Cell Based Bio Sensors

Cell-Based BiosensorsPrinciples and Applications

Artech House SeriesBioinformatics & Biomedical Imaging

Series EditorsStephen T. C. Wong, The Methodist Hospital and Weill Cornell Medical College Guang-Zhong Yang, Imperial College

Advances in Diagnostic and Therapeutic Ultrasound Imaging, Jasjit S. Suri, Chirinjeev Kathuria, Ruey-Feng Chang, Filippo Molinari, and Aaron Fenster, editors

Biological Database Modeling, Jake Chen and Amandeep S. Sidhu, editors

Biomedical Informatics in Translational Research, Hai Hu, Michael Liebman, and Richard Mural

Cell-Based Biosensors: Principles and Applications, Ping Wang and Qinjun Liu, editors

Data Mining in Biomedicine Using Ontologies, Mihail Popescu and Dong Xu, editors

Genome Sequencing Technology and Algorithms, Sun Kim, Haixu Tang, and Elaine R. Mardis, editors

High-Throughput Image Reconstruction and Analysis, A. Ravishankar Rao and Guillermo A. Cecchi, editors

Life Science Automation Fundamentals and Applications, Mingjun Zhang, Bradley Nelson, and Robin Felder, editors

Microscopic Image Analysis for Life Science Applications, Jens Rittscher, Stephen T. C. Wong, and Raghu Machiraju, editors

Next Generation Artifi cial Vision Systems: Reverse Engineering the Human Visual System, Maria Petrou and Anil Bharath, editors

Systems Bioinformatics: An Engineering Case-Based Approach, Gil Alterovitz and Marco F. Ramoni, editors

Text Mining for Biology and Biomedicine, Sophia Ananiadou andJohn McNaught, editors

Translational Multimodality Optical Imaging, Fred S. Azar and Xavier Intes, editors

Cell-Based BiosensorsPrinciples and Applications

Ping WangQingjun Liu

Editors

Library of Congress Cataloging-in-Publication DataA catalog record for this book is available from the U.S. Library of Congress.

British Library Cataloguing in Publication DataA catalog record for this book is available from the British Library.

ISBN-13: 978-1-59693-439-9

Cover design by Pilar Colleran

© 2010 Artech House685 Canton StreetNorwood, MA 02062

All rights reserved. Printed and bound in the United States of America. No part of this book may be reproduced or utilized in any form or by any means, elec-tronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher.

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10 9 8 7 6 5 4 3 2 1

v

Contents

Foreword xi

Preface xiii

Acknowledgments xvii

CHAPTER CHAPTER 1 Introduction 1

1.1 Defi nition of Cell-Based Biosensors 11.2 Characteristics of Cell-Based Biosensors 31.3 Types of Cell-Based Biosensors 41.4 Summary 10

References 11

CHAPTER CHAPTER 2 Cell Culture on Chips 13

2.1 Introduction 132.2 Cell Immobilization Factors 14

2.2.1 Physical Factors 142.2.2 Chemical Factors 152.2.3 Biological Factors 15

2.3 Basic Surface Modifi cation Rules 162.3.1 Hydrophilicity Improving 172.3.2 Roughness Changing 182.3.3 Chemical Coating 18

2.4 Typical Methods 202.4.1 Special Physical Structure 222.4.2 Microcontact Printing 242.4.3 Fast Ink-Jet Printing 262.4.4 Perforated Microelectrode 272.4.5 Self-Assembled Monolayer 292.4.6 Microfl uidic Technology 30

2.5 Summary 33References 33

vi Contents

CHAPTER CHAPTER 3 Mechanisms of Cell-Based Biosensors 37

3.1 Introduction 373.2 Metabolic Measurements 38

3.2.1 Cell Metabolism 383.2.2 Extracellular pH Monitoring 403.2.3 Other Extracellular Metabolite Sensing 433.2.4 Secondary Transducers 44

3.3 Action Potential Measurements 443.3.1 Action Potential 453.3.2 The Solid-Electrolyte Interface 473.3.3 Cell-Electrode Interface Model 523.3.4 Cell-Silicon Interface Model 543.3.5 Secondary Transducers 55

3.4 Impedance Measurements 563.4.1 Membrane Impedance 563.4.2 Impedance Model of Single Cells 573.4.3 Impedance Model of Populations of Cells 593.4.4 Secondary Transducers 61

3.5 Noise Sources 623.5.1 Electrode Noise 623.5.2 Electromagnetic Interference 633.5.3 Biological Noise 63


CHAPTER CHAPTER 4 Microelectrode Arrays (MEA) as Cell-Based Biosensors 65

4.1 Introduction 654.2 Principle 684.3 Fabrication and Design of MEA System 69

4.3.1 Fabrication 694.3.2 Different MEA Chips 744.3.3 Measurement Setup 77

4.4 Theoretical Analysis of Signal Process in MEA Systems 794.4.1 Equivalent Circuit Model of Signal Process 794.4.2 Impedance Properties Analysis of MEA 804.4.3 Analysis of Extracellular Signal 82

4.5 Application of MEA 844.5.1 Dissociated Neural Network on MEA 844.5.2 Slice on MEA 864.5.3 Retina on MEA 884.5.4 Pharmacological Application 89

4.6 Development Trends 924.6.1 Lab on a Chip 924.6.2 Portable MEA System 92

Contents vii

4.6.3 Other Developmental Trends 924.7 Summary 93

References 93

CHAPTER 5 HAPTER 5 Field Effect Transistor (FET) as Cell-Based Biosensors 97

5.1 Introduction 975.2 Principle 985.3 Device and System 100

5.3.1 Fabrication of FET-Based Biosensor 1005.3.2 FET Sensor System 102

5.4 Theoretical Analysis 1035.4.1 Area-Contact Model 1045.4.2 Point-Contact Model 105

5.5 Application 1065.5.1 Electrophysiological Recording of Neuronal Activity 1065.5.2 Two-Way Communication Between Silicon Chip and Neuron 1085.5.3 Neuronal Network Study 1095.5.4 Cell Microenvironment Monitoring 112

5.6 Development Trends 1145.7 Summary 115

References 116

CHAPTER 6 HAPTER 6 Light Addressable Potentiometric Sensor (LAPS) as Cell-Based Biosensors 119

6.1 Introduction 1196.2 Principle 121

6.2.1 Fundamental 1216.2.2 Numerical Analysis 122

6.3 Device and System 1246.3.1 Device 1246.3.2 Microphysiometer System 1266.3.3 Detecting System of Cell-Semiconductor Hybrid LAPS 129

6.4 Application 1326.4.1 Signaling Mechanism Study 1336.4.2 Functional Characterization of Ligand/Receptor Binding 1346.4.3 Identifi cation of Ligand/Receptor 1366.4.4 Drug Analysis 137

6.5 Developing Trend 1436.5.1 LAPS Array System for Parallel Detecting 1446.5.2 Multifunctional LAPS System 145


viii Contents

CHAPTER 7 HAPTER 7 Electric Cell-Substrate Impedance Sensor (ECIS) as Cell-Based Biosensors 151

7.1 Introduction 1517.2 Principle 152

7.2.1 Electrochemical Impedance 1527.2.2 Cell-Substrate Impedance 1547.2.3 AC Frequency and Sensitivity Characteristics of Interdigitated Electrodes 156

7.3 Device and System 1607.3.1 Device Fabrication 1607.3.2 Bioimpedance Measurement System 161

7.4 Theoretical Analysis 1647.4.1 Lumped Model 1647.4.2 Analytical Model 1657.4.3 Data Calculation and Presentation 165

7.5 Applications 1677.5.1 Monitoring of Cell Adhesion, Spreading, Morphology, andProliferation 1677.5.2 Monitoring of Cell Migration and Invasion 1697.5.3 Monitoring of Cellular Ligand-Receptor Interactions 1707.5.4 Cytotoxicity Assays 172

7.6 Development Trends 1737.6.1 High-Throughput Screening 1737.6.2 Integrated Chip 175


CHAPTER CHAPTER 8 Patch Clamp Chip as Cell-Based Biosensors 179

8.1 Introduction 1798.2 Theory 179

8.2.1 Conventional Patch Clamp 1798.2.2 Patch Clamp Chip 181

8.3 Sensor Device and System 1828.3.1 Patch Clamp Chip Device 1828.3.2 Patch Clamp Chip System 1888.3.3 Cells Preparation 193

8.4 Biomedical Application 1948.4.1 Ionic Channels Research 1948.4.2 Drug Discovery 1998.4.3 Drug Safety 200

8.5 Development Trends 2028.6 Summary 203

References 203

Contents ix

CHAPTER CHAPTER 9 Other Cell-Based Biosensors 207

9.1 Quartz Crystal Microbalance (QCM) as Cell-Based Biosensors 2079.1.1 Introduction 2079.1.2 Principle of QCM 2089.1.3 QCM Sensors and Measurement System 2109.1.4 Biomedical Application 211

9.2 Surface Plasmon Resonance (SPR) as Cell-Based Biosensors 2179.2.1 Introduction 2179.2.2 The Principle of SPR 2199.2.3 SPR Sensors and Measurement System 2209.2.4 Biomedical Application 221

9.3 Immune Cell-Based Biosensors 2259.3.1 Introduction 2259.3.2 Mast Cell–Based Biosensors 2269.3.3 Dendritic Cell–Based Biosensors 2279.3.4 B Cell–Based Biosensors 229


CHAPTER 1CHAPTER 10 Developments of Cell-Based Biosensors 233

10.1 Introduction 23310.2 Cell-Based Biosensors with Integrated Chips 233

10.2.1 Integration Chip of the Same or Similar Functional Sensors 23410.2.2 Multisensors Involve Sensing Elements with Different Functions 23510.2.3 Multifunctional Chip Monitoring Different Parameters 236

10.3 Cell-Based Biosensors Using Nanotechnology 23710.3.1 Nano-Micropatterned Cell Cultures 23810.3.2 Nanoporous-Based Biosensor 23910.3.3 Nanoprobes to Intracellular Nanosensors 240

10.4 Cell-Based Biosensors with Microfl uidic Chips 24110.4.1 Microfl uidic Flow 24210.4.2 Soft Lithography 24310.4.3 Dielectrophoresis 245

10.5 Biomimetic Olfactory and Gustatory Cell-Based Biosensors 24610.5.1 Bioelectronic Nose and Bioelectronic Tongue 24710.5.2 Olfactory and Gustatory Biosensors with Special Receptors 24710.5.3 Olfactory and Gustatory Cell-Based Biosensors 248References 250

Glossary 255

About the Editors 261

List of Contributors 262Index 263

xi

Foreword

The fi eld of biosensors and bioelectronics has enveloped many new areas such as molecularly sensitive receptors, biomimetic sensors, nanotechnology, and more. One of the most enduring topics, however, is that of cell-based biosensors, which are able to detect directly biological effects and to convert them, via the living cell, to electrical signals. Hence, the cell-based biosensor serves as the archetypal inter-face between a biological and an electronic system.

Interdisciplinary skills are essential for the development of cell-based biosen-sors and the knowledge of engineers, physicists, chemists, and biologists must be combined to ensure success. This book fulfi lls this demand, describing in detail the fundamentals, design, technology, and applications of cell-based biosensors along with concrete realizations of the art.

This volume systematically deals with the concepts, technology, and develop-ment of cell-based biosensors. It combines descriptions of microelectronics and information technology with biological fundamentals to introduce the basic prin-ciples and applications of cell-based biosensors. It provides a topical description of research progress in cell-based biosensor models, sensing techniques, and novel microstructure biosensor developments in the fi eld over the past 10 years. In ad-dition, many innovative applications of cell-based biosensors in areas such as bio-medicine are detailed.

As one who has had the duty and privilege of tutoring graduates and research-ers in the elements of biosensors and bioelectronics in both university and com-mercial environments over the past 30 years, I am pleased to see this monograph on biosensors and am delighted to write this foreword.

The authors’ 10 years of their own research in the area have furnished them with suffi cient material and confi dence to contribute a series of original results and observations to the fi eld of cell-based biosensor throughout this book. The authors rightly aspire to stimulate the invention of new technologies for both the study and the application of biological science.

Cell-based biosensors have a long pedigree, but remain a hot subject today. They attract a lot of attention from research groups in various fi elds, such as mi-croelectronics, cell biology, electrochemistry, and mathematical modeling. A funda-mental attribute of the area is to provide in-depth studies of cellular structure and activity. This theme of scientifi c interest features strongly in this book.

This book covers surface-coating materials, surface-cell interface models, sen-sor design and fabrication rules, and systems and applications from a professional view. It describes principles, developments, applications, and promising aspects

xii Foreword

of cell-based microelectrode arrays (MEAs), fi eld-effect transistors (FETs), light-addressable potentiometric sensors (LAPS), and electrical cell impedance sensing (ECIS). Cells are not only considered as sensing elements, but also as rich informa-tion sources accessed using techniques such as cell impedance spectra, microenvi-ronment evaluation, and electrophysiological signals, which are surveyed in multi-functional integrated chips. These are complemented with other novel technologies that could be employed for cell measurements. This book is timely since the subject of cell-based biosensors has begun to coalesce into a defi nable subdiscipline of bio-sensors with its own principles and features.

Ping Wang and Qingjun Liu deliver good interdisciplinary knowledge and re-search backgrounds in both engineering and biology as editors of this book. They have taken on the task of surveying the broad fi eld of cell-based biosensors from a perspective that stresses the underlying principles. They construct an outline of the fi eld that includes principles and applications from living cells through biological to electronic interfaces and methods from characterization through synthesis to technological application. At the same time, they provide a reasonably comprehen-sive description of the particular classes of cell-based biosensors that have become important in biosensors and bioelectronics.

I believe this monograph is one of the fi rst to have taken this interdisciplinary approach to the broad subject of biosensors and bioelectronics. It is targeted to-ward graduate students, researchers, and lecturers in the fi eld of biosensors and bioelectronics, who will fi nd it a very useful text and reference.

Professor Anthony P. F. TurnerEditor-in-chief of Biosensors & Bioelectronics

Cranfi eld UniversityCranfi eld, United Kingdom

October 2009

xiii

Preface

In the second half of the twentieth century, interest in the development of biosen-sors, which conventionally are electric sensing devices, has increased considerably. Scientifi c research was followed by an emerging demand from various application fi elds. In the 1960s, the technique of chemical sensors grew rapidly and resulted in the possibility for direct detection of various ion and molecular types with certain selectivity limits. The research and development of conventional macrosensors soon turned in the direction of microsensors and even nanosensors as a result of the miniaturization in biosensors and expanding applications, including biology and medicine.

In a decade, cell-based biosensors have become a research hotspot in biosen-sors and bioelectronics fi elds because they can detect the functional information of biological living cells. They are characterized by their high sensitivity, excel-lent selectivity, and rapid response and have been applied in many fi elds, such as biomedicine, environmental monitoring, and pharmaceutical screening. Currently, cell-cultured technology, silicon micromachining technology, and genetic technol-ogy have dramatically promoted the exploration of cell-based biosensors.

Although the practical application of cell-based biosensors has been developing rapidly, the theoretical background of their operation is clarifi ed only partly or not at all in many cases. There are debates about the signal excitation mechanisms, the signal conditioning methods, and the interpretation of practically measurable and theoretically expected results. Developing the cell-based biosensors means conduct-ing considerable basic research at the same time. This is one of the main and com-mon commercialization barriers of cell-based biosensors.

This book provides a survey of this fi eld from a systems engineering perspec-tive. The structure of this book is simple and builds upon basic concepts. There are 10 chapters, each reviewing a fundamental block in our survey of cell-based biosensors. In each chapter, it provides details relevant to the section.

Chapter 1 briefl y introduces the development history and basic concept and knowledge of cell-based biosensors, including the defi nition, characteristics, and main types of cell-based biosensors.

Chapter 2 provides some basic knowledge about cell culture on chips. This is one of the essentials that could impact the whole cell-based biosensors work pro-

xiv Preface

cedure, and it is quite different from traditional cell culture. Here, a lot of work is needed to improve the surface characteristics.

Chapter 3 deals with the mechanism and models of cell-based biosensors. Served as a transducer and an interface in cell-based biosensors, it is also very es-sential in how a cell couples to transducers and how it receives and responds to outside stimuli.

Chapter 4 describes the microelectrode array (MEA) as a cell-based biosensor that is designed for transferring and recording cellular action potential including frequency, amplitude, wave shape, velocity, and so on. Due to simple fabrication, good biocompatibility, and convenience for observing in a microscope as well as detecting cellular multiparameters, MEA has been extensively applied on cell-based biosensors.

Chapter 5 describes the cell-based fi eld-effect transistor (FET) sensor fabricated by semiconductor technology. This type of sensor signifi es another sensing effect used to record extracellular signals. As a highly organized living microstructure, the cell itself expresses much information according to the changing environment outside the entity.

Chapter 6 proposes a light addressable potentiometric sensor (LAPS). As a novel kind of semiconductor device, LAPS can detect extracellular ions concentra-tion and indicate the metabolism information of cells, instead of conventional elec-trodes. The microphysiometer is a typical application of LAPS in biology. On the sensor, living cells are cultured and confi ned in the microenvironment. By detecting its response, the variation of certain molecular concentrations can be monitored.

Chapter 7 mentions the electric cell substrate impedance sensing (ECIS). Bio-impedance technology has been developing for a long time, covering the electric currents associated with the life processes and their biopotentials. Based on the bioimpedance technology, ECIS probes the electrochemical processes in the cell, and the tissue thus owns the capability of monitoring physiology changes, which have great differences among various cells and tissues.

Chapter 8 introduces the patch clamp chip. Patch clamp technology is now a golden standard in the research of electrophysiology. However, it has certain inevitable limitations and can hardly be applied to drug screening and cellular com-munication in neural networks. The emergence of a planar patch clamp chip makes it possible to record the electrophysiological process of ionic channels in a highly parallel and automatic way.

Chapter 9 proposes other new cell-based biosensors: quartz crystal microbal-ance (QCM), surface plasmon resonance (SPR), and immune cell-based biosensors. At the same time, those cell array-based biosensors can be used in fundamental studies of multicellular interactions in the immune system and other areas of cell biology.

Chapter 10 discusses the developing trends of cell-based biosensors combining with up-to-date technologies in science and engineering, such as microelectronics, nanotechnology, and molecular biology to fabricate the integrated, multifunction-al, intelligent, or smart cell-based biosensors chips.

Preface xv

We are indebted to our coauthors for their personal knowledge and expertise and generous contribution to their chapters. Each chapter provides a gateway to the fi eld it represents. As previously mentioned, many sources have been used to complete the information presented here including papers, books, and trade litera-ture containing relevant, up-to-date materials. These sources are referred to and listed in the references sections at the end of each chapter.

Ping Wang, Ph.D.Editor

Professor at Zhejiang University, ChinaDeputy Director of Biosensors National Special Lab

Zhejiang, Hangzhou, ChinaOctober 2009

xvii

Acknowledgments

We would like to acknowledge all of the contributors for our book: Wei Zhang for Chapter 2; Hua Cai for Chapter 3; Lidan Xiao and Qingmei Chen for Chapter 4; Jun Zhou for Chapter 5; Hui Yu for Chapter 6; Zhaoying Hu for Chapter 7; Peihua Chen for Chapter 8; and Chunsheng Wu for Chapter 9.

We also give many thanks to Jun Wang, Liping Du, and Chengxiong Wu, Gong Cheng, Weiwei Ye, Ning Hu, Shuai Zhen, and Zhiyuan Liu for their help in compil-ing and correcting notes.

We thank Artech House Publishers for their willingness to give prompt assist-ance during the proposal and manuscript stage. We also thank Mark Walsh and Christine Daniele, who encouraged us with their tireless support of this book. Our hope is that books like this provide a continuous stream of knowledge for students, researchers, and engineers who are working in the fi eld.

We would be remiss if we did not acknowledge those institutes and founda-tions that have fi nancially supported us overall with teaching and research work on cell-based biosensors topics through the past over 10 years: the National Natural Science Foundation of China and National Distinguished Young Scholars Fund (Grant No. 30627002, No. 60725102, No. 30700167), the State Key Laboratory of Transducer Technology of China (Grant No. SKT0702), the Zhejiang Provincial Natural Science Foundation of China (Grant No. Y2080673), the National Basic Research Program of China (973 Program, Grant No. 2009CB320303), and the National High Technology Research Program of China (863 Program, Grant No. 2007AA09210106).

Ping Wang and Qingjun LiuEditors

Zhejiang UniversityHangzhou, China

October 2009

1

C H A P T E R 1

IntroductionPing Wang

1.1 Defi nition of Cell-Based Biosensors

Cell-based biosensors are special devices that employ immobilized living cells as sensing elements, combined with sensors or transducers to detect the intracellular and extracellular microenvironment condition, physiological parameters, and pro-duce responses through the interaction between stimulus and cells.

The main feature of cell-based biosensors is that they employ living cells as receptors in contrast to the other types of biosensors that contain only materials extracted from living things. They consist of two main parts: one is from living cells, which is the primary transducer used in the fi rst sensing element receiving and producing signals; the other belongs to the secondary transducers used in convert-ing the physiological signals to electrical signals. Picking up, isolating, and immo-bilizing the living cell on the surface of transducers, and designing and fabricating the special sensor chips to assure good coupling and get accurate signals from cells are the main work in cell-based biosensors research.

In a decade, cell-based biosensors have become a research hotspot in biosen-sors and bioelectronics fi elds because they can detect the functional information of biological active analytes. They are characterized by their high sensitivity, excellent selectivity, and rapid response, and they have been applied in many fi elds, such as biomedicine, environmental monitoring, and pharmaceutical screening. Currently, cell-cultured technology, silicon micromachining technology, and genetic technol-ogy have promoted exploration of cell-based biosensors dramatically.

The basic schematic diagram of cell-based biosensors is shown in Figure 1.1. It mainly consists of two parts: one is living cells or a neural network cultured on the surface of a transducer, and the other is a transducer including potential sensing and chemical sensing, sometimes also with stimulus elements. The living cell serves as the sensing element or primary transducer to respond to external stimuli, such as electric and chemical stimuli, antiviral drugs, and various receptor ligands. Then it will produce corresponding outputs or changes, such as extracellular changes of molecules or ions, action potential and impedance change induced by the cellular metabolism, and so on. Transducers or secondary transducers such as silicon fi eld-

2 Introduction

effect devices can detect these responses and convert them into electrical signals. These make up the whole cell-based biosensors.

Generally, when external stimulation, such as drugs, chemicals, and electric stimuli, are added into cell-based biosensors, cells cultured on a chip would pro-duce action potential and ionic or molecular changes that can be detected by de-vices underneath a thin layer of electrolyte. The potential and current changes cou-ple with the transducers, so that the extracellular environmental changes can be monitored by the cell-based biosensors. These sensors can be applied to measure the extracellular action potential, impedance, and transmission path of ionic chan-nels, and they can detect the transmission velocity of biological signals along the layer of neurons.

Culturing living cells on devices is one of the essentials that could determine the performance of the whole cell-based biosensor. What we do is quite different from common cases, in which cells are mostly cultured directly on a petri dish, slide, or other glass or polymeric plastic culture dishes. Due to good surface hydrophilic-ity and negatively charged of its material, most cells could locate and spread well on the culture dish. Besides, many conventional operation manuals and protocols are available for different types of cells, including cardiac myocytes, neurons, glial cells, epithelium, embryonic cells, and so on. It is a big challenge to culture cells on chips comprised of silicon or other conductive metals and to gain good contact between the substrate and cell membrane. However, the main problem is that the material itself is not attractive to cells in roughness, hydrophilicity, surface func-tional groups, and viscosity. Hence, more work is needed to improve the surface characteristics of transducers.

To assure good coupling and get accurate signals from living cells, study on the mechanism and models of coupling models between cells and transducers is neces-sary to design and fabricate transducer chips better. When cells serve as transduc-ers, it is very important to know how cells sense the stimulating signals, how cells response to external stimulation, and how cells couple to the transducers. Besides, developing new transducer chips means conducting considerable basic research on the various detection techniques to complete cell metabolic measurements, action potential measurements, impedance measurements, and so on.

Figure 1.1 Basic schematic diagram of cell-based biosensors.

1.2 Characteristics of Cell-Based Biosensors 3

Cell-based biosensors have the potential of providing rapid, sensitive, low-cost measurement technology for monitoring analyte concentrations. Cells are equipped with a host of receptors that can transduce chemical and biological signals into electrical ones. If effi ciently coupled to an electronic readout device, cells could function as versatile biosensors in a variety of applications [1]. By using living cells as sensitive elements, cell-based biosensors are able to respond to many kinds of chemical and biological analytes and detect functional information. It has been im-plied that the on-off of cellular receptors and ionic channels induces the migration of electriferous clusters and ions on both sides of cellular membrane, which will couple with microelectronic devices. When detection system is improved, cells can become sensitive units of biosensors for environment detection or drug discovery [2]. Besides, some researchers have demonstrated two-way, noninvasive commu-nication between external electronics and cells cultured on the chip [3]. Changes of extracellular metabolism products, such as ions and large biomolecules, are in-duced by the transformation of intracellular physiological status. Thus, we can deduce the intracellular physiological state by detecting the metabolism products.

1.2 Characteristics of Cell-Based Biosensors

A common special feature of cell-based biosensors is that they employ living cells as receptors, in contrast to other types of biosensors that contain only materials extracted from living things. Unique combinations of enzymes or highly sensitive physiological receptor mechanisms become available that are present in intact cells but may be impossible to duplicate using isolate enzymes in the biosensor. Another advantage should be that the materials can fulfi ll their biological functions within their natural biological media. In these circumstances, bioactive compounds may have the best activity and lifetime, and they can even be regenerated or resynthe-sized by the living cells. Thus, a better stability of biosensors may be expected. If the living cells perish, abrupt observable changes will occur in the sensor’s behavior, instead of a slow drift due to the receptor dissolution that is characteristic of other types.

The common problems of living cell–based biosensors can be summarized as follows [4]:

The natural environmental conditions, in which the cell can stay alive for •long period, must be maintained continuously, and this requires severe con-trol of physical and chemical parameters of the environment.

The metabolism of the cells must be maintained and they must be fed •continuously.

The living cells must be immobilized around or on the surface of the trans- •ducer without limiting their biological functions. The supramolecular or-ganic chemistry and cellular mimicry should be exploited in the future.

The lifetime of sensors is mainly determined by the lifetime of the cells. •

The particular advantages of using cell-based biosensors are as follows:

4 Introduction

They are less sensitive to inhibition by solutes and are more tolerant of sub- •optimal pH and temperature values than enzyme electrodes, though they must not exceed the narrow range in case of the cells dying.

A longer lifetime can be expected than with the enzymatic sensors. •

They are much cheaper because active cells do not need to be isolated. •

Because cell-based biosensors have lots of advantages (e.g., long-term record-ing in noninvasive ways, fast response, and easy fabrication), they have extensive applications, including pharmaceutical screening, cellular physiological analysis, toxin detecting, peripheral nerve regeneration, and environment monitoring, as well as in vivo recordings; thus, they are also promising in fi elds of neuronal pros-theses and reconstruction of damaged sense organs.

Although the practical applications of cell-based biosensors have been develop-ing rapidly, the theoretical background of their operation hasn’t been clarifi ed com-pletely in many cases. There are debates about the signal excitation mechanisms, the signal conditioning methods, and the interpretation of practically measurable and theoretically expected results. Developing cell-based biosensors means con-ducting considerable basic research at the same time. This is one of the main com-mercialization barriers of cell-based biosensors. Despite recent successful research results, cell-based biosensors also have some disadvantages beyond these general problems. They are as follows:

Some types of cell-based biosensors may have a longer response time than •enzymatic sensors.

They need more time to return to the baseline level after using. •

Cells contain many enzymes, and care must be taken to ensure selectivity. •

1.3 Types of Cell-Based Biosensors

At present, the secondary transducers used in cell-based biosensors mainly include the microelectrodes array (MEA), fi eld-effect transistor (FET), light addressable po-tentiometric sensor (LAPS), electric cell-substrate impedance sensor (ECIS), patch clamp chip, quartz crystal microbalance (QCM), surface plasmon resonance (SPR), and so on.

MEA, which is designed for transferring and recording cellular action poten-tial including frequency, amplitude, wave shape, and velocity, is shown in Figure 1.2. Using micromachining technology, MEA is fabricated by depositing Au, Ir, Pt, or other metals on glass or silicon substrate to form electrodes, connecting leads, depositing the passivation layer, and exposing the electrode sites where the cells or tissues contact. Usually MEA is composed of microelectrodes arranged in a matrix, connecting leads and welding pads. Due to simple fabrication, good biocompat-ibility, and convenience for observing in microscope as well as detecting cellular multiparameters, MEA has been extensively applied in cell-based biosensors. Being array sensor, MEA is predominant in long-term, real-time, noninvasive measure-

1.3 Types of Cell-Based Biosensors 5

ment for signal coupling and transferring between cells compared with the patch-clamp technology [5].

Some key technologies of MEA for cell-based biosensors are still underway. Many problems need to be solved. For example, substrate surface is easily eroded when dipped in the solution for a long time. The gap between cells and electrodes is diffi cult to control by cellular swimming or moving when adhered to the MEA. These will affect the veracity of measurement. MEA is very effective, not only for monitoring the cellular electrophysiological activity, but also for screening and evaluating pharmacology and toxicology. It can be used for observing signal trans-fer of cardiac myocytes and fi ltering drugs for the heart. It can also record extracel-lular action potentials and neuronal responses to different substances, which is very important in pharmacological research. For example, some researchers cultured a spinal neuron network of a mouse on a 64-channel MEA (ITO electrodes) and measured the neuronal response after using TMPP (a drug inducing convulsion).

FET is fabricated by semiconductor technology. This type of sensor signifi es another transducer used to record extracellular signals, as shown in Figure 1.3 [6]. As a highly organized living microstructure, the cell itself expresses much informa-tion depending on the changing environment outside the entity. Up to now, the standard MOS process has been applied to fabricate cell-based FET sensors with tiny changes compared with an insulated-gate fi eld-effect transistor (IGFET). The metal gate connection of the FET structure is replaced by a reference electrode in the solution. Sensitive fi lms such as silicon dioxide and silicon nitride are de-posited on the gate area which is covered by different types of electrogenic cells. Environmental factors infl uence the cells, which can be simulated through the fl uid perfusion system. After receiving different stimuli from the chamber electrolyte, the cell response inside seems like a complete black box to us with regard to parallel activation of different signaling pathways. The simple way to analyze cell response is to decode the information from the cell metabolism expressed extracellularly.

Cell-based FET sensors focus on the ionic concentration variations near the gate area and the action potential on the cell membrane. If there is any change, the

Figure 1.2 Schematic diagram of MEA for cell-based biosensors.

6 Introduction

surface potential of the dielectric layer will be lifted, which induces the change in the density of mobile defect electrons. The cell-silicon interaction causes a modula-tion of the current along the inversion layer driven by a voltage between source and drain. To record ac modulation signal, it was defi ned by the source-voltage relative to the reference potential [7].

The fi rst attempt to apply an ISFET in neurophysiological measurements was made in 1970. Subsequently, a single invertebrate neuron and a vertebrate neuron, as well as cardiac myocyte monolayers and brain slice, were cultured on the gate of cell-based FET sensors for biological measurement. The mechanism explanation was modeled and described in several papers. Yates et al. described the electrical double layers at colloidal oxide-water interfaces with the site-binding model [8]. Bergveld et al. [9] discussed the determination factors of interfacial potential by double-layer capacitor equations. Fromherz et al. [6] developed a planar two-di-mensional area-contact model and a point contact model to evaluate the ac-signal transfer on the basis of linear response theory. Recently, extracellular acidifi cation and respiration detection are hot spots by ion-sensitive fi eld-effect transistor (IS-FET). Cell-based FET arrays in high density are also highlighted in the recent devel-opment trend. Combined with an additional insulated spot of silicon, a capacitive extracellular stimulation of an individual cell is demonstrated to be possible. This idea develops a two-way communication chip between cell and silicon chip that could simultaneously implement stimulation and record activities on the cell-silicon interface. The cell monitoring system (CMS) even combines different microsensors, including arrays of different ion-sensitive FETs and cell potential FETs, with dif-ferent gate areas and materials. With the rapid development of the semiconductor industry, high-density cell-based FET sensor arrays with better resolution could be achieved. Before long, this type of sensor can be widely used in drug screening and

Figure 1.3 Schematic diagram of FET for cell-based biosensors. (From: [6]. Reproduced from Solid State Electronics, © 2008, with permission from Elsevier B.V.)


neural network transduction research, with advantages such as fast response, low cost, and noninvasive long-term recordings.

LAPS is a semiconductor device proposed by Hafeman et al. in 1988 [10]. Hafeman et al. detected the extracellular pH with LAPS, instead of conventional electrodes, to indicate the metabolism of cells. On the basis of LAPS, the commer-cially available cytosensor microphysiometer was then released in 1990 by Mo-lecular Device Corporation. This can monitor small fl uxions of acidifi cation in extracellular microenvironment of 104–106 cells. Nowadays, most research work on LAPS is on the basis of this microphysiometer.

The microphysiometer is a typical application of silicon technique in biology. Living cells are cultured and confi ned in micro environment on the sensor, as shown in Figure 1.4. Variations of certain molecules are then monitored by detecting bio-logical responses of living cells. microphysiometer is different from most other ana-lyzing equipment. Instead of determining the characteristics of analytes, it detects the effect on cells induced by the analyte. In most circumstances, acidic products of cell metabolism are related to the consumption of ATP. During important cell metabolic process (i.e., the metabolism of glucose, aminophenol, and fatty acid), produced proton released into the microenvironment causes the extracellular pH change. The microphysiometer can measure such a small change and can indicate the undertaken cell metabolism. The microphysiometer has been widely used in measurement in biology, pharmacology, toxicology, and so on.

Another realization of LAPS as a cell-based biosensor is the cell-semiconductor hybrid LAPS device. Excitable cells such as cardiac myocytes or neurons are cul-tured and fi rmly attached to the sensor surface. Then, by detecting the resulting photocurrent, extracellular potential signals of cells can be measured. The cell-sem-iconductor hybrid LAPS device can possibly be used for single cell analysis [11].

ECIS can be used in electrical impedance spectroscopy and chemical analysis techniques to investigate bioelectrical properties of cell membranes and mechanisms

Figure 1.4 Schematic diagram of LAPS for cell-based biosensors.

8 Introduction

of the excitable cell responses to chemical or electrical stimuli, as shown in Figure 1.5. Compared with conventional methods such as patch-clamping and fl uores-cent microscopy, the electrochemical means has the advantages mentioned previ-ously. In particular, during the maturation of microelectromechanical system and nanoscale technologies, it creates great opportunities for simplifi ed automated and high-through approaches for basic research in cells. Furthermore, these techniques can be incorporated to simultaneously measure parameters as much as possible by integrating different methods into one chip. Thus, time is supposed to be reduced while obtaining more information about cells [12].

Dittami and Ayliffe designed and fabricated a platform for EIS of small regions of the cell membrane and the measurement of the chemical concentration adjacent to the cell membrane [13]. He reported that the neurotransmitter release was mod-ulated in phase with the positive peak of the sine stimulus, which highlighted the potential of the device to spatially resolve the cell membrane’s electrical properties, as well as the intracellular components.

The patch clamp chip technology is widely used in the fi elds of electrophysiol-ogy and neuroscience, as shown in Figure 1.6. It is now a golden standard in the research of electrophysiology. However, patch clamp technology has certain inevi-table limitations, such as a low throughput. It can hardly be applied to drug screen-ing and cellular communication in neural networks. The emergence of a planar patch clamp technology makes the highly parallel and automatic electrophysiology recording of ionic channels possible. Scientists have made great efforts to improve conventional patch clamp technology by developing a new confi guration of the

Figure 1.5 The basic schematic diagram of ECIS for cell-based biosensors.

Figure 1.6 Schematic diagram of patch clamp chip for cell-based biosensors.


conventional microelectrode or a new generation of microelectrode. Improvement of the electrode confi guration was fi rst achieved by Sophion Bioscience. Afterward, Neuropatch and Flyion produced a novel automatic patch clamp instrument. How-ever, these systems were still based on a single microelectrode and could not be used in high-throughput applications.

In the late 1990s, scientists developed a patch clamp chip and raised the con-cept of guiding cells onto a microaperture, which replaced the glass microelectrode with a planar structure. This novel chip can record many cells simultaneously and can be combined with multiple measurement methods easily. Planar patch clamp technology will be a potential and effective approach in the study of ion channels and drug discovery. The key technique is how to fabricate a smooth chip with an aperture and with a diameter of 1 μm or even smaller. So far, some materials, such as silicon, quartz crystal, glass, and polymers, have been utilized to fabricate patch clamp chips. At fi rst, silicon was chosen undoubtedly because of the convenient application of the standard semiconductor technology, while the choice of glass is for its application in conventional patch clamp. Polymer, such as PDMS, is well known as Sylgard, which is widely applied in microfl uidics. They all had different advantages and disadvantages, and were chosen for different interests.

QCM and SPR are now also widely used commercially available analytical techniques suitable for the detection of biomolecular interactions, as shown in Fig-ure 1.7. Recent experimental results have demonstrated that the cells adhering to the sensor chip and responding to stimulators could introduce the changes of re-sponse signals in both QCM and SPR.

Based on the piezoelectric effect, QCM is a very sensitive technique to detect mass changes in the fi eld of biosensors. The primary advantages of QCM over traditional methods include high sensitivity (in the range of nanograms), nonin-vasiveness, long measurement periods, and being label free. The technique pos-sesses a wide detection range from a monolayer of small molecules to much larger masses bound to the surface, even including complex arrays of wholes cells. The signal transduction mechanism of the QCM relies on the piezoelectric effect in quartz crystal. When an alternating electric fi eld is applied across the quartz crystal through electrodes covering the quartz surface, a mechanical oscillation of char-acteristic frequency is produced in the crystal. Thus, the mass changes can cause the pressure changes on the crystal surface and subsequently lead to the resonant frequency of the crystal shifting. Lots of successfully used cell-based biosensor ap-plications of QCMs, such as drug analysis, cell adhesion, cell exocytosis, and epi-thelial cell-microparticle interaction, have been introduced.

Figure 1.7 The basic schematic diagram of QCM for cell-based biosensors.

10 Introduction

SPR is an optical technique that uses the evanescent wave phenomenon to measure changes in the refractive index very close to a sensor surface. In recent years, the SPR-based biosensors have been widely used to study biomolecular inter-actions. SPR is an optical detection process that occurs when a polarized light hits a prism covered by a gold layer. Free electrons at the surface of the gold layer absorb incident light photons and convert them into surface plasmon waves. Perturba-tions at the gold surface of SPR, such as an interaction between probe molecules immobilized on the chip and captured target molecules, induce a modifi cation of resonance conditions, which in turn are seen as a change in refl ectivity and can be measured. This is the basis for molecule SPR measurements. Recent experimental results demonstrated that when cells are cultured on the surface of SPR, the at-tachment and spreading could be monitored in real time with intracellular signal transduction events refl ected.

Finally, the immune system is one of the most complex biological systems. It protects against disease by identifying and killing pathogens and tumor cells. Be-cause immune cells can recognize and respond to antigens with very high sensitiv-ity and specifi city, a number of immune cells have been investigated to explore the feasibility of being used as sensitive elements in cell-based biosensors. Both mast cells and B cells are used as examples of the immune cells–based biosensors for promising applications in pathogens detecting. At the same time, those cell array–based biosensors can be used in fundamental studies of multicellular interactions in the immune system and other areas of cell biology.

This chapter has fi rst summarized the various kinds of cell-based biosensors and then introduced their working principles, design, and fabrication methods. Afterward, it introduced their typical applications in biomedicine, environment monitoring, and so on. Finally, it described the future development trends and pos-sible commercial applications.

1.4 Summary

The most important reason for developing cell-based biosensors is that by using living cells components it is possible to respond directly to incoming information from an external physical or chemical stimulus. This functional information, with additional qualitative or quantitative analytical information, can be very important with respect to clinical diagnostics, pharmacology and drug screening, cell biology, toxicology, and environmental monitoring. By means of such biosensors, it is pos-sible to study the effects of pharmaceutical compounds, toxic substances, pollut-ants, and so on in a physiological system and especially in cellular metabolism.

The model of the cell-silicon, cell-metal electrode interface and the detection models of MEA, FET, LAPS, and ECIS are very important for improving the prop-erty of cell-based biosensors. For example, the characteristics of transmembrane ionic current are given based on the conductance and permeability of cellular membrane. With the development of micro electronic mechanical system (MEMS) and cell biology, the research on cell-based biosensors has reached the cellular and molecular level. Cells provide and express a series of elements such as naturally evolved receptors, ion channels, and enzymes that can be the targets of biological

1.4 Summary 11

active analytes. When stimulated, the living cell responds and takes action—induce electronic activity, excrete something, or absorb something. Cell-based biosensors that treat cells as biological sensing elements have the capacity to respond to ana-lytes in a physiologically relevant manner.

References

[1] Neher, E., “Molecular Biology Meets Microelectronics,” Nat. Biotechnol., No. 19, 2001, p. 114.

[2] Xu, G. X., et al., “Cell-Based Biosensors: Towards the Development of Cellular Monitor-ing,” Chin. Sci. Bull., Vol. 47, No. 22, 2002, pp. 1849–1856.

[3] Michael, P. M., et al., “The Neurochip: A New Multielectrode Device for Stimulation and Recording from Cultured Neurons,” J. Neurosci. Methods, No. 87, 1999, pp. 45–56.

[4] Harsanyi, G., Sensors in Biomedical Applications: Fundamentals, Technology and Applica-tions, Lancaster, PA: Technomic, 2000.

[5] Grattarola, M., and S. Martinoia, “Modeling the Neuron-Microtransducer Junction: From Extracellular to Patch Recording,” IEEE Trans. Biomed. Eng., Vol. 40, No. 14, 1993, pp. 35–41.

[6] Fromherz, P., “Joining Microelectronics and Microionics: Nerve Cells and Brain Tissue on Semiconductor Chips,” Solid State Electronics, No. 52, 2008, pp. 1364–1373.

[7] Fromherz, P., “Semiconductor Chips with Ion Channels, Nerve Cells and Brain,” Physica, No. E16, 2003, pp. 24–34.

[8] Yates, D. E., S. Levine, and T. W. Healy, “Site-Binding Model of the Electrical Dou-ble Layer at the Oxide/Wafer Interface,” J. Chem. Soc. Faraday Trans., Vol. I, 1974, pp. 1907–1818.

[9] Bergveld, P., et al., “Theory and Application of the Material Work Function for Chemi-cal Sensors Based on the Field Effect Principle,” Meas. Sci. Technol., Vol. 9, 1998, pp. 1801–1808.

[10] Hafeman, D. G., J. W. Parce, and H. M. McConnell, “Light-Addressable Potentiometric Sensor for Biochemical Systems,” Science, No. 240, 1988, pp. 1182–1185.

[11] Xu, G. X., et al., “Cell-Based Biosensors Based on Light-Addressable Potentiometric Sen-sors for Single Cell Monitoring,” Biosen. Bioelectron., No. 20, 2005, pp. 1757–1763.

[12] Giaever, I., and C. R. Keese, “Micromotion of Mammalian Cells Measured Electrically,” Proc. Natl. Acad. Sci. USA, Vol. 88, No. 17, 1991, pp. 7896–7900.

[13] Dittami, G. M., et al., “A Multilayer MEMS Platform for Single-Cell Electric Imped-ance Spectroscopy and Electrochemical Analysis,” J. MEMS, Vol. 17, No. 4, 2008, pp. 850–862.

13

C H A P T E R 2

Cell Culture on ChipsWei Zhang and Qingjun Liu

2.1 Introduction

Cell culture is one of the essentials that could impact the whole cell-based biosensor work procedure, and it is quite different from common cultures. In vivo, cell adher-ence and spreading are infl uenced by the interaction mediated between cell-cell and cell-extracellular matrix (ECM). However, it is still uncertain about the adherence mechanism in vitro. It is consistent with the ECM complex molecule, which plays similar role when cells adhere onto substrate coated with laminin or fi bronectin. The most common methods place cells directly on petri dish, slide, and other glass or polymeric plastic culture dishes. Attributing to a good surface hydrophilicity, most cells could locate and spread well on the glass or plastic dish. Besides, many conventional operation manuals and protocols are available for different types of typical cells, such as cardiac myocytes, neurons, glial cells, epithelial cells, embry-onic cells, and so on.

On cell-based biosensors, a complete contact between the bilayer and insulator silicon dioxide results in an affi nitive medium. For example, the electric fi eld chang-es elicited by action potentials could pass through SiO2 thin layer onto the polar surface of the chip. This is mainly attributed to the outstanding protein molecules in the lipid bilayer of the cell membrane, which could deposit on the substrate to mediate the cell adherence. These molecules could fi ll the gap between cell mem-brane and substrate with electrolyte and make it copolar. In the action potential du-ration, corresponding currents are elicited by ion streams on the cell membrane in the conductive gap, which enhances the voltage coupling between cells and chip.

It is a big challenge to culture cells on chips comprised of silicon or other con-ductive materials and, furthermore, to gain a good contact between substrate and cell membrane. However, the main problem is that the material itself is not suitable to cells in roughness, hydrophilicity, surface functional groups, or viscosity. There-fore, a lot of work is needed to improve the chip surface characteristics.

14 Cell Culture on Chips

2.2 Cell Immobilization Factors

Typical substrate materials are glass and silicon. Silicon dioxide is a kind of stable, harmless, nontoxic inertia material. In modern biosensors, many types of substrates like silicon nitride and photoresist such as SU-8 and other organic materials are used. Silicon carbide is another kind of good substrate material, as it is stable and could avoid ion or chemicals invasion. Silicon nitride is also widely used in sub-strate, because of its good properties in spreading and depositing in spite of a high dielectric constant. All the materials used in surface treatment process need to be biocompatibility tested fi rst.

Biocompatibility is an important concept used extensively within biomateri-als science, but there still exists a great deal of uncertainty regarding means and mechanisms. As biomaterials are being used in increasingly diverse and complex situations, more and more applications involve in tissue engineering, invasive sen-sors, drug delivery, gene transfection systems, medical nanotechnologies, and bio-technology. Materials are selected, or occasionally developed, on the basis that would be nontoxic, nonimmunogenic, nonthrombogenic, noncarcinogenic, nonir-ritating, and so on.

Cell immobilization on the surface of chips is essential for biosensor designs and applications. Furthermore, a fi ne coupling with the substrate is expected to be obtained. Fundamentally, some factors that affect cell immobilization on the surface of chips should be investigated fi rst. In terms of materials, several factors such as roughness, elasticity, functional groups, and landscape on the surface are drawing the most attention. Because the materials chosen in biosensor designs are commonly nonelastic, the majority of factors that affect immobilization of cell cul-ture on chips are grouped into three main aspects. One is a physical factor, includ-ing surface roughness and landscape, and the others are chemical factors, including surface functional groups and electric charge, and biological factors.

2.2.1 Physical Factors

Cell adherence and spreading on the substrate is a dynamic process. The cell has to change shape to make a better contact with the substrate. It is quite different to culture cells on the microstructure surface with channels or on edges and corners compared with that on smooth surface. Generally, a cell is apt to be directed, and it migrates along the protuberant part or fi ber orientation, which is called contact inducement in cell culture. Cell orientation is infl uenced evidently by depth and width of the channel. The reason is that cell adherence and spreading is a kind of microcosmic mechanical response, like fi broblast cells. Most adherent cells press the substrate to adhere and spread on it. As a result, cells can hardly survive on absolutely smooth substrates due to not having enough stress.

Roughness of the chip surface is also very crucial for cell adherence. Usually, surfaces of all kinds of materials are not absolutely polished, but fl uctuant in ran-dom. In a given length of L, using the function f(x) in profi le description and the arithmetic square difference of setover between profi le and center is called Ra, which could be considered as a parameter of surface roughness (2.1).

2.2 Cell Immobilization Factors 15

( )( )Ra f x dx L= ∑ (2.1)

A cell membrane itself is not absolutely smooth, but with transmembrane pro-teins, surface antigens, receptors, and so on. Neither too smooth nor too rough is suitable for cell adherence. It should match the surface roughness with the mem-brane, and this should be chiefl y considered in biosensor designs.

2.2.2 Chemical Factors

On the surface of silicon insulator layer, some chemical groups such as Si-O-Si, Si-O-O, and Si-O-H exist. Among those bond states, Si-O-H group is most con-tributive to the material hydrophilicity, which is approved by the measurement of the silicon surface energy spectrum. Enhancement of hydrophilicity makes it easier for cells to adhere onto the surface. Embedding the hydroxyl ions into the surface of silicon dioxide could bring in the Si-O-H group, which plays an essential role in changing the surface hydrophilicity.

Additionally, amino silane is a good choice for surface modifi cation because of its hydrophilicity. It depends on the property, toxicity, or other respects of the different kinds of silanes in practical operation. Diethylenetriamine (DETA) is typi-cally used in surface modifi cation [1]. When applied onto the surface, it could make it easier to form a self-assembled monolayer and promote the cell adherence to a certain extent, with the amino group extruding out of the surface.

Another important factor is electric charge. Mammalian cell membrane surface is charged. As a result, some work is needed for immobilization on glass or polysty-rene surface, which has a similar charge density.

Glass is comprised of silicon and oxygen atoms. One oxygen atom could com-bine with a pair of silicon atoms to make a bonding to capture a hydrogen atom, or to get charged. There is approximately one oxygen atom per square nanometer of glass surface. In soft glass, one in three atoms is charged on average, and surface electric charge density is near 0.3 per square nanometer. Electric charge density in cell lipid bilayer is less than 1 per square nanometer. Glass surface is easier for cell adherence. When the charge density matches with the density on cell membrane, it facilitates cell adherence. Aminosilane is commonly used in modifying the proper-ties of glass surface.

Usually glass is chosen for cell culture because its charge property matches with cell membrane after being treated. It was discovered that it could enhance the elec-tric charge density of the chip surface via immobilizing the amino acid group with abundant positive charging, such as laminin. Thus, it could signifi cantly improve the cell adherence.

2.2.3 Biological Factors

Recent researches on cell growth factors and extracellular matrix convey that some segments of the peptide own the special function, which could determine the prop-erty of the whole factors or matrix, such as RGD, IKVAV, and YIGSR [2, 3]. And these segments could bind with the intergrin on a cell membrane to promote a series


of biochemical reactions on the membrane and inside the membrane. It may help the cell adhere and spread onto the substrate. The RGD segment is the binding site of collagen protein, which facilitates the adherence of many types of cells. Mean-while, YIGSR segments are the active peptide sites of axon spreading promotion on laminin protein molecular. IKVAV sequence on PA22-2 can help neurites extend on the surface. Immobilization of these segments on the chip could improve the surface property, as neurons are prone to land on them and spread. Some peptide sections are immobilized on chips to help cell adherence. Under room temperature, dip the chip into a phosphatic buffer solution with 100 μg/mL poly ornithine for 24 hours. After rinsing, dip the chip into phosphatic buffer solution with 8 μg/ml laminin protein for 24 hours. Thus, a membrane 4 nm thick was formed on the chip, which could help the adherence of cells and avoid the hydration of silicon dioxide on chip surface.

Besides the factors of these materials, environmental controls during incuba-tion are also very important for cell survival on chips, including temperature, me-dium components, pH, osmotic pressure, and gas pressure.

Cell culture in vitro is sensitive to environment condition changes. The most suitable temperature for mammalian cells is 35°C–37°C. Generally, cells could sur-vive in lower temperatures—even in 4°C. Low temperatures only stunt the growth of cells. Nevertheless, high temperatures over 40°C are fatal to cells. It is because most kinds of enzymes are inactivated under high temperature, which affect cell metabolism and growth speed predominantly. They are also related to gene ex-pressions, action potential transduction, and so on. As a result, it is important to guarantee precise temperature control during the cell incubation and measurement process. Temperature control units have been designed in most chip platforms. However, a slight change, like 0.5°C, has little effect on chicken embryonic myo-cyte action potential measurements.

The components in this medium include salts, glucose, amino acids, and vi-tamins. Now serum is also requested in many cell line cultures, in which some peptides could promote cell growth, differentiation, and adherence. Cells are also very sensitive to pH and osmotic pressure changes in solution. pH value is adjusted to 7.4±0.2 by bicarbonate or other typical buffers. And the osmotic pressure of sample should be 260–320 mOsm/L, with a fl uctuation of 10 mOsm/L. Gas control during incubation is also very important for cell metabolism. Carbon oxide is one of the components in pH balance system. Typical CO2 pressure in air is about 0.1 kPa; however, it should be kept in 5–10 kPa in cell culture process.

The whole operation of chip packing and cell culture should be ensured in a sterilized environment. Additionally, all the subjects involved in the cell’s cul-ture, including substrate, petri dish, and other materials, should have good biocompatibility.

2.3 Basic Surface Modifi cation Rules

Patterning cells effectively on chips has recently drawn considerable attention due to its important role in fundamental cell biology, tissue engineering, cell-based bio-sensors, and other bio-MEMS devices. Many methods in biofunctionalized polymer

2.3 Basic Surface Modifi cation Rules 17

surfaces analysis are well considered in surface characterization, including spectral methods (x-ray photoelectron spectroscopy, Fourier transform infrared spectros-copy, atomic force microscopy (AFM), and others) as well as nonspectral methods (water contact angle, dye assays, biological assays, and zeta potential).

Focusing on the factors that affect the cell immobilization, a series of methods are performed on the surface. Traditionally, some physical methods such as the sur-face hydrophilicity improvement, roughness changing, building special structure and chemical substance or biochemical molecular printing are all available for cell immobilization on biosensors.

2.3.1 Hydrophilicity Improving

Hydrophilicity is one of the fundamental factors that affect the soakage property of material. A uniform hydrated layer exists outside the cell membrane, which de-termines the suitable parameter of the cell adherence surface. Water contact angle measurement reveals that methyl (CH3), bromine (Br), and vinyl (CH=CH2) groups result in hydrophobic surfaces, while amine (NH2) and carboxyl (COOH) func-tion groups lead to moderately wettable surfaces. Meanwhile, polyethylene glycol (PEG) and hydroxyl (OH) groups create more wettable surfaces. It helps to improve hydrophilicity of silicon surface by the means of hydroxyl ion embedding or silane pretreatment.

Hydroxyl ion implantation into the silicon signifi cantly affects structural prop-erties of the surface. It is known that a surface with better hydrophilicity facilitates cell adherence and proliferation. By modulating the energy and the dosage of im-plantation, Fan et al. pursued an appropriate experiment condition and made it work in tissue repair engineering to enhance the cell adherence ratio on hydroxyl ion modifi ed silicon surface [4]. On silicon surface treated with hydroxyl ion, cell adherence increases signifi cantly. In the aggregation region, the axon outgrowth and formation of synapses are well developed. Ion implantation increases neither the thickness of chip surface nor the conductivity of plating layer. However, it indeed improves the signal-to-noise ratio in spite of some sensitivity loss. That is because the essential aspect of ion channel and action potential measurement is to detect the concentration of hydrogen ions, which is decreased to an extent due to the implantation of Si-O-H groups.

Amino silane is a kind of chemical coupling reagent. KH550 is one of the widely used reagents with a molecular structure as H2N(CH2)3Si(OC2H5)3, in which both amino and triethoxy groups exist. The amino end group combines with the organic material fi rmly, while the triethoxy end group reacts with a little water to become part of the silane alcoholic group, which should combine with silicon surface after disassembling. Thus, amino silane can improve the adherence property between glass fi ber and resin, in term of glass fi ber complex material intensity promotion. As a result, it is commonly applied in protein chips, DNA chips, or other cell chip fabrications.

Additionally, because of amino group hydrophilicity, amino silane is a good choice for surface modifi cation. In practical operation, different kinds of silanes are applied depending on their properties. Taking KH550 as an example; commonly,


it is diluted into 0.5%–2% with water, alcohol, or a mixture, especially deion-ized water, ethanol, or isopropyl alcohol without fl uoride ion. Usually it is diluted with 95% ethanol and adjusted pH to 3.0–5.5 with hydrochloric acid or acetum solution.

Hydroxyl group on the surface could react with silane molecular to construct the structure on a glass surface, which could change the property of glass and make it easier for cell adherence. More applications on a silicon surface are presented in the following passages.

2.3.2 Roughness Changing

Silicon is a kind of proper material for cell chip fabrication. However, its current polishing surface is diffi cult for cells to adhere to. Fan et al. studied neuron culture on different roughness silicon surfaces and concluded that neurons could adhere to and spread on the silicon surface with appropriate roughness [5]. Roughness is basic physical parameter of material surface. Cells tended to be normal morphology and function on silicon surfaces with an average roughness of 20–50 nm. Under the same conditions, it is not suitable for neuron cultures with roughness lower than 20 nm or higher than 50 nm. In their experiments, silicon was dipped in hy-drofl uoric acid to enhance the roughness to 25 nm from the original 3 nm; thus, it dramatically improved the surface property for neurons adherence. Compared with chemical components or reagent depositions, changing roughness needs no coating but promotes cell adherence effectively. It not only makes great progress in biocom-patibility, but also avoids noise due to some additional medium or chemical groups changing. Hence, it ensures the normal signal conduction between cell and chip.

After pretreatment in reactive ion etching, some columnar structures in nano-scale, called silicon grasses, appeared on the silicon surface. With normal lithog-raphy technology as a mask, part of the silicon region was reformed with wet chemical etching. In this way, a structure with different roughness (silicon grass and wet chemical etching area) was achieved. Meanwhile, astrocytes were cultured on a nano-patterned surface of silicon. The results showed that a certain type of de-formed astrocytes preferred to adhere on wet chemical etching areas, while primary astrocytes of newborn rats would like to land on silicon grass instead [6].

2.3.3 Chemical Coating

Silicon or silicon dioxide itself is not suitable for cell adherence, so that some chemi-cal substances with special functional groups are necessary to improve the surface for attachment. This is called chemical coating. It is widely used in inducing cell adherence and spreading along the pattern coated on the substrate. The most direct way to change the substrate adherence property is to coat a well-biocompatible material on the whole surface, such as immobilizing a certain adherence molecular or extracellular matrix.

Dissociated cells adherence process could be divided into four steps, as shown in Figure 2.1:

2.3 Basic Surface Modifi cation Rules 19

Glycoprotein secoisolariciresinol diglucoside (SDG) from medium blood se-1. rum fi rst absorbs on the surface of the culture dish due to electric charge.Cells dissociated from integrate tissue are deposited on the surface after 2. 1–2 hours.Condition factor (CF) combines with glycoprotein and cell surface glyco-3. protein (CSG), which mediates cell to touch the surface.Following cell immobilization and spreading fl at, about 24 hours later, a 4. single layer appears.

As a result, either the relation acquisition between cell surface and substrate or the structure and function stabilities relies on the existence of absorption fac-tors in glycoprotein. Most neurons are anchorage-dependent cells, which behave poorly on glass or silicon substrate. Hence, a good sticker like poly-lysine or rat tail collagen is needed to be coated fi rst on the substrate to help neuron adhering and spreading.

Matsuzawa et al. coated laminin as lines on the surface of glass, and then adsorbed butyldimethysilane (BDMS) and aminopropyldimethysilane (APDMS), respectively, on it to promote hippocampal neurons to adhere and spread onto the surface [7]. Thus neurons well adhered along the pattern lines. Additional results were visualized where neurons cultured on APDMS adsorption surfaces showed a special appearance with several short branches and a long axon; neurons on a BDMS adsorption surface changed little in axon length, with a basic neuritis and branches reduction. It is necessary to choose the appropriate substance to coat on the surface for the fi nal target cell pattern acquisition.

Clark et al. did further research where their group combined simple chemi-cal coating and topology [8]. In their study, they cultured chicken embryo brain neurons on micro-patterned laminin surface. They modulated the width between laminin lines and found the lines’ interval reduction could enhance the neuron ad-herence, especially when reducing to 2 μm. The neuron cluster grew more uniform and glomerate along the laminin-coated track. With the technology developing dra-matically, AFM makes it possible to control the thickness of protein coating. An-tonik et al. coated the laminin on the silicon surface and utilized the cantilever tip of AFM to scratch on the surface to reduce the thickness of protein coating [9].

However, a few disadvantages limit this simple method of application in cell-based biosensors. First, coating on chip surfaces infl uences the interface between cell and substrate. Additional coating might cause the current leakage to culture medium or other cells via a material plating layer or even other electrodes. As a

Figure 2.1 Fresh dissociated cells spreading and adherence process.


result, a lot of superimposed noise is brought into a single electrode, which enor-mously reduced the signal-to-noise ratio. In other ways, the risk of bacteria con-tamination raises and coating on substrate means additional sterilization.

2.4 Typical Methods

In recent years, cell-based biosensor is becoming a hot topic in science. It means to immobilize the living tissue or cells directly on sensors or transducers, which could specially response to chemical substances, potential change, or other immune in-teraction in intra- or extracellular microenvironments. One of the most important parts of biosensor is living cell or tissue, which receives or sends out biological signals, such as ion concentration change, electrical current, or voltage fl uctuation. The other parts are mainly physical components, including devices and periphery equipment. As a result, extraction, dissociation, purifi cation of cells, and suitable device design determine the system together. Solving a well interface and obtaining a precise combination and a swift response reveals the orientations of the future development.

A fast MEMS development has obviously reformed biological, chemical, and medical areas. Technologies such as 3D DNA chip, lab on chip, and μTAS chip are all based on biological theory and MEMS fabrication technology.

The main problem in cell and biosensor chip combination is how to immobilize the cell on a target position and obtain a good contact between the cell membrane and chip, because most of the time target cells may be surrounded by many other cells or tissues such as support cells and extracellular matrix. Meanwhile, it is vital to ensure the stability of the gap between cell and electrode because of its sensitivity to action potential during the recording process (in Figure 2.2 [10]).

Different types of chips are applied in a biosensor area, and methods used for cell immobilization are also various. Some efforts are needed for cell immobiliza-tion. Considering the signal detection and amplifi cation, neither complicated 3D structures nor changes are allowed in chip surface and shape. Electric property makes it diffi cult to etch or implant ions into the surface. If biodegradable materi-als are applied in chip surface, the signal detected would always be changing. Some other methods must be developed.

Several recent novel techniques, with or without protein mediation, were devel-oped, including the metallic and perforated electrode fabrication [11], microcontact printing [12], microfl uidic channels [13, 14], and ink-jet printing [15]. In addition, extracellular matrix such as poly-lysine, poly-ornithine, laminin, fi bronectin, and collagen result in a better adherence and spreading of cells and tissues on surface. However, most of these patterning techniques focused on guiding cells on substrates of a single type material—generally a mechanical device in contact with substrate or controlling the immobilization process. Palyvoda et al. coated NH2-terminated SAM on Au surface to guide neurons immobilization on glass wafers and found that dissociated neurons from rat embryos could form good neuronal networks on the 11-amino-1-undecanethiol–coated surface after 6 days [16]. The high adher-ence and survival of neural cells are evidence of biocompatibility and nontoxicity. Other groups [17, 18] also discovered that SAM might play a role as the support

2.4 Typical Methods 21

structure for the cell culturing due to its long-term stability. However, the subject of extracellular potential detection has not been studied very much. Currently, mi-crofl uidic technology has been widely utilized in precise immobilization.

Commonly, three types of strategies are applied in immobilizing cells on sen-sors. First, electrode array design combines with topology method, which guides synapse orientation by artifi cial microstructures [19]. Thus, it helps to dissociate the cells for a long-term measurement,or record the distribution and current change of neuronal network typically via fi eld effect transistor (FET) array [20]. A practi-cal method is provided for long-period detection between cells and microelectronic circuits. Various microstructures are fabricated on silicon for trapping cells and test separately. Some researchers pointed out that a microhole with a diameter of 150 µm was appropriate for the cell adherence [21]. However, it was supposed that the network oriented as the ideal model and formed synapses artifi cially, which might not tell the truth in vivo. In addition, it is diffi cult to control the process exactly to fabricate the complicated structure on a chip.

Another way is to utilize some chemicals or reagents to guide the neurites spreading along the track. Some chemicals and proteins such as poly lysine, amino silane, poly ornithine, some peptide sections, and complex compounds, could pro-mote the adherence and combination of neural cells and neurites on chips. How-ever, technology is still lacking in single cell measurement. Many cells prefer to cluster in a coating region, which make it diffi cult to locate the target during the experiment. The microcontact printing method integrates the lithography technol-ogy and chemical coating using a self-assembled monolayer, which guides cells to attach to the target region effectively.

Figure 2.2 Micrograph of cells cultured directly on surface of a chip. (From: [10]. Reproduced from Sensors and Actuators B: Chemical. © 2004, with permission from Elsevier B.V.)


Lately, amazing materials development plays a helpful role in cell culture on chips. Polydimethyl siloxane (PDMS) is satisfactorily biocompatible, stable, non-toxic, and suitable for cells as a novel organic material. Scaffolds made in PDMS could be used to hold cells and support them for a more complex 3D structure, which is more suitable for cell culture.

2.4.1 Special Physical Structure

Cells, especially neurons, could hardly survive on raw silicon or silicon dioxide, due to its poor biocompatibility, physical properties, and chemical properties. For the sake of a good interface between silicon and cells, some modifi cations are needed on the silicon surface. Fromherz and Zeck suggested that a single neuron body could be fi xed by a picket fence of polyimide, while the neurite could spread through the poles and form a network in plane, and it was supported by a stable test model (Figure 2.3). The stimulation by electric pulse from capacitance stimuli unit on a chip did no harm to the neuron. At the same time action potentials and other signals propagated via a neuronal network from one former nerve cell to the next were recorded [22].

Maher et al. suggested that we could reduce contact resistance by etching a neural well on chips [23]. It could prevent a cell from growing outside the surface of an electrode, which was outstanding progress in cell chip fabrication. On a silicon chip, with common lithography technology, microwells slightly larger than the neuron were etched. There was a 16-well array on a chip, and each electrode leader connected with an exterior circuit at the bottom of the well. Thus, electrode and cell were one-to-one correlative, which made progress in effi ciency and consist-ency in measurement. Hippocampal neurons dissociated from mice were posited in these wells via micromanipulation technology. The main problem was that neurites had to move outside the neural well to synapse with other neurons, which made

Figure 2.3 Neurons culture on silicon chip. (a) Stimulator wings (St) and transistor (S, source; D, drain; G, gate) were marked on neuron silicon chip. Scale bar: 20 μm. (b) A single neuron was trapped inside the poles, neurites were spread outside and connected with others. Scale bar: 20 μm. (c) Micro polyimide poles on biosensor. Scale bar: 100 μm. (From: [22]. Reproduced from Proceed-ings of the National Academy of Sciences. © 2001, with permission from the National Academy of Sciences, U.S.A.)


it diffi cult to form a more complex network. Additionally, the responses were dif-fi cult to analyze due to signals from different neurons in each well.

Many researchers dedicate themselves to the study of cell culture on a nano-patterned silicon surface. Turner and his coworkers utilized reactive ion etching technology to deal with the silicon, after which some special grass with nanoscale was achieved [24]. The morphology of the cells cultured on the special structure could be detected by confocal scanning microscope (CSM) and scanning electronic microscope (SEM). The results revealed that primary cortex astrocytes preferred to land on the special grasses than other astrocytes.

It is a good idea to reconstruct neuronal network in 3D cell culture, which has been proposed to replace the traditional 2D cell culture systems. Microfabrication is an ideal way to produce inexpensive 3D cell culture microenvironment [25]. Wu et al. at the University of Georgia had an idea to utilize SU-8 to form a microstruc-ture on silicon, in which they rebuilt the neuronal network and marked it with fl uorescence dye. SU-8 has good stability and biocompatibility, and it is easy to fabricate into any kind of microstructure. Human neuroblastoma cells (SH-SY5Y) were cultured in SU-8 microfabricated microwells, as shown in Figure 2.4 [26]. Furthermore, SH-SY5Y cells within microwells were characterized with voltage-gated calcium channel (VGCC) function and resting membrane potential by using confocal microscopy. Neural progenitor cells within microwells were characterized with cell morphology and neuronal network formation. SH-SY5Y cells cultured in SU-8 microwell microstructures formed 3D tissue and exhibited higher membrane potential than cells on 2D substrates. In response to 50-mM high K+ depolarization, cells in microwells were less responsive in comparison to cells on 2D substrates in intracellular Ca2+ uptake [27].

Figure 2.4 SH-SY5Y cells cultured in microstructures. (a) Phase contrast images show SH-SY5Y cells interfaced with the microwell network patterned on day 5 and (b) into differentiation on day 13. (c) Fluorescent image for the same fi eld as in (a), showing neuronal extensions along the microchannels. Cells were labeled with FITC. (d) SEM image shows the cross-sectional profi le of patterned network in microwell after cell culture. Scale bar: 100 μm. (From: [26]. Reproduced from Colloids and Surfaces B: Biointerfaces. © 2006, with permission from Elsevier B.V.)


A special structure called adherence islands was made, which had some certain size, shape, and pattern. After gold plated onto the silicon, self-assembled mono-layer (SAM) was adsorbed on to the micro physical structure. The fi rst layer of SAM is alkanethiol. A stamp made of PDMS with rubber property could adsorb this kind of compound after plasma oxidation and make a new pattern on chip. Peeling off the PDMS mold, another poly glycol with a termination of alkanethiol combined with gold region, which resist the protein adsorption. As a result, the process used a simple way to make gold plating layer adsorbing protein with a certain size (2–8 μm) on alkanethiol region, which resisted protein originally. When exposed to pure ECM, such as laminin and fi bronectin, these adsorption regions could immobilize cells to islands with protein covered.

2.4.2 Microcontact Printing

Microcontact printing is probably best known as the soft lithography technique. It could effectively guide cell to survive on chips and become more and more impor-tant in neuronal network research. In the elastomeric membrane method, proteins and cells could be patterned on substrates through the holes of a micropatterned PDMS membrane, which is peeled off later to allow cells to spread from the initial pattern. This method can be applied on a variety of surfaces, such as glass and plas-tic, and provides a unique approach for studying cell spreading and migration when the surfaces are precoated with mediating proteins.

With the rapid development of material science, a lot of novel biological ma-terials are gradually applied in the chemistry engineering, biology, electronics, and medicine industries. PDMS is a novel organic material with excellent antioxida-tion, thermal stability, biocompatibility, and low surface tension. It is convenient to be fabricated to any required pattern.

The fabrication of the stamp in microcontact technology is very important. The fi rst step is to pattern on PDMS by lithography. Molds for the production of PDMS stamps were usually produced as described by Love and his coworkers [28]. A photomask was created in a CAD drawing tool and printed on transparent fi lm at a high resolution. Then, the photomask was placed at the fi eld to transfer the reduced image to a substrate.

To prepare silicon wafers for the deposition of photoresist, Si <100> wafers were sonicatied in acetone and methanol and dried at 180°C. Positive photoresist was spin-coated onto the wafer at 4,000 rpm for 40 seconds to a thickness of about 1.3 μm. The coated wafers were baked at 105°C for 4 minutes. Photoresist-coat-ed silicon must be protected from light during preparation. The next step was to choose an appropriate cell intimate substance, which was usually silicon or glass, and load on the surface to mediate the cell adherence. Some proteins with special peptide terminated are usually used for immobilizing different types of cells. The fabrication process of a stamp is shown in Figure 2.5(a).

Compared with other technologies, microcontact technology could behave well on patterning cells to the target region, especially on two or more different kinds materials mix regions. This technology makes it much easier and more effi cient to pattern cells on planar or nonplanar surfaces. It could also be employed to form a


cell network in vitro on a required region, even at a low cell density, as shown in Figure 2.5(b, c).

Neurons in cell culture make synaptic connections with each other to form in-teractive networks. The random nature of in vivo network organization in culture complicates the investigation of synaptic interactions. A novel strategy to create cultured neuronal networks in vitro with defi ned composition and connectivity is needed. The Offenhäusser group reported a self-ordered in vitro culture on a grid of laminin A, which featured an array of 14 × 14-μm nodes connected by 6-μm tracks [30, 31]. Heller et al. patterned networks of hippocampal neurons on peptide-coated gold substrates prepared by microscope projection photolithogra-phy and microcontact printing, as shown in Figure 2.5(d). A 19-amino acid peptide segment of laminin A (PA22-2), which included IKVAV cell adherence domain, was used to direct patterns of cell adherence in primary culture [29]. Dispersed hip-pocampal cells isolated from a neonatal mouse were grown on peptide-patterned gold substrates for 7 days, and then membrane potential was recorded. Neurons showed a preferential adherence onto peptide-coated arrays. As a result, peptide-modifi ed gold surface served as a convenient and effective substrate for growing ordered neuronal networks that were compatible with existing multielectrode ar-ray recording technology.

Figure 2.5 Microcontact fabrication process and reconstruct network in vitro. (a) PDMS stamp fab-rication scheme; (b) microcontact stamp mold, scale bar 100 μm; and (c) reconstructing a neuronal network in vitro. (From: [12]. Reproduced from the Journal of Neuroscience Methods. © 2006, with permission from Elsevier B.V.) (d) Fluorescent image of patterned hippocampal neurons after 4 days’ incubation on gold coated with PA22-2. (From: [29]. Reproduced from Biomaterials. © 2004, with permission from Elsevier Ltd.)


This method works dramatically in patterning neural cells and networks ar-tifi cially, especially for cells at a low density. However, it may adsorb many cells onto protein-covered regions, which could break uniform distribution of cells and hardly track single cells. The stamp microfabrication process is also diffi cult in micro scale.

2.4.3 Fast Ink-Jet Printing

Former methods have mentioned that some small islands of adhesive protein or cells arranged over an inhibitory background are employed in patterned cell cul-tures, especially in neuron cultures. Lately, more techniques have included contact masks using photolithography, photochemistry, and microstamping. However, they are limited by their use of an unalterable master pattern, such as one mask or solid stamp.

Ink-jet printing, a method using low-cost desktop printers that patterns mate-rial by depositing microscopic droplets under programmable hardware control has been developed recently. In contrast to desktop ink-jet printers, which print dyes or pigments onto paper, Sanjana and Fuller printed biologically active materials onto coverslips or chips [15]. Ink-jet printing has outstanding advantages in that the patterns are programmable by computer and it is fl exible. It becomes much easier to deposit multiple layers of different materials to build 2D or more complicated structure. Less cost is also necessary. In their work, polyethylene glycol (PEG) was chosen as the inhibitory background material, which has been shown to provide a long-term inhibition of cell adherence in several studies of patterned neural cell culture [32, 33]. PEG is a kind of material, which is nonreactive with the glass print head, as well as nontoxic to tissues and cells. In their experiment, a collagen and poly-D-laminin (PDL) mixture was chosen as an adhesive part, which was printed by ink-jet on top of the PEG background according to the pattern set in the program.

An ink-jet printing system consists of tightly controlled microscopic droplets that are 10–100 μm in diameter when put onto the substrate. While continuous jet printers elicited a continuous pressurized stream that broke ink up into small droplets, the printer ejected single droplets in response to a pressure impulse in the ink chamber [34]. The pressure impulse is generated by a piezo crystal that deforms in response to a voltage pulse generated under computer control, usually such as mouse and keyboard clicking. The droplet loaded and adhered onto the substrate. Liquid evaporated and left a round protein deposition that would administer to cell adherence. The surface chemistry presented using a uniform covalently bounded layer of PEG as the inhibitory background and a mixture of collagen and PDL printed on top as the cell adhesive foreground. In principle this chemistry also has fl exibility. Neurons and glia cells were plated onto the substrate (Figure 2.6). Pat-terns for neurons included controlled micro-islands, lines of dots as narrow as 65 μm, gaps as small as 8±2 μm, and arbitrary shapes. The mean density of synapses in pattern and control cultures was observed. Synapses are abundant phosphopro-teins found in virtually all presynaptic terminals [35]. In both pattern and control


cultures, synapses were not signifi cantly different, and similar synaptic density re-sults had been found in high density, unpatterned hippocampal cultures [36].

The advantages of the method are that it is programmable and relatively inex-pensive. In addition, ink-jet printing has some other potential advantages in neuro-science, including the ability to fabricate gradients, layered patterns, and nonpla-nar structures much easier than many mold pattern-based approaches. Meanwhile, much smaller dot size is still expected, which is optimal lower than the 65-μm resolution. New technologies in drop generation based on print heads are different from the way it is used in this area, such as acoustic droplet generators, which may push to an even lower resolution and printing reliability.

2.4.4 Perforated Microelectrode

In the past few years, microelectrode arrays (MEAs) have been widely developed in parallel, noninvasive, real-time, long-term, and extracellular monitoring of cell electrophysiological activity [37–39]. And it is a powerful tool for high-throughput drug screening, intercellular signal propagation for networks, and so on. However, electrodes on MEA are limited to less than 100, so it is becoming vital for measure-ments to enhance the distribution of seeding cells available. Furthermore, extracel-lular signals of cells are rather small, on the order of 10–400 μV for neurons and 500–1,000 μV for cadiomyocytes. The probability of harvesting cells on an elec-trode can be easily increased by utilizing high-density cultures. But it does not work in all cases. Low-density cultures allow for a study on signal propagation along the pathways. Without a particular cells placement technique, it is hard to place cells on

Figure 2.6 Fast ink-jet pattern and cell culture. (a) Diagram of a piezo drop-on-demand ink-jet printing operation process. (b) Microscopic ink droplets were ejected individually through an orifi ce by means of a pressure impulse delivered by a piezo crystal. Each droplet, 10–100 μm in diameter, took a small distance (~1 mm) to the glass substrate. The print head was moved robotically in two-dimension above the substrate as droplets ejected, leaving a pattern of round dots onto the sub-strate. (From: [15]. Reproduced from the Journal of Neuroscience Methods. © 2004, with permission from Elsevier B.V.)


a single electrode. Thus, a perforated electrode technology has been used to enhance the cell coverage on electrodes.

The device was a 5 × 5 mm2 chip fabricated by standard industrial CMOS tech-nology with subsequent dedicated micromachining steps for electrodes deposition and immobilization features realization [11]. The area for cells culture was in the center of chip, including a 4 × 4 array platinum electrodes, and the platinum layer was fabricated during the post-CMOS micromachining (Figure 2.7). For cell suck-ing, the chip was mounted on a modifi ed dual-in-line (DIL) package that was able to apply pressure from the chip backside. The DIL package had a 1-mm hole in the body, which was fabricated by laser cutting. And a silicon tube connected to the pressure setup was inserted into the hole and sealed by using particular glue. Cell placement was achieved via applying slight underpressure from the backside setup. Thus, it could pull the cells toward the electrode center. Surface chemistry was also inevitable before underpressure was applied. Neonatal rat cadiomyocytes were used in measurements of the electrical activities. The cadiomyocytes were puri-fi ed by using gradient centrifugation and seeded on chips. Plating medium-featured higher serum content enabled better adherence to the surface of chip [40].

During placement, the underpressure was 20 kPa lower than ambient pres-sure until all electrodes or holes had been occupied with cells. Cell clusters were assumed to be an incomplete cell separation during the trypsinization. Most of the orifi ces (90%) were occupied with single cells, 4% of the orifi ces were occupied with two or more cells, and 6% were not occupied. Unoccupied orifi ces were most probably a consequence of clogging.

This technology is advantageous in a wide variety of applications ranging from low- to high-density cultures and from cardiomyocytes to neurons. The cells were sucked for just a few seconds during the placement, and no other pressure was applied afterward. In consequence, it was not obviously harmful in cells, and it dramatically enhanced the coverage rate on a single electrode. Different applica-tions are allowed for signal recording of individual cells or clusters and even signal transmission in networks.

Figure 2.7 Microholes on the surface of electrode and myocytes culture. (a) Perforated platinum electrodes. (b) Immobilized neonatal rat cardiomyocytes on the orifi ces. (From: [11]. Reproduced from Journal of Micromechanics and Microengneering. © 2007, with permission of IOP Publishing Limited.)


2.4.5 Self-Assembled Monolayer

Self-assembled monolayer (SAM) technology has emerged as a promising approach to defi ne the length scale of material science as small as those conveniently acces-sible by lithography [41, 42]. SAM of thiols on gold has been intensively researched in recent years because of its promising future in microelectronics and biotechnol-ogy. It can be a model system for more complex membranes [43, 44]. There are many approaches in SAM, including headgroup-substrate interaction, endgroup-substrate interaction, chain-chain interaction, and endgroup-endgroup interaction [17, 45]. All of these approaches are attempted to realize the contact attraction and repulsion that guide cells to locate onto the target region.

The surface was modifi ed with a novel SAM, which could effi ciently guide the cell adherence on gold region [46]. To ensure the surface is totally clean, the chip was fi rst immersed in acetone for 10 minutes in an ultrasonic bath, and then 5 min-utes in an ethanol ultrasonic bath. The gold regions were consequently immersed in a 20-mM mixture of alkane thiols of 11-Mercaptopropionic acid (11-MUA) and 3-Mercaptopropionic (3-MPA, 1:10 V/V) for at least 16 hours to create SAM. And the silicon oxide background was passivated with methoxy-polyethyleneglycol-silane (methoxy-PEG-silane) solution prepared in nitrogen-fi lled reaction fl asks by adding 3 mM PEG-silane in anhydrous toluene containing 1% triethylamine. The reaction proceeded under nitrogen at 60°C for 18 hours. Nonbounded groups were removed by ultrasonic washing in toluene and ethanol for 5 minutes each, followed by rinsing in deionized water and drying under nitrogen. Then it was exposed to a mixture of 30-mM NHS (N-hydroxy succinimide) and 150-mM EDAC (1-ethyl-3-(3-dimethylamino-propyl) carbodiimide esters for 30 minutes, which could acti-vate the terminated carboxyl in alkane thiols. Then the chip was sterilized in 75% ethanol for 15 minutes, and soon treated with adherence peptides (KRGD) in a phosphate buffer solution (PBS) with a concentration of 0.1 mg/ml. After each step, the chip was rinsed with its original solvent and deionized water for 5 minutes, respectively, to remove other unbound molecules.

A density of 2 × 105 cells/mL NIH3T3 cells were plated onto the peptide-patterned substrate, and cells were allowed to adhere to the substrates for 24 hours under the standard culture condition. Also, three different sizes of gold electrodes were designed for a single cell impedance measurement. Cells attached to covalent-ly bound electrodes increased the measurable electrical signal strength by 48.4%, 24.2%, and 19% for three sizes of electrodes, respectively. Compared with cells attached to physically adsorbed electrodes, the results demonstrated that both elec-trode size and surface chemistry played key roles in cell adhering, spreading, and the impedance characteristics of cell-based sensors.

A report on a simple technique to precisely position cortical neurons in a serum-free medium on 2D electrode arrays and pad size effects on neuron cell culture and immobilization was also well investigated [16]. Gold patterns were produced on glass substrates using microfabrication processes. Also, 1-amino-1-undecanethiol SAM was coated only on the gold surface. Cortical neurons were cultured on the arrays for measurement. And the undecanethiol thin fi lm was essential for generat-ing cell adhesive areas for the rat cortical neurons. A 50 μm × 50 μm SAM pad size was found to be suitable for single cortical neuron immobilization, while larger


pads provided excellent neuron coverage (Figure 2.8). The technology might enable neurons precisely localized during stimulation and surveillance for both biological research and medical applications.

Monolayers with different terminated groups were found to have different ef-fects on cell adherence [43]. The interaction of human fi broblasts with CH3, PEG, and OH terminated SAMs was similarly weak when cell spreading, and fi bronectin matrix formation and growth were observed on COOH and NH2.

Compared with other methods to immobilize cells onto the target region, SAM is a simple and effective technology, which does not need complicated microfabri-cation, any expert experiences, or precise alignment equipments. Meanwhile, it is a novel method available for cell manipulation in micrometer scale, although the signal decreases to some extent due to the multimonolayer.

2.4.6 Microfl uidic Technology

Micro total analysis systems (μTAS), also called lab-on-chip, integrate analytical processes for sequential operations such as sampling, sample pretreatment, analyti-cal separation, chemical reaction, analyte detection, and data analysis in a single microfl uidic device. Microfl uidic-based research has made signifi cant progress over the past few years and has recently become a hot topic. Microfl uidic chip-based systems for biological cell studies have attracted signifi cant attention because of their advantages, including low reagent and power consumption, short reaction time, portability for in situ use, low cost, versatility in design, and potentials for parallel operation and for integration with other miniaturized devices. Microfl uidic technique has played an increasingly important role in cell biology discoveries, neu-robiology, pharmacology, and tissue engineering.

A comprehensive review of microfl uidics for cellomics was presented, which covered the microfl uidic devices for cell sampling, trapping, sorting, cell treatment,

Figure 2.8 SAM deposit process on substrate and neurons on gold regions. (a) SAM deposit pro-cess. (b) Fluorescence images of (1) single rat embryo cortical neuron cultured on 50-μm × 50-μm SAM coated gold pads, and (2) cell-to-cell connection on the 50-μm × 50-μm SAM pads with 50-μm gap. (From: [16]. Reproduced from Biosensors and Bioelectronics. © 2006, with permission from Elsevier B.V.)


and cell analysis [47]. Sequentially Erickson focused on integrated microfl uidic devices for cell handling and cytometry, dielectrophoretic cellular manipulation and sorting, and general cellular analysis [48]. Manz et al. presented a series of comprehensive reviews concentrated on μTAS that covered development history and theory of miniaturization, fabrication of microfl uidic system, and microfl uidic standard operations, including sample preparation, injection, manipulation, reac-tion, separation, and detection [49–51].

Instead of a solid stamp, the microfl uidic patterning uses a stamp with a network of microchannels to deliver mediating proteins or cells to the substrate [14, 52, 53]. The technique reduces the potential damage to the mediating proteins by the solid stamp and can pattern cells on substrates of polymers or metals. Compared with solid stamps, this technology allows more complex pattern design, which could realize multitype protein or cell deliveries onto the inconsistent surface.

A 3D microfl uidic system was fabricated and used to deliver proteins and mam-malian cells on a planar substrate in Figure 2.9. The 3D topology of the microfl u-idic network in the stamp made it a versatile one with which to pattern multiple

Figure 2.9 Fluorescence and phase-contrast pictures of two cell types deposited on a tissue culture dish in a concentric square pattern by using the 3D stamp. (a) Two kinds of cell suspensions of ECVs and BCEs were introduced into the three sets of channels of stamps and were allowed to land and attach to the surface of culture dish. These cells were cultured with the stamp in place for 24 hours to grow and spread into a confl uent layer. The pictures were taken immediately after the PDMS stamp was removed. (b) Fluorescence image of cells. (c, d) Phase-contrast pictures of cells. (From: [13]. Reproduced from Proceedings of the National Academy of Sciences. © 2000, with permission from the National Academy of Sciences, U.S.A.)


types of proteins and cells in a complex, discontinuous structures surface. Channels formed by the stamp when it was in contact with the substrate surface. It limited the migration and growth of cells in the channels with the channel structure in con-tact with the surface. And the cells could land and form a confl uent layer [13].

The membrane had two levels of structures. One level provided a plane of channels, which opened for contact with the substrate that was to be patterned. The other level provided the vertical channels that connected these channels in the membrane to those in the slab. The master in the bottom layer required two steps of photolithography, while the master for the top layer was fabricated with a single step. To fabricate the PDMS membrane, a drop of PDMS prepolymer between the master and a Tefl on sheet was sandwiched. To ensure the vertical channels were not blocked by a thin underlayer of PDMS, pressures in the range of 10 kPa to 50 kPa were required. Once the PDMS had cured, it was peeled off and attached to the wafer by van der Waals interactions.

Another fabrication of microfl uidic membrane was designed with an array of 50 μm × 50 μm vertical wells buried horizontally, interconnecting microfl uidic channels [54]. The wells were spaced by 150 μm, and horizontal channels were formed with widths of 40, 20, and 10 μm. Then the PDMS membrane was aligned onto a commercially available microelectrode array (Panasonic MED64). Patterns were etched so that lines of varying widths could connect eight electrodes in a line, as shown in Figure 2.10. Patterning of neurons was achieved. Alignment of the microfl uidic structures to MED64 probes was also achieved via an XYZ stage.

Microfl uidic technology could directly realize cells selection, transport, and po-sition on biosensors. This successful hybrid system helps to reduce the cell damage during the transport process, which could ensure a better cell activity than the sol-id stamp mediated process mentioned earlier. Nevertheless, a precise microfl uidic platform fabrication is quite complicated and not fl exible and alternative enough.

Figure 2.10 Patterning on the electrode chip. (a) Perfl uoropolymer patterned in the dark fi eld on the chip. (b) A bright fi eld micrograph of a fi xed cell culture patterned on top of the electrodes. (From: [54]. Reproduced from Sensors and Actuators B: Chemical. © 2002, with permission from Elsevier Science B.V.)

2.5 Summary 33

2.5 Summary

In this chapter, basic factors including physical, chemical, and biological factors that affect cell immobilization are introduced. Cells prefer to locate onto the sub-strate with appropriate roughness, hydrophilicity, and electric charge density. A series of basic surface modifi cation ways on biosensors are also presented. Some ef-fective typical methods are already well developed, like direct microscale structures, indirect solid or soft stamp mediated patterns, some metallic electrode fabrication, and SAM.

Some novel pathways are being developed based on electrophoretic and elec-tromagnetic technologies, which guide cells using electric or magnetic fi eld force. Different methods are utilized to help to obtain an affi nitive coupling between cell membrane and substrate. However, there are still some problems, and a lot of efforts are needed for more effective, alternative, and feasible technology development.

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2.5 Summary 35

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37

C H A P T E R 3

Mechanisms of Cell-Based BiosensorsHua Cai and Ping Wang

3.1 Introduction

In a general sense, cell-based biosensors are useful because they harness highly evolved cellular pathways. These properties make such sensors highly attractive for detection of chemical and biological analyte, for detection of environmental toxins, and for drug screening. The development of cell-based biosensors represents the convergence of several technologies. This type of sensor is divided into two stages: primary transducer and secondary transducer. Living cells serve as the pri-mary transducers, converting the detected molecular signals into signals measured via means such as an extracellular electrode or optical detector [1].

The basic principle of cell-based biosensors is shown as Figure 3.1. The living biological cells serve as the primary transducers to receive the stimulation signals, and the cells will produce different given responses, such as cell metabolism, action potential, and impedance change. The secondary transduction can detect these re-sponses from cells, convert the detected cellular signals into electrical signals, and send to the electric system. All those components make up the whole cell-based biosensors.

According to the principle of cell-based biosensors, we need to understand the following two basic mechanisms:

As primary transducers, how do cells receive the stimulation signals? • Cell biology cannot be fully understood without knowledge and understanding of biochemistry, metabolism, molecular biology, and genetics. Here, we will focus on the basis of cellular functions as they relate to cellular metabolism, electrical activity in cells, cellular membrane impedance characteristics, and cell adhesion and motion. The specifi cs of these functions will be explored in detail where it is necessary for an understanding of the signals and re-sponses. However, the sensitivities of cell-based biosensors to biological tox-ins and chemical agents depend on the type of cells used and the nature of the agents.

38 Mechanisms of Cell-Based Biosensors

How are cellular responses coupled to the secondary transducers when cells •respond to the outside stimulation? As cells sense the stimulation signals, they will produce a great deal of relevant information, such as changes of extracellular acidifi cation, action potential, and cellular membrane im-pedance. Corresponding to the parameters, there are a variety of different measurement techniques. Microphysiometer can be used to analyze the cell membrane bound receptors, and study nonreceptor mediated events on cell metabolism (e.g., the effect of viral infection and toxicological effects). The action potential system can be used to see changes in the action potential. Impedance techniques can be used to monitor such things as cellular adhe-sion, motility, and proliferation of both electrically active and nonelectrically active cell types.

We will discuss the measurement principle and techniques of these parameters by these questions:

How are the parameters produced, and what do they represent? •

What are the mechanisms and models of the biosensors to monitor these •parameters?

What are the transducers used to monitor these parameters? •

3.2 Metabolic Measurements

3.2.1 Cell Metabolism

A general feature of living, heterotrophic cells is the uptake of metabolites (carbon sources), the production of energy (ATP), and the excretion of acid waste prod-ucts (e.g., lactic and carbonic acid). Carbon sources include sugars, amino acids, and fatty acids. In regular culture conditions, glucose and glutamine are present in

Figure 3.1 The basic principle of the cell-based biosensor.

3.2 Metabolic Measurements 39

high concentrations and are taken up by cells and broken down into energy and waste products. Under aerobic conditions, glucose is converted via pyruvate and acetyl CoA into CO2 yielding energy (respiration). The corresponding pathways are glycolysis, citric acid (TCA) cycle, and oxidative phosphorylation. Under an-aerobic conditions, glucose is converted via pyruvate into lactate and energy. The corresponding pathway is glycolysis, connected to the reduction of pyruvate to lac-tate by lactate dehydrogenase. By comparing the amount of protons produced per ATP molecule (Table 3.1), it is evident that glycolysis yields the greatest number of protons. Many cultured eucaryotic cells might predominantly use glycolysis as the main energy-yielding pathway.

The extracellular acidifi cation of cells sitting in the fl ow chamber can be meas-ured with the cytosensor microphysiometer, and thus a functional response of cells to receptor stimulation can be monitored under noninvasive conditions and in real time (Figure 3.2). Upon stimulation of a membrane-bound receptor, which can be G protein-coupled, tyrosine kinase-coupled, or an ion channel, a signal transduc-tion cascade is initiated. Many steps in this cascade are either directly or indirectly energy dependent—for example, phosphorylations by protein-kinases and produc-tion of second messengers (cAMP, IP3). In addition, the opening of channels pro-vokes a subsequent consumption of energy. To maintain ionic homeostasis, pumps must actively transport ions across the plasma membrane at the expense of ATP. Under steady state conditions, one cell produces ≈108 protons per second. After receptor stimulation, this will be raised between 10% and 100%, depending on the cell type, the receptor, and the coupling pathway. In order to yield this increase, additional 107−108 ATP molecules per cell must be produced (and consumed) per second. Calculations suggest that the production of cAMP as a second messenger, as well as protein phosphorylation by kinases, only accounts for a small propor-tion of the increased acidification. Ion pumps, which pump at the expense of ATP to maintain homeostasis, are mainly responsible for this effect. Some receptors di-rectly activate the Na+/H+ exchanger, an antiport that directs Na+ into the cell and H+ out of the cell. The result is an acidifi cation of the extracellular medium, which

Table 3.1 Summary of Principal Energy-Yielding Pathways

CarbonSource Pathway Reactiona

ATPYield

H+ perATP

Glucose Glycolysis Glucose→2 lactate−+2H+ 2 1.000

Glucose Respiration Glucose+6O2→6HCO3−+6H+ 36b 0.167

Glutamine Respiration Glutamine+9/2 O2+3H2O→5 HCO3−+2NH4

++3H+ 27 0.111

Pyruvate Respiration Pyruvate−+5/2 O2+H2O→3 HCO3−+2H+ 15 0.133

Fatty acidβ-Osidaton, respiration C2nH2n−1O2

−+(6n−2)O2→2nCO3−+(2n+1)H+ 17n-6 0.129(n=9)

a Assumes that all ATP produced is hydrolyzed and all CO2 produces hydrates and dissociates into H+ and HCO3-. At pH 7.4, 95%

of the CO2 does so (pK=6.1).b Assumes that each cytosolic NADH produced in the glycolytic part of the pathway yields 2 ATP. If a more effi cient shuttle system

is used to pass the NADH to the mitochondrion, the yield rises to 3 ATP and the overall yield of ATP for the reaction becomes 38

[2].


can directly be detected with the cytosensor system. This is associated with an increase of intracellular Na+. Other receptors (e.g., nAChR) open cation channels so that Na+ and K+ can follow their concentration gradients. The Na+/K+-ATPase has to pump the cations at the expense of ATP to reverse this effect. In fact, mam-malian cells use more than 20% of the total energy to feed the Na+/K+-ATPases of the plasma membrane. Other receptors, which couple via a G protein to PLC, lead to the release of intracellular Ca2+, which then has to be pumped back into intracel-lular stores with the help of Ca2+-ATPases.

Lipid-soluble and some small noncharged molecules, such as alcohol, can pass through the plasma membrane passively, whereas ions (including protons) have to use specifi c channels or transporters. CO2 and lactic acid, the waste products from aerobic and anaerobic glucose degradation, either are hydrolyzed inside the cell to HCO3

-/H+ and lactate/H+, respectively, or can pass the plasma membrane in an unhydrolyzed state and are hydrolyzed outside the cell. Inside the cell, CO2 is hydrolyzed with the help of the enzyme carboanhydrase into H+/HCO3

-. The latter can actively be transported outside the cell via an antiport (sodium in, bicarbonate out). Lactate is excreted with facilitated transport by monocarboxylate carriers and anion exchange proteins. With the hydrolysis, either intra- or extracellular protons are generated. Intracellular potons have to be transported outside. Although spe-cifi c proton channels do exist, the most important transporter for protons is the Na+/H+ exchanger, where one H+ directed outside the cell leads to the infl ux of one Na+ (antiport). The Na+ infl ux is counteracted by the Na+/K+ pump at the expense of ATP (active transport) [2].

3.2.2 Extracellular pH Monitoring

Extracellular pH monitoring is based on the detection of H+ concentration near the cell, and the principal is shown in Figure 3.2. So the chip as extracellular pH moni-toring device is composed of the H+ sensitive membrane. Due to distinct membranes, the mechanisms are different, and we will introduce the SiO2 as an example. The H+-sensitivity of the transducer can be explained using the site-binding theory.

3.2.2.1 Site-Binding Theory

This model was fi rst introduced in 1974 by Yates et al. to describe the properties of an oxide aqueous electrolyte interface [3] and was generalized in 1986 by Fung et al. to characterize ISFETs with oxide gate insulators [4]. It is founded on the thermodynamical fundamentals of the equilibrium reactions of the surface groups and the Gouy-Chapman-Stern theory of the ion distribution at an electrolyte-solid interface.

In the site-binding model, the oxide surface (i.e., pH-sensitive surface) con-tains the site in three forms: A-O−, A-OH, and A-OH2

+, as shown in Figure 3.3. The acidic and basic characters of the neutral site A-OH are characterized by the equilibrium constants Ka and Kb, respectively, and can be written as the following equations:


_aK

sA OH A O H +− ←⎯⎯→ − + (3.1)

2

bKsA OH H A OH+ +− + ←⎯⎯→ − (3.2)

where

[ ]

sa

A O HK

A OH

− +⎡ ⎤ ⎡ ⎤−⎣ ⎦ ⎣ ⎦=−

(3.3)

Figure 3.2 Upon receptor stimulation, signal transduction pathways are induced. The correspond-ing ATP consumption is compensated by the increased uptake and metabolism of glucose, which results in an increase in the excretion of acid waste products. The extracellular acidifi cation is mea-sured by the silicon sensor.

Figure 3.3 Schematic representation of the site-binding model.


[ ]

2 sb

S

A HK

A OH H

+

+

⎡ ⎤−⎣ ⎦=⎡ ⎤− ⎣ ⎦

(3.4)

According to (3.1)–(3.4), A-OH2+, A-OH, and A-O- represent positive, neutral,

and negative surface sites, respectively, and [A-OH2+], [A-OH], and [A-O-] are the

numbers of these sites per surface area. Note that the HS+ activity results in [H+]S,

the surface concentration. The activity coeffi cient is effectively included in the dis-sociation constants. The relationship between [H+]S and the bulk concentration of H+ ions ([H+]b) is given by the Boltzmann equation:

0expS b

qH H

KT

ψ+ + −⎛ ⎞⎡ ⎤ ⎡ ⎤= ⎜ ⎟⎣ ⎦ ⎣ ⎦ ⎝ ⎠ (3.5)

where

q is the elementary charge;

K is the Boltzmann’s constant;

T is the temperature;

ψ0 is the pH-dependent surface potential.

In fact this surface potential is generated by the net surface charge, σ0.

( )0 2q A OH A Oσ + −⎡ ⎤ ⎡ ⎤= − − −⎣ ⎦ ⎣ ⎦ (3.6)

Knowing that the total number of sites per unit area is

[ ] 2SN A OH A OH A O+ −⎡ ⎤ ⎡ ⎤= − + − + −⎣ ⎦ ⎣ ⎦ (3.7)

The relationship between [H+]b, ψ0, and σ0 can now be derived from (3.3)–(3.7) in terms of Ka, Kb, and Ns, which are the characterized parameters of a specifi c oxide.

1 2 1 2

10 0 1ln ln sinh

4a

bb S a b

K qH

K KT qN K K

ψ σ+ −⎛ ⎞ ⎛ ⎞⎡ ⎤ − = +⎣ ⎦ ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠ (3.8)

For this calculation, it is assumed that 2(KaKb)1/2<<1 and σ0<<qNs.In order to derive the relation between pH = −log10[H+]b and ψ0, σ0 should be

expressed in terms of ψ0. It can be shown that in practical cases, the double layer capacitance can be approximated by a simple constant capacitance CDL (derived by the Gouy-Chapman-Stern model), which gives this relation between σ0 and ψ0:

0 0 DLCσ ψ= (3.9)


Equation (3.8) thus describes in a direct way the relationship between ψ0 and the pH but needs further explanation for practical application. Here, the reference point on the pH-scale, the pH value for which ψ0 = 0, follows from (3.8) that

( ) ( )1 2

10

1log

2pzc a b a bpH K K pK pK= − = + (3.10)

This value is called the pH at the point of zero charge (pHpzc). Ka and Kb are equilibrium constants, pKa = −log(Ka), pKb = −log(Kb), ψ0 = 0 implies σ0. In other words, pHpzc gives the value of the hydrogen ion concentration, which results in an electrically neutral surface. The fi nal equation can thus be written as follows:

( ) 10 0 12.303 sinhpzc

q qpH pH

KT KT

ψ ψ

β− ⎛ ⎞

− = + ⎜ ⎟⎝ ⎠ (3.11)

where

( )1 222 s b a

DL

q N K K

KTCβ = (3.12)

Apart from the value of pHpzc, β is the only parameter that determines the 0

pH

ψ

relation. This parameter depends mainly on the surface reactivity expressed by Ka

and Kb and site density, Ns. Equation (3.11) can be distinguished by two regions of

the 0

pH

ψ curve, given as follows:

When 0q

KT

ψβ<< ,

( )0 2.3031 pzc

KTpH pH

qβ

ψβ

= −+

(3.13)

When 0q

KT

ψβ>> ,

( ) 2 22.303 lnpzc

q q qpH pH

KT KT KT0 0 0ψ ψ ψ

β β− ≈ + ≈ (3.14)

From these equations it can be seen that the larger β is, the higher the reactivity of the surface, and then the more ψ0 behaves in a Nernstian manner, with a maxi-mum sensitivity (for pH) of the 59 mV per decade. Surfaces with a low value of β behave nonlinearly with a low sensitivity for pH.

3.2.3 Other Extracellular Metabolite Sensing

In order to detect other extracellular metabolite, it is perspective to fabricate a number of regions that are selective to various ions—such as K+, Na+, Ca2+, and


Cl− based on, for instance, chalcogenide glass ion-sensitive mebrane materials—on the surface of a single transducer.

3.2.3.1 Ion-Sensitive Membrane Based on Chalcogenide Glass

The main potential-determining process, which takes place at the interface between the chalcogenide glass membrane and the solution, is the exchange of primary ions between the solution and the exchange sites at the modifi ed surface layer of the glass. Sensitivity of this type of sensors is governed by the ionic exchange current density at such an interface. Besides their advantageous sensing parameters, chalco-genide glasses are characterized with enhanced chemical durability and endurance, including those in aggressive media, and feasibility of all-solid-state sensor devices. These remarkable properties provide their successful use in, for example, industrial control and environmental monitoring applications [5].

3.2.4 Secondary Transducers

The secondary transducers used to detect cell metabolite should be ion sensitive. Several silicon-based biosensors have been developed for cell-metabolism detec-tion. The common types of secondary transducers are the ion-sensitive fi eld-effect-transistor (ISFET) and the light-addressable potentiometric sensor (LAPS). With reference to ISFET and LAPS, the same operating principle is exploited, based on the properties of the electrolyte-insulator-semiconductor (EIS) system.

3.2.4.1 ISFET

ISFET is a transistor without the metal gate. An electrolyte is placed directly in con-tact with the gate insulation materials, which confer pH sensitivity, such as silicon nitride (Si3N4), aluminum oxide (A12O3), and tantalum oxide (Ta2O5). A pH varia-tion corresponds to a shift in the threshold voltage of ISFET. The sensitivity can be directly calculated from the input characteristics. Cell-based biosensors based on ISFET for cell-metabolism detection will be introduced in Chapter 5 [6].

3.2.4.2 LAPS

LAPS is as commonly used as ISFET and indicates a heterostructure of silicon/silicon oxide/silicon nitride. The silicon nitride on a LAPS surface senses small changes in extracellular acidification, and a LAPS chip can detect the corresponding potential change. This transducer is now experimentally utilized by several research groups and is preferred to ISFET for several reasons. Cell-based biosensors based on LAPS for cell-metabolism detection will be introduced in Chapter 6 [7].

3.3 Action Potential Measurements

Electrical activity in living cells is responsible for much of the complex behavior of organisms. Sensory processing, cardiac function, muscle control, thought, and so

3.3 Action Potential Measurements 45

on are all partially controlled and regulated by the electrical activity and response of different cells in the body. Electrophysiology, which treats electrical phenomena produced by or in living cells, has obtained increased importance in the fi eld of drug discovery.

Intracellular recording techniques of the electrical activity in neurons are still the standards for measurements, with good signal-to-noise ratio as well as tem-poral resolution. However, simultaneous recording from several neurons becomes technically diffi cult. Therefore, the development of noninvasive techniques for the controlled stimulation and recording of individual neurons becomes a key compo-nent for the design of the whole system.

It has become possible to detect and to measure rapid changes in the mem-brane potential of neurons by using voltage-sensitive dyes as optical probes. This method allows recording from multiple sites and therefore enables the monitoring of spatiotemporal optical parameters directly related to neural activity. However, disadvantages of this method are the toxicity of the dyes on illumination, which makes it unsuitable for long-term recording, and that stimulation of neurons using this method is not possible at present.

An alternative noninvasive stimulation and recording method for cultured neu-rons is to use electrodes that are based on substrate-embedded microcircuits. Usu-ally these devices are prepared on glass or silicon substrates carrying a fi xed pattern of metal microelectrodes added to the surface. Alternatively, electrical signals of neurons can be monitored by microelectrode array or a silicon chip, such as FET and LAPS.

Adhesion of neurons to surfaces in biological tissue (e.g., to glia cells, in syn-apses) or in cell culture (on glass or silicon) may seriously change the features of the electrical signals. Transmembrane ion currents have to flow across through the narrow cleft between membrane and surface. They give rise to a drop of voltage that may, in turn, affect the ion channels. The electrical properties of cell adhesion can be probed by taking advantage of the neuron-silicon junction.

3.3.1 Action Potential

The role of different ionic channels in establishing a potential across the cell mem-brane will be discussed, as well as modulation of this potential with changing mem-brane properties. This basic information will be used to understand action potential (the intrinsic electrical activity of cells) and how changes in the cellular membrane can affect them. This will be important when interpreting changes in the measured action potential (AP) due to pharmacological manipulation.

While there are a plethora of different cell types, the fundamental mechanisms for electrical activity are quite similar. Structurally, cells are composed of a lipid bi-layer membrane enclosing an intracellular ionic solution. This membrane contains numerous proteins, receptors, ionic channels, and ionic pumps that are responsible for maintaining the ionic concentrations within the cell and the intracellular poten-tial relative to the extracellular.


3.3.1.1 Hodgkin-Huxley (H-H) Model

H-H equation (Hodgkin-Huxley) suggests that the electrical behavior of the mem-brane may be represented by the network shown in Figure 3.4. The current can be changed either by changing the membrane capacity or by movement of ions through the resistances in parallel with the capacity. The ionic current is divided into components carried by sodium and potassium ions (INa and IK), and a small leakage current (IL) made up by chloride and other ions. Each component of the ionic current is determined by a driving force and a permeability coeffi cient. Thus, the sodium current (INa) is equal to the sodium conductance (gNa) multiplied by the difference between the membrane potential and the equilibrium potential for the sodium ion (ENa). Similar equations apply to IK and IL. The sodium and potassium conductance (gNa and gK) vary with time and membrane potential, while the other components are constant.

Thus, the total membrane current is divided into a capacitive current and an ionic current:

m m Na K L

dVI C I I I

dt= + + + (3.15)

where

Im is the total membrane current density;

INa, IK, IL is the ionic current density;

V is the displacement of the membrane potential from its resting value;

Cm is the membrane capacity per unit area;

t is time.

The individual ionic currents are obtained as

( )( )( )

,

,

Na Na Na

K K K

L L L

I g E E

I g E E

I g E E

= −

= −

= −

(3.16)

Figure 3.4 Electrical circuit representing membrane.


where ENa and EK are the equilibrium potentials for the sodium and potassium ions. EL is the potential at which the leakage current due to chloride and other ions is zero.

As the sodium conductance and potassium conductance have been determined, we collect the equations that give the membrane current Im as a function of time and voltage:

( ) ( ) ( )

( )

( )

( )

4 3 ,

1 ,

1 ,

1

m m K K Na Na L L

n n

m m

h h

dVI C g n V V g m h V V g V V

dtdn

n ndtdm

m mdtdh

h hdt

α β

α β

α β

= + − + − + −

= − −

= − −

= − −

(3.17)

where

( )

( )

1010

80

2510

18

20

3010

0.01 10,

1

0.125 ,

0.1 25,

1

4 ,

0.07 ,

1

1

n V

V

n

m V

V

m

V

h

h V

V

e

e

V

e

e

e

e

α

β

α

β

α

β

+

+

+

+−

−

=+

=−

=

=

=+

Potentials are given in mV, current density in μA/cm2, conductance in m×mho/cm2, capacity in μF/cm2, time in ms, and the α and β in ms−1. The expressions for α and β are appropriate to a temperature of 6.3°C; for the other temperature T°C,

they must be multiplied by a factor 6.3

103T

φ−

= [8].

3.3.2 The Solid-Electrolyte Interface

Before fi nding appropriate models and simulation tools for a better understanding and for a more accurate interpretation of signals recorded extracellularly from pop-ulations of neurons, we will introduce and model the solid-electrolyte interface.

When a solid is placed into an electrolyte (a solution where charge is carried by the movement of ions), an electrifi ed interface immediately develops. This occurs for any solid (metal, semiconductor, and insulator) immersed in an electrolyte. At the instant when a metal is placed in an ionically conducting solution, the metal


and the solution are electroneutral. Chemical reactions occur immediately whereby electrons are transferred between the metal and the electrolyte (A+ + e− → A). This results in the formation of an electric fi eld between the electrode and the electrolyte that infl uences further chemical reactions (thus making them electrochemical). This induced electric fi eld inhibits the reduction reaction (A+ + e− →A) while accelerat-ing the oxidation reaction (A → A+ + e−). These two competing reactions eventually reach an equilibrium condition whereby the currents due to electron transfer to and from the metal are equal. This equilibrium exchange current density fl ows across the interface in both directions, resulting in a net current of zero.

The electric fi eld generated by these electron transfer reactions also has an impact on the electrolyte. Water dipoles orient themselves in the fi eld in a layer at the metal surface forming what is known as the hydration sheath. Just beyond the water dipoles are solvated ions (the result of the electron transfer with the metal) that form a layer, the locus of which is known as the outer Helmholtz plane (OHP). There is also specifi c adsorption of ions (cations or anions) at the electrode surface interspersed with the orientated water dipoles. The contact adsorption of these ions tends to be very chemically dependent and partly oblivious to the charge on the metal. It is possible to have anions specifi cally adsorb to the surface of a negatively charged metal. The locus of centers of these ions is known as the inner Helmholtz plane (IHP) (although in some texts it is the locus of the orientated water dipoles that is termed the IHP) and can affect the overall charge density profi le of the inter-face. The net result of these reactions, adsorptions, and orientations is the creation of the electrical double layer (or simply double layer): an electrifi ed interface de-scribing the interphase region at the boundary of an electrolyte. This is illustrated in Figure 3.5(a), where the arbitrary case of positive ions at the OHP and electrons at the metal surface has been assumed. The choice of an unsolvated negative ion for specifi c adsorption was also arbitrary and independent of the other charges in the system. The space charge region shown has a graded profi le with the strongest fi eld at the interface, diminishing to zero in the bulk electrolyte, as will be discussed in more details next.

Figure 3.5 (a) Interface between electrolyte and solid (b) including the interfacial capacitance (CI), charge transfer resistance (RI), diffusion-related Warburg elements (Rw and Cw), and the solution (spreading) resistance (Rs).


The whole circuit model developed thus far appears as shown in Figure 3.5(b). This works well for simple electrode systems in pure electrolytes (those without proteins). For instances where proteins are present, it is necessary to use empirical methods for determination of parameters in the electrode model. The parameters related to the electrode area must also be adjusted if the geometrical area of the electrode does not match the physical area, as will be discussed further later.

3.3.2.1 Interfacial Capacitance: Helmholtz, Gouy-Chapman, and Stern

The total interfacial capacitance was the series combination of both the Gouy-Chapman model and that of Helmholtz.

1 1 1

I H GC C C= + (3.18)

where CI is the total interfacial capacitance, CH is the Helmholtz capacitance, and CG is the Gouy-Chapman capacitance due to the diffuse ion cloud.

Helmholtz assumed the charge of solvated ions was confi ned to a rigid sheet at the OHP, and was equal and opposite to that in the metal. With the orientated water dipole layer acting as a dielectric, the model predicted the interface would behave like a simple capacitor:

0 rH

OHP

Cd

ε ε= (3.19)

where CH is the capacitance per unit area (F/m2), ε0 is the permittivity of free space (8.85419 × 10−12 F/m), εr is the relative permittivity of the electrolyte, and dOHP is the distance of the OHP from the metal electrode.

The simple model of Helmholtz suffered from an inadequacy; it neglected the dependence of capacity on potential that had been observed experimentally. Since the OHP was determined by how close the solvated ions could get to the electrode, there was no accommodation for movement of those ions. From 1910 to 1913, Gouy and Chapman modifi ed the simple Helmholtz model (a rigid sheet of sol-vated ions) by considering mobile solvated ions at the electrode surface.

0 0cosh2

rG

D t

zVC

L V

ε ε ⎛ ⎞= ⎜ ⎟⎝ ⎠

(3.20)

where the fi rst term (ε0εr/LD) is simply the capacitance per unit area of two plates separated by a distance LD and effects of mobile charges are compensated for by the hyperbolic cosine. LD is the Debye length. The Debye length characterizes the spatial decay of potential and can be viewed as the characteristic thickness of the diffuse layer. V0 is the potential at the electrode (x=0), and Vt is the thermal voltage (kT/q).


3.3.2.2 The Charge Transfer Resistance

The capacitance that develops at the electrode/electrolyte interface and how it changes with the concentration of the electrolyte and the applied potential was discussed earlier, but this does not describe the entire electrical picture. If a DC po-tential is applied across the interface, a current may fl ow under certain conditions. Thus, it is important to consider the addition of a resistive path in parallel to the capacitive in the electrical model of this interface.

The theoretical value for the charge transfer resistance that appears in parallel with the interfacial capacitance under low fi eld conditions (nonrectifying system) can be calculated by

0

tt

VR

J z= (3.21)

in Ω·cm2. Vt is the thermal voltage (kT/q); J0 is the exchange current density (A/cm2); and z is the valence of the ion involved in the charge transfer reaction.

Here, the resulting current (J) can be calculated directly from Ohm’s law,

0t t

t t

J zJ

R V

η η= = (3.22)

ηt is the overpotential due to charge transfer through the double layer.For instances where higher currents are required (as in stimulation of neural

tissue), it is no longer possible to defi ne a pure resistance term. However, by assum-ing that one exponential term of (3.22) tends toward zero while the other increases in magnitude, the total current may be estimated by

0 exp

2t

t

zJ J

V

η⎛ ⎞= ⎜ ⎟⎝ ⎠

(3.23)

where a nonrectifying system has been assumed.

3.3.2.3 Diffusion and the Warburg Impedance

The situation changes when the current density (AC or DC) is so large that reac-tants are not able to diffuse from the bulk to the interface fast enough. The current becomes diffusion limited resulting in a diffusion overpotential (ηd). This additional impedance must be placed in series with the charge transfer resistance (Rt), since physical diffusion and charge transfer must occur as a serial process; reactants dif-fuse to the interface where they contribute to oxidation or reduction reactions.

Warburg proposed a model for this frequency dependent diffusional impedance:

w

kZ

f= (3.24)


where k is a constant determined by the electrochemistry and mobility of the ions involved in the charge transfer reaction and f is the excitation frequency.

The Warburg impedance elements may be theoretically determined by the fol-lowing equations, provided that the diffusion is dominated by a single ion species and the electrode is operated near equilibrium:

3

2 0

10 tw

VR

z qn fDπ= (3.25)

1

2ww

CRπ

= (3.26)

1

12w w

w

Z j CR

π−

⎡ ⎤= +⎢ ⎥

⎣ ⎦ (3.27)

where f is the frequency in hertz, D is the diffusion coeffi cient (cm2/sec) of the ion in question, Rw is in Ω·cm2, and Cw is in C/cm2. Substitution of (3.26) into (3.27) reveals that the Warburg impedance has a constant magnitude of

2w wZ R= (3.28)

with a constant phase of −45°.

3.3.2.4 The Spreading Resistance

The fi nal circuit element that must be included in the basic electrode/electrolyte model is the spreading resistance. As the name implies, this resistance models the effects of the spreading of current from the localized electrode to a distant counter electrode in the solution. It can be calculated by integrating the series resistance of shells of solution moving outward from the electrode where the solution resistance (R in watts) is determined from

LR

Aρ

= (3.29)

where ρ is the resistivity of the electrolyte (Ω·cm), L is the length (centimeters), and A is the cross-sectional area (cm2) of the solution through which the current passes.

Of specifi c interest for microelectrodes fabricated using planar, integrated cir-cuit fabrication techniques produce square and circular electrodes with one side exposed to the electrolyte. For a circular electrode of radius r (cm), the spreading resistance is given by

4 4

sRr A

ρ ρ π= = (3.30)


in Ω where A is the area of the circular electrode in cm2.For a rectangular electrode of length l and width w (both in cm), Kovacs calcu-

lated the spreading resistance using the formula

ln 4

s

lw

Rl

ρ

π

⎛ ⎞⎜ ⎟⎝ ⎠

= (3.31)

From these equations it can be seen that the spreading resistance varies with the square root of the electrode area (for symmetrical shapes) rather than being directly proportional, as was the case for the circuit elements discussed so far.

3.3.3 Cell-Electrode Interface Model

Here we analyze the coupling between a patch of a neuronal membrane and a microelectrode with a lumped circuit approach; the analysis of the infl uence of the equivalent circuit elements of the neuron-microelectrode junction, with respect to their physical meanings, was carried out.

In principle, a coupling of ionic signals in a neuron and electronic signals in the semiconductor can be attained by electrical polarization. If the insulating lipid bilayer is in direct contact with the insulating silicon dioxide, a compact dielectric is formed. An electrical fi eld across the membrane, created by neuronal activity, po-larizes the oxide such that the electronic band structure of silicon is affected. And vice versa: an electrical fi eld across the oxide, as caused by a voltage applied to the chip, polarizes the membrane such that the conformation of membrane proteins is affected. However, when a nerve cell grows on a chip, we cannot expect that the lipid and oxide to form a compact dielectric layer. Cell adhesion is mediated by protein molecules that protrude from the membrane and that are deposited on the substrate. These proteins keep the lipid core of the membrane at a certain distance from the substrate, stabilizing a cleft between cell and chip that is fi lled with elec-trolyte. That conductive cleft suppresses a mutual polarization of silicon dioxide and cell membrane.

Based on the H-H theory and the solid-electrolyte interface, when the cell is cultured on electrodes, shown as Figure 3.6, we can draw the simplifi ed circuit schematic of the cell/electrode junction.

From the equivalent circuit, we can get the following:

MM M ionic

dVI C I

dt= + (3.32)

( )M Jj J

M ionicseal electrode a

d V VV VC I

R Z Z dt

−+ = +

+ (3.33)

sealseal

lR

d w

ρ= (3.34)


where VJ is the polarization voltage of electrode. The thickness d is the (average) patch-to-insulator distance. ρseal is the sealing resistivity, and l and w are the length and width of the portion of electrode coupled to the patch of neuronal membrane, respectively.

The extracellular voltage monitored from cells cultured over a microelectrode array is due to the fl ow of current by (or through) the electrode. Recall that for a wave traveling down a cylindrical axon (from core conductor theory) the total transmembrane current is given by:

2

2m m

total m ionic

d V dVI K C I

dtdt= = + (3.35)

22

aK

Rv= (3.36)

where Vm is the transmembrane potential, Cm is the membrane capacitance per unit area, and Iionic is the current due to fl ow of ions through ionic channels in the cellular membrane. The constant K is dependent on the radius of the fi ber (a), the axoplasm resistance (R) (which may be loosely correlated to the seal resistance (Rseal)), and the conduction velocity (v). For the case of cultured cardiac cells, the system is not a cylindrical conductor, and this equation will not hold exactly. The relationships involved with the determination of the constant K are different, mak-ing extrapolation of the seal resistance and conduction velocity impossible from the measured action potential data using (3.35) and (3.36). Development of suitable equations for the case of a sheet of cells cultured over an electrode array is beyond the scope of this book. However, the general assumptions of local current loops (fl ow through the membrane, along the exterior of the cell, back through mem-brane, and along the interior of the cell) still hold for the case of traveling waves. Since traveling wave behavior was observed, (3.35) can act as a general guideline

Figure 3.6 Conceptional drawing of a cultured cell coupled to a microelectrode with simplifi ed cir-cuit schematic of the cell/electrode junction. Vin is the transmembrane voltage, Cm is the basal mem-brane capacitance, Im is the basal ionic current, Rseal is the seal resistance, Zelectrode is the electrode impedance, and Za is the input impedance of the amplifi er, bias resistors, and parasitics to ground.


for the anticipated relationship between the total membrane current and the trans-membrane potential.

Assuming that (3.35) will generally hold for the case of a traveling wave in a sheet of cardiac tissue, it is expected that the total current will be proportional to the second derivative of the transmembrane potential. The current will fl ow through the seal resistance, generating a potential at the electrode equal to the product of Itotal and Rseal.

This voltage is transduced by the electrode to provide either a replica of the sec-ond derivative of the transmembrane voltage or a third derivative of membrane po-tential. For the electrodes utilized herein, the impedance was almost purely capaci-tive in the frequency range of interest. However, combined with the 1MΩ resistance to ground at the amplifi er input, the pole was at a low enough frequency so that the AP was not differentiated. Thus, second derivative behavior was observed.

As mentioned previously, the ability to record APs from individual cells re-quires microelectrodes comparable in size to the cells themselves. This calls for electrode dimensions on the order of tens of micrometers or smaller unless the cells are interlinked as in a myocardial syncitium. Electrodes much larger than cells will not yield recordable signals due to decreasing seal impedance (voltage division of the signal), whereas electrodes that are too small will have electrical outputs domi-nated by Johnson noise of the electrode impedance’s real component [1].

3.3.4 Cell-Silicon Interface Model

There are other methods to detect extracellular action potential, such as silicon chip. The FET and LAPS can be used as such secondary transducers.

A fundamental step in using planar substrate microtransducers is the formation of a long-term (i.e., days) sealing between a patch of membrane of a living neuron and the surface of the recording device. The sealing region is bounded by the patch of membrane on one side and by the transducer surface on the other side—that is, by the surface of an insulator (typically Si3N4 or SiO2)—whenever an FET is uti-lized as a transducer. Consequently, in this confi guration, the patch-to-transducer coupling is capacitive. Due to a lack of direct experimental data on the thickness and physico-chemical properties of the volume surrounded by the leaky capacitor (the membrane) and by the other capacitor (the FET insulator), we will give the following equivalent circuit description.

Figure 3.7 shows the equivalent circuit of the patch-to-FET sealing. Vm is the transmembrane potential, Cm is the membrane capacitance per unit area, and Iionic is the current due to fl ow of ions through ionic channels in the cellular membrane. Rseal is the sealing resistance between the neuron membrane and the insulated FET; Cs is the insulator capacitance. From the equivalent circuit, we can get:

MM M ionic

dVI C I

dt= + (3.37)

( )M JJ

M ionicseal

d V VVC I

R dt

−= + (3.38)


sealseal

lR

d w

ρ= (3.39)

where VJ is the polarization voltage of the insulated FET. The thickness d is the average patch-to-insulator distance. ρseal is the sealing resistivity, and l and w are the length and width of the portion of the insulated gate FET coupled to the patch of neuronal membrane, respectively.

The whole circuit of the model is determined by the secondary transducer struc-ture (e.g., FET cell-based biosensors, introduced in Chapter 5, and LAPS cell-based biosensors, introduced in Chapter 6).


3.3.5.1 Patch Clamp as Cell-Based Biosensors

The preferred method with the highest information content for characterization of ion channel function and its regulation is the patch clamp technique that allows reliable recording of ionic currents under a defi ned membrane voltage. This tech-nically demanding method is now being automated by several groups and will be introduced in Chapter 8.

3.3.5.2 Microelectrode Array (MEA)

MEA is a valuable tool to record the electrical activity of electrogenic cells with a high information content with respect to drug action in an intact cellular envi-ronment of cardiac myocyte cultures, brain slices, either acute or maintained in organotypic long-term culture, and explanted retinas. Using planar metallic micro-electrodes (diameter of 10 to 30 μm), it offers the possibility for noninvasive extra-cellular recording from as many as tens of sites simultaneously. MEA is an ideal in

Figure 3.7 Conceptional drawing of a cultured cell coupled to a silicon chip with simplifi ed cir-cuit schematic of the cell/silicon junction. Vin is the transmembrane voltage, Cm is the basal mem-brane capacitance, Im is the basal ionic current, Rseal is the seal resistance, and Cs is the insulator capacitance.


vitro system to monitor both acute and chronic effects of drugs and toxins and to perform functional studies under physiological or induced patho-physiological con-ditions that mimic in vivo damages. By recording the electrical response of various locations on a tissue, a spatial map of drug effects at different sites can be gener-ated, providing important clues about a drug’s specifi city. MEA will be introduced in Chapter 4.

3.3.5.3 FET Arrays

FET arrays are always treated as MEA with amplifi ers. The neuron is placed on an oxidized surface of silicon with an integrated array of FET with open metal-free gate oxide. Electrical signals of neurons can be monitored by a FET device, where the neuron sits on top of the nonmetallized gate inducing an electrical signal in the FET by capacitive coupling. FET as cell-based biosensors will be introduced in Chapter 5.

3.3.5.4 LAPS

LAPS is a surface potential detector with spatial resolution, based on silicon tech-nology. By illuminating parts of its surface, a localized photo-induced current fl ows, the amplitude of which depends on the local surface potential properties. In this way, any desirable detection site can be freely selected by moving the light pointer across the sensor surface. Therefore, surface potential measurements are no longer restricted to discrete sites, like the gate electrodes of FET arrays. The capability of the LAPS to measure surface potentials in a spatially resolved manner suggests that this device could be employed to detect extracellular potentials. It will be intro-duced in Chapter 6.

3.4 Impedance Measurements

3.4.1 Membrane Impedance

Impedance techniques have been used to study organs in the body, explanted neural tissues, whole blood and erythrocytes, cultured cell suspensions, bacterial growth, and anchorage dependent cell cultures. There is a great deal of relevant informa-tion regarding the characteristics of biological material. Most signifi cant are the frequency dependent dielectric properties of biological materials that yield insight into the expected behavior within different frequency ranges.

In many cases, the capacitance of the cell membrane, cell/substrate separa-tion, and cell/cell separation can all be monitored and determined. While all of these measurements provide biologically relevant information, there is still a need to examine the membrane properties of cultured cells. In particular, the effects of different compounds on the ionic channels of populations of cells cultured in vitro should be considered.

Where involved with high-frequency capacitance measurements, an important feature becomes obvious from theoretical consideration. As sketched in Figure 3.8,

3.4 Impedance Measurements 57

the cells reach the substrate with an almost spherical morphology [9]. The area of closest contact between basal membrane and surface is therefore rather small but increases continuously while the cell spreads. Since the cell membrane is essentially insulating and restricts current fl ow, current leaving the electrode in the adhesion area has to fl ow in the narrow clefts underneath the cells before it can escape in the bulk electrolyte at the cell perimeter. Current fl ow within the adhesion area is furthermore dependent on the applied frequency.

In order to monitor changes in cellular membrane capacitance and conduct-ance, the cell/electrode interface was modeled. These models were used in the de-sign of the impedance system as well as the electrode array.

The ionic channels affect not only the transmembrane potential, but also the electrical impedance characteristics of the cellular membrane. This will be discussed along with a basic electrical model of the membrane. For measurement of this im-pedance using extracellular electrodes (as will be discussed in later chapters), cel-lular adhesion and motility can greatly impact the result. Thus, an understanding of the mechanisms responsible for these phenomena is critical for interpretation of the measured impedance and development of appropriate control experiments.

There has been a signifi cant effort in this book to describe the impedance char-acteristics of populations of cells cultured over large area electrodes and single cell cultured over small microelectrodes. These impedance measurements have provid-ed information about proliferation, motility, and cellular adhesion for both electri-cally active and nonelectrically active cell types.

3.4.2 Impedance Model of Single Cells

A schematic view of a cell positioned over a microelectrode is shown in Figure 3.9. The total measured impedance consists of the electrode impedance (Ze), the resis-tance between the electrode and the bulk electrolyte due to the thin layer of medium between the cell and the passivation layer (Rseal), the membrane capacitance and ion channel resistance over the electrode (Cm1 and Rch1), the membrane capacitance and ion channel resistance of the top and sides of the cell (Cm2 and Rch2), and the solution resistance (Rsoln) and the counter-electrode impedance (Zco). Note that in reality Rseal is distributed with the capacitance and conductance of the membrane in the region over the passivation layer. For this measurement, the counter-electrode impedance, solution resistance, and electrode impedance should all be negligible (assuming platinized electrodes) so that the impedance measurement is dominated by the seal resistance and the cell membrane properties. It is clear that Rseal must be

Figure 3.8 Time course of cell spreading on a culture substrate and the concomitant increase in the adhesion area’s projection on the substrate.


on the same order as (or larger than) the membrane impedance if changes in mem-brane properties are to be observed.

The model components were estimated by assuming a circular cell of radius r1 centered over a microelectrode of radius r0. The total seal resistance was esti-mated by assuming a uniform cell to substrate separation (t) and using the standard formula

L

RA

ρ ⋅= (3.40)

where R is the resistivity of the medium, L is the length over which the current fl ows (radially outward from the electrode), and A is the cross-sectional area the current fl ows through (equal to the perimeter of the circular ring of current times the cell to substrate separation (t)). Note that while changes in the effective resistivity of the solution are possible with small t, these effects are assumed to be negligible and the bulk resistivity of the medium was used. The seal resistance is calculated by inte-gration of (3.40) over the distance between the outer edge of the electrode and the outer edge of the cell, where the length L is equal to the incremental change in ra-dius, and the area A is equal to the perimeter times the cell to substrate separation

1

0

1

0

ln2 2

r

sealr

rR dr

rt rt rρ ρ

π π

⎛ ⎞⎛ ⎞= = ⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠∫ (3.41)

The capacitance and resistance values could be calculated from

21 0m AC r Cπ= (3.42)

Figure 3.9 Schematic of a cell positioned over an electrode. The measured impedance consists of the electrode impedance, the resistance between the electrode and the bulk electrolyte due to the thin layer of medium between the cell and the passivation layer, the membrane capacitance, and the ion channel resistance over the electrode.


21 0m AG r Gπ= (3.43)

22 1m AC r Cπ= (3.44)

22 1m AG r Gπ= (3.45)

where CA and GA are the capacitance and conductance of the cellular membrane per unit area.

In order to observe changes in the cellular membrane impedance due to modu-lation of ionic channel conductance, it is necessary for the seal resistance to be suf-fi ciently high (tens to hundreds of MΩ). However, as this seal resistance increases, the effective area of the membrane through which the current can fl ow along the bottom side of the cell is reduced. In the extreme of infi nite seal resistance, the area is limited to that of the electrode itself. A limited membrane area results in higher impedance and therefore more noise in the measurements. It is therefore expected that there may be an optimum electrode size for monitoring changes in membrane properties for a cell of a known diameter.

3.4.3 Impedance Model of Populations of Cells

In the following, we review in some detail the model proposed by Giaever et al. [10]. This model is based on the fact that the cell monolayer geometrically blocks the current fl ow with respect to the current fl ow measured in the absence of cells naked response. Between the electrode surface and the bottom of the cells, there is a space occupied by the electrolyte. This space is implicitly assumed to be larger than a few nanometers, and, therefore, even in the presence of cells, the electrical behavior of the current fl owing through the electrode in contact with the electrolyte is analogous to the behavior of the naked electrode. Once it crosses the electrode-medium interface, the current fl ows following different paths. The relative impor-tance of the different paths depends on the specifi c impedances that are developed by the monolayer attached to the electrode. This model proposes that there can be two paths: either between the substrate-cell spaces or through the cell membrane.

In a simplifi ed manner, the resistance produced by the cellular monolayer is due to the current passage underneath cells (distributed effect) and the current passage crossing intercellular spaces, through a junction resistance (lumped effect). The current that directly crosses the cell presents an essentially capacitive behavior.

As mentioned before, the analysis in a cellular unit provides the system re-sponse in the presence of cells. In this sense, cells are represented as circular disks of radius, rc, and the analysis is carried out up to the one-cell limit where a proper boundary condition is imposed. Figure 3.10 shows the outstanding variables in the current distribution, the drops in potential developed in the system, and the volume utilized for the integral analysis [10]. Electrical properties are considered by both the specifi c impedance of the naked electrode, Zn(w), and the specifi c im-pedance due to the presence of cells (basically, the impedance of two cellular mem-


branes connected in series considering that each of both membranes only shows a capacitive behavior), Zm(w).

Based on different balances, which are related to drops in potential and conser-vation of current, an ordinary differential equation of second order for the poten-tial developed in the cell-substrate space, V, can be obtained.

2

22

10

d V dVV

r drdrγ β+ − + = (3.46)

where

2 1 1,

n m

n m

n m

h Z Z

V Vh Z Z

ργ

ρβ

⎛ ⎞= +⎜ ⎟⎝ ⎠

⎛ ⎞= +⎜ ⎟⎝ ⎠

(3.47)

Because of the geometry assumed for the cell, the formulation is simplifi ed when expressed in cylindrical coordinates. ρ is the known resistivity of the medium, h is the height of the cell-substrate space, Vn is the applied potential, and Vm is the potential beyond the monolayer.

The general solution of (3.46) is given by

( ) ( )1 0 2 0 2V C I r C K rβ

γ γγ

= + + (3.48)

where I0 and K0 are the modifi ed Bessel functions of fi rst and second kind, respec-tively, of zero order.

Because K0 diverges at the origin, the constant C2 must be zero. The constant C1 is determined with the boundary condition

( ) ( )2c c

bmr r r r

c

RV V I

rπ= =− = (3.49)

Figure 3.10 Giaever and Keese model. The cell is represented by a circular disk of radius rc, and there is a space of height h between the substrate and the cell. The current going out the electrode, In, distributes in one of two directions: part of the current flows in a radial direction, I, and the remaining current directly crosses the cell, Im. The drops in potential for the system are described by the applied potential, Vn, the potential in the electrolyte bulk, Vm, and the potential in the cell- substrate space, V. In the end of the cell, r=rc, there is a tight junction between neighbor cells func-tioning as an electrical resistance (lumped effect).


where I is the current in the cell-substrate space and Rb is a resistance (by unit area) due to the formation of intercellular junctions. This condition implies that the po-tential drop at the end of the cell can be considered a lumped effect. The current under the cell that reaches its boundary cannot cross the limit of the system because the neighboring cell is identical to the cell under study; thus, by symmetry, in this region the current must axially cross the cells through a tight junction that acts elec-trically as a resistance. This type of analysis by unit cells is justifi ed when all cells are equivalent. In such cases, the boundary condition (restricting the analysis to only one cell) results are simple, such as in the model described in this section. However, the region studied, rigorously, is not a unit cell because it does not fi ll the space. In this analysis, there are spaces between consecutive regions that are not considered within any region. Nevertheless, this is a minor concern, since the results obtained under this formulation agree with those obtained with a formulation based on re-gions of rectangular base that does fi ll the space.

Once the constants are determined, the variables of the system are defi ned, and so the specifi c impedance of the cell-covered electrode can be established, which is given by

( )( )

cov 0

1

1 1

1 12

m

n n m

n n m ccb

n mc

ZZ Z Z

Z Z Z Z I rrR

Z ZI r

γγγ

⎛ ⎞⎜ ⎟+⎜ ⎟= +⎜ ⎟+ ⎛ ⎞

+ +⎜ ⎟⎜ ⎟⎝ ⎠⎝ ⎠

(3.50)

where

cr hρ

α = (3.51)

This model then has two adjustable parameters: α and Rb. A greater complex-ity can be achieved by considering the specifi c impedance through the cells, Zm(W), as a capacitive impedance with the particular value of the capacitance becoming an additional parameter [11].


3.4.4.1 MEA

The planar electrodes can be used to monitor proliferation, motility, and adhesion of populations of cells. Impedance characteristics of populations of cells cultured over large area electrodes and single cells cultured over small microelectrodes are both studied. These impedance measurements have provided information about proliferation, motility, and cellular adhesion for both electrically active and non-electrically active cell types.


3.4.4.2 ECIS

Electric cell-substrate impedance sensor (ECIS) provides a novel method to per-form real-time cell monitoring like never before without the use of radioactive trac-ers, or biological markers. The core of ECIS technology is based on measuring the change in impedance of a small electrode to AC current fl ow. It will be introduced in Chapter 7.

3.5 Noise Sources

Previously, we discussed the mechanisms and models of several types of cell-based biosensors. In the detection of signals, the noise is a very serious problem, so it is necessary to research the noise sources of the detected signals. We will focus on three different types of noises as follows: electrode noise, electromagnetic interfer-ence, and biological noise.

3.5.1 Electrode Noise

As with most circuit elements, there is an intrinsic noise associated with the metal-electrolyte interface. This noise has been empirically shown to be thermal, follow-ing the standard Johnson noise equation for the rms voltage noise of a resistor:

4rms noise NV kTR f= Δ (3.52)

where k is the Boltzmann’s constant (1.38 × 10−23J/K), T is the absolute tempera-ture in Kelvin, RN is the real part of the electrode impedance in ohms, and Δf is the bandwidth of interest.

For reference, this theoretical thermal noise voltage is plotted versus RN in Figure 3.11. It is important to remember that RN is the effective resistance of the electrode and will be attenuated with the electrode capacitance changes as the fre-quency increases.

Figure 3.11 Theoretical noise voltage plotted versus the real part of the electrode impedance at 37°C. Noise is quoted as nV per root hertz, since it is the bandwidth not the actual frequency that is signifi cant.

3.5 Noise Sources 63

For practical applications, the electrode impedance is measured in the band-width of interest, and the obtained resistance is used in (3.55) to estimate the ther-mal noise. This noise is compared to the anticipated signal levels to determine whether electrode impedance reduction is required. For impedance measurements where homodyning techniques are employed, the effective bandwidth of the meas-urement is signifi cantly reduced, making this thermal noise less signifi cant. How-ever, saturation of any amplifi cation prior to the homodyning stage is possible, making this noise an issue for most practical systems [12].

3.5.2 Electromagnetic Interference

Electromagnetic interference (EMI; also called radio frequency interference or RFI) is an unwanted disturbance that affects an electrical circuit due to electromagnetic radiation emitted from an external source. The disturbance may interrupt, obstruct, or otherwise degrade or limit the effective performance of the circuit. The source may be any object, artifi cial or natural, that carries rapidly changing electrical cur-rents, such as an electrical circuit, the Sun, or the Northern Lights.

EMI can be induced intentionally for radio jamming, as in some forms of elec-tronic warfare, or unintentionally, as a result of spurious emissions and responses, intermodulation products, and the like. It frequently affects the reception of AM radio in urban areas. It can also affect cell phone, FM radio, and television recep-tion, although to a lesser extent.

3.5.3 Biological Noise

Noise is a ubiquitous source of energy in the nervous system, arising from the sto-chastic properties of biophysical mechanisms underlying both electrical excitability and synaptic transmission. Ionic concentrations in the cellular media and membrane conductance present stochastic features, and neurotransmitter releases in response to presynaptic stimuli is a random process, as is the activity of ionic membrane channels. Embedded in such a noisy environment, neurons operate a spatial and temporal integration of the input stimuli, encoding them into fi ring or bursting patterns that carry information on the inputs in both spike rate and timing. These complex responses are generated through the activation of ionic channels, which behave as gates for transmembrane currents; the switching between opening and closing is almost stochastic, due to thermal excitation of channel macromolecules with multiple stable states. Such a channel noise has been shown to induce a variety of well-known behaviors in neuronal processing, including response unreliability to repetitive stimulations, missing spikes during fi ring activity, and spontaneous action potentials in resting state. Recently, some studies have proposed a functional role in clustering ionic channels assemblies for preferred input processing, according to a phenomenon known as stochastic resonance.


3.6 Summary

This chapter deals with the mechanisms and models of cell-based biosensors. We studied the cell metabolic measurements, action potential measurements, and im-pedance measurements. There are many other types of cell-based biosensors, like patch clamp cell-based biosensors and B-cell based biosensors. The mechanisms and models are not discussed here, but they will be introduced in Chapter 8.

Now that the new type of cell-based biosensors developed, the basic theory and defi nition of cell-based biosensors are assured. We just need to understand the construction and signal transduction, and we should master the two core parts of cell-based biosensors: living biological cells serve as the primary transducer and a secondary transduction.

References

[1] Kovacs, G. T. A., “Electronic Sensors with Living Cellular Components,” Proc. IEEE, Vol. 91, 2003, pp. 915–929.

[2] Hafner, F., “Cytosensor Microphysiometer: Technology and Recent Applications,” Bios-ens. Bioelectronics, Vol. 15, 2000, pp. 149–158.

[3] Yates, D. E., S. Levine, and T. W. Healy, “Site-Binding Model of the Electrical Dou-ble Layer at the Oxide/Wafer Interface,” J. Chem. Soc. Faraday Trans., Vol. 70, 1974, pp. 1807–1818.

[4] Fung, C. D., P. W. Cheung, and W. H. Ko, “A Generalized Theory of an Electrolyte-Insu-lator-Semiconductor Field-Effect Transistor,” IEEE Trans. Electron. Devices, Vol. 3, 1986, pp. 8–18.

[5] Mourzina, Y., et al., “Ion-Selective Light-Addressable Potentiometric Sensor (LAPS) with Chalcogenide Thin Film Prepared by Pulsed Laser Deposition,” Sens. Actuators B, Chem., Vol. 80, 2001, pp. 136–140.

[6] Fanigliulo, A., et al., “Comparison Between a LAPS and an FET-Based Sensor for Cell-Metabolism Detection,” Sens. Actuators B, Chem., Vol. 32, 1996, pp. 41–48.

[7] Adami, M., M. Sartore, and C. Nicolini, “PAB: A Newly Designed Potentiometric Alternat-ing Biosensor System,” Biosens. Bioelectronics, Vol. 10, 1995, pp. 155–167.

[8] Hodgkin, A. L., and A. F. Huxley, “A Quantitative Description of Membrane Current and Its Application to Conductance and Excitation in Nerve,” J. Physiol., Vol. 117, 1952, pp. 500–544.

[9] Wegener, J., C. R. Keese, and I. Giaever, “Electric Cell-Substrate Impedance Sensing (ECIS) as a Noninvasive Means to Monitor the Kinetics of Cell Spreading to Artifi cial Surfaces,” Exp. Cell Res., Vol. 259, 2000, pp. 158–166.

[10] Giaever, I., and C. R. Keese, “Micromotion of Mammalian Cells Measured Electrically,” Proc. Natl. Acad. Sci., Vol. 88, 1991, pp. 7896–7900.

[11] Urdapilleta, E., M. Bellotti, and F. J. Bonetto, “Impedance Analysis of Cultured Cells: A Mean-Field Electrical Response Model for Electric Cell-Substrate Impedance Sensing Tech-nique,” Phys. Rev. E., Vol. 74, 2006, p. 041908.

[12] Borkholder, D. A., “Cell Based Biosensors Using Microelectrodes,” Ph.D. dissertation, Stanford University, Palo Alto, CA, 1998.

65

C H A P T E R 4

Microelectrode Array (MEA) as Cell-Based Biosensors

Lidan Xiao, Qingmei Chen, Qingjun Liu, and Ping Wang

4.1 Introduction

Bioelectric potential has caught the attention of many researchers studying the ion channels and membrane structure on the cellular and molecular levels since twen-tieth century. Transmembrane potential and extracellular potential are two main measurements to express the activity of various ions, and both of them could be re-corded by the electrodes. The patch clamp, fi rst developed by Neher and Sakmann, has become the fundamental tool to directly record the transmembrane potential. It applies a micropipette with an open tiny tip impaling into the plasma membrane to record, even from a single ion channel. To improve the effi ciency, the planar patch clamp (introduced in Chapter 8) has been recently developed to monitor the cellular electrophysiology in a high-throughput way. However, in some cases it is not necessary to know the actual transmembrane potential changes, except when the action potential occurs. The extracellular potential is adequate to provide the information about the activities of cells. Different from intracellular measurement, the extracellular electrodes are closely located on the surface of the cells to detect the potential changes relative to the reference electrode positioned in the bulk so-lution surround the cells. Although it could not be as refi ned as the patch clamp to monitor the ion channels, the extracellular potential measurement has its own distinctive advantages. First, in a noninvasive and long-term manner it can monitor the activities of the cells without piercing the electrode into the cellular membrane. Second, there is no need to pay intensive labor to get giga sealing between electrode and the cellular membrane, which is indispensable in patch clamp measurement. Third, it is convenient for high-throughput pharmacological assays and suitable for studying the cell-cell communication due to its parallel detection. However, the amplitude of action potential detected by extracellular electrodes is much smaller than those recorded by intracellular electrodes. The signal shape and time course

66 Microelectrode Array (MEA) as Cell-Based Biosensors

are also quite unlike. Until now, the famous devices for extracellular potential de-tection have included microelectrode arrays (MEA), fi eld-effect transistors (FET), and light-addressable potentiometric sensors (LAPS). In this chapter, we will focus on introducing the MEA cell-based biosensor. FET and LAPS will be introduced in Chapter 5 and Chapter 6, respectively.

With more than 30 years of development, MEA has been exquisitely fabricated and systematically applied in bioelectric physiology. The essence of the design is to sandwich a thin metallic fi lm between two insulative layers and expose a small electrode site for sensing the electric potential changes generated by the objective cells, which is fi rst proposed by Thomas in 1972 [1]. They fabricated a 15 × 2 Ni/Au electrode array on a glass substrate, and the electrodes are square and 7 µm in diameter. With the in vitro culture technique, they found it possible to record ro-bust signals of 20–1,000 μV high after the dissociated chick myocytes had formed a confl uent contracting layer over the electrodes. However, their MEA failed to sense the potential changes from a single cell or even small group of cells on an electrode.

In 1977, Gross et al. also developed the MEA and successfully recorded the extracellular potential of explanted brain ganglion of snail Helix pomatia laid over the electrodes [2]. The max amplitude of single-action potential was 3 mV, which is found dependent on the cellular size. The fi rst extracellular recordings from sin-gle dissociated neurons were reported by Pine [3]. With relatively more detailed knowledge about intracellular potential, he also combined an extracellular MEA electrode with the intracellular stimulating pipette to validate the MEA technique and simultaneously analyze the extracellular signal.

In the 1980s, the MEA was more and more widely applied to monitor different types of cells and tissues (or slices). After Pine’s work, the research on neuron and neural networks continued in several other groups [4–6]. The system for simultane-ously stimulating and recording in a same MEA chip is available, and it provided an effective way to do some fundamental research on neural activities. Later, the concept of using the neural networks as the biosensor was proposed to screen some chemical compounds and drugs [7]. These efforts have led the MEA tech-nology to a new application in the areas of pharmacology and toxicology. MEA is also applied to monitor primary cultured cardiacmyocytes and embryonic stem cell derived cardiomyocytes. The high-throughput system QT-screen is a design to specially test the drug-induced prolongation of the QT interval, which is much known in the electrocardiogram (ECG) [8]. Apart from recording the signals from cultured cell, MEA also can detect the signal propagation in the tissue or slice. The mapping of the network and the distribution of the signaling pathway in the tis-sue or slice could be better understood. In 1981, Jobling and his pioneering group demonstrated the effectiveness of the FET using the microelectrodes as the gates in recording the fi eld potential from hippocampal slices with stimulus [9]. Much later, MEA was further developed to analyze various slices. Except for the in vitro appli-cation described earlier, the probes embedded with microelectrodes were designed to implant into the tissue for long-term recording and/or stimulation [10]. It has potential prospects in the fi eld of neurosurgery for impairments on nervous system suffered from disease or injuries [11]. In a word, in the design of MEA, the size,

4.1 Introduction 67

material, and distribution of the microelectrodes should be adjusted to the various cell types, the slices, or in vivo tissue.

The commercial products of MEA system have been developing for several years. Figure 4.1 shows the three typical MEA chips from Multichannel Systems (Multichannel Systems, MCS, GmbH), Ayanda, and Panasonic. MCS (http://www.multichannelsystems.com/) was found in 1996, and its products are mainly fab-ricated on glass. The minimum of the diameter of electrode and interconnect dis-tance are 10 μm and 30 μm, respectively. It also provides the whole setup, including software and hardware for synchronous signal stimulating, recording, and analyz-ing. The Ayanda MEA biochips (http://www.ayanda-biosys.com/physiology.html) are fabricated on transparent microchips and adapted to the commercially avail-able MEA60 signal amplifi cation and data acquisition system from MCS. They offer various microelectrode geometries ranging from planar to 3D tip-shaped elec-trodes. The MED64 System (also known as Panasonic MED System) (http://www.med64.com/index.html) obtains reliable, long-term, continuous recording and two-dimensional, real-time analysis of neural/myocardial activity. The MED64 is also a complete, fully integrated solution.

Hence, MEA is a potential platform for study on cellular electrophysiology. It is famous for its long-term and noninvasive performance. In this chapter, we offer a systematical review of MEA from the modeling, design, fabrication, and applica-tion standpoints.

Figure 4.1 The commercial products of the MEA chips. (a) The 64-channel MEA from Multichan-nel Systems (MCS). (b) The layout of microelectrodes of (a). (c) The Ayanda MEA60 Biochips. (d) MED64 from Panasonic.


4.2 Principle

Based on microfabrication technique, the MEA device contains multiple metallic sites with stable physical and chemical properties to deliver electric signals from the cells to the display output. Figure 4.2 shows a diagram of the principle when MEA monitors the activity of cultured cells. When the cellular components in the diagram are substituted by tissues or slices, the basic principle is almost the same.

In the sensing process, the spiral platinum wire with large surface area is im-merged into the bulk media as the reference electrode and grounded. When the cellular components are coupled onto the microelectrodes, the electric fi eld changes due to the action potential could induce the electric potential changes around the cell itself and on the microelectrodes. Because of the block from cellular mem-brane, the minute of media between cells and microelectrodes, and the interface of electrode-electrolyte, both the shape and amplitude of signals detected by mi-croelectrodes vary from the transmembrane potential. Only the fast phase of the transmembrane potential could be refl ected in the recorded signal.

Due to the overlapped cultured cell or multilayer of cells in the slices, the signal may be the plus of multiple electric fi elds generated by different cells. The electrode therefore could not be too large and is always designed to match the cellular size.

In recording, part of a microelectrode is covered by cellular components while the other part is exposed into the bulk media. The voltage is approximately equal to the voltage of covered region of the electrode multiplied by the ratio of the cov-ered region (Acovered) to the area of the entire electrode (Aelectrode). It is shown in (4.1). With largely covering the electrode, the voltage could be enhanced.

coveredcovered

electrode

AV V

A= × (4.1)

Figure 4.2 The schematic of the principle of the MEA.

4.3 Fabrication and Design of MEA System 69

In addition, the intensity of the signal is inversely related to the gap between microelectrode and its local cells. Therefore, the cells are required to be cultured or placed as close to the electrode as possible. It usually resorts to the various proteins to provide a compatible environment for cells growing onto the electrodes.

The signals from sensory electrodes are loaded out to the outside setup. To get high signal-to-noise ratio (SNR), they are fi rst output to external or integrated preamplifi ers. The amplifi ed signal is then sampled and displayed on computer.

4.3 Fabrication and Design of MEA System

4.3.1 Fabrication

In defi nition, general MEA is a “sandwich” structure (Figure 4.3) in which a thin metallic layer is fi rst deposited on the insulative substrate as electrodes and traces, and then a passivation layer is coated on it to passivate traces from electrolyte while uncovering the sensory sites for sensing electric signals. As a cell-based biosensor, the materials of the chip should meet the requirement of biocompatibility and good coupling of cell-electrode, and it should allow the normal cellular function. In addi-tion, the size of the electrodes and the thickness of all layers also affect transduction of the electric signal and performance of MEA. In its 30-year development, even though the general fabrication process of MEA has not changed in essence, much improvement has been done to better monitor the activities of electrogenic cells and tissues.

The two “bread layers” that sandwich the metal fi lm are strictly required to be insulative for avoiding the current shunting to electrolyte or substrate. Generally, the most widely used substrates include silicon, glass, and polymer. Because silicon is a semiconductor material, a layer of SiO2 must be fi rst grown onto it to insulate the silicon substrate from electrodes and electrolyte. However, the semiconductor-silicon oxide-metal structure forms the parasitic capacitance, which will weaken the signal. In any case, maturely utilized in standard integrated circuit fabrica-tion technology, silicon is still a popular material for MEA fabrication. Glass is another broadly utilized substrate because it is transparent, highly insulative, and

Figure 4.3 The “sandwich” structure of the microelectrode.


temperature resistant. To reduce cost, recently the parylene or polyimide-based MEA has been developed for both in vivo and in vitro detection. It provides good fl exibility and mechanical strength in addition to biocompatibility, insulative prop-erty, and temperature resistance, which is necessary for in vivo detection. Com-pared with glass-based MEA, the cardiomyocytes cultured on polymer-based MEA show no effects of the substrate types on the culture parameters such as beat rate or conduction velocity [12].

Another “bread layer” is the passivation layer, which almost utilizes two kinds of materials: polymer compounds, alternating Si3N4 and SiO2 layers or individual Si3N4 layer. The former is patterned by photolithography, while the latter is depos-ited by the plasma-enhanced chemical vapor deposition (PECVD). The polymer compounds, including photoresist and polyimide, are patterned under the ultravio-let light to expose the sensory site. They exhibit biocompatible and good dielectric features with dielectric constant lower than 2.7. In general, the thickness of poly-mer coating on the electrodes is about several micrometers, which is much thicker than alternating SiO2/Si3N4/SiO2 or individual Si3N4 layers and quite erosible by chemical cleaning or cracked under ultrasonic cleaning. The individual Si3N4 layer is a good choice for uniform coating on the surface. It simplifi es the fabrication process more than that of the alternating SiO2/Si3N4/SiO2 layer and shows imper-meability to the ions. Because the dielectric constant of Si3N4 is 7.5, about 1 μm of it will be coated onto the surface for secure insulation. Another accessible choice is the alternating SiO2/Si3N4/SiO2 layer. It combines the merits of ionic impermeabil-ity by Si3N4 layer and the insulative property from SiO2 layer (ε = 3.9). Although the individual SiO2 layer is also insulative, it cannot completely prevent the ion in the electrolyte from passing through it and will activate the metallic traces to com-municate with the bulk electrolyte. Thus, it is not adopted much individually.

The metallic layer is the core part in the sandwich structure. Since it is directly exposed to the cells, tissues, or slices, its biocompatibility and property is expressly important. Low impedance of the microelectrode decreases the thermal noise of electrodes and increases the possibility of successfully obtaining extracellular sig-nals from the objective cells or tissues. In published papers, there are mainly several types of materials for the metallic electrodes: naked Au, naked Pt, iridium tin oxide (ITO), iridium (Ir) or iridium oxide, Au or Pt grown with titanium nitrite (TiN), and Au or Pt plated with platinum black.

Comparatively, the exchange current density of the metal Pt and Ir is much larger than that of the Au [13]. Except for the material properties, changing surface morphology is an effective way to lower the impedance and improve the perform-ance of the electrodes. The impedance of both TiN, Ir and platinum black electrodes are much lower than Au electrodes, as their surface is much rougher than that of naked Au or Pt electrodes. With respect to the electrical and mechanical properties, TiN [Figure 4.4(a)] microelectrodes exhibiting regular columnar morphology are found to be superior to the iridium with irregular cracks upon anodic oxidation. The impedance of the sputtered iridium can be varied in a large range and less used in practical experiment [14]. In the MEA developmental process, the most com-monly and conveniently used metal is still the Au plated with platinum black, since its fabrication is much cheaper and more available than others. The “fl uffy” layer of platinum black [Figure 4.4(b)] could increase the surface area of Au electrode


for even about 100 times than the geometric area. However, there are no standard parameters in the process of electroplating platinum black on Au electrode, and the fl uffy structure is relatively unstable, especially in the stimulating process. Thus, its lifetime could not be as long as the TiN coated electrodes. The transparent ITO mi-croelectrode fi rst introduced by Gross displayed biocompatibility for cell growing [15]. It also successfully records the action potentials from cultured neurons with visual observation but at the price of low SNR. In any case, its visualization still makes it a suitable option for traces.

In the design, the size of the electrode should be considered. Generally, the diameter of a single cell is 10–30 μm. The size of the electrode is usually designed to match with the size of the cell. This will decrease the area of electrode that is exposed to the electrolyte and lower the leaked current. The center-to-center dis-tance between neighboring electrodes is another important parameter in the MEA design. Breckenridge et al. applied a current of 400 nA on one electrode to record the neighboring electrodes. It is found that the signal attenuated to 50% by the electrode at a distance of 30 μm and 10% by the electrode at a distance of 128 µm. For reducing the electric interference between electrodes, the center-to-center distance is usually more than 100 μm [17].

The fl ow chart in Figure 4.5 is a general fabrication process on glass-based MEA.

The design and fabrication of planar MEA can be described as follows:

The Cr (30 nm) and Au (300 nm) are sequentially sputtered or evaporated 1. onto the glass substrate. Cr layer is used to enhance the adhesion of the Au layer onto the substrate [Figure 4.5(a, b)].The photoresist is then spin-coated onto the metallic layer. It protects the 2. pattern of electrodes and traces by a standard photolithographic technique [Figure 4.5(c)].The exposed metallic layers with Cr and Au are etched away separately. 3. Then the photoresist staying on the electrodes and traces is removed in the photoresist developer [Figure 4.5(d)].

Figure 4.4 The surface morphology of microelectrodes with different treatment. (a) Microelectrode sputtered with TiN (http://www.multichannelsystems.com/). (b) Microelectrode electroplated with platinum black. (From: [16]. Reproduced from Advances in Network Electrophysiology Using Multi-Electrode Arrays. © 2006, with permission from Springer-Verlag GmbH.)


The insulative layer of alternative SiO4. 2/Si3N4/SiO2 is sequentially depos-ited onto the surface of the chip by the PECVD process [Figure 4.5(e)].The photoresist is then spin coated onto the chip. Under UV light exposure, 5. the insulative layer on the sensory sites is exposed and then etched away [Figure 4.5(f, g)].For improving the performance of MEA, the surface of sensory sites are 6. treated. In this step, we take the TiN as the surface treatment option. It is reactive sputtered onto the chip in a nitrogen/argon atmosphere and then the TiN on photoresist is removed by lift-off process. In this way, only the sensory sites are treated with TiN [Figure 4.5(h, i)].

If the surface is treated by platinum black, the plating process can be done after removing the photoresist around the sensory sites in step 5. It applies constant cur-rent of 5 nA/mm2 onto the electrodes in electrolyte of chloroplatinic acid for about 30 seconds.

Next, we introduce the fabrication of 3D MEA, as it shares the design idea of planar MEA.

Like the planar MEA, silicon and glass are commonly selected as the substrate of 3D MEA. In the process of 3D MEA fabrication, the fi rst and vital step is how to enable the substrate shape pyramid at the electrode sites, which does not exist in the process of the planar MEA. In 2002, it was reported that the fi rst active cell layer inside the acute cerebellar slice was placed about 15–30 μm from slice border [18]. Therefore the height of 3D electrodes was designed ranging from 40 to 70

Figure 4.5 (a–i) General fabrication process of MEA.


μm [19–22] to make good contact between the electrodes and the active cells in the slice. On the supposition of silicon substrate, the process fl ow is shown in Figure 4.6.

Generally, 3D MEA fabrication mainly includes the following steps:

Deposit a SiO1. 2 or Si3N4 layer as a protection mask on silicon. Use photolithography to defi ne the basic size of the electrodes using the 2. fi rst mask.Etch away the unwanted deposition SiO3. 2 or Si3N4 in buffered HF solution. Then the silicon tips are formed by anisotropic etching in KOH solution. Here, the concentration, temperature, and etching time of the KOH solu-tion are especially noticed and sternly controlled, because they play signifi -cant roles in the height and shape forming of electrodes. A popular KOH solution mainly includes 44 wt%, 65°C [21].This step is similar to the planar MEA fabrication, which has been de-4. scribed earlier. Briefl y, the next steps include evaporating or sputtering the metal layer, patterning defi ned electrodes, connecting leads and bonding pads, and depositing the passivation layer onto the connecting leads.Deposit the top passivation layer with silicon nitride and expose the metal 5. of the electrode tip. We obtain the fi nal 3D electrode.

Because glass has the high chemical resistance to almost all chemicals, it is diffi cult to structure in hydro-fl uoric acid (HF) solution unless we have following chemical reaction: SiO2 + 6HF → H2SiF6 + 2H2O. Thus, in order to form a tip protuberance, a mask for protecting the glass locally is required. Figure 4.7 shows the whole tip-forming process [23].

First, the glass is etched vertically. Then, the mask is underetched more and more with a continuing etching process. Finally, the mask is fully underetched and is released into the HF solution. This is the moment when the etching procedure has to be stopped to protect the tip.

During fabrication, mask pattern and etching conditions are the keys to form the sharp tips. Theoretically, wet chemical etching of glass in HF solutions is pre-sumed to be an isotropic process. The mask with a diameter of twice the required tip height is fi t for the tip-shaped structure protruding. In practice, however, HF

Figure 4.6 Process fl ow of 3D MEA on silicon substrate. (a) Deposition of SiO2; (b) mask pattern-ing; (c) unwanted SiO2 removal; (d) deep Si RIE; (e) removal of SiO2 mask and photoresist; (f) Si tips forming; (g) isolation layer deposition; (h) deposition metal; (i) removal unwanted metal; and (j) top passivation layer and exposure the electrodes and bonding pads.


glass etching processes are more complicated because some side-reaction products settling at the glass surface impede the etching process. As a consequence, differ-ent morphologies of the glass tips generate on the different etching conditions. On Pyrex glass substrate, low HF concentrations (5%) make tips with a circular but rough shape; however, higher HF concentrations (49%) make tips very fl at but smooth. On soda-lime glass substrate, low HF concentrations (5% and 10%) can obtain very sharp and high tips with a smooth surface, but higher HF concentra-tions (>25%) accelerate the etch rate, leading to an uncontrolled etching process as well as an unusable obtained structure. According to experiment results, the fl oat glass etching in a 10% HF solution at 20°C has been considered optimal for 3D MEA fabrication [23].

Usually, most 3D electrode tips are cone-shaped [20, 21, 24], and few are cylin-drical electrodes [22]. Figure 4.8 shows two types of 3D electrodes.

4.3.2 Different MEA Chips

Specifi c MEA layout and structure have been increasingly developed to meet the demand of specifi c biological and operational problems, yet the essential design

Figure 4.7 Schematic of glass etching process in HF solution. Due to isotropic glass etching proper-ties, the mask is underetched symmetrically until detachment. In order to get very sharp tips, the etching process should be stopped right after mask detachment. (From: [23]. Reproduced from Advances in Network Electrophysiology Using Multi-Electrode Arrays. © 2006, with permission from Springer-Verlag GmbH.)

Figure 4.8 (a) SEM of cone-shaped electrodes on glass substrate. (From: [21]. Reproduced from Chemistry & Biology, © 2006, with permission from Elsevier Science Ltd.) (b) SEM of cylindrical elec-trodes on silicon substrate. (From: [22]. Reproduced from Sensors and Actuators A. © 2006, with permission from Elsevier B.V.)


principle still obeys the “sandwich” structure. By purposes of design, specifi c MEA chips are classifi ed into three types.

The fi rst type is to do some promotion on the MEA for obtaining more valu-able information from specifi c kinds of electrogenic cells or tissues. Changing the spatial distribution of microelectrodes has been an effortless but effective way. The “HexaMEA,” featuring a hexagonal layout, aligns 60 electrodes in a special con-fi guration with varying electrode diameters and interelectrode distances. It ideally resembles the regularity of the retina’s architecture (http://www.multichannelsys-tems.com). For stimulating specifi c afferent pathways and recording the corre-sponding responses in acute hippocampal slices, microelectrodes are designed in a tissue-conformal and partially high-density distribution (Figure 4.9) [25].

For placing live cellular layers in slicing tissue directly onto the sensory sites, the planar structure of the microelectrodes is substituted by the 3D MEA, whose microelectrode is shaped with a pyramid tip. When we study the electrophysiology of the in vivo tissue, probe arrays are adopted more frequently. The fundamental

Figure 4.9 The various designed conformal MEA. (a) cMEA#1: 60 electrodes with diameters of 30 μm and spacings of 50 μm. It has a 3 × 20 rectangular array. (b) cMEA#2: 64 electrodes with squares of 40 μm and spacings of 60 μm. It has a 2 × 8 subarray to stimulate Schaffer Collateral (SchC) fi bers and a 4 × 12 subarray to record output responses from CA1 pyramidal cells. (c) cMEA#3: 60 electrodes with diameters of 30 μm and spacings of 50 μm. It has two 3 × 7 subarrays to stimulate perforant pathways (PP) and record in dentate gyrus (DG) and one 3 × 6 subarray to record CA3 output. (d) cMEA#4: electrodes of 39R and 49S with diameters of 30 μm, 20 μm and spacings of 50 μm, 50 μm, respectively. It has one stimulation subarray consisting of seven triplets for stimulating SchC. It also has four linear subarrays of seven or eight electrodes to record from DG, CA3, and CA1. (From: [25]. Reproduced from the Journal of Neuroscience Methods. © 2006, with permission from Elsevier B.V.)


principle of probe arrays is almost the same as that of general MEA. Differently, the amplitude of the signal recorded by probe is much larger than that from the cultured cells for its good adhesion naturally formed between tissue and probe. The Utah probe (http://microfab.utah.edu) and Michigan probe [10] are two typi-cal designs of out-of-plane probes and in-plane probes. Except these silicon-based probes, the polymer based in-plane probe with the distinctive advantage of fl ex-ibility is under development.

The second type of improved MEA is to precisely position the cells growing onto the defi ned electrodes or patterns. Here are several techniques to integrate with the MEA: physical structure, protein coating by technique of microcontact printing, microfl uidic channel, and perforated electrode. The interested reader could refer to Chapter 2 for a detailed description. Besides these methods, the light-addressed microelectrode chip with high-revolution distribution attempts to address cells by switching a photo conductor by a focused laser beam [26]. It is structured with 3,600 electrodes with a width of 20 μm and a distance of 10 μm (Figure 4.10). The cellular size distance between electrodes allows recording from the cells lying between electrodes and makes the stimulation in short distance effective.

The third type of MEA is to integrate with functions for increasing the effi -ciency in massive assays. CMOS-MEA, shown in Figure 4.11, is a highly integrated biochip comprising the microelectrodes and the fully integrated CMOS circuitry for stimulating and recording the cellular activity [27]. Each electrode has its own mini circuit to fi lter and amplify the signal for improved SNR and programmable control. However, the size of the mini CMOS circuit causes the distance between electrodes to be quite large, which is not benefi cial for obtaining high-resolution acquisitions.

In the large-scale pharmacological assays, it is diffi cult to fi nish the amount of work by the conventional MEA. The QT-screen system is specially designed to evaluate the effects of drugs on the QT interval prolongation of beating cardiomyo-cytes in 96 wells simultaneously (Figure 4.12). [8].

Figure 4.10 The light-addressable MEA chip. (a) In the multilayer structure, the focal laser illumina-tion induces local charges in a thin layer of amorphous silicone, which insulates two layers of leads. Each of their intersections can form an electrode, which is activated by the laser. (b) This shows the photo of the packaged chip of light-addressable high-density MEA. In the chip, there are 3,600 po-tential electrodes at positions optimally suited for the preparation and question of the experiment. (c) This shows the magnifi ed distribution of white area in (b). (From: [26]. Reproduced from the Journal of Biosensors and Bioelectronics. © 2001, with permission from Elsevier B.V.)


4.3.3 Measurement Setup

The signaling process of the MEA measurement system is shown in Figure 4.13. In recording mode, when the microelectrodes are polarized by the electric fi eld changes generated by the cells or tissues, the weak signal will fi rst be preamplifi ed and then sampled to display in a personal computer. In stimulation mode, it selects several electrodes as the stimulation sites to feed in a current or voltage pulse to stimulate some nodes in cellular network while keeping the recording mode on. In the MCS setup, the users can connect the output of a stimulus generator as the digital input. It is allowed in the MC_Rack software to set the duration of a TTL pulse to the sampling rate. The TTL pulse must be long enough to cover at least one data point, and the voltage should be in the range of 0–5V. When stimulating, the connection between electrodes and amplifi ers will switched OFF and ON instantly by the TTL

Figure 4.11 The photograph of integrated circuit with MEA on a single chip. (a) The chip includes parts of the reference electrode, electrode array, temperature sensor, A/D and D/A converter, and digital control. (b) This shows the block diagram of the architecture of the circuits on the chip. (From: [27]. Reproduced from Biosensors and Bioelectronics. © 2004, with permission from Elsevier B.V.)

Figure 4.12 (a) The photograph of QT screen. (b) This shows the overview of the 96-well plate. It shows the magnifi cation of the well. It reveals a substrate-integrated, round gold microelectrode with a 100-μm diameter and a large octagonal reference electrode in each well (http://www.multi-channelsystems.com).


pulse driver to keep the recording property. This process is fi nished by an electronic blanking circuit that has been integrated into the amplifi er.

A preamplifi er is directly connected to the output pins of MEA to provide the amplifi cation stage of about 10. Its performance greatly infl uences the SNR by three parameters: input impedance, input noise, and bandwidth. In the sign-aling pathway, the voltage source produced by electrogenic cells, the resistance representing microelectrodes (including impedance of the double layer), and the input impedance of the preamplifi er are connected serially. Thus, with larger input impedance, the voltage distributed by preamplifi er is much closer to the voltage source. In our experiment, the input impedance of MPA8I (Multichannel Systems, MCS, GmbH) is 1012 Ω parallel to 10 pF, which is much larger than the impedance of microelectrodes of 105–106 Ω at 1 kHz. The MPA8I also has input noise of 1.5 μV much lower than both the intrinsic noise of the microelectrodes and the signal amplitude with several decades of microvolt to several millivolts.

The raw data from the preamplifi er is then amplifi ed by a multichannel fi lter amplifi er with fi xed gain and bandwidth. The fi lter amplifi er combines a bandpass fi lter and a signal amplifi er in one instrument. Usually, the bandwidth of 1–5,000 Hz is suitable for application on the extracellular potential recording, such as spike and fi eld potential recording from neurons or cardiomyocytes.

The analog input signals are acquired from the data source and digitized by in-tegrated 16-channel analog-digital converter, which is integrated into the main unit shown in Figure 4.13. The recorded signals are converted in real time into digital data streams at sampling of up to 50 kHz per channel. This sampling frequency will not miss the fastest biological signals. The sampled digital data is transferred to the computer via a full-speed USB port.

The design of the measurement setup has the trend of being integrated and miniaturized for fi eldable measure. It requires the system be robust, compact, and controllable to the environment for the cellular growth. Coupled with software, the

Figure 4.13 It schematically shows the signaling process of the MEA system.

4.4 Theoretical Analysis of Signal Process in MEA Systems 79

setup will be more versatile and adequate to obtain and analyze the extracellular potential from various parameters.

4.4 Theoretical Analysis of Signal Process in MEA Systems

4.4.1 Equivalent Circuit Model of Signal Process

There are mainly three components in the equivalent circuit model of electric signal process present in Figure 4.14. First, the electric source driving the circuit is the transmembrane potential. When depolarization occurs, the transmembrane poten-tial will rapidly increase mainly due to the infl ux of Na+. This fast change in trans-membrane fi eld will induce the potential change on the microelectrodes.

Second, the Hodgkin-Huxley model (H-H model) is applied to express the cel-lular double-lipid layer structure, ion channels, and cellular signal processes. Via different positions, the membrane is divided into two groups of top membrane and bottom membrane. Two H-H models are serially connected to express these two groups, respectively. Only the bottom H-H model is present in the Figure 4.14.

Third, the space between the cells and microelectrodes is the vital part of the signaling process. When adhesion occurs between the cells and microelectrodes, there is still a minute volume of electrolyte that forms an electric double layer (EDL) with the electrode. The equivalent circuit of EDL could be expressed as the Randles model. Every parameter in this model is directly dependent on size and performance of microelectrode and ionic concentration of electrolyte. In addition, this minute volume electrolyte also induces a side track for ionic fl ow to the bulk electrolyte. It is expressed as the sealing resistance (Rseal) in parallel to the cel-lular model. This model has been shown in Figure 4.15. The readers could turn

Figure 4.14 The equivalent circuit of the signaling pathway in MEA systems. Vin: the intracellular potential; CM: the capacity of the cellular membrane; IM: the current source of the cellular mem-brane; joint A: the junction between the cell and electrode; Rseal: the sealing resistance between the cell and the chip; Rinput: the equivalent of the input of preamplifi er; Vout: the output potential of the microelectrode; Vreference: the grounded bulk media potential.


to Section 3.3.3 for the particular illustration about the whole equivalent circuit and the elements in the model. The output from microelectrodes (Vout) is fed into the preamplifi er relative to the reference electrode. Rinput is the equivalent high-impedance input of the preamplifi er.

The main difference of the whole equivalent circuit of MEA from FET or LAPS is the electrolyte-electrode interface. In MEA, the sensory site is the metallic fi lm. When contacted with electrolyte, a complex electrochemical process will happen, which is expressed as the Randles model. In FET or LAPs devices, cells are cultured on the silicon substrate grown with an insulative layer of SiO2. There is no Faradaic current fl owing across it, and only a single capacity is used to express the equiva-lent circuit of this interface. Even with different working principles, the extracel-lular potentials recorded by these three devices are all synchronizing to and induced by the activities of action potential generated by the objective cells or tissues.

4.4.2 Impedance Properties Analysis of MEA

Impedance spectroscopy (IS) technique is a versatile electrochemical tool that pro-vides quantitative information for characterizing the intrinsic and interfacial prop-erties of the material. It applies an alternating current with low amplitude (about several millivolts) to noninvasively study the static properties and dynamic changes. IS method is applied to deeply and extensively study the interfacial properties of MEA without destroying the samples. It provides information to analyze the char-acterizations of the electrolyte-electrode interface, surface morphology of micro-electrodes, organic coating on microelectrodes, and the couple between cells and microelectrodes, and to help MEA optimally and successfully detect the extracel-lular potential.

The basis of the impedance properties is the Randles model and expressed as the equivalent model showed in Figure 4.15. In this model, Zcpe is the constant phase element impedance, Rct is the charge transfer resistance, Zw is the Warburg impedance, and Rs is the spreading resistance. Since the Zw, due to diffusion of chemical reactants in solution, does not signifi cantly contribute to the overall im-pedance, it is omitted from the Randles model. The plot of the impedance is shown in Figure 4.15.

The recorded impedance as a function of frequency is the main variant used to derive an expression to estimate the thermal noise spectral density according to the Johnson-Nyquist formula:

( ) ( )( )2 RevS kT Zω ω=

(4.2)

Figure 4.15 The equivalent circuit of electrode-electrolyte interface.


where k represents the Boltzmann constant and T is the absolute temperature. The thermal noise is considered a dominated noise source for the micro size electrodes and greatly infl uences the signal quality. From this formula, the thermal noise spec-tral density is proportional to the real part of the overall impedance. Because of the extremely high impedance at low frequency, the thermal noise spectral density is mainly distributed at this frequency band, which causes the baseline of recorded signal to drift. Hence, low impedance is a benefi t for reducing the noise and increas-ing the SNR.

Increasing the surface area or roughness of microelectrodes is an effective way to lower the impedance. Electrodepositing “fl uffy” platinum black or “columnar” TiN on Au or Pt microeletrodes is the method adopted most often, which could enhance the contact area between electrolyte and electrode by up to 100 times. Comparing the impedance spectroscopy (Figure 4.16) of 1 cm2 Pt, Pt black, and TiN electrodes, the corresponding value of the parameters in the model is changed: the Zcpe is calculated to decrease by a factor of 100, and paralleling the Rct causes the overall impedance to be reduced at low frequency [28] (in Figure 4.15, the Warburg impedance is omitted).

Coating protein on the substrate promotes attachment between the cells and the microelectrodes. Because the protein layer is organic and not conductive, the impedance of an electrode coated with protein is larger than that of a bare elec-trode. However, the difference is not so obvious that the signal pathway will not be infl uenced greatly. Hence, the impedance changes induced by the protein coating are often neglected [28].

The last “layer” is the cultured cells. When the cells ideally attach on the micro-electrodes, the communication between microelectrodes and bulk electrolyte will be blocked due to the cellular membrane structure. Two factors are mainly considered

Figure 4.16 The plots of transmembrane potential and fi rst- and second-derivative potentials of cardiomyocytes from neonate mouse.


on the coupling quality: the cellular coverage rate on microelectrodes and the gap between the cultured cells and microelectrodes. The fi nite-element model based on Randles circuit was proposed to study the relationship between the impedance and these two factors [29]. With partial coverage of small electrodes, at least 100% of the impedance increase can be expected. By this model, at frequency 10 kHz, the normalized impedance change (Zwith_cell-Zwithout_cell)/Zwithout_cell is about 1.4 and 0.9 for cell-electrode gap of 0.15 μm and 0.015 μm, respectively. It seams that the good coupling between cells and microelectrodes will increase noise via increasing the impedance. Contradictorily, good coupling reduces the lateral current fl ow (or voltage division) due to the large sealing resistance and farthest refl ects the extra-cellular ionic concentration changes onto the sensory electrodes. In other words, the signal quality will be enhanced.

All the methods for decreasing noise and increasing the signal are adopted for obtaining extracellular signal with good SNR. Even though organic coating and coupling quality infl uence the noise level, it is minor to the whole impedance. The characterization including material properties and surface morphology of the mi-croelectrodes is the most important factor, which will decrease the impedance by a factor of 100. In another aspect, the coupling quality determined by the coverage rate and gap is the dominating point via increasing the sealing resistance. The abil-ity to record APs from individual cells requires microelectrodes comparable in size to the cells themselves. Cardiomyocytes will interconnect like a high-density net via cellular gap junctions. Thus, the size of the electrodes is not confi ned into the size of individual cardiomyocytes.

4.4.3 Analysis of Extracellular Signal

As it is known, the signal source is substantially the ionic fl ux across the cellular membrane when action potential occurs. By the equivalent circuit model, the cells couple with the MEA by the transmembrane potential through the cellular mem-brane capacity and the transmembrane current through the ionic channels. The signals outside the cell and detected by extracellular metallic electrodes are usually considered the fi rst or second time derivative of the transmembrane potential. In Figure 4.16, the top plot is the intracellular action potential signal from cardiomyo-cytes from neonatal mouse (Oxsoft HEAET, [30]). The followed two plots are the fi rst derivative and second derivative, respectively. The typical waveshape of the extracellular signal recorded by microelectrodes is resembled to second derivative plot [31, 32]. For neurons, the intracellular potential and extracellular potential also share the similar relationship. Nevertheless, it’s possible for the waveforms to resemble the fi rst or third derivative, depending on the coupling quality and the performance of microelectrodes [33].

The particular illustration about this kind of relationship is based on the equiv-alent circuit and multiprograms, such as NEURONS, MATLAB, and HSPICE, and they are combined to build and analyze it. The reader is directed to the literature [34] for a deeper understanding of the analysis of intracellular and extracellular potential.

It is important to choose a suitable data display type to better present the re-corded data. The most familiar display methods are trace plot [Figure 4.17(a)] and


raster plot [Figure 4.17(c)]. Trace plot, with x-axis as time and y-axis as amplitude, is mainly used to view the electrode raw data, analog raw data, or mixed data stream. It could be plotted on a tissue image as a background picture to align the traces to the electrode positions and map the signal transmission route in the tissue [Figure 4.17(b)]. Raster plot is used to visualize spike patterns, especially for trig-gered data. Only information about the time course could be showed in the raster plot.

One crucial task of signal processing is to analyze the waveforms recorded from multisites and then extract the parameters to help understand the cellular or organic activity. In the neural physiological fi eld, the spike analysis technique is usually applied to organize the spikes into several types, which may represent dif-ferent parts of a neuron or different neurons types in a network. It is prerequisite for studying many types of brain functions. The basic problems of spike analysis are how to distinguish the smaller spikes from the background noise, and how to dissociate spikes from cells overlap. The developed algorithm includes steps of spike detection and sorting should handle these situations. In commercial soft-ware, MC_Rack (Multichannel Systems, MCS GmbH), the threshold mode and the waveform mode are the two available ways to detect the spikes from the raw sig-nal. They are simple and helpful for the physiologist to distinguish the spikes from background noise when SNR is good enough, but not effective enough to in poor quality situations. Several other spike-sorting methods are presented, such as prin-cipal component analysis, cluster and template matching, artifact neural network analysis, and wavelet analysis. It is concluded that there are no methods versatile and robust enough to solve all the problems, especially to classify the highly over-lapping groups of bursting action potentials. The sorted spikes can be displayed by their respective colors in the waveform groups or two-dimensional projections.

A series of information could be extracted from sorted spikes. The features and changes of the extracellular potential are usually expressed in the terms of ampli-tude, rate of beating, time duration, propagation delay, power spectral density, and slope of the major decline or rise. The statistical result of these parameter values could be evident for evaluating the drug effect and other applications.

Figure 4.17 The display methods of the recorded signal. (a) The original trace plot and the fi ltered trace plot. (b) The signal map combining the trace plot and tissue image (http://www.multichan-nelsystems.com). (c) Raster plot (http:// www.multichannelsystems.com).


4.5 Application of MEA

MEA permits long-term monitoring of electrogenic cells in a noninvasive way. For improving the quality of the culture environment, the chamber for a cell culture and its package are suggested to be delicately designed. Someone improved the conventional chamber by adding a lid to it, which weakened the osmotic strength of media due to evaporation. For keeping normal concentrations of carbon dioxide (CO2) and oxygen (O2), and maintaining suitable pH, it then incorporated with a transparent hydrophobic membrane (fl uorinated ethylene-propylene) which was selectively permeable to O2 and CO2, and relatively permeable to water vapor. In this package, the neurons still exhibited robust spontaneous electrical activity after more than a year in culture [35].

Besides, parallel recording makes MEA possible to conveniently study the sig-nal transmission across the neural network or in the myocardial syncytium. Thus, MEA has been considered as a fundamental tool in network study and a potential platform for pharmacological assays while treating targeted cells as the sensitive components.

4.5.1 Dissociated Neural Network on MEA

The dissociated neural networks from the mammalian tissues are used as a model to study the central nervous system, especially the brain. Although it lacks some features of the intact brain, it still shows the fundamental behaviors, such as the intrinsic circadian activity, recurring fi ring patterns, and synaptic plasticity. It is proved that the dissociated neural network is an invaluable model to study the learning, memory, and information processing in the brain. When cultured on the MEA, the spikes from the different sites of the neural network could be synchro-nously recorded. This stable neuron-MEA signaling system could help the scientists to pick up the temporal-spatial properties of population spikes for neural coding.

The neurons dissociated from murine frontal cortical tissues display the intrin-sic circadian activities in the pattern of random spiking at about 5–7 days. After three weeks, they are considered mature and remained spontaneously active and pharmacologically responsive for more than half a year [36, 37]. Figure 4.18 shows the neural network cultured on the 64-channel MEA at 3 weeks [38]. The variety of spikes and burst might be the recurring potential and could be categorized into several types [36]. In Van Pelt’s group research, they summarized two character-istics on extracellular spikes of the rat cerebral cortex cell: (1) after a long-term recording network burst, the cultured cortical network presented consistent burst discharge patterns with age-dependent fi ring rate profi les and mean durations; and (2) the individual neurons at all ages tend to fi re in specifi c and persistent temporal relationships to one another within the generalized network bursts [39]. And the bursting patterns, including the periodic and random ones, could be switched each other when exposed to or washed out from the pharmacological agents [40, 41].

Coupling with some other accessible techniques, it is fl exible to constitute some structure on MEA to defi ne an experimental model for studying the adaptive prop-erties and synaptic plasticity. The MEA with 30 μm Pt electrodes integrated with

4.5 Application of MEA 85

clustering structures is one typical design to induce the neural network to grow in the defi ned pattern (in Figure 4.19) [42]. The pattern is constituted with fi ve chambers 3 mm in diameter and interconnected via 800-μm-long and 300-μm-wide channels. The topology of the chamber is shown in Figure 4.19(a), and chamber D is the stimulated cluster. Figure 4.19(b) shows the cellular growth in 11 DIV in the clustered MEA. It mainly uses latency response to delivered stimuli and post-stimulus time histogram (PSTH) to evaluate and study the activity of the networks. Compared with the conventional MEA, there are some special characteristics be-tween the subpopulation of different clusters: the existence of asynchronous bursts, more variable PSTH, and longer latency. This research on the behavior of the sub-population networks is a meaningful phase for in vitro study of the functional and anatomical aspects of distributed learning process.

Figure 4.18 This shows the neural network cultured on the 64-channel MEA at 3 weeks. (From: [38]. Reproduced from Biosensors and Bioelectronics. © 1995, with permission from Elsevier B.V.)

Figure 4.19 The MEA with fi ve subchambers. (a) The photograph of the MEA chip with fi ve sub-chambers and the topology. (b) In vitro E18 cortical neurons cultured on clustered MEA at 11 DIV. The microelectrode site in picture is 40 μm in diameter. (From: [42]. Reproduced from Sensors and Actuators A. © 2005, with permission from Elsevier B.V.)


4.5.2 Slice on MEA

Compared with the dissociated cultures, slices hold the closer characteristics to that of the intact tissues. A hippocampal slice is the optimal object to study excit-atory and inhibitory transmission as well as synaptic plasticity. Usually, stimulation of Schaffer collateral axons at the CA3/CA1 border or in the CA1 region evokes fi eld excitatory postsynaptic potentials in the CA1 region [Figure 4.20(b)] [43] and large population spike responses in the dendritic and pyramidal cell layer. When stimulating on the dentate gyrus, it generates generally small responses in the CA3 region via the mossy fi ber pathway. That is to say that MEA is allowed to trig-ger and record conventional fi eld potential a long time, and three circuits exist in hippocampus. Additionally, the researchers illustrated that different “classical” protocols (paired-pulse, long-term potentiation (LTP), and chemical long-term de-pression (LTD)) could reveal synaptic plasticity mechanisms, and MEA could be allowed to discriminate the effects of known pharmacological compounds. Fur-thermore, researchers have tried to design high-density conformal MEA for current source density (CSD) analysis [25].

In 2006, Hofmann and Bading cocultured the slices of entorhinal cortex (EC) and dentate gyrus (DG) in the identical MEA [44]. After 9 days coculture, stimu-lation in EC induced the signals in the EC and DG regions (Figure 4.21), which showed that functional connections between EC and DG existed. The experiment indicates that MEA is also feasible for studying the neural network behavior and axonal regeneration between different tissues.

Next, we introduce the application of 3D MEA on acute slices. Comparing with the planar MEA, 3D MEA has better SNR and could obtain larger signal am-plitude. Thus, researchers began to study the neural network on acute slices using 3D MEA [45–47].

In 2007, Mapelli and D’Angelo used 60 conic electrodes (8 × 8 matrix without corner electrodes with base diameter of 40 μm, height of 30 μm, and space by 100 µm center to center) to study the spatial organization of long-term synaptic plas-ticity at the input stage of cerebellum [47]. They recorded the spontaneous signals from granular layer (GL) and Purkinje cell layer (PCL), as well as evoked activity from GL and molecular layer (ML) (Figure 4.22). The results showed that the GL

Figure 4.20 Recording of fEPSP at the stratum radiatum in CA1 when stimulating schaffer collat-eral axons in CA3. (a) Hippocampal slice on the plannar MEA. (b) Paired-pulse (with 40-ms interval) facilitation of evoked response. (From: [43]. Reproduced from the Journal of Neuroscience Methods. © 1999, with permission from Elsevier Science B.V.)


was usually silent [electrode (3, 5)] except for the occasional low-frequency rhyth-mic fi ring from Golgi cell [electrode (5, 4)]; however, spontaneous high-frequency discharge was usually found in the PCL [electrode (5, 3)]; stimulation on mossy fi ber (Mf) induced the typical N1-N2-P2 complex from GL [electrode (2, 4)] and a wave complex with inverted polarity from ML [electrode (1, 5)]. It suggested that local mossy fi ber–Golgi cell–granule cell inhibitory circuits existed. In addition, Mapelli and D’Angelo analyzed the case of excitation, inhibition, and plasticity after injecting bicuculline (the plots not shown). The results suggested that bicucul-line could increase the active surface, and inhibition was stronger beside the most excited regions. They also studied the long-term synaptic plasticity before and after injecting bicuculline and the spatial organization of synaptic plasticity.

All experiments and the results indicated 3D MEA was feasible for acute slice experiments and effective for neural networks. That is to say, 3D MEA has great value in study for the neural and brain sciences.

Figure 4.21 (a) Coculture of EC and DG on MEA after 9 days in vitro. (b) Evoked signals on MEA electrodes in EC and DG after electrical stimulation of EC (asterisk). EC and DG locations are indi-cated by dotted lines. (From: [44]. Reproduced from the Journal of Physiology-Paris. © 2006, with permission from Elsevier Ltd.)

Figure 4.22 The middle image shows a slice of the cerebellar vermis on 3D MEA; the left traces show the spontaneous activity on different electrodes; the right traces show evoked activity on two different electrodes (white dots: stimulus position). (From: [47]. Reproduced from Journal of Neurosci-ence. © 2007, with permission from Elsevier Science Ltd.)


4.5.3 Retina on MEA

The vertebrate retina, an easily accessible part of central nervous system, is a light-sensitive part located in the inner layer of the eye. It is composed of three layers of nerve cell bodies and two layers of synapses [Figure 4.23(a)]. Different from the brain slices, slicing process on a retina is avoided, and the live cell could be directly present on the surface of the planar electrodes. Guenther et al. proposed a concept of “retinasensor,” and they isolated the retina with pigment epithelium to place on the 60 TiN coated MEA [48]. It could not only detect the microelectroretinogram (micro-ERG) (0.2–100 Hz) generated by the whole slice but also the ganglion cell spikes (0.2–2.8 kHz). Figure 4.23(c) binds the view of slice and the micro-ERG re-corded by MEA. In the white area, the retina and retinal pigment epithelium (RPE) fall apart and only the retina is present, while both are present in the gray area. It can be seen that the signal shows good SNR only in the central part with the retina/RPE. Similar to the electroretinogram (ERG) shown in Figure 4.23(b), the micro-ERG is also induced by the three components of a-, b-, and c-waves. In addition,

Figure 4.23 The micro-ERG recorded from retina by MEA. (a) The structure of the retina. When placing on MEA, the layer of ganglion cell present onto the surface of microelectrodes. (b) The waveform and components of ERG. (c) This combines the view of slice and the micro-ERG recorded by MEA. (d) The micro-ERG changes with the changes of the light intensity. The numbers in the left indicate the attenuation of light by neutral density fi lters in log units. (From: [48]. Reproduced from Advances in Network Electrophysiology Using Multi-Electrode Arrays. © 2006, Reproduced with permis-sion from Springer-Verlag GmbH.)


the micro-ERG clearly shows the light-on and light-off response, and its amplitude is observed to be dependent on the intensity of the light stimulus [Figure 4.23(d)]. It is also sensitive enough to assess the drug effects on the retinal functions.

The ganglion cell is a type of neuron located near the inner surface of retina, which could transmit visual information from retinal photoreceptors to the brain nervous system. Usually, the stimulus on the retina is from light with two states (ON and OFF), light with colors, or electrical stimulus. By exposing to blue light, the intrinsically photosensitive retinal ganglion cells from different stages of the embryonic chick show three types by their fi eld potential responses (sorted by pa-rameters of latency, peak fi ring rate, and total number of light-induced spikes) recorded by TiN-coated MEA [49]. The external applied electrical stimulus is an-other means to study the signaling mechanisms in the retina and the coding process of RGCs. With different stimulus modes and different forms of electric pulses, the response of the retina can be refl ected in the ganglion cells. By modeling these kinds of stimuli as the input and the neural spikes as the output, it could put forward the coding/encoding of the retinal images by ensembles of the ganglion cells [50].

4.5.4 Pharmacological Application

In the past 10 years, the live biological cells have been progressively used as the pharmacological models to study the functional characterization of drugs, patho-gens, and toxicants. It puts forward the drug discovery to the cellular and molecular level. In 1995, Gross’s group proposed the concept of using the neural networks cultured on MEA as the biosensor [38]. Without the homeostatic control of the central nervous system, the in vitro neural networks are still a highly stable system and sensitive to minute chemical agent changes in its culture environment. Similarly, some other electrogenic cells and tissues, such as cardiomyocytes, brain slices, and retinal networks, are also gradually proposed to be studied as the pharmacological models. With the development of stem cell technology, more efforts are invested into the fi eld of stem cell–based biosensor. Stem cells are defi ned by two important characteristics: the ability to proliferate by a self-renewal process and the potential to form at least one specialized cell type. Being genetically normal, they demon-strate uniform physiological responses and can be maintained in culture for a long period of time. Stem cells potentially contribute a fl exible and effective platform for pharmacological research [51].

On the MEA, the presentation of drug discovery via electrogenic cells or tis-sues is the extracellular potential changes due to the infl uences of drugs on cellular structure or activity. Taking the neural networks as an example, such infl uences can be classifi ed into four types: direct metabolic effects, specifi c synaptic effects, transmission effects that stop action potential propagation, and generic membrane effects mediated through nonsynaptic Ca2+ or K+ channels or by the generation of new channels (ionophores) [52]. With this mechanism, the pharmacological ap-plications of MEA are almost oriented in drug evaluation and toxicity detection. In the following section, we will review these applications on cardiomyocytes and neural networks, respectively.


4.5.4.1 Pharmacological Application on Cardiomyocytes

Many drugs interfere with the normal operation of ion channels, which regulate heart activities. The potassium channel openers (KCOs) strongly activated the ATP-sensitive potassium channels (KATP channels) in cellular membranes, which could help to cure many diseases. In the assay, three types of KCOs, pinacidil (PIN), cro-makalim (CROM), and SDZ PCO400 (SDZ), were added into the cardiomyocytes cumulatively [31]. The 64-channel MEA was the evaluating platform to determine the relative effects of these three KCOs on the sodium infl ux. The signal shapes, rate of beating, and propagation speed are the three main parameters to evaluate the effects. EC50 (the concentration of an agonist that is required to produce the half maximal response) is the standard value to describe the action after presence of KCOs. From Figure 4.24, rate and magnitude of voltage change in depolarizing process both decrease concentration dependency. In the presence of CROM (0.1 μM and 1.0 μM), it caused a more pronounced reduction in sodium infl ux than in the presence of PIN or SDZ. Meanwhile, the assay also shows that the beating rate and propagation speed slowed down.

Figure 4.24 The signal shapes of cardiomyocytes in the presence of different KCOs. (a) Pinacidil (PIN); (b) SDZ PCO400 (SDZ); and (c) cromakalim (CROM) (0.1 and 1.0 μM versus controls). All three drugs caused a concentration-dependent reduction in the rate of change of voltage over time. The extent of depolarization (the magnitude of voltage change) also decreased concentration dependency in the presence of these types of KCOs. The presence of CROM affected the sodium component of the recording more signifi cantly than the presence of PIN or SDZ(C). (From: [31]. Re-produced from Analytical and Bioanalytical Chemistry. © 2007, with permission from Springer-Verlag GmbH.)


Similarly, other groups also conducted experiments to evaluate the effects of some drugs on other ionic channels. One researcher applied the cardiac clusters in a differentiated manner from murine embryonic stem cells as the model to evalu-ate drugs like L-type Ca2+ channel antagonist verapamil and Na+ and K+ channel blockers (tetrodotoxin, 4-aminopyridine, and sparfl oxacin) [53]. In Gilchrist’s the-sis, he studied the effects of the drugs quinidine and nifedipine on sodium channels and calcium channels, respectively [54]. Potentially, MEA could sensitively tell the minute changes of action potential due to different drugs and give them the cor-responding signal shape as the “signature.”

Besides, earlier cardiovascular safety assessment of noncardiovascular drugs draws more attention than before. The QT screen is a 96-channel device for high-throughput pharmacological screening, and each channel contains one microelec-trode (Φ = 100 μm) [8]. It is specially designed to natively analyze the phenom-enon of QT prolongation and the induced arrhythmia in vitro. In work from other groups, MEA is also used as a platform for detecting environmental toxicity like pesticides [55] to the cardiomyocytes. In a word, it is a meaningful method to quantitatively give the profi les of drugs or toxins on cardiomyocytes.

4.5.4.2 Pharmacological Application on Neural Networks

Neurotoxicology is now a special fi eld to fi nd the possible existence of a link be-tween exposure to a specifi c compound and a risk of neural system effects. When exposing to neurotoxins, the neuron itself and the neural network will alter the nor-mal activity of the nervous system or cause damage to nervous tissues. Tetrodotoxin (TTX), a potent neurotoxin with no known antidote, is a sensitive voltage-gated sodium channel blocker for nerve cell. When the spinal cord cultured in vitro is ex-posed to TTX with different concentration, the spike rate is dependent on the con-centration and the IC50 is fi tted as 4±1 (mean 9/S.E.M., n=3 cultures) [56]. This nM range is consistent to the level anticipated to induce lethality (LD50) for TTX of 9.4 nM. It proves the potential ability of toxin detection via MEA in a quantitative way. Keefer et al. used the 64-channel ITO microelectrode arrays to detect the effect of trimethylopropane phosphate (TMPP) on the cultured networks [57]. By the action of TMPP with concentration of 2–200 μM, the spike activity was reorganized into synchronous quasi-periodic burst episodes. These changes in network activity con-fi rmed classifi cation of TMPP as a potential epileptogenic compound. Some other groups also put forth efforts on neurotoxins research, such as heavy metal from the environment [58], botulinum toxin (BoNT) from food contamination [59], and acute neurotoxins like trimethyltin chloride (TMT) [60]. With robotic cell culture, biocompatible MEA, and stable testing setup, it provides a reasonable quantitative range of toxin that damages the human neural network.


4.6 Development Trends

4.6.1 Lab on a Chip

The conventional MEA mainly provides the solitary function of stimulating and recording the electrogenic cells or tissues. It is one of the developmental trends to control the microenvironment for the biological source and comprehensively moni-tor the other parameter values on the MEA chip. Other laboratory functions could integrate with microelectrode arrays onto a single chip. The microfl uidic channels could be bonded onto the surface of the MEA chip for controlling the minute cul-ture volume and the sites for cell growing. The high-integrated microchannel on MEA could digitally control the ingredients in the culture fl uid. It will enhance the sensitivity of the cellular response on the drug and the effi ciency in mount of drug-screening assays with massive parallelization.

Functionally integrating with other means of detection helps describe the cel-lular activities concretely. By integrating the impedance electrode and LAPS, the integrated MEA could detect changes of the extracellular potential, impedance, and ionic concentration changes in a synchronous manner. The multiparameter detection helps us to roundly understand the cellular activity and function more exactly.

4.6.2 Portable MEA System

Instrumentation is another trend in the developmental process of an MEA system. Signal attenuation and the size of whole MEA chip are two problems in design of the MEA chip. With the highly developed technology of microfabrication, it could be expected that the MEA systems will be fully integrated with end-to-end process-ing, analysis, archiving, and display into a single unit.

One of the driving forces for developing the MEA system is to detect the out-door environment. It needs to work outside the laboratory and sense the chemical and biological toxins in air and water. Thus, the incubator with regulated tempera-ture and pH should meet the requirement of the longevity of the cultured cellular components and also be integrated for stable monitoring in various environmental conditions. In addition, the software for analyzing the recorded signal will be more reliable and accurate.

4.6.3 Other Developmental Trends

The population spike coding is a fi eld for studying the behavior of the neural net-work. The regulatory circuits of many physiological functions and information coding in neural systems could be further explored. The knowledge on neurosci-ence could help people to recognize the network and develop the artifi cial neural network (ANN).

In addition, MEA is a chip that directly interfaces with the neuron and could communicate with the neuron in an electric stimulating way. It provides the fi rst step in the project of brain-computer interface (BCI). It could help to recover the

4.7 Summary 93

function of the injured parts, such as the visual sense, auditory sense, and even the recognizing ability.

4.7 Summary

With the development of microfabrication and other technologies, MEA has been fi nely fabricated to noninvasive, long-term, and multisites monitoring the electri-genic cellular components. It has become one of the fundamental tools in neurosci-ence research. The relatively simple design makes it possible to be custom designed to meet various requirements. The electric communication between MEA and cel-lular components makes the manual intervention and rehabilitation on the nervous system possible. With this development, the screening throughput and quality have been greatly improved, and it could also provide fast responses of the cell to drugs or chemical compounds. Hence, the effi ciency of massive drug-screening assays is enhanced. Due to its ambiguous mechanism and uncertain experimental conditions, it is still diffi cult to provide the standard of the waveforms or any parameters to indicate the effects of drugs on the cellular components. Extracellular potential ex-presses the limited information about the ion channels, so it is necessary to combine with other drug-screening methods for secondary screening to reach the optimiza-tion. In addition, more efforts are needed to precisely and systematically evaluate the effects of different drugs.

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97

C H A P T E R 5

Field Effect Transistor (FET) as Cell-Based Biosensors

Jun Zhou, Qingjun Liu, and Ping Wang

5.1 Introduction

In the previous chapter, we introduced microelectrode arrays (MEA) as one type of extracellular recording sensors. MEA adopts the metallic fi lm depositing on glass or silicon substrate as a sensitive part to form cell-electrode coupling. Here we concen-trate on another extracellular recording sensor based on semiconductor technology. Silicon is suitable as an electronically conductive substrate for three reasons [1]. (1) Coated with thermally grown silicon dioxide (thickness 10–1,000 nm), silicon is a perfect inert substrate for culturing neuronal cells. (2) The thermally grown silicon dioxide suppresses the transfer of electrons and concomitant electrochemical pro-cesses that lead to corrosion of silicon and damage of the cells. (3) An established semiconductor technology allows the fabrication of microscopic devices in direct contact with the cells. The sensor devices based on fi eld effect principle could be suitably coupled with living cells or tissues, which allows noninvasive long-term recording. The sensitive region could also be deposited by different materials for specifi c ion detection and cell cultural environment monitoring. Up to now, array of 64 metal electrodes has still not met the density requirement of neuronal network study. With the help of high-density fi eld effect transistor (FET) array, dynamics of functional neuronal network grown in vitro, as well as acute brain slice, can be studied at suffi cient resolution.

After Bergveld fi rst employed the metal free gate FET for the extracellular ion concentration measurement [2], more than 600 papers devoted to ion sensitive fi eld effect transistor (ISFET) have appeared and another 150 on related devices, such as enzyme modifi ed FET (ENFET), immune-reaction based FET (IMFET), and chemi-cally sensitive FET (CHEMFET). Several articles have reviewed the development of FET in biological area [3–6]. In the early 1990s, we believed silicon interface could well have been coupled with living cells or tissues when Peter Fromherz mounted single cell on the metal free gate to detect extracellular electrophysiologi-cal signals [7].

98 Field Effect Transistor as Cell-Based Biosensors

The most important reason for developing cell-based FET biosensors is that only by using living components capable of a direct response to incoming informa-tion can the effect of an external physical or chemical stimulus on a living system be investigated. By designing stimulator and recording structure on the same chip, we could mimic the response from the outer stimulus and record the response immediately. By means of integrated biosensors on the same silicon wafer includ-ing temperature sensor, ENFET, ISFET, and cell-based FET biosensors with a fl uid handling system [8], it could be possible to study the effects of pharmaceutical compounds, toxic substances, pollutants, and so on, on a physiological system, especially on cellular metabolism.

5.2 Principle

First, we briefl y introduce the principle of fi eld effect on the fundamental struc-ture named the insulated-gate fi eld-effect transistor (IGFET). The transistor con-sists of a single crystal known as substrate with two heavily doped regions that are diffused into the top surface of the crystal. The current that fl ows from one of these two regions to the other must fl ow through the narrow channel in the sub-strate between them.

An extremely thin layer of silicon dioxide, a near-perfect insulator, is deposited upon the surface of the substrate between the source and drain regions. A metal-lic plate, called the gate, is deposited upon the surface of this insulator. Note that the gate, insulator, and substrate form a capacitor. Due to this metal (gate)–oxide (barrier)–semiconductor (channel) construction, the insulated-gate transistor is fre-quently designated as MOSFET.

In addition to a choice of N-channel versus P-channel design, IGFETs come into two general types: enhancement (E-type) and depletion (D-type). In E-type, the source and drain regions are doped with the same type of material. The substrate is doped with material of opposite polarity. For example, if the substrate is of P-type material, the source and drain regions are N-type. The result is that the source, chan-nel, and drain form a sort of N-P-N sandwich with two junctions between them. The effect is as if there were two junction diodes back to back [see Figure 5.1(a)].

Normally, current will not fl ow through the channel between the source and drain because of the P-N junction between the drain and the substrate in the current

Figure 5.1 (a) Basic structure of insulated-gate FET, and (b) N-channel MOSFET (enhancement type) with positive gate bias.

5.2 Principle 99

path. But as a positive bias applied to the gate, this charges the capacitor (gate). The gate atop the oxide takes on a positive charge from the gate bias battery. The P-type substrate below the gate takes on a negative charge. An inversion region with an excess of electrons forms below the gate oxide. The more electrons left in the channel, the more the material of this channel takes on the characteristic of N-region. This region now connects the source and drain N-type regions, forming a continuous N-region from source to drain. Thus, the MOSFET is a unipolar device [see Figure 5.1(b)].

Cell-based FET sensors can generally be classifi ed into two categories: cell me-tabolism detection and extracellular potential measurement. The cell is the smallest self-sustaining biological entity, and they process multiple incoming information signals by means of a parallel activation of different signaling pathways. According to different types of input (i.e., physical or chemical stimulus), they respond with an appropriate reaction pattern. Cell metabolism parameters are changes in the extracellular pH, the concentration of ions, oxygen consumption, CO2 produc-tion, the redox potential, and other metabolic products. Extracellular potential measurement methods need certain types of electrogenic cells, such as neuronal cells, muscle cells, and so on. Because the electrolyte will be brought in direct con-tact with the gate insulator layer, the design of the cell-based FET needs to take into consideration, for example, how to successfully encapsulate all regions of the device other than the gate region that will be exposed to the electrolyte solution. In the meantime, a reference electrode in the solution replaces the metal gate to provide bias voltage.

Currently, cell-based FET sensors adopt enhancement-type n-channel MOS-FET (see Figure 5.2). Under applied positive gate bias, the positive charges on the gate will attract minority carriers and form a channel that links the source and

Figure 5.2 A cell cultured on FET. (From: [1]. Reproduced from Physica E: Low-Dimensional Systems and Nanostructures. © 2003, with permission from Elsevier Science B.V.)


drain together. When bias is applied between the source and drain, the modula-tion of the drain-source current is determined by the gate voltage change. Cells are cultured on dielectric material like SiO2 or Si3N4. As soon as an action potential occurs, the surface potential of the dielectric layer will be lifted, which induces the change in the density of mobile defect electrons. The neuron-silicon interaction causes a modulation of the current along the inversion layer. According to the current-voltage relationship of FET, we can get the calibration voltage on the gate (VG). This voltage is assumed as extracellular potential, which is well correlated with the increase of membrane voltage (VM). Ion-sensitive fi eld-effect transistor sensors are often performed in a fl ow-through chamber to measure the acidifi cation rate or oxygen consumption rate in the fl ow-stop mode. The materials on the gate area are selective with respect to specifi city and sensitivity. The working mechanism is more or less the same as cell-based FET.

5.3 Device and System

5.3.1 Fabrication of FET-Based Biosensor

The fabrication of the FET is based on standard MOSFET process. In the fi rst step, the wafer (N-type silicon, <100>, 8–12 Ω • m) is thermally dioxide (above 1,000°C) in wet atmosphere leading to an oxide layer thickness of about 1,000 nm [see Figure 5.3(a)]. After oxidation, the source and drain areas of the FET are photolithographi-cally defi ned and etched with buffered HF [see Figure 5.3(b)]. The chip surface must be cleaned and dried to ensure good photoresist adhesion. If negative resist is used, the resist remains on the surface where it is exposed. The photoresist without being exposed to high-intensity ultraviolet light will be washed away. The pattern in the P well region is formed after the sequence procedure of mask alignment, photore-sist exposure, development, and etching. To dope the source and drain region with boron ions, a two-step diffusion process is applied [see Figure 5.3(c)]. The constant-

Figure 5.3 (a–g) Fabrication chart of FET-based biosensor.

5.3 Device and System 101

source diffusion step is used to establish a known dose doping in a shallow layer on the surface of the silicon. The second diffusion is called drive-in step, which intends to move the diffusion front to the desired depth. Ion implantation is also used to achieve more accurate doping. An oxidation and diffusion step results in a fi eld oxide layer of 400–500 nm on the source and drain region. The sheet resistance of these areas after oxidation and diffusion will be measured, by which an average resistance of the source/drain region can approximately be calculated. The gate area of the FET and the contact holes are photolithographically defi ned and etched by buffered HF [see Figure 5.3(d)]. On these areas, a thin oxide layer is grown by ther-mal oxidation at 700°C in a wet atmosphere [see Figure 5.3(e)]. In some articles, the gate region will be deposited by a thick layer of Si3N4 in an LPCVD system using ammonia and silane [9]. Because the silicon nitride layer prevents the penetration of alkali ions, this biocompatible passivation layer contributes to device protection and leakage currents reduction. In a further photolithography step, the contact ar-eas are opened and etched by buffered HF [see Figure 5.3(f)]. Because aluminum is compatible with silicon IC processing and relatively inexpensive, a certain thickness aluminum layer is coated by the lift-off technique [see Figure 5.3(g)]. This method is used to protect the gate and the fi eld oxide area from being damaged by the evapo-ration of metal. The lead wire should be covered by passivation layer.

To our common knowledge, cells in vitro grow randomly on the surface of the chip. In order to record signals, we suggest two ways to overcome this diffi culty. One is to send the cell to the destination. The other is to increase the FET unit num-ber in the chip. The fi rst method uses pipette to suck the cell and then place onto the sensing area of the chip. This work needs experience, and usually has low suc-cess ratio. The second method needs high skill in chip design and fabrication, but involves less operational factor in the experiment. For the latter solution, the chip assembly becomes an important issue. First, the individual chip must be separated from the wafer. Then, good chips are sorted out by visual inspection. Usually, the chip is stuck to the chip carrier by epoxy cement. The chip connections are wire-bonded to the chip carrier using thermosonic bonding. Next, the chip is selectively encapsulated in order to expose the gate region. For the same purpose, a very thin funnel made from silicone is fi xed onto the chip using silicone glue. In the follow-ing process, a glass ring is fi xed onto the chip carrier using silicone glue. Figure 5.4

Figure 5.4 Encapsulated FET array chip. (From: [10]. Reproduced from Trends in Biotechnology. © 2001, with permission from Elsevier Science Ltd.)


shows the packaged chip using a standard 28 dual in-line package (DIP) ceramic chip carrier, which has a bath chamber assembled on the device [9].

5.3.2 FET Sensor System

Fromherz et al. established a typical FET sensor system to detect the action poten-tial of a Retzius cell from a leech. Planar technology was used to fabricate p-channel insulated gate FETs. Source and drain were made on the <100> surface of n-Si by boron diffusion. A thin layer of gate oxide covered n-type Si between the source (S) and drain (D), while p-type silicon was insulated by a thick fi eld oxide. The electrolyte was maintained at ground potential (Ag/AgCl electrode). Bulk silicon, source, and drain were held at positive bias voltages (p-channel FET). The positive-bias voltage provided an accumulation of mobile, positive defect electrons near the surface. The positive voltage change in the neuron during an action potential lifted the surface potential of Si and reduced the density of mobile defect electrons. The neuron-Si interaction caused a modulation of the current along the inversion layer driven by a voltage between source and drain. The source-drain current was mea-sured by a current-voltage converter. A neuron was mounted on a thin insulating layer of the gate oxide on an n-type Si in an electrolyte. The neuron was impaled by a microelectrode. Current was injected to stimulate the cell. The membrane poten-tial was measured by a voltage follower [7].

Offenhäusser et al. [9] produced a 4 × 4 array of FETs by microfabrication techniques (see Figure 5.5). Their system consists of a fi rst stage preamplifi er, which converts the current signal of a single FET unit into a voltage, and a second stage amplifi er and control unit, which compensates for the offset and amplifi es the sig-nal. The temperature of the FET array will be controlled by a heating system, which is included in the second stage control unit. The whole recording system is connected to a computer that is equipped with a 16-channel A/D-conversion board including processor unit and memory for real-time data recording and an IO-board for the control of the amplifi er stage. Compared with the above basic system structure, many small modifi cations of the structure have been reported. In order to have an easier encapsulation, a backside contacted FET was used [11]. Backside contacts were engineered by deep reactive ion etching and a gas phase boron doping process of the holes using planar diffusion sources. To obtain higher resolution, Lambacher et al. produced a fi eld-effect transistor array with 128 × 128 sensor transistors (pitch 7.8 μm) on 1 mm2. This system is called a multitransistor array (MTA) and can be used to record electrical fi eld potentials in cultured brain slices. After achieving stimulation from the silicon chip to the neurons cultured on the chip, stimulation and recording synchronously have been considered into the

Figure 5.5 FET cell sensor system.

5.4 Theoretical Analysis 103

chip design. The contact interface consists of an open-gate FET for recording and a capacitor for stimulation [12, 13].

Another important sensor system is based on ion sensitive fi eld-effect transis-tor (ISFET), which can be applied to detect the extracellular acidifi cation rate of cell culture. This idea was fi rst introduced by Bergveld in 1970s [2]. Without gate metal, the silicon dioxide was used as a pH-sensitive dielectric because the charge of the oxide layer can also induce the conducting channel. Subsequently, Al2O3, Si3N4, Ta2O5, and SnO2 were used as pH-sensitive dielectrics because of the higher pH response [14–16]. In order to avoid poor isolation between the device and so-lution, ISFET encapsulation and new structure fabrication have been developed. Esashi and Matsuo applied anisotropic etching technique to make a needle-like ISFET device [17]. Figure 5.6(a) shows one of the original designs—a needle-like device drawing. Yin created separate extended gate ion-sensitive FETs with ITO glass structure, as shown in Figure 5.6(b) [18]. For the purpose of more physiologi-cal parameters measurement, one integrated sensor based on ISFET principle was designed to measure both the cell’s respiration and its acidifi cation.

5.4 Theoretical Analysis

We usually place electrogenic cells or tissues onto the transistors with an open gate and observe the modulation of the source-drain current. How can we better under-stand the physics of the signal transfer from cell to biosensor? Fromherz developed a planar two-dimensional area-contact model and a point contact model to evalu-ate the ac-signal transfer on the basis of linear response theory. When a neuron is attached to the surface of oxidized silicon, a conductive cleft of electrolyte is left between neuron and chip. It is insulated from the conductive silicon by silicon dioxide and from the conductive cell plasma by the plasma membrane. The cleft forms a core, while the silicon dioxide and the membrane forms the coats, just like a “sandwich cable” [19]. An electrical stimulation of the neuron gives rise to a cur-rent through the membrane in the junction. The voltage that mediates the coupling

Figure 5.6 (a) Design of silicon needles with a place for integrated buffer amplifi ers. (From: [4]. Reproduced from Sensors and Actuators B: Chemical. © 2003, with permission from Elsevier Science Ltd.) (b) EGFET measurement system. (From: [18]. Reproduced from Materials Chemistry and Physics. © 2001, with permission from Elsevier Science Ltd.)


of the junction is defi ned as the transductive extracellular potential (TEP). It spreads along the core to the bulk electrolyte and also through the lower coat to the silicon. As a result, the TEP is determined by the current balance in the core-coat conductor of the junction related to space coordinates x, y and the time t.

5.4.1 Area-Contact Model

First, we introduce the area-contact model according to the specifi c circuit shown in Figure 5.7(a). The current along the cleft is balanced by the displacement cur-rent through silicon dioxide and by the ionic and displacement current through the attached membrane. The conservation of electrical charge per unit area of the junction is expressed by (5.1), where the left-hand side refers to the balance of cur-rent per unit length in the cleft, while the right-hand side refers to the capacitive and ohmic current per unit area through the membrane and the capacitive current per unit area through the silicon dioxide. If the bath electrolyte is kept on ground potential (VE=0), the model introduces three voltages: the electrical potential VM in the cell, the potential VS in the substrate, and the TEP VJ in the junction.

( )1 J JS MJ JS JM JM M J

J

V VV VV c c g V V

r t t t t

⎡ ⎤ ∂ ∂⎡ ⎤ ⎡ ⎤∂ ∂−∇ ∇ = − + − + −⎢ ⎥ ⎢ ⎥ ⎢ ⎥∂ ∂ ∂ ∂⎢ ⎥ ⎣ ⎦ ⎣ ⎦⎣ ⎦

(5.1)

Parameters are the sheet resistance of the cleft rJ, the area-specifi c capacitance between the membrane and the junction cJM, the area-specifi c capacitance between the junction and silicon dioxide cJS, an area-specifi c leak conductance gJM of the attached membrane. Voltage-dependent ion conductance is not included in (5.1). The specifi c capacitance cJM in the attached membrane could be assumed to be the same value as in the free membrane. The sheet resistance rJ can be expressed by the width dJ and the specifi c resistance ρJ of the cleft rJ with rJ = ρJ/dJ.

Considering a circular junction of radius aJ, which is homogeneous with con-stant values of rJ, gJM, cJM, and cJS, the profi le of the ac voltage VJ(a,ω) along the ra-dial coordinate a for a periodic stimulation VM(ω) is given by (5.2) with the length constant λ and the time constant τ of the cable and with the time constant τM of the membrane. After solving (5.2) with the periphery kept on ground potential to be

Figure 5.7 (a) AC circuit of area-contact model. (b) DC circuit of point-contact model. (From: [1]. Reproduced from Physica E: Low-Dimensional Systems and Nanostructures. © 2003, with permission from Elsevier Science B.V.)


zero, the transfer function h(ω,a) = VJ(a,ω)/VM(ω) is obtained. Due to the frequency factor, we get two expressions of transfer functions. In the high-frequency domain, the transfer function becomes independent of the position and is controlled by the capacitive voltage division in the upper and lower coat as h = cJM/(cJM + cJS). In the low-frequency domain, the transfer function is given in terms of the modifi ed Bes-sel function of order zero I0 as h = 1−I0(a/λ)/I0(aJ/λ). At different positions along the radius of a circular homogeneous contact, the amplitude and phase of transfer functions under different frequencies are computed and plotted in Figure 5.8. The value of the parameters are aJ = 10 μm, rJ = 1 GΩ, cJM = 5 μF/cm2, cJS = 0.3 μF/cm2, and gJM = 0.1 mS/cm2.

2 22

2

2

(1 ) ( ) (1 )

1, ,

J JM M

JM JS JMJM

JM J JM JM

d di V a i V

a dada

c c c

g r g g

λλ ωτ ωτ

λ τ τ

⎡ ⎤+ − + = − +⎢ ⎥

⎣ ⎦+

= = =

(5.2)

5.4.2 Point-Contact Model

Usually we know neither the shape of the junction nor the positions of the transistor in the junction, which result in unknown structural factors existing in the area-con-tact model. In that case, it is convenient to use the point-contact model describing the core-coat conductor. The equivalent circuit is shown in Figure 5.7(b). The con-ductive cleft is represented by a global ohmic conductance GJ, attached membrane, and silicon dioxide by the global capacitances CJM and CJS. In this model, global ion-specifi c conductance is taken into account in the attached membrane. The re-versal voltages originate from the concentration differences of the ions between cell and environment, which fl ow through the conductance i

JMg . They are assumed to be the same as in the free membrane. When area-specifi c parameters are determined with respect to the area AJM of the attached membrane as cS = CS/AJM, cM = CJM/AJM, and gJ = GJ/AJM, Kirchhoff’s law is expressed by (5.2), where VJ and VE are the potentials in the junction and in the bulk electrolyte. A comparison of area-contact equation and point-contact equation shows that the Laplace operator is replaced by a constant rJgJ. To match the two models, we must express the area-specifi c

Figure 5.8 (a, b) Theoretical transfer function and the frequency at different positions along the radius. (From: [19]. Reproduced from Physical Review E. © 1997, with permission from the American Physical Society.)


conductance gJ = GJ/AJM by the sheet resistance rJ. Various averaging methods lead to a relation 1

J JG r θπ− = between global resistance and sheet resistance with a scal-ing factor θ = 4-6. For a circular junction of radius aJ with 2

JM JA a π= , we obtain 1

J JG r θπ− = .

0( ) ( )J J i iS M

J J E JS JM JM M Ji

dV dVdV dVg V V c c g V V V

dt dt dt dt

⎡ ⎤ ⎡ ⎤− = − + − + − −⎢ ⎥ ⎢ ⎥

⎣ ⎦ ⎣ ⎦∑ (5.3)

These two models imply that the ion concentrations in the narrow cleft be-tween cell and chip are not changed with constant rJ, constant gJ, and constant V. A change of the ion concentrations in the cleft may become important when the density of ion channels in the junction is high and when these channels are open for an extended time interval. An electrodiffusion version of the area-contact and of the point-contact model needs to compensate for these effects.

5.5 Application

The cell-integrated extracellular sensor hybrid-system can be used for many ap-plications such as drug screening, neurophysiology, toxicology, and environmental measurements. The system shows very high sensitivity to minute amounts of chemi-cal changes within the cellular environment. With the development of recording single-cell activity mounted on the gate of the insulated gate FET, the directions of further study are as follows:

The microscopic nature of the cell-semiconductor contact will be investigated •with respect to its structure and electrical properties. The purpose is to detect electrophysiological signals or even ionic exchange on the membrane.

Hybrid systems are assembled with neuronal networks joined to microelec- •tronic circuit. The purpose is to study the neuronal plasticity and communi-cation between neurons.

5.5.1 Electrophysiological Recording of Neuronal Activity

FET with a metal free gate in an electrolyte has been used to record the electrical activity of individual neuronal cells. A fast opening of sodium channel current and a delayed opening of potassium channel current generate an action potential. The neuronal excitation drives ionic and capacitive current through the membrane at-tached to the chip. The voltage is defi ned as transductive extracellular potential VJ(t). Several cells are coupled to the surface of FET to detect transductive extracel-lular potential. A Retzius cell of the leech attached by a glass pipette was mounted on the thin insulating layer of the gate oxide on n-type Si in an electrolyte. The gate was coated with poly-L-lysine. Extracellular voltage VJ(t) recorded by a transistor and corresponding intracellular voltage VM(t) measured by glass pipette are shown in Figure 5.9. There are three types of VJ(t) recordings according to two positions

5.5 Application 107

that cells grow on the FET. Type a and type b couplings were observed with the cell body on the transistor, while type c coupling was observed with the axon stump on the transistor. Type a response resembles the fi rst derivative of the intracellular voltage VM(t). Type b response resembles the intracellular voltage VM(t) itself. Type c response resembles the inverted fi rst derivative of VM(t) [20]. Neurons from a rat hippocampus have also been recorded. Elicited by current pulses, the neurons fi re action potentials and sometimes burst spikes. Transistor records are obtained by signal averaging, locking the transistor signal to the maximum of the intracellular transient. The amplitude of the extracellular potential VJ(t) is around 0.15 mV [21]. There is no unique response to action potentials; the shape of the extracellular re-cord depends on the cell type and the cell area attached to the chip.

The action potential VM(t) gives rise to two positive transients in VJ(t), one in the rising phase and the other in the falling phase. The small amplitude of the tran-sistor record is a consequence of a high junction conductance gJ. From an ac meas-urement, the junction conductance is estimated to be 600–700 mS/cm2. The shape of the transistor response can be interpreted in terms of (5.4): (1) The positive peak in the rising phase is related to the sodium current. Considering 0Na

M OV V− < , it must be connected with 0Na Na

JM FMg g− < (i.e., a depletion of sodium channels in the junction). In other words, the sodium inward current through the free membrane

Figure 5.9 (a–c) A-, b-, and c-type couplings. The upper row shows the intracellular voltage VM(t); the lower row shows the extracellular voltage VJ(t) on the gate oxide. A- and b-type couplings are observed for cell body on gate, and c-type couplings, for axon stump on gate. (From: [1]. Repro-duced from Physica E: Low-Dimensional Systems and Nanostructures. © 2003, with permission from Elsevier Science B.V.)


gives rise to a capacitive outward current through the attached membrane. (2) The positive peak in the falling phase can be assigned to a potassium outward current through the attached membrane, considering an accumulation of potassium chan-nels with 0K K

JM FMg g− < and 0KM OV V− < [22].

1( )( )

1i i i

J J JM FM M O INJiM

g V g g V V jβ

= − − ++ ∑

(5.4)

5.5.2 Two-Way Communication Between Silicon Chip and Neuron

The integration of electronic ac neuronal circuitry requires a bidirectional electrical communication between silicon chip and neuronal cell. A stimulation achieved by capacitive coupling needs tight coupling between oxidized silicon and neuron. There is no Faradic current fl owing across the electrode/electrolyte interface in contrast to systems with a metallic interface. The silicon electrode was designed on an n-type silicon wafer. Sixteen radial lanes were opened on the wafer and were p-doped with boron. At each inner end of the lanes, stimulation spots (diameter 20–50 μm) were etched and covered by a thin layer of oxide (about 10 nm). By applying voltage steps to the stimulation spot, the attached neuron was excited electrically, as shown in Figure 5.10 [23]. Figure 5.10(a) shows a neuron (N) attached to a stimulation spot at the end of a p-doped lane, which is covered by a thin layer of spot oxide. Re-sponses to the stimuli are intracellular, detected by an impaled microelectrode (ME 1), and an extracellular microelectrode (ME 2). Figure 5.10(b) shows that given extracellular stimulation, the step of a 4.8-V height is a subthreshold stimulus. Ac-tion potentials are elicited by steps of 4.9V and 5V.

Furthermore, with a combination of an insulated-gate fi eld-effect transistor and an insulated spot of silicon, the integrated chips are able to stimulate the neu-ron and record the extracellular potential synchronously. Voltage pulses applied to the silicon elicit an action potential in the neuron. At the same time, a change of the voltage in the neuron membrane modulates the source-drain current through the gate oxide of the FET.

Figure 5.10 (a) Silicon-neuron junction. (b) Response of Retzius cells to step voltage. (From: [23].Reproduced from Physical Review Letters. © 1995, with permission from the American Physical Society.)

5.5 Application 109

The two-way interface is shown in Figure 5.11(a). A single neuron covers a stimulation spot and a transistor, separated by recessed oxide. The electrolyte is on ground potential, the source is held at a voltage VSE, the drain is at a voltage VDS, and the bulk silicon is at a voltage VBS to the source. The voltage VST of the p-doped stimulation spots forms the reversed p-n junction that prevents a spread of the stimuli in the chip. After applied by a voltage step of ΔVST=+5V, an action potential appeared with a delay of 25 ms. The response of the transistor resembled an inverted action potential, which is shown in Figure 5.11(b) [12].

5.5.3 Neuronal Network Study

To study the dynamics of a functional neuronal network in vitro, researches cur-rently develop toward two directions. One is to separate individual neuronal cells from the tissue and culture-defi ned neuronal networks by controlling neuritis out-growth; the other method is to obtain an acute brain slice and analyze the neuronal network given by brain.

Culturing neuronal cells to form a neuronal network is not an easy task. Re-searchers hope to intensify the detection unit in order to harvest more signals from self-organized neuronal networks, but this kind of trial requires a large-scale multi-electrode device. More importantly, the experiment results are not predictable and hard to control. Sprossler et al. established a measuring system of a 4 × 4 FET array for acquisition, storage, and analysis of electrical signals from a network of electro-genic cells [24]. This system allows simultaneous recording from up to 16 channels. The time-delayed recording of cardiac muscle cells recorded from different sites could estimate the difference of the origin, as well as the direction and the veloc-ity of the burst pattern among the cells. A more advanced method is to fabricate a neuronal network with a defi ned topology of synaptic connections. Chemical guid-ance, topographical guidance, and electrical guidance have been used to defi ne a unique way for cell neurites’ growth direction, as mentioned in previous chapters.

In principle, low-density cultures of the neuronal cells drastically reduce the structural complexity, compared with the large number of closely packed neurons

Figure 5.11 (a) Two-way interface. (b) Action potential and the response of the transistor. (From: [12]. Reproduced from Physical Review E. © 1997, with permission from the American Physical Society.)


with unknown synaptic connectivity in brain tissue. A problem of chemical pat-terns is that cell adhesion is often not suffi cient to keep cell bodies and neurites on the given structures for an extended culture time and to withstand the forces ex-erted by the grown cones and the grown neurites [25]. In that respect, topographi-cal structures are superior because they are able to guide growing neurites and keep neurites and somata in place [26].

Neurons from the pond snail Lymnaea stagnalis were used to form a small neu-ronal network with defi ned geometry by combining mechanical immobilization of cell bodies and topographical guidance of neurites. The neurons were immobilized on a silicon chip by microscopic picket fences of polymide. After the cells formed a network with electrical synapses by outgrowth in brain-conditioned medium, pairs of neurons were noninvasively stimulated and recorded [27]. Later, the neurons were cultured in pits and grooves of a polyester fi lm on a silicon chip. The contact interface consists of an open-gate fi eld-effect transistor for recording and a capaci-tor for stimulation, as shown in Figure 5.12(a). After coating with poly-L-lysine and adding culture medium, neurons were put into the pits individually. Two days later, cell bodies were reliably immobilized by the pits, and neurites outgrowth was well governed by substrate topography, as shown in Figure 5.12(b). The experi-ment fi rst observed spontaneous network activity. Without any stimulus applied, the fi ring pattern shows neuron 4 drives other neurons and is stimulated by the input from the network itself. This activity pattern gives evidence for the existence of a network with functional synapses between the neurons. After applying 5 volt-age pulses of 2V to the stimulator beneath, neuron 1 evoked an action potential in neuron 1 after 20 ms, and subsequently induced fi ring in neurons 4, 2, and 3, as shown in Figure 5.12(c) [13]. This experiment proves a designed neuronal network outside a biological entity with a defi ned topology of synaptic connections and noninvasive supervision from a semiconductor is possible. It also points out the crucial problem of yielding with the bottom-up approach of cell-chip engineering.

Another research method is to record spatial patterns of neuronal fi eld poten-tials in acute brain slices using transistor arrays. The neuronal network already exists, and there is no need to put much effort on culturing a network in vitro.

Figure 5.12 (a) Bidirectional contact with capacitor for stimulation and transistor for recording. The small, bright area in the center (10 μm × 3 μm) is the open gate of a fi eld-effect transistor. (b) Four neurons in pits on bidirectional contacts connected by neurites grown in linear grooves. (c) Extracellular control of network activity. Neuron 1 is stimulated from a capacitive stimulator, and the response of other neurons is recorded by transistor. (From: [13]. Reproduced from Advanced Func-tional Materials. © 2005, with permission from Wiley-VCH Verlag GmbH & Co. KgaA.)

5.5 Application 111

However, the recordings are obtained at the bottom of acute slices far from the cur-rent sources because of an inactive tissue layer and possibly a fi lm of bath electro-lyte on the substrate. Moreover, to understand the network in brain tissue requires stimulation and recording of electrical activity at a high spatiotemporal resolution over a large area of tissue. To overcome these problems, electrolyte-oxide-semi-conductor (EOS) transistors have been introduced to record signals from cultured slices of rat hippocampus [28]. Up to now, we still could not accurately elicit and detect the electrical excitation of every single neuron in a functional area. The main current work is to yield time-resolved images of electrical fi eld potentials. Although the EOS transistor signals matched micropipette recordings of fi eld potentials, the detection was achieved only in one dimension. Later, high-density 2D MTAs are implemented by an extended complementary metal oxide semiconductor (CMOS) technology and tested with snail neurons [29]. Based on MTAs designing thought, integrated transistors are invented with an electrolyte-oxide-metal-oxide-semicon-ductor (EOMOS) structure, as shown in Figure 5.13 [30].

Organotypic slices from rat hippocampus were stimulated with a tungsten mi-croelectrode positioned in the pyramidal layer of the CA3 area. The excited brain slice gives rise to an electrical fi eld potential (i.e., the changes of the local extracel-lular voltage with respect to the bath electrode on ground potential). The local fi eld potential at the surface of the chip couples through the insulating electrolyte/chip interface to the gate and proportionally modulates the source-drain current as in the situation of calibration. A complete functional image in terms of the fi eld po-tential was obtained on an area of 1 mm2 at a resolution of 7.8 μm in space [30]. After stimulation in stratum pyramidale of the CA3 region with a tungsten micro-electrode, fast propagating waves of presynaptic action potentials were recorded, as well as patterns of excitatory postsynaptic potentials across and along cornu ammonis, as shown in Figure 5.14. From the different time frames, a propagating velocity of 0.25 m/s could also be evaluated, which correspond to the typical value for the unmyelinated mossy fi bers. Further research based on electrolyte/oxide/semiconductor fi eld-effect transistors (EOSFET) discusses the problem on the loss of signal amplitude from the center of a slice to the bottom [31]. The loss of signal amplitude is mainly caused by layers of inactive tissue in the slice. The result shows

Figure 5.13 Schematic cross section of a transistor with an EOMOS structure. (From: [30]. Repro-duced from the Journal of Neurophysiology. © 2006, with permission from the American Physiological Society.)


that the signal due to an excitatory postsynaptic potential (EPSP) at the bottom of the slice was about 40% of the maximum at its center.

5.5.4 Cell Microenvironment Monitoring

More widely expressed information of cells concerning physiological signals rather than electrophysiological signals are measured by ion sensitive fi eld-effect transistor (ISFET). This type of FET sensor is mainly focused on the extracellular acidifi ca-tion rate of culturing cells, because the pH value in the microenvironment of living cells acts as an important regulator for a number of cellular functions [32]. Usually, the measurements of cell pH were performed using microelectrodes and magnetic resonance spectroscopy. However, microelectrodes may cause damage to cells and magnetic resonance spectroscopy is unsuitable for continuous, long-term monitor-ing of living cell microenvironment.

For measurement, the pH-sensitive ISFET was operated in constant charge mode with adjustable constant source drain voltage UDS and source drain current IDS. On sensor chips fabricated in NMOS technology, the gate substrate voltage UGB and the source substrate voltage UBS were constant. The source voltage relative to the reference electrode potential UGS was used (normally a separate conventional Ag/AgCl electrode was used in contact to the solution above the ISFET’s gate insulator) as output signal [see Figure 5.15(a)]. Changes in the pH of the solution above the ISFET caused a shift of the threshold voltage and the ISFET’s characteristic IDS-UDS curve. This shift was compensated by the electronic sensor and could easily be meas-ured as a shift of the same amount in the voltage UGS [see Figure 5.15(b)] [8].

Lehmann et al. fabricated a four-ISFET array to measure the pH of adherent tumor cells in vitro. The array was built on a 5-inch wafer by a 1.2-μm CMOS-process [33]. An 80-nm Al2O3 layer was sputtered on the ISFETs as the pH-sensitive gate isolator. A fl uid perfusion system was also added for cell culture media. After the constant pumping stopped, the ISFET detected the ongoing cell acidifi cation. The ISFET without cells showed nearly linear characteristics, whereas ISFET with cells displayed a saturation behavior. This saturation is due to a growing pH differ-ence between the cell-ISFET-interspace and the acidifi cated medium above the cells, which is caused by diffusion limitation of the protons out of the cell-ISFET cleft. Because of the higher proton concentration in the cleft, the proton diffused to the

Figure 5.14 Time-resolved images of fi eld potential in a cultured hippocampal slice measured by MTA: (a) after 1.0-ms stimulation and (b) after 5-ms stimulation. (From: [30]. Reproduced from the Journal of Neurophysiology. © 2006, with permission from the American Physiological Society.)

5.5 Application 113

medium and reduced the increase in acidifi cation [see Figure 5.16(a)]. Enhanced fl ow rates had no infl uence on the slope of pH increase. After Triton X-100 was injected into the medium through a fl uid perfusion system, the acidifi cation peak of the ISFET with cells increased while the ISFET without cells showed no obvious peak. This indicates that Triton X-100 improves the adhesion of the cells on the sensor surface and enhanced cell metabolism. The less removed protons from the cell-ISFET cleft decreased the signal of UGS [see Figure 5.16(b)].

As we all know, a living cell always combines complex biochemical and bi-ophysical processes to maintain its physiological function. Monitoring only the extracellular acidifi cation rate is not enough. In order to obtain more informa-tion from the living cell, this ISFETs system is designed with other sensors, like temperature sensors (diodes), conductivity sensors (interdigitated electrodes), and oxygen sensors (amperometric), all integrated in a silicon wafer at the bottom of a cell culture chamber. With a fl uid perfusion system for cell culture media, cells can be kept alive or be stimulated by added drugs [4]. Baumann et al. developed the cell monitoring system (CMS), which allows parallel and noninvasive measurement of different parameters.

Figure 5.15 (a) Sketch of an ISFET with reference-electrode and electrical connections. (b) Charac-teristic line for different pH values. (From: [8]. Reproduced from Sensors and Actuators B: Chemical. © 1999, with permission from Elsevier Science S.A.)

Figure 5.16 (a) ISFET with and without cells is shown in a pump stop-and-go cycle. (b) The whole measurement. (From: [33]. Reproduced from Biosensors and Bioelectronics. © 2000, with permission from Elsevier Science B.V.)



Nearly 40 years have elapsed since Bergveld invented ISFET and applied this sen-sor to the biological measurement fi eld. We have recorded extracellular informa-tion from electrogenic cells, including cell metabolism and membrane potential change. Compared with the complex environment people live in, a single cell could not provide adequate information. Thanks to recent advances in the semiconduc-tor industry, high-density cell-based FET array sensors with better resolution can express more information we want through a large cultured neuronal network. Manufacturing by a smaller scale, the detection of single synaptic events will be realized in the near future. In that case, we would fi nd out how the pre- and post-synapses function together, what the difference in contribution is, when the critical time period for stimuli application is. We have mentioned topographically defi ned neuronal networks with microprinting or microscopic grooves to improve in vitro neuronal network cultivation. For more precise environmental monitoring, the idea of integrated chip sensors with fl uid perfusion systems is developed. Many semi-conductor sensor devices such as cell-based FET, enzyme FET, temperature diode, and surface plasmon resonance can be appropriately designed and fabricated in the same silicon wafer. This integrated sensor, which is equipped with many different sensors, minimizes the size of the traditional sensing system. Moreover, the sensor could also well cooperate with large neuronal networks. We are looking forward to this multiparameter detection sensor system. The more information we get from the cells, the better our understanding of the environment.

Nanobioelectronics, the integration of biological processes and molecules with nanoscale fabricated structures, offers the potential for electronic control and sens-ing of biological systems. As a specifi c example, carbon nanotubes have been sug-gested for use as prosthetic nervous implants in organs such as eyes and ears. To achieve this goal, it requires the parallel preparation of fully functional biological systems and nanoelectronic systems integrated together. This opens up another new cell-monitoring area using a nanodevice as an investigative tool. Bradley et al. [34] have used the interaction between a biological system and a nanodevice to learn not about the electronic component, but about the biological component. This rep-resents a signifi cant step in nanotechnology. In addition, the particular nanodevice used includes a network of nanotubes. This type of nanodevice is ideal for nano-bioelectronics, in which stable nanostructured semiconductors are made available at the scale necessary to interface with biological materials. The device geometry, with the conducting channel of the transistor in direct contact with the biological system, allows excellent communication between the two components. Such direct communication is not possible with conventional transistors, where the conduct-ing channel is buried within the structure. The nanoelectronic devices continue to function as transistors, where the cell membranes preserve their properties and these two component systems interact. As a result, it should be possible to connect living cells directly to these nanoelectronic devices. The devices also operate in a buffer environment, allowing monitoring of certain cell functions by subjecting the cell to different environments. Current technology allows the device size to be reduced to 20 nm, which is signifi cantly smaller than the size of a typical cell area

5.7 Summary 115

if immobilized onto a surface. Thus, an array of devices would allow the detailed monitoring of a living cell in a buffer. In addition, the electronic devices allow the application of local electric fi elds, which will likely infl uence cell functions, opening the way to a functional integration of electronics and biological matter.

5.7 Summary

The concept of cell-based FET sensors utilizing living cells as the sensing element coupled to the metal free gate of the FET offers the possibility of gaining informa-tion about the reaction between cells and external stimuli. Bergveld fi rst employed the SiO2-gate ISFET for the extracellular ion-concentration measurement. Recently, cell-based FET sensors with Si3N4 or Al2O3 as pH-sensitive gate insulators have been realized for extracellular acidifi cation as well as for respiration measurements. However, solid-state physics and neurophysiology have been developing dramati-cally during past 50 years. Still, we have to face the electrical transfer between two different charge carriers (i.e., electrons in the semiconductor and ions in the neurons). More effort has been made to elucidate the mechanism: two-dimensional area contact model is developed to describe current and voltage in iono-electronic interface; the properties of the cleft are measured by optical and chemical method. However, communication between chip and cell still seems unpredictable by the area-contact model. Parameters such as scaling factor exist to modify the result of the simulation due to unstable conditions in experiments like cell adhesion and cell position.

Considering the defects in simulation, we may attempt to optimize recording by improving the cell-chip contact or by lowering the noise of the transistors. The better contact requires the width of the cleft reduction or its specifi c resistance enhancement, enhancing the inhomogeneity of channel distribution using recom-binant methods. Cell-chip contact enhancement methods are mentioned in Chapter 2. Up to now, the state of a single cell or cell system can be monitored by means of various methods, which can be distinguished into two basic families. The fi rst family of methods utilizes the energy metabolism of cells and, in principle, can be extended to all cell types. It is sensitive to a wide range of cellular events, like consuming energy for synthesis of biological molecules, maintaining gradients of ionic concentration across the cell membrane, mechanical motion, and a variety of purposes. Signal parameters such as changes in the extracellular pH, the concentra-tion of ions, oxygen consumption, CO2 production, the redox potential, and other metabolic products (e.g., glucose and lactate) can be measured by various well-established ISFET-based single chemical sensors and biosensors, or multiparameter sensor systems. The second family of methods can detect different electrogenic cell and neuronal network cultured in vitro.

With the development of IC manufacturing, more complex circuits including preamplifi er, noise degeneration, high pass, and low pass fi lter can be integrated on single chip, which improves SNR. The resolution of the chip will also improve to obtain a better signal. We may record multisite under one cell to analyze the ef-fect caused by different distribution of ion channels. We may also study the signal transduction by detecting single synaptic events. This functional information with


additional qualitative or quantitative analytical information can be very important with respect to clinical diagnostics, pharmacology and drug screening, cell biol-ogy, toxicology, and environmental monitoring. By means of such sensors, it could be possible to study the effects of pharmaceutical compounds, toxic substances, pollutants, and the like on a physiological system and in particular on cellular metabolism. Although these experiments would not shake the foundations of neu-robiology, the whole package could help to rouse the attention of the neuroscience community to an emerging technique.

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[14] Moss, S. D., C. C. Johnson, and J. Janata, “Hydrogen, Calcium, and Potassium Ion-Sensi-tive FET Transducers: A Preliminary Report,” IEEE Trans. Biomed. Eng., Vol. 25, No. 1, 1978, pp. 49–54.

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5.7 Summary 117

[17] Esashi, M., and T. Matsuo, “Integrated Micro Multi Ion Sensor Using Field Effect of Semi-conductor,” IEEE Trans. Biomed. Eng., Vol. 25, No. 2, 1978, pp. 184–192.

[18] Yin, L. T., et al., “Study of Indium Tin Oxide Thin Film for Separative Extended Gate IS-FET,” Mater. Chem. Phys., Vol. 70, No. 1, 2001, pp. 12–16.

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[22] Fromherz, P., “Extracellular Recording with Transistors and the Distribution of Ionic Con-ductances in a Cell Membrane,” Eur. Biophys. J., Vol. 28, 1999, pp. 254–258.

[23] Fromherz, P., and A. Stett, “Silicon-Neuron Junction: Capacitive Stimulation of an Individ-ual Neuron on a Silicon Chip,” Phys. Rev. Lett., Vol. 75, No. 8, 1995, pp. 1670–1673.

[24] Sprössler, C., et al., “Long-Term Recording System Based on Field-Effect Transistor Arrays for Monitoring Electrogenic Cells in Culture,” Biosens. Bioelectron., Vol. 13, No. 6, 1998, pp. 613–618.

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[27] Zeck, G., and P. Fromherz, “Noninvasive Neuroelectronic Interfacing with Synaptically Connected Snail Neurons Immobilized on a Semiconductor Chip,” Proc. Natl. Acad. Sci. USA, Vol. 98, No. 18, 2001, pp. 10457–10462.

[28] Besl, B., and P. Fromherz, “Transistor Array with an Organotypic Brain Slice: Field Potential Records and Synaptic Currents,” Eur. J. Neurosci., Vol. 15, No. 6, 2002, pp. 999–1005.

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[32] Wolf, B., et al., “Monitoring of Cellular Signalling and Metabolism with Modular Sensor-Technique: The PhysioControl-Microsystem (PCM®),” Biosens. Bioelectron., Vol. 13, No. 5, 1998, pp. 501–509.

[33] Lehmann, M., et al., “Non-Invasive Measurement of Cell Membrane Associated Proton Gradients by Ion-Sensitive Field Effect Transistor Arrays for Microphysiological and Bio-electronical Applications,” Biosens. Bioelectron., Vol. 15, 2000, pp. 117–124.

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119

C H A P T E R 6

Light Addressable Potentiometric Sensor (LAPS) as Cell-Based Biosensors

Hui Yu, Qingjun Liu, and Ping Wang

6.1 Introduction

As illuminated in Chapters 4 and 5, MEA and FET array are two of the most com-monly used types of cell-semiconductor hybrid biosensor systems for extracellular potential detection at present. MEA and FET typically consisting of 16–64 record-ing sites present tremendous leads for data acquisition from networks of electrically living cells. Such recordings offer a noninvasive and long-term method to measure multiple parameters of cells and tissue, including cell coupling, the extracellular action potential, transmission path of ionic channels, and transmission velocity of biological signals along with the layer of neurons or cardiomyocytes, which are virtually inaccessible using standard intracellular recording techniques [1, 2]. However, these methods are confi ned to measuring the potential only at the pre-determined active measuring sites, such as the tip of the individual microelectrode and the gate-electrode of individual FET, which makes it diffi cult to culture cells on where the very sites are designed. Additionally, because of restriction of micro-electronic fabrication, the active sites of MEA and FET are separated by 50–200 μm, which limits the high-level integration of arrays [3, 4]. All these are barriers limiting the microelectronic devices to be applied to cell detection. In particular, the ion-selective fi eld effect transistor (ISFET) can also be used to detect the concentra-tion of certain ions in solutions, including sometimes in extracellular environments. However, ISFET faces some diffi culties in managing some critical parameters, in-cluding the extremely low lifetime, drift, and stability.

Light addressable potentiometric sensor (LAPS) is a semiconductor device pro-posed by Hafeman in 1988, and it is now as commonly used as ISFET [5]. LAPS indicates a heterostructure of silicon/silicon oxide/silicon nitride, excited by a mod-ulated light source to obtain a photocurrent. The amplitude of this light-induced photocurrent sensitive to the surface potential LAPS is able to detect the potential

120 Light Addressable Potentiometric Sensor (LAPS) as Cell-Based Biosensors

variation caused by an electrochemical event. Therefore, in principle, any event that results in the change of surface potential can be detected by LAPS, includ-ing the change of ion concentration [6], redox effect [7], and so on. LAPS shows some advantages when compared to ISFET while constructing cell-based biosen-sors. The easier fabrication process of LAPS is fully compatible with the standard microelectronics facilities. The encapsulation of LAPS is much less critical, since no metal contact is formed on the surface. Besides, the extremely fl at surface makes it compatible to incorporate into very small volume chambers, which is impor-tant for small dose measurement. Therefore, LAPS seems promising for biomedical application.

Nowadays, several research groups are experimentally using LAPS, and many attempts have been made to commercialize this system, such as the potentiometric alternating biosensor system (PAB) [8] and the physiocontrol microsystem (PCM) [9]. Traditionally, cell-based biosensors involving LAPS are dedicated to the detec-tion of extracellular acidifi cation of living cells. In the U.S. market, there is one available system called the Cytosensor Microphysiometer, released in 1990 by Mo-lecular Devices Corp. (Sunnyvale, California). The Cytosensor Microphysiometer can detect the acidifi cation rate of several cells in the extracellular microenviron-ment with an ultra-high sensitivity down to 0.5 mpH. This system has been widely used in several aspects of the biomedical fi eld, such as receptor analysis and drug analysis.

Besides these conventional applications related to cell metabolism, LAPS has shown great potential in cell-semiconductor hybrid research recently, which is used in extracellular potential detection [10]. After cells are cultured on the LAPS sur-face and fi rm contact forms between the cells and the semiconductor, extracellular potential signals such as the extracellular action potential can be coupled to the LAPS surface. Therefore, detection of the fl uctuation of the resulting photocurrent can refl ect the transmembrane potential variation of target cells. The most impor-tant characteristic of LAPS for the cell-semiconductor hybrid is the addressing abil-ity. Cells can be cultured at any position on a LAPS surface, instead of certain pre-determined detecting sites on MEA or FET. Focused light sources can be employed to address the target cells cultured on any position of the device to be monitored. Furthermore, with the spatial resolution of LAPS down to the level of cell size, and the light point focused to the diameter of several microns, any desired single-cell monitoring can be achieved.

Due to the spatial resolving power, LAPS also has an advantage for array sens-ing application [11]. Usually, no additional sensor structure is needed to realize the LAPS array sensing. In fact, LAPS is an integrated sensor itself, whose integra-tion level is defi ned by the spatial resolution and the illuminating system. Thus, miniaturization with high integration level can be achieved. Many efforts have been drawn on the integration of LAPS [12, 13]. Among these attempts, most are focused on the multisensing of different ions. Our lab proposed an electronic nose with multi-LAPS (MLAPS) for environmental detection. It can detect H+, Fe3+, and Cr6+ simultaneously [12]. Schooning et al. proposed a 16-channel handheld pen-shaped LAPS that can detect the pH of 16 spots on the surface [14]. For biomedical sensing, our lab reported a novel microphysiometer to detect several different ions in the cell metabolism [15]. Besides integrating LAPS to detect different ions, other

6.2 Principle 121

possible attempts are also performed to integrate both the abilities of ion concen-tration detection and extracellular potential signal detection, although it is still a long way from realistic application [16].

This chapter introduces recent signifi cant advances in the research and devel-opment of cell-based biosensors involving LAPS for biomedical applications. The chapter is organized as follows. First, the detecting principle of LAPS is described, including numerical analysis of LAPS in theory. Then, devices and detecting sys-tems are described in details. Typical applications of a LAPS system are then pre-sented as an important part of this chapter. Finally, concluding points and future prospects are discussed.

6.2 Principle

6.2.1 Fundamental

LAPS is a semiconductor-based potential sensitive device that usually consists of the metal-insulator-semiconductor (MIS) or electrolyte-insulator-semiconductor (EIS) structure. For constructing cell-based biosensors, electrolyte is needed for living cells; thus, LAPS with EIS structure is always adopted. LAPS with EIS structure is schematically shown in Figure 6.1(a). The LAPS consists of the heterostructure of Si/SiO2/Si3N4. An external DC bias voltage is applied to the EIS structure to form accumulation, depletion, and inversion layer at the interface of the insulator (SiO2) and semiconductor (Si) in different working status. When a certain light pointer illuminates the LAPS chip, the semiconductor absorbs energy and leads to energy band transition (i.e., produces electron-hole pairs). Usually, electron and hole would compound soon and current is unable to be detected by peripheral circuit. If LAPS is biased in depletion, an internal electric fi eld appears across the depletion layer, and the width of the depletion layer is a function of the local value of the surface

Figure 6.1 (a) Working principle of the LAPS. (b) Characteristic I-V curves of n-type LAPS.


potential. When a modulated light pointer illuminates the bulk silicon, light in-duced charge carriers are separated by the internal electric fi eld, and thus photocur-rent can be detected by the peripheral circuit. The amplitude of the photocurrent depends on the local surface potential. Therefore, by detecting the photocurrent of LAPS, localized surface potential can be obtained [5].

The characteristic sigmoid-shape current-voltage curve of n-type LAPS is shown in Figure 6.1(b). Three regions can be identifi ed as the cutoff region, the working region, and the saturated region.

In the cutoff region, there is no photocurrent. In the working region, the photo-current rises almost linearly with the decreasing voltage. The bias voltage is set to the point of the infl ection of this curve and kept constant during the long time detec-tion process of LAPS. At this point the photocurrent is most sensitive to change in the surface potential. In the saturated region, the photocurrent is saturated, which means that the change of bias voltage within this range will not cause any change in the photocurrent. Since the photocurrent changes with the bias voltage, if the bias voltage applied is kept constant, external potential changes coupled to the bias voltage can be determined by detecting the change of the photocurrent.

By illuminating parts of the surface of the device with a modulated light pointer, additional charge carriers are generated and ac-photocurrent fl ows. This photocur-rent is due to a rearrangement of charge carriers in the depletion layer of the semi-conductor. The arrangement of charge carriers is voltage dependent. If an addition-al potential is applied, the characteristic photocurrent-voltage curve of the LAPS shifts along the voltage axis. The most common functions of LAPS are detecting pH and extracellular potential signals, discussed in the following paragraphs.

For pH detection, a layer of Si3N4 is fabricated on the surface of LAPS. Accord-ing to the site-binding theory, a potential difference, which is related to the concen-tration of H+ in the electrolyte, forms at the interface of insulator (Si3N4/SiO2) and solution [17, 18]. This potential is coupled to the bias voltage applied to the sensor. A larger concentration of H+ provides a larger value of this potential difference, causing the I-V curve to shift along the axis of bias voltage [Figure 6.1(b)]. When the bias voltage keeps constant in the working region, change of the photocurrent indicates the pH change of the electrolyte.

LAPS is a surface potential detector with spatial resolution. The light pointer used for LAPS detection can be focused by microscope and optical lens, which sug-gests that LAPS is possible for cell analysis on any nonpredetermined testing site. After cells are cultured on the LAPS, a focused laser, 10 μm in diameter, is used to illuminate the front side of the chip to address the cells to be monitored. Excitable cells such as cardiac myocytes or neuron cells can generate extracellular action potential. This potential is coupled to the bias voltage applied to the LAPS, which correspondingly changes the amplitude of the photocurrent. Thus, by monitoring the photocurrent at a constant bias voltage, extracellular potential signals can be detected [10].

6.2.2 Numerical Analysis

Numerical analysis helps in further understanding the working principle of LAPS, as well as optimizing the sensor and system. In this section, the electric circuit model

6.2 Principle 123

of LAPS is introduced in a simple way. The equivalent circuit model of the interface of the cell semiconductor was described in Chapter 3, giving a direct view of the way that LAPS is able to detect the extracellular potential signals.

Modeling of LAPS focuses mainly on the EIS system. Here we will briefl y intro-duce the circuit model most commonly used, as shown in Figure 6.2 [19, 20].

Figure 6.2(a) shows the qualitative view of charge distribution within the EIS system. Charge is mainly distributed in four regions: charge in the semiconductor Qs, the interfacial charge Qo, charge in the electrolyte space-charge region Qd, and counterion chage at the inner Helmoltz plane (IHP) Qβ, which is negligible here. Since the system is electric neuter, sum of the charge is zero.

0s o dQ Q Q+ + = (6.1)

According to the site-binding theory [17], and with reference to a silicon nitride (Si3N4) insulator, the interfacial charge is:

Figure 6.2 (a) Qualitative view of charge distribution within the EIS system. (b) Equivalent circuit representation. (c) Simulation of I-V curve of LAPS.


2

2

[ ] - - [ ][ ] [ ] - [ ]

s

o sil nits s s N

H s K K HQ e N e N

H K H K K H K

+ ++

+ + ++ + +

⎛ ⎞⎛ ⎞= +⎜ ⎟ ⎜ ⎟+ + +⎝ ⎠ ⎝ ⎠

(6.2)

where Nsil and Nnit are the silanol and amine binding site density, respectively; K+, K−, and KN+ represent the dissociation constants of the surface chemical reactions.

The charge density in the electrolyte space-charge region can be estimated by the Gouy–Chapman–Stern theory.

( ) ( )1/ 2

0 08 sinh2

dd e

e V VQ KTC

KTε ε

⎡ ⎤−= ⎢ ⎥

⎣ ⎦ (6.3)

Qs can be estimated from the Poisson’s equation

( ) ( ) 1/ 2- /

0 0 02 1 1s seV KT eV KTs s sQ KT p e eV KT n e eV KTεε ⎡ ⎤= ± + − + − −⎣ ⎦ (6.4)

An equivalent circuit for the LAPS is shown in Figure 6.2(b). V is the applied bias voltage, Cd is the Gouy-Chapman layer capacitance, Ch is the Helmoltz layer capacitance, Ci is the insulator capacitance, Cs is the depletion region capacitance, and Rs is the resistance that models the recombination process. From Figure 6.2(b), we can easily get these two equations:

d o d hV V Q C− = (6.5)

o s o iV V Q C− = (6.6)

In (6.1)–(6.6), Ci and Ch are determined from the experiment; thus, we can get Qd, Qo, Qs, Vd, Vs, and Vo from these six equations. The relationship between the bias voltage and photo-induced current can be simulated, shown in Figure 6.2(c), which is consistent with I-V curve from the experiment, shown in Figure 6.1(b).


Due to the advantages of LAPS for constructing biosensors, many attempts have been made to commercialize the LAPS system. In this section, we will introduce the most popular design of the LAPS chip and system setup. First, we present the ba-sic structure and fabrication of the LAPS chip. The Cytosensor Microphysiometer system along with acidifi cation detection is then presented. After that, the detecting system for a cell-semiconductor hybrid device is introduced, as well as the detection of extracellular potential signals.

6.3.1 Device

The optimization of a LAPS heterostructure of Si/SiO2/Si3N4 might provide the sur-face potential sensitivity of LAPS the ability to measure the extracellular potential


of the cells. For extracellular acidifi cation detection, Si3N4 is required for its higher sensitivity to H+ than SiO2. However, for the measurement of action potential, which is measured as a direct change of surface potential of the insulating layer and not as a pH response of the insulating layer, SiO2 alone can be used if it is prevented from getting totally hydrated during the whole measurement period [21].

As mentioned before, one of the most important features of LAPS is the ad-dressing ability. Spatial resolution of LAPS is important in highly integrated image sensing and single cell monitoring. Nakao et al. revealed the effects of the thick-ness of silicon chips and wavelengths of the light source to spatial resolution [22]. By thinning the bulk silicon to a thickness of 20 μm and illuminating the backside with laser at a wavelength of 830 nm, special resolution of 10 μm was obtained. Yoshitaka deposited an ultrathin silicon fi lm of 0.5 μm on transparent substrate and achieved good resolution down to 5 μm [23]. Thus, optimization of LAPS in-volves changing the thickness of insulator and bulk silicon [4]. High spatial resolu-tion means accurate imaging and more information. Besides, to achieve single cell monitoring, spatial resolution down to the scale of single cell is required, which is usually 10–100 μm. However, in some circumstances, including the commercially available Cytosensor Microphysiometer, it does not take advantage of the spatial resolution of the LAPS, but detects the mean acidifi cation caused by 104–106 cells. Spatial resolution is less critical in this system.

Fabrication of LAPS is easy and fully compatible with the standard microelec-tronics facilities (Figure 6.3). The detailed fabricating process of the LAPS chip most commonly used is explained here:

A silicon chip, usually 400–500 1. μm in thickness, with a resistance of about 5–10 Ω·cm, is chosen as the substrate. The surface is cleaned with a stand-ard chemical process.Oxidation is carried out to form a silicon oxide layer of about 30–50 nm in 2. thickness on the silicon surface.Deposit a thin layer of Si3. 3N4 on the upper side of the bulk, usually 50–100 nm in thickness, by plasma-enhanced chemical vapor deposition (PECVD) as a sensitive layer.Remove oxide layer on the backside by etching with 10% HF. Etch the sili-4. con bulk from the back side to reduce the thickness if necessary, then wash with deionized water and other organic solutions.An aluminum membrane, about 300 nm in thickness, is evaporated on the 5. backside of the silicon chip to form an ohm contact.

Figure 6.3 The fabricating process of a LAPS chip.


6.3.2 Microphysiometer System

The commercialized Cytosensor Microphysiomter, which can detect extracellular acidifi cation, is shown in Figure 6.4. To state, the main part that decides the size of this equipment is the fl owing system, which consists of the fl uid tubes, peristaltic debubbler, and valve. The whole process, including valve control, voltage control, data sampling, and signal processing, can be manipulated by the computer. The LAPS chip works as the working electrode. A platinum counter electrode and an Ag/AgCl reference electrode are placed nearby the fl uid inlet and outlet, respec-tively. These three electrodes form a three-electrode system, which can keep the potential between the counter electrode and working electrode steady.

After sensor chip fabrication, a testing chamber should be coupled on the sen-sor to confi ne the testing environment for measurement. Under steady state con-ditions, one cell produces about 108 protons per second [25]. To detect such a small parameter, the testing chamber and fl uid system should satisfy the following objectives:

The cell volume/medium volume ratio should be maximized to increase 1. measurement sensitivity.The exchange rate of fl owing liquid should be optimized to fl ush out spent 2. medium and/or introduce reagents.The distance between measurable species and the silicon chip should be 3. minimized such that protons must diffuse to reach the silicon electrode.

A full view of the fl uid fl owing system of the microphysiometer is shown in Figure 6.4. A peristaltic pump transports the culture medium through a heated degasser and via a selection valve and the plunger into the sensor chamber, con-taining the cells. The medium used for running the cytosensor microphysiometer is buffered with only 1 mM of phosphate. Sodium bicarbonate is not present, but in

Figure 6.4 Schematic diagram of the cytosensor microphysiometer. (From: [24]. Reproduced from Biosensors and Bioelectronics. © 2000, with permission from Elsevier Science S.A.)


order to keep the osmotic balance, an appropriate amount of NaCl has to be added to the medium. This low buffer capacity ensures the high sensitivity of the system, allowing it to detect small changes in extracellular acidifi cation.

The testing chamber for the Cytosensor Microphysiometer [Figure 6.5(a)] is defi ned as a porous microchamber, which is constructed with two porous mem-branes and a spacer. The porous membranes are made of biocompatible porous polymer material. A preferred material is a porous polycarbonate membrane. The pore size of this membrane can be selected for use with different cells; for example, large pore size (generally 3–12 μm) is chosen for eukaryotic cells. In operation, a tissue plunger, with an inlet and an outlet, is inserted into the capsule insert and tightly pressed against the membranes. A seal is formed by ridge on the plunger. Liquid can fl ow from the inlet in above, through, between, and below membranes and through the outlet. This chamber can be minimized to a volume of 2.8 μL. Contained fl uids and agents can enter the porous microchamber when placed in a fl owing stream, but cells cannot escape. In the capsule cup, spontaneously adhering cells are seeded directly on the inner surface membrane. However, for other cells that do not spontaneously adhere to porous membranes, a polymer matrix such as a collagen sponge is used to deposit cells. The cells in the polymer matrix are then incorporated into the porous microchamber.

An alternative design of the fl ow chamber for extracellular acidifi cation detec-tion in the PAB system is shown in Figure 6.5(b). The counter electrode is inserted inside the inlet channel, while the reference electrode is in the outlet. The LAPS is separated from the cells immobilized onto a glass coverslip by a tefl on spacer, whose thickness defi nes the microvolume of the testing chamber. Thermal control is achieved by a Peltier cell connected to appropriate circuitry and fi xed on the top of the chamber. Heating is provided near the cells by a metal cylinder (usually alu-minum). The right-hand side of the fi gure shows the top view of the gasket used to prevent medium leakage, the coverslip where cells are grown, and the tefl on spacer, giving an indication of the fl ow circuit inside the “sandwich.” One advantage of this design is the potential of utilizing several LEDs at different wavelengths ad-dressing regions covered by biological sensing elements with various specifi cities (e.g., different enzymes), which renders the PAB system extremely useful for com-bined assays of complex samples.

6.3.2.1 Acidifi cation Detection with Microphysiometer

As an example, for functional detection with microphysiometer, extracellular acid-ifi cation rate (ECAR) detection is shown in Figure 6.6 [16]. Before monitoring ECAR, calibration of the LAPS chip is carried out. Three species of solutions with pH of 3.32, 7.04, and 9.45 are used as the solutions to be measured. As we can see in Figure 6.6(a, b) good linearity with high sensitivity to H+ of 53.9 mV/pH is obtained.

Extracellular acidifi cation result is shown in Figure 6.6(c). The fl ow in the chamber is controlled in the ON-OFF mode. When the fl ow is switched to OFF mode, metabolites are accumulated in the testing chamber, causing the pH to drop below the baseline. The rate of the change of pH in extracellular microenvironment is the acidifi cation rate. When the fl ow is switched to ON mode, the liquid fl ow


washes out the protons and thus causes the pH to rise back up to the baseline. By detecting in such cycles, extracellular acidifi cation rate can be visualized. In Figure 6.6(c), after the electrolyte solution containing acetylcholine at the concentration of 100 mg/ml was pumped in to the chamber as the inhibitor, the output didn’t

Figure 6.5 (a) Schematic fi gure of the cell assay device used in microphysiometer. (From: [24]. Reproduced from Biosensors and Bioelectronics. © 2000, with permission from Elsevier Science S.A.) (b) PAB system. (From: [8]. Reproduced from Biosensors and Bioelectronics. © 1995, with permission from Elsevier Science B.V.)


drop even with the fl ow off, showing the cells slowed or stopped acidifi cation, which means the cells were probably killed.

6.3.3 Detecting System of Cell-Semiconductor Hybrid LAPS

The cell-semiconductor hybrid LAPS is used to perform high-sensitivity extracel-lular potential detection. To give a more detailed description of the detecting system of cell-semiconductor hybrid LAPS biosensors, the schematic diagram is shown in Figure 6.7. For LAPS detection, a light source should be applied to illuminate either side of the silicon chip to generate the light-induced photocarriers. In nonspatial re-solved system, such as the Cytosensor Microphysiometer, which detects the average response of an illuminated area, simply an LED can be used to illuminate a certain testing area on the backside of the sensor chip [Figure 6.5(a)]. Preferred is the in-frared LED. However, the most outstanding advantage of LAPS is the addressing ability. In a spatial resolved LAPS system for cell-semiconductor hybrid detection or chemical imaging processes, a highly focused laser is used as the illuminating source (Figure 6.7). The laser is power supplied by the laser generator and focused by the optical lens. This focused laser works as the addressing tool to choose any detection site on the sensor surface. Generated photocurrent of LAPS contains information

Figure 6.6 (a) I-V curves in different pH solution. (b) Calibration for H+ detection. (c) Extracellular acidifi cation detection of cardiac myocytes and the effect of acetylcholine (100 mg/ml) on cardiac myocyte. (From: [16]. Reproduced from Biosensors and Bioelectronics. © 2009, with permission from Elsevier Science B.V.)


on the exact illuminated area. Thus, by scanning the sensor surface with this fo-cused laser, a high-resolution chemical distribution image is acquired. This focused laser, with a diameter down to 10 μm, can even address any single cell on the sen-sor surface. While the laser is focused on a single cell, turbulence of the resulting photocurrent corresponds to the response signals of this cell, such as the transmem-brane potential signal. Therefore, when used as a cell-semiconductor hybrid device, LAPS can overcome the most critical problem of cell positioning that researchers encounter in the application of FET and MEA cell-based biosensors. Cells are no longer constrained on predetermined testing sites, but anywhere available on the LAPS sensor. In this case, the laser should be visible for observing, and preferred is the red light with long wavelength.

In Figure 6.7, a potentiostat (EG&G Princeton Applied Research, M273A) is employed to control the bias voltage across the silicon bulk to form the depletion layer inside. In the running process, the bias voltage of LAPS is applied between the platinum counter electrode and the silicon working electrode, and the photocurrent fl ows through the working electrode to peripheral equipment. Preamplifi cation is also performed in the potentiostat, which transforms the current signal into poten-tial signal.

In the LAPS system, the surface potential signal is amplitude modulated by the high-frequency light signal, resulting in the high-frequency photocurrent signal. Thus, to obtain the original surface potential signal, demodulation is required after preamplifi cation. Lock-in amplifi er is always used for small signal detection, as it can greatly increase the SNR, usually an improvement of the SNR for over 106 times. In our system, the lock-in amplifi er (Stanford Research System, SR830) is employed. The lock-in amplifi er detects only the signals in the narrow band near the objective frequency, determined by the reference frequency. Thus, in order to get the corresponding surface potential signal from the photocurrent signal, it is important to keep the internal reference frequency exactly the same as the carrier

Figure 6.7 Detecting system for cell-semiconductor hybrid LAPS device. (From: [26]. Reproduced from Biosensors and Bioelectronics. © 2006, with permission from Elsevier Science B.V.)


frequency of the photocurrent signal, which is the light frequency. In Figure 6.7, the laser generator supply is controlled by a signal generated by lock-in amplifi er, which has the same frequency as the internal reference signal used for demodula-tion. The result of the lock-in amplifi er includes the amplitude and phase informa-tion of the photocurrent signal, which refl ects the change of the surface potential signal of the LAPS chip.

After signal demodulation by lock-in amplifi er, data is then sampled by a 16-bit acquisition card to the computer for data screening and further processing by the software. Programming can be performed with different programming languages, among which labVIEW is recommended.

6.3.3.1 Extracellular Potential Detection with LAPS Device

Typical recording of the cell-semiconductor hybrid LAPS device is shown in Figure 6.8. Cardiomyocytes or neurons are cultured on the surface of LAPS. The system detects the current in the cleft between cells and the silicon electrode. The activi-ties of cardiomyocytes are verifi ed by visually identifying the cellular contractions. However, depending on the age of the culture and the cell density, the proportion of contraction and beating frequency varies correspondingly. All the obtained extra-cellular responses on different sites of the chip show obvious signal shapes and am-

Figure 6.8 The extracellular signal of a single cardiomyocyte or neuron on the LAPS. (a) The typical response of neuron under the effect of acetylcholine. (b) The spontaneous potentials of mitral cell under the effect of glutamic acid. (From: [26]. Reproduced from Biosensors and Bioelectronics. © 2006, with permission from Elsevier Science B.V.) (c) Extracellular potential of the cardiomyocyte. (d) With the drugs screening parameters of the signals indicated. Amplitude and duration are defi ned sepa-rately for each stroke of the spike. Amplitude is simply a measurement from baseline to the peak of each stroke. The duration is defi ned as the width of the stroke when the stroke amplitude is 50% of the maximum. (From: [29]. Reproduced from Biosensors and Bioelectronics. © 2007, with permission from Elsevier Science B.V.)


plitudes of up to 40 μV. These shapes are similar to the various categories recorded by the FET and MEA [27, 28].

Similar to the shapes of signals recorded by FET [30] array and MEA [28], the parameters of drugs analysis can be divided into single-spike parameters and multispike ones. The single-spike parameters characterize the morphology of in-dividual extracellular potentials [Figure 6.8(a)], in which there are three major features of the signal shape, or strokes, and each of them is described by amplitude and duration, as shown in Figure 6.8(d). The multispike parameters characterize the propagating nature of the potentials. The interspike interval (ISI) is the time difference between the beat points (this can be represented by the peaks of stroke 1) of two consecutive extracellular potentials. Indeed, the ISI refl ects the beating frequency on a special time, which is a conformity analysis parameter to cardiovas-cular pharmacology.

The formation of the signals is mainly contributed to the changes of the ion currents in the junction area between cells and sensor [30, 31]. The positive up-stroke at the beginning of the signals is attributed to the stimulus current injected by neighboring cells. The fast negative spike is due to a coupled Na+ ion current, while slower negative and positive continuation represents different intensities of Ca2+ and K+ ion currents. Once a certain drug is administered, the activities of the ion-channels in the membrane are either reduced or enhanced. Thus, the beating frequency, amplitude, and duration are extracted as parameters of drug effects. The sum of the absolute amplitudes of stroke 1 and stroke 2 is selected as amplitude of the whole signal. The duration is the sum of three strokes. At the same time, to eliminate the diversities of the baseline (e.g., the diversity of the beating frequency of different cellular clusters), the normalized method is used.

6.4 Application

LAPS has many advantages for constructing cell-based biosensors. Since the fi rst publishing of the Cytosensor Microphysiometer, it has been widely used by re-searchers. Besides, the newly proposed cell-semiconductor LAPS device for extracel-lular potential detection is considered a useful tool for cell electrophysiology study. In this section, applications of LAPS for cell-based biosensors are introduced in cell biology, pharmacology, toxicology, environment measurement, and so on. Several reviews have been published to introduce the applications of the microphysiometer [6, 24, 25, 32, 33]. Here we mainly represent the application of LAPS for cell-based biosensor in ligand/receptor analysis and drug analysis.

With the help of the microphysiometer, research work has been carried out on the binding process of cells responding to ligands and receptors, including the second messenger mode. Interactions between ligands in solution and receptors in cell membranes are the basis of some of the most important intercellular regula-tory mechanisms, and these interactions are the targets of a large fraction of pre-scription drugs. By detecting the extracellular acidifi cation rate change, which can refl ect the metabolism of cells, interactions between ligands and receptors can be monitored.

6.4 Application 133

6.4.1 Signaling Mechanism Study

Microphysiometer has been used to study the signaling mechanism. By monitoring the extracellular acidifi cation rate after certain ligand/receptor binding, the transfer or channel inhibition can be observed and thus we can obtain evidence for signaling tranduction.

Several lines of evidence indicate that a variety of G protein–coupled receptors modulate Na+/H+ exchanger. Indirect evidence for modulation of Na+/H+ exchange includes the observation that some responses to stimulation of α2-adrenergic recep-tor are sensitive to amiloride analogs and extracellular pH and require Na+. Smart and Wood described the investigation of the signal transduction mechanisms acti-vated by both G-protein and non–G-protein coupled hormone and neuropeptide re-ceptors [34]. Neve et al. monitored the extracellular acidifi cation rate of C6 glioma cells and L fi broblast expressing dopamine D2 receptor with the microphysiometer [35]. Different inhibitors of the D2 receptor, such as Na+/H+ exchange G protein and cAMP enzymes, were analyzed. Results show that recombinant D2 receptors expressed in two lines of mammalian cells promote intracellular alkalinization by acceleration of Na+/H+ exchange. The insensitivity of this response to pertussis toxin indicates that D2 receptors modulate Na+/H+ exchange by a signaling path-way distinct from the many signaling pathways regulated by the pertussis toxin-sensitive G proteins, G1 and G0. Mukhin et al. researched on rat aortic smooth muscle (RASM) cells [36]. Response curves show that the epidermal growth factor receptor blockade attenuated extracellular signal-regulated protein kinase (ERK) activation, but not Na+/H+ exchanger type 1 (NHE-1) activation by Angiotensin II (Ang II) and 5-hydroxytryptamine (5-HT), suggesting the EGF receptor and NHE-1 work in parallel to stimulate ERK activity in RASM cells, converging distal to the EGF receptor but at or above the level of Ras in the Ras-MEK-ERK pathway. Conclusion is given as identifying the NHE as a new regulator of ERK activity in RASM cells.

Since the microphysiometer is a generic way to measure the ligand/receptor binding, it is usually used together with other biological methods. Garnovskaya et al. used both the microphysiometer and the fl uorometric imaging plate reader (FLIPR), which can measure the extracellular acidifi cation rate and intracellular pH, respectively, to assess the activation of NHE-1 by hypertonicity [37]. Results indicate hypertonicity rapidly activated NHE-1 in a concentration-dependent man-ner. Inhibitors of Ca2+/calmodulin (CaM) and Janus kinase 2 (Jak2) attenuated this action, whereas neither calcium chelation nor inhibitors of protein kinase C, the Ras-ERK1/2 pathway, Src kinase, and Ca2+/calmodulin dependent enzymes had signifi cant effects. Together with immunoprecipitation studies, a novel pathway, that hypertonicity induces activation of NHE-1 in CHO-K1 cells in large part, was proposed. Blanpain et al. studied the ability of the mutants to bind and signal in response to chemokines. Chemokines’ binding and activation of Gi-mediated sig-naling pathways, such as calcium mobilization and inhibition of adenylate cyclase, were not affected [38]. However, the duration of the functional response, as mea-sured by a microphysiometer, and the ability to increase [35S] guanosine 5’-3-O-(thio) triphosphate binding to membranes were severely affected for the nonpalmi-toylated mutant. Together with other results of immunofl uorescence studies, it is


concluded that palmitoylated cysteines play an important role in the intracellular traffi cking of CCR5 and are likely necessary for effi cient coupling of the receptor to part of its repertoire of signaling cascades.

6.4.2 Functional Characterization of Ligand/Receptor Binding

With the help of the microphysiometer, functional characterization of ligand/recep-tor can be studied. To characterize ligand/receptor, usually time-dependent response and dose-dependent response of ligand/receptor binding are monitored. The time-dependent response shows the dynamic characteristics of ligand/receptor binding, while dose-dependent response represents the activation dose of the receptor.

Real-time monitoring cell metabolism with microphysiometer provides an al-ternative way of observing dynamic characteristics of cell biology caused by bind-ing of ligands and receptors. Wada et al. studied the dynamic response process of the extracellular acidifi cation rate of human medullary cells activated by the granulocyte-macrophage colony stimulating factor (GM-CSF) [39]. Using the mi-crophysiometer, it is obtained that the response to GM-CSF is dose related and can be restrained by antibody of anti–GM-CSF. From the results, it is proposed that the response can be cataloged into two components: fast response and slow response. And it is proved that the fast response is activated by the reverse transfer of Na+-H+, while slow response is due to the increase of glucose metabolism. Miller et al. monitored the metabolism of medulloblastoma cells expressed with nicotinic acetylcholine receptor [40]. Carbachol and d-tubocurarine are used as stimulus and inhibitor, respectively. Resulting metabolic responses to these two drugs are consistent with those obtained by other biochemical analyzing methods. Interest-ingly, the dynamic of this response also showed two different metabolic actions, fast response and slow response. This indicates that the duration of cell metabolic variation activated by acetylcholine receptor is much longer than the opening of the receptor channel. Conventional electrophysiological methods are not able to perform such long-time and real-time detection to achieve this observation. McCo-nnell et al. studied characteristics of response to muscarinic acetycholine receptor [25]. Extracellular acidifi cation variation caused by the muscarinic acetylcholine receptor is found to be a fast and instantaneous response. In 30 seconds after the addition of carbachol as the activation, the acidifi cation reached a peak and attenu-ated quickly. New steady status was achieved in several minutes. On the other side, cellular response to binding of the nerve growth factor (NGF) and its correspond-ing receptor was proved to be a slow variation. After reaching its maximum of acidifi cation rate, a plateau was followed for a long time. Okada et al. investigated the functional characteristics of purinoceptors in Chinese hamster ovary (CHO) cells [41]. It is found that Uridine 5’-triphosphate (UTP) increased the extracel-lular acidifi cation rate biphasically—namely, a transient and a steady response. As shown in Figure 6.9, the transient phase reached a peak (four- to fi vefold the basal extracellular acidifi cation rate in amplitude) within 20 seconds and was followed by the steady phase, which was sustained for more than 1 minute at amplitude less than twofold the basal extracellular acidifi cation rate.

Besides long-time detection of dynamic characteristics of receptor response, dose effect characteristics of concentration-dependent receptor stimulation can also

6.4 Application 135

be monitored. Dunlop et al. evaluated the functional activity of a series of 5-HT1A receptor ligands in a cell line expressing the human 5-HT1A receptor (h5-HT1A · CHO) using the agonist-stimulated increase in extracellular acidifi cation rate [42]. Figure 6.10(a) shows the concentration-dependent increase in the rate of extra-cellular acidifi cation. Along with the increase in concentration of 8-OH-DPAT, acidifi cation rate increases obviously. Figure 6.10(b) shows the log-concentration response curves of several 5-HT1A receptor agonists, which indicates the ability to stimulate an increase in extracellular acidifi cation rate when applied to h5-HT1A CHO cells.

Ellis et al. evaluated the effective inhibition of the tyrphostin 4-(3-chloro-anilino)-6, 7-dimethoxyquinazoline (AG1478)’s cellular receptor response to EGF challenge in BaF/ERX cells, indicating a need to maintain sustained levels of inhibi-tor [43]. As shown in Figure 6.11, the extracellular acidifi cation is obviously re-strained, along with the increase of AG1478 concentration. The IC50 was found to be approximately 0.3 μM. They also evaluated the duration of inhibition following exposure to AG1478 using the LIM1215 cells (Figure 6.11). Cells exposed to EGF at 0, 12, and 18 minutes post-removal of the inhibitor did not respond instantane-ously to EGF challenge. However, a signifi cant delayed response was seen in all

Figure 6.9 Extracellular acidifi cation rate response of CHO cells stimulated with 30 μM UTP. (a) A representative potentiometric recording of extracellular pH is shown in mV. (b) Absolute values of extracellular acidifi cation rates (−mV/s). Extracellular acidifi cation rates every 3 seconds during 120-second “fl ow-off” were plotted before and during UTP application. (c) Extracellular acidifi cation rate ratio was estimated by dividing the extracellular acidifi cation rate during the application of UTP by the basal extracellular acidifi cation rate (before the application) at the corresponding time point during fl ow-off. UTP elicited a rapid and transient increase in extracellular acidifi cation rate that was followed by a steady phase. (From: [41]. Reproduced from the European Journal of Pharmacology. © 2002, with permission from Elsevier Science B.V.)


cells around 24 minutes. Cells challenged 24 minutes after removal of the inhibitor showed immediate response.

6.4.3 Identifi cation of Ligand/Receptor

The identifi cation of specifi c and functional orphan receptors will facilitate studies on their physiological roles and the search for receptor agonists and antagonists. Due to lack of proper radioactive label, screening this type of ligand and receptor with radio receptor assay (RRA) is too diffi cult. Besides, as the mechanism of this type of receptor is not known in detail, it is also diffi cult to solve this problem with biochemical methods. As mentioned before, the microphysiometer is a more generic characterization of ligand/receptor, which is usually insuffi cient for identifi cation of ligand/receptor. Therefore, other methods for ligand/receptor identifi cation are usu-ally used along with the microphysiometer to give precise identifi cation of ligand/receptor.

Fujii et al. frequently measured the extracellular acidifi cation rate in CHO cells expressing orphan GPCRs [44]. In the screening of various synthetic compounds, including known bioactive peptides, they found that CHO cells expressing an or-phan GPCR, FM-3, were responsive to neuromedin U in the microphysiometric assay. Kramarenko et al. discovered that bradykinin (BK) activates extracellular signal-regulated protein kinase 1 and 2 (ERK) in the human embryonic kidney (HEK) 293 cells [45]. By employing fl uorescent measurements of intracellular Ca2+, measuring changes in the extracellular acidifi cation rate (ECAR) as a refl ection of the Na+/H+ exchange (NHE) with a cytosensor microphysiometer, and assessing ERK activation by Western blotting with a phosphor-specifi c ERK antibody, it is found that signals, obtained by exposure of HEK 293 cells to BK, were blocked

Figure 6.10 (a) Exposure of h5-HT1A•CHO cells to the 5-HT1A receptor agonist 8-OH-DPAT re-sults in a concentration-dependent increase in extracellular acidifi cation rate. Cells were exposed to successive 6-minute applications of 8-OH-DPAT, at the concentrations indicated from lowest to highest, and a 30-minute wash period was employed between successive agonist exposures. (b) Log- concentration response curves for the stimulation of extracellular acidifi cation rate in h5-HT1A•CHO cells by h5-HT1A receptor agonists. Results are expressed as a percentage of the response observed in the presence of a maximally effective concentration of 8-OH-DPAT, and represent mean values (+S.E.M.) from three independent experiments. (From: [42]. Reproduced from the Journal of Pharma-cological and Toxicological Methods. © 1998, with permission from Elsevier Science Inc.)

6.4 Application 137

by HOE-140 (B2 receptor antagonist) but not by des-Arg10-HOE-140 (B1 receptor antagonist) (Figure 6.12). Thus, it is concluded that HEK 293 cells express endog-enous functional BK B2 receptors.

6.4.4 Drug Analysis

Drug analysis is an important application of cell-based biosensors using LAPS as the secondary trasducer. By monitoring extracellular parameters after drug treatment,

Figure 6.11 Analysis of AG1478 inhibition of EGF-induced cell stimulation using Cytosensor Micro-physiometer. (a) Dose-dependent inhibition by AG1478. BaF/ERX cells were inhibited with AG1478 as indicated and the extracellular acidifi cation rate (ECAR) was measured, following stimulation with EGF (16 nM). (b) Duration of AG1478 inhibition. LIM1215 cells were preincubated with AG1478 (5 μM) for 30 minutes. The inhibitor was then removed and the cells challenged with EGF (16 nM) at the times indicated. (From: [43]. Reproduced from Biochemical Pharmacology. © 2006, with permis-sion from Elsevier Inc.)


corresponding drug effect can be investigated. Both the microphysiometer and the cell-semiconductor hybrid LAPS device can be used for drug analysis, depending on different extracellular parameters to be measured.

6.4.4.1 Drug Analysis with the Microphysiometer

The functional measurement of physiology makes the microphysiometer a valuable tool in drug analysis research by allowing application of the instrument to screening of perspective pharmacologically active agents, characterizing dose responses and structure-activity relationships, and investigating mechanisms of action.

Initial studies of irritancy testing using human keratinocytes grown on cov-erslips tested half-log serial dilutions of eight irritants previously characterized as having in vivo ocular irritancy ranging form mild to severe [6]. Bruner et al. tested 17 product formulations and chemicals well characterized with historical data ob-tained at Procter and Gamble using the rabbit low volume eye test (LVET) [46].

Figure 6.12 BK stimulates ECAR in HEK293 cells. (a) ECAR measurements. BK (white circles) stimu-lates ECAR, whereas the BK B1 receptor agonist des-Agr9-BK (dark circles) does not. Cells were ex-posed to perfusate containing a drug during the timespan encompassed by the gray box. (b) ECAR stimulated by 10-6 M of BK in various buffers, including Ham’s F12 medium, with and without 10-5 M MIA, a balanced salt solution containing NaCl or TMA substituted mM per mM for sodium. *P<0.05 versus BK alone; IP<0.01 versus BK in balanced salt solution with NaCl. (c) Effects of BK B2 (HOE-140) and BK B1 (des-Arg10-HOE-140) receptor antagonists on BK-stimulated ECAR. Antagonists (10-5 M) were added 30 minutes prior to addition of BK. All experiments were performed at least four times. IP<0.01 versus BK alone. Error bars in (b) and (c) represent the S.E.M. (From: [45]. Reproduced from Biochemical Pharmacology. © 2008, with permission from Elsevier Inc.)

6.4 Application 139

These materials represent the range of activities commonly encountered in ocular irritancy testing of cleaning products: soap, shampoos, detergents, fabric softener, and four single chemicals. Keratinocytes grown on coverslips were also used in the microphysiometer to determine the 50% decrease in the rate (MRD50).

Pharmacology testing can be carried out with the microphysiometer. Rabinowitz et al. applied the microphysiometer to screen for effects of cation channel blockers on the metabolism of a variety of human and murine cell lines [47]. At concentra-tions suffi cient for cation channel blockade, most of these drugs have little or no ef-fect on cellular metabolism as measured by acid release. In contrast, the potassium channel blocker clofi lium triggers sustained increases in acid release at low con-centration. Acid release persists in media containing high extracellular potassium. Attempts to identify physiological correlates to this response revealed that low con-centrations of clofi lium but not other potassium channel blockers cause lymphoma apoptosis. Fischer et al. measured the potency of four new drug molecules (i.e., EMD84021, EMD94309, EMD 96785, and HOE 642), which are inhibitors of the isoform 1 of the Na+/H+ exchanger [48]. The CHO K1 cells were enriched in the NHE-1 isoform of the Na+/H+ antiporter. The IC50 values of the four compounds were obtained in good agreement with more elaborate biological assays. Results show that the microphysiometer approach is a fast and simple method to measure the activity of the Na+/H+ antiporter.

Besides pharmacology tests, in vitro toxicology has also been studied with the microphysiometer. Cao et al. continuously monitored perturbations in metabolic rate of the human liver cell line ATCC-CCL-13 when exposed to each of 10 drugs (Figure 6.13) [49]. All drugs produced concentration and time-dependent reduc-tion in acidifi cation rate following 24-hour exposure. Recovery after drug removal was compared. Excellent correlation (r = 0.958) was gained between IC50 value of 24-hour exposure obtained from the cytosensor with the 10 drugs and their published human lethal blood concentrations. One advantage of this methodology over other in vitro assays is that the microphysiometer allows the determination of time points at which reversible change becomes irreversible.

Physiological responses to toxic insult can be characterized both by intensity and duration. Most in vitro toxicological assays measure intensity. The microphysi-omter is well suited to measure not only intensity, but also duration of alterations of cellular metabolic activity. Therefore, besides the response of irritancy insult, recovery from this insult can also be characterized by the microphysiomter. Parce et al. exposed human keratinocytes to both dimethylsulfoxide (DMSO) and dose ethanol (0.1 versus 0.8) for 5 minutes at corresponding MRD50 concentration to equalize acute effects, and then metabolic activity was monitored for 2 hours [50]. The acidifi cation rate of keratinocytes exposed to DMSO returned to its initial con-trol value within the observation period, suggesting complete recovery. In contrast, the acidifi cation rate of the cells exposed to ethanol continued to decline, reaching about 10% of the control value by 2 hours after exposure. Another example was carried out in triethanolamine and heavy-duty laundry detergent. Keratinocytes exposed to triethnolamine recovered to their initial acidifi cation rate, while those exposed to the detergent did not.

Getting more information about the multifunctional cellular processing of in-put and output signals in different cellular plants is essential for basic research as


well as for various fi elds of biomedical applications. The concentrations of the ex-tracellular ions, such as Na+, K+, and Ca2+, may change along with the alteration of cell physiology. In order to analyze simultaneously the relations of the extracellular environmental H+, Na+, K+, and Ca2+ under the effects of drugs, our lab has devel-oped a novel microphysiometer based on multi-LAPS [15, 51]. The surface of the LAPS is deposited with different sensitive membranes by silicon microfabrication technique and the poly(vinyl chloride) (PVC) membrane technique. When fabricat-ing K+ sensitive membrane, we used 2 mg valinomycin as electroactive substance, 1.3 ml tetrahydrofuran (THF), and 1.3 ml cyclohexanone as solvent, 0.066g PVC powder as bulk material, and 1–2 gutta di-butyl phthalate as plasticizing agent. When fabricating Ca2+ sensitive membrane, we used 0.24 ml formamide as an ac-tive substance, 4.72-ml tetrahydrofuran as solvent, 0.15g PVC powder as bulk material, and paucity tetraphenylboron sodium as plasticizing agent.

Three different sensitive membranes are illuminated in parallel with light sourc-es at different frequencies and measured online by parallel processing algorithm in Figure 6.14(a). Different sensitive (H+, K+, Ca2+) membranes are illuminated on the sensor, simultaneously with three light sources at different frequencies (3 kHz for K+, 3.5 kHz for Ca2+, and 4 kHz for H+). The photocurrent comprises these three frequency components, and the amplitude of each frequency component might be measured online by software FFT analysis, as shown in Figure 6.14(b). Dilantin (e.g., phenytoin sodium, a sort of anti-epilepsy drug) has signifi cant effects of tran-quilization, hypnosis, and anti-seizure. Moreover, dilantin is also one of the anti-ar-rhythmia drugs. It is proven that dilantin has membrane stabilizing action on neu-ral cells because it can reduce pericellular membrane ions (Na+, Ca2+) permeability,

Figure 6.13 (a) Effect of 24-hour exposure of human liver cells to different concentrations of etha-nol (50 mM, 100 mM, 300 mM) contained control cells. (b) Correlation of in vitro cytotoxicity of the fi rst 10 MEIC chemicals with their in vivo toxicity to humans. The r value is 0.958 for 24-hour exposure. Each numeral identifi es the test drug used: 1, acetaminophen; 2, acetylsalicylic acid; 3, ferrous sulfate; 4, diazepam; 5, amitriptyline; 6, digoxin; 7, ethylene glycol; 8, methanol; 9, ethanol; 10, isopropanol. (From: [49]. Reproduced from Toxicology In Vitro. © 1997, with permission from Elsevier Science Ltd.)

6.4 Application 141

inhibit Na+ and Ca2+ infl ux, and stave K+ effl ux, thus prolonging refractory periods, stabilizing pericellular membranes, and decreasing excitability [Figure 6.14(c)].

6.4.4.2 Cell-Seminconductor Hybrid LAPS Device for Drug Analysis

Cell-semiconductor hybrid LAPS device is a newly proposed idea [10]. For cell-semiconductor hybrid LAPS device, cells are cultured on chip surface and fi rm at-tachment is achieved before testing. Focused laser light is involved as both the source of photocurrent and the addressing tool. By detecting the electric signals of the nearby surface, different information of living cells attached to the LAPS sur-face can be obtained. The main application of the cell-semiconductor hybrid LAPS device for now is drug analysis.

Cell-semiconductor hybrid LAPS devices can detect extracellular potential sig-nals similar to FET and MEA. Its most outstanding advantage against conventional FET and MEA is the addressing ability of LAPS. Test can be carried out at any de-sirable position of LAPS surface, thus diminishing the requirement of cell culture.

Figure 6.14 Microphysiometer studies based on multi-LAPS. (a) The schematic drawing of the sys-tem of the multi-LAPS to different extracellular ions (H+, K+, and Ca2+). (b) Simultaneous illumination at the three sensitive membranes with three light sources at different modulated frequencies. (C) H+, K+, and Ca2+ analyzed simultaneously by multi-LAPS. (From: [51]. Reproduced from Biosensors and Bioelectronics. © 2001, with permission from Elsevier Science B.V.)


Some pilot experiments have been carried out by researchers, and the results show the possibility of primary application in study of some drug effects.

Liu et al. have constructed a cell-semiconductor hybrid device for some applica-tions in drug analysis [29]. As an agent of β-adrenoceptor agonist that contributes to cardio-activity, isoproterenol (ISO) enhances the L-type calcium channel activ-ity, which causes an increase in Ca2+ signal. As shown in Figure 6.15, it is obvious that after administration of ISO, the beating frequency, amplitude, and duration of cardiomyocytes were all increased in a dose-depended manner (0.1, 1, 10 μM). The cellular contractibility all recovered after washing drugs out at above concen-trations. Whereas, as a negative one, carbamylcholine (CARB) had the opposite effect to ISO, increasing K+ conductance in cardiacmyocytes, and signals indicated

Figure 6.15 Plots comparing the response of cardiomyocytes to the CARB, ISO, and physiological solution as control. The concentrations of drugs are all 1 μM. The drugs affect (a) the beat rate, (b) the amplitude, and (c) the duration of each extracellular potential. (d) Different effects of drug concentration on the beat rate. Each data point represents an average over 50 seconds. The experimental data is the average value of six times of repetition. (From: [29]. Reproduced from Biosensors and Bioelectronics. © 2007, with permission from Elsevier Science B.V.)

6.5 Developing Trend 143

a decreasing trend. Figure 6.15(a) shows the changes of curves to ISO and CARB at concentration of 1 μM. We could see that the parameters display the two drugs dis-tinctly. Furthermore, if we differentiated parameters to each stroke shown in Figure 6.15(b–d), more approving results could be obtained. According to those changes, we know that ISO and CARB have direct effects on the duration and amplitude of strokes 2 and 3, which accords with the pharmacological increase of the Ca2+ or K+ ion current, respectively. Thus, cooperated with Na+, K+, and Ca2+, targets of a concrete drug can be evaluated synchronously by the biosensor system.

The LAPS biosensor system has been reported to detect heavy metal ions ac-cording to changes in parameters describing spontaneous beating of cardiomyo-cytes under the different toxic effects [52]. The effects of heavy metal ions on cell function were evaluated by comparing the changes of the sensor signals before and after the cells were exposed to the toxins. Figure 6.16 shows the change of fre-quency, duration, and amplitude of the signals after the addition of 10 μM heavy metals for each type (Fe3+, Hg2+, Pb2+, Cd2+, Cu2+, and Zn2+). Exposure of beating cardiomyocytes to 10 μM Fe3+ decreases the frequency, amplitude, and duration to about 50% of the basal signal. Similar curves were found for Pb2+ and Cd2+ solu-tions with a smaller decrease of amplitude and duration; however, a slight progres-sive increase of frequency was observed. On the contrary, the three parameters all increased in Hg2+ solution. There were no apparent trends with regard to Cu2+ and Zn2+ toxic effects on measured parameters (only duration on Zn2+ showed a slight increase). Compared with biosensors using pure enzymes, cell-based biosensors, which use whole cells as the biorecognition elements, and LAPS for biosensors can detect agents functionally [53, 54]. Metal ions are found to have effects on the cellular organelles and components, such as cell membrane, mitochondrial, lyso-some, endoplasmic reticulum, nuclei, and some enzymes involved in metabolism, detoxifi cation, and damage repair [55]. All these systems are considered to infl u-ence metal induced cellular responses simultaneously. Therefore, incorporated with whole cells, cell-based biosensors would offer potential physiological monitoring advantages over devices based on isolated enzymes or proteins. And with the help of living cells, especially mammalian cells, we could not only detect, but also evalu-ate toxicities of heavy metals with cellular physiological changes.

6.5 Developing Trend

Compared with MEA and FET array, an obvious disadvantage of LAPS was its single channel output, which made it diffi cult to simultaneously measure a large number of sites and perform high-throughput experiments. In the aspect of sensor chips, the feasibility of light-addressing electrodes and the fabrication of sub-μm electrodes on photoconductor layers have been published [56]. This novel design concept even will benefi t MEA systems by lowering the density of electrodes caused by the problem of wiring a great number of electrodes, which results in a low num-ber of good cell–electrode contacts.


6.5.1 LAPS Array System for Parallel Detecting

Our lab has reported a novel design that could signifi cantly increase the measure-ment rate of LAPS [57]. By illuminating the LAPS simultaneously at several different positions, each of which is illuminated with a light pointer modulated with different frequencies, the surface potential at all illuminated regions can be measured simul-taneously by analyzing the resulting photocurrent. Using this method, the rate to

Figure 6.16 Effect of heavy metals on the spontaneous beating. (a) Frequency. (b) Amplitude. (c) Duration. The effect of each metal (10 μM) was expressed as a percentage change compared to the baseline recorded (at time 0 minute) before the administration of the ions (at time 0–10 minutes). After a washing cycle (at time 10 minutes), the changes reach a steady level (at time 10–40 minutes) (mean±S.E.M.; n=7). (From: [52]. Reproduced from Biosensors and Bioelectronics. © 2007, with per-mission from Elsevier Science B.V.)

6.5 Developing Trend 145

obtain a complete image of the surface potential distribution across a LAPS wafer can be drastically increased compared to the conventional system. However, the multilight LAPS needs to equip a signal generator for each light source. To obtain an 8 × 8 image, the system needs to provide 64 signal generators. Moreover, with LEDs as the light sources, this system has a lower resolution and precision. So this method is unsuitable for accurate imaging. To solve this, our lab also has presented a novel imaging system, shown in Figure 6.17. With a microlens array, a single laser is separated into a focused laser line array. Every focused laser is modulated sepa-rately to a different settled frequency. With a line-scanning control, only eight scans are required to obtain an 8 × 8 image. Moreover, with different sensing materials fabricated on different sites of the silicon electrode, this device can be used to detect several components of sample in parallel.

6.5.2 Multifunctional LAPS System

As LAPS can detect the extracellular potential signals of cells as well as the me-tabolism substances, multifunctional detecting system of LAPS array is taken into consideration. Our lab has proposed an idea to primarily perform a multifunctional LAPS system (Figure 6.18) [16]. A laser light with the wavelength of 690 nm is used for illumination in extracellular potential detection. The laser is modulated at 10 kHz by the lock-in amplifi er (SR830, Stanford Research System), and the power is about 0.2 mW. The laser is focused to about 10 μm in diameter through an optimized microscope so that it can be used to address a cluster of cells on the sensor chip. Four LEDs with the wavelength of 625 nm are driven at four different frequencies of crime numbers to avoid harmonic interference with a power of 50 mW. These LEDs illuminate the four testing areas for acidifi cation detection. These fi ve light sources illuminate the sensor chip simultaneously. Signals at these fi ve different frequencies, which are all included in the resulting photocurrent signal, respectively, represent information generated about the fi ve different testing sites.

Figure 6.17 Schematic diagram of the line-scanning light sources based on microlens array.


The detecting system is designed to sample the overall signal and extract signals at the fi ve different frequencies.

6.6 Summary

In this chapter, LAPS as cell-based biosensor is introduced and regarded as a power-ful tool in biomedical application. The LAPS system has been commercialized and has been well known as the Cytosensor Microphysiometer for 20 years. It has been widely used in areas including receptor/ligand research and drug analysis. On the other side, the cell-semiconductor hybrid LAPS device is a newly proposed idea for extracellular potential detection. It is better than conventional FET and MEA in certain aspects (i.e., cell culture and positioning). This hybrid cell-based biosensor has also been used for primary drug analysis. LAPS is well compatible for construct-ing biosensors and shows great potential in integrated biosensors.

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6.6 Summary 147

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6.6 Summary 149

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151

C H A P T E R 7

Electric Cell-Substrate Impedance Sensor (ECIS) as Cell-Based Biosensors

Zhaoying Hu, Qingjun Liu, and Ping Wang

7.1 Introduction

Bioimpedance has been discussed for a long time, covering the electric currents and biopotentials associated with the life processes. Bioimpedance measurements probe the electrochemical processes in the cell and tissue, and thus own the capability of monitoring physiological changes and deciphering the information hidden inside a cell, which is impossible to be detected with classical analytical techniques. Many investigations have been carried out to exploit this potential technology in areas such as electrical impedance tomography, body composition, cell micromotion, or-gan viability, skin hydration, and skin pathology [1].

The historical perspective and methodological development of impedance spec-troscopic studies of tissue and cell suspensions have been reviewed elsewhere [2, 3]. Impedance spectroscopic studies of cells in suspension have provided insight into the function and properties of cells [4]. However, this technique gains its practi-cal applicability to cell-based screening only for decades, greatly promoted by the emergence of microfabrication technology. The research on cell impedance sensing may be divided into two main categories, one for adherent cells grown on elec-trodes and the other for cells transported and/or immobilized in microfl uidic chan-nels (i.e., applicable to both adherent and nonadherent cells). The former category is vastly reported and researched for its comparative simplicity, while the latter one is expected to be a new trend for its precise control and measurement of single cell. In this chapter, we mainly focus on adherent cell impedance measurement. More established theories and applications may contribute to the comprehensive under-standing of the whole system.

The electric cell-substrate impedance sensing (ECIS) technique was pioneered by Giaever and Keese, who fi rst applied impedance measurement to cell morphol-ogy monitoring on planner electrode [5]. The ECIS system is capable of real-time, noninvasive, label-free impedance measurements. To increase sensitivity, reproduc-ibility, biocompatibility, cell number, and throughput, diverse electrode layouts and novel designs have been researched. And further experiments and mathematical

152 Electric Cell-Substrate Impedance Sensor (ECIS) as Cell-Based Biosensors

modeling have revealed the mechanism of impedance measurement of cellular be-havior. In the past 20 years, impedance-based sensing technology has emerged as one of the most important and interesting label-free technologies for in vitro cell-based assays, and real-time and dynamic information of cellular response [6–11].

The effective and simple nature of impedance-based technology leads to a broad market prospect in commercialization. The products developed by different com-panies are listed in Table 7.1. The fi rst commercialized product is known as ECIS by Applied BioPhysics. Another electronic-based biosensor is represented by the “Real-Time Cell Electronic Sensing” (RT-CES supplied by ACEA Biosience; http://www.aceabio.com). To improve the sensitivity and reproducibility of the signal, an array of interdigitated electrodes (IDEs) is located in the bottom of each well of a 16- or 96-well plate. Another label-free cellular dielectric spectroscopy ana-lyzer called CellKey has been invented by MDS (CellKey supplied by MDS Sciex; http://www.mdssciex.com). There are also IDEs in a 96-well plate. They are used to measure fast changes in impedance within the radio-wave range to monitor cellular consequences of ligand-receptor interaction. The Bionas 2500 analyzing system incorporates IDEs structure with other microsensors to monitor acidifi cation rate, oxygen consumption, and adhesion (cell impedance) of cells.

In this chapter, we will explore the impedance-based sensing technologies from the electrical characteristics of the cell, the frequency characteristic of the cell-covered electrode, and impedance output for various cellular responses, includ-ing cell adhesion and spreading, cell migration, receptor-mediated signaling, and cytotoxicity.

7.2 Principle

7.2.1 Electrochemical Impedance

Impedance spectroscopy is a powerful method for characterizing electrical proper-ties of material and their interfaces with electrically conducting electrodes. Gener-ally, the impedance Z of a system is determined by applying an electrical voltage perturbation (stimulus signal) of small amplitude and detecting the current response

Table 7.1 Commercialization of Impedance-Based Biosensors

Company Product Cell-Based AssaysSelected Reference

Applied Biophysics ECIS Cell adhesion, proliferation, barrier function, receptor-mediated signaling, and wound healing [12]

ACEA Biosciences and Roche

xCELLigence(RT-CES)

Cell adhesion, proliferation, cytotoxicity, receptor-mediated signaling, barrier function, immune-cell signaling, cell migration, and invasion [10]

Bionas Bionas 2500 Cell adhesion, proliferation, metabolism, and cytotoxicity [13]

MDS Sciex Cellkey Cell adhesion, proliferation, receptor-mediated signaling [9]

7.2 Principle 153

passing through the measured sample. From its defi nition, impedance is the quo-tient of the voltage-time function ν(t) and the resulting current-time function i(t):

( )( )

( )( )

m sin

sinm

v t V tZ

i t I t

ω

ω θ= =

+ (7.1)

( ) ( ) ( )( )cos sin

( )V j

Z Z jZ Z j ZI j

ωω θ θ

ω= = + = +′ ′′ (7.2)

The impedance is a complex value, since the current does not only vary in the amplitude (as is the case for a pure ohmic resistance), but also shows a phase shift θ compared to the voltage-time function. Equation (7.1) can be transformed to com-plex domain as (7.2). Thus, the value can be described either by the modulus |Z| and the phase shift φ or alternatively by the real part and the imaginary part of the impedance. Impedance value can be illustrated in many different ways. Generally, the most popular methods are the Bode plot using |Z| and φ as a function of log f and the Nyquist plot using ZR and ZI.

Many processes account for the response of impedance measurement, which is mainly the transport of electrons or charged ions through the interfaces of bulk sample and electrodes besides the diffusion processes. For this reason the imped-ance spectrum is often analyzed by means of an equivalent circuit. In electrochemi-cal impedance spectroscopy, usually four elements are used for the description of the impedance behavior (Table 7.2): ohmic resistance, capacitance, constant phase element, and Warburg impedance. Electrical properties of the investigated system are fi nally modeled from experimental data. The equivalent circuit for typical elec-trode-electrolyte interface has been mentioned in Chapter 3.

Table 7.2 Impedance Elements Defi ned to Represent the Processes in Electrochemical Reaction

Impedance Element Defi nitionPhaseAngle

Frequency Dependence

R Z R= 0° No

C 1CZ

j Cω=

⋅ ⋅ 90° Yes

CPE (Constant Phase Element)

( )0

1( )

1

CPE n

W

ZQ j

Z j

ω

σ

ω

=⋅

= −0–90° Yes

W(infi nite)

2 2

1 1

2 O O R R

R T

n F D c D cσ

⎛ ⎞⋅= +⎜ ⎟⋅ ⋅ ⋅ ⋅⎝ ⎠ 45° Yes

W(fi nite)0

tanhW

l j DZ R

l j D

ω

ω

⋅ ⋅=

⋅ ⋅ 0–45° Yes


ω = angular frequencyl = length of diffusion regionD = diffusion coeffi cientR0 = diffusion resistance for ω = 0cO, cR = concentration of oxidized and reduced species

7.2.2 Cell-Substrate Impedance

Bioimpedance technology has gained lots of applications in human body–related investigations such as body water ratio and breath rate. Also in vitro, impedance sensing is a very important and inspiring technology. Many kinds of mammalian cells adhere to substrate to grow and propagate, whereas cells from blood cells and cancer are apt to live in suspension status and spread aggressively. During cell culture, cells interact with substrate by changing their morphology according to the stiffness, energy, or some proteins on the substrate; interconnection with adjacent cells through tight junctions are also subject to the stimulus factor surrounding their environment [14]. We need effective means to measure these signals and reveal the mechanism underneath the phenomenon. Typically, optical methods including mi-croscopy and fl uorescence detection are used. Besides, mechanical, electrical, and sonic measurements have also gained their roles in the detection. Among these, electrochemical measurement is one of the most convenient, low-cost, and quick methods.

ECIS is one of the electrochemical techniques that can be applied to monitor the adhesion and spread of mammalian cells quantitatively and in real time. Cell membrane is considered a high value capacitor with low conductivity (about 10−6

S/m) but complex pattern, which mainly consists of phospholipids that form a bilayer lipid membrane (BLM) about 7 nm thick [1]. The cell maintains life ac-tivity by controlling the membrane permeability. If the integrity of membrane is destroyed, the cell dies. An electric double layer covers the wetted outer cell mem-brane surface. The whole cell has a net charge revealed by its elecrophoretic mobil-ity. The cell membrane capacitance is of the order of 1 μF/cm2, which is considered to be frequency dependent.

To measure the bioimpedance of cultured cells, gold fi lm is deposited on proper substrate such as glass or silicon to fabricate specifi c electrode pattern. The method is based on measuring the changes in the effective electrode impedance (Figure 7.1). When the planar gold electrode is immersed in the culture medium, cells at-tach and spread on the electrode. With the increase of coverage over the electrode,

Figure 7.1 Principle of electrical impedance measurement on planar electrodes.

7.2 Principle 155

the measured electrical impedance increases for cells, constraining the current fl ow and changing its path. At DC and low frequencies, current passes beneath the cells and fl ows out beside cells. Lateral conductance in the double layers is another pos-sible path. At higher frequencies, current penetrates the cell membrane. The mem-brane effect disappears, and current fl ows according to local ionic conductivity. The changing impedance can be interpreted to reveal relevant information about cell behaviors and coordination of many biochemical events [15]. This method has been proving noninvasive [8]. The small electrical current for measurement has no detectable effect on living cells, so that long-term experiments are reliable.

According to the measurement mechanism, the planar electrodes used for im-pedance sensing can be classifi ed into two prototypes as follows:

The monopolar system1. : A small electrode is used as the working electrode, and the other large electrode as the counter electrode. The area ratio of these two electrodes is <1/100, which provides the ability to differentiate electrode reactions between them. Due to high impedance, the current density and there-fore the voltage drop are much higher at the small electrode, which results in dominating the total impedance changes. However, it is not the case in po-tentiometry, and electrode area is unimportant under no-current conditions. Giaever and Keese fi rst presented ECIS [Figure 7.2(a, b)] that small thin fi lm gold electrodes with a diameter of 250 μm were located at the bottom of the 1 cm2 cell culture well. Each array contains eight wells, and each

Figure 7.2 Monopolar electrodes and IDEs. (a) Electrode layout of ECIS. (b) Arrangement of four small working electrodes (0.03 cm2) and a common counter electrode (0.6 cm2). (From: [16]. Repro-duced from the Journal of Biochemical and Biophysical Methods. © 1996, with permission from Elsevier B.V.) (c) Array of electrodes with area sizes of 0.00025 cm2, 0.0004 cm2, 0.0013 cm2, and 0.004 cm2. (From: [17]. © 2004, Biotechnology & Bioengineering. Reproduced with permission of John Wiley & Sons, Inc.) (d) Interdigitated, parallel line electrode array. (e) Castellated electrode structures. (f) Electrode structures with disc electrodes added on the electrode lines.


well has either a single electrode or 10 electrodes and a single large counter electrode. The simple layout and relative scale dispense the necessity of high-resolution and high-quality microfabrication technology. The interdigitated system2. : A plurality of independently operating IDE units connect to a terminal strip to form one branch of an electrode. The other identical one is parallel spaced, and together they form the interdigitated system. Two facing branches weigh equally in measurement because they have identical dimensions in geometry. As the cells cover either one of the two branches, impedance changes in the same manner; however, it does not always hold true for any situation, which will be discussed later in this chapter. The parameters involving the electrode width, the distance between two neighboring electrodes, the length of the fi nger, and the total number of fi ngers deserve attention for optimal performance in impedance sens-ing of cells. The diverse schemes of IDEs are presented in Figure 7.2(d–f). Ehret et al. reported comprehensive experiments on monitoring cellu-lar behavior using IDE structures [6]. The interdigitated structure exploits advantages of the changed conditions for the current fl ow in the vicinity of the electrodes surface. It demonstrates a much higher sensitivity toward surface changes compared with the conventional design.

These two systems have both advantages and disadvantages. No preference can be given without specifi c applications. As for drug screening industry, the interdigi-tated system is more pervasive. The large counter electrode in the monopolar sys-tem hinders further miniaturization, which is essential in a high-throughput drug-screening system. Because the area excluding the working electrode is relatively large, only a few cells on the working electrode contribute to the whole impedance measured, which results in fl uctuation among experiments. Furthermore, a large number of cells are required to be initially seeded on the electrode to obtain suf-fi cient number on the working electrode. The overall area of the IDEs commonly covers up to about 50% of the area of each well, lowering the well-to-well distribu-tion of responses.

7.2.3 AC Frequency and Sensitivity Characteristics of Interdigitated Electrodes

Although the ECIS techniques had signifi cant advances in the past 20 years, some fundamental problems have not been solved satisfactorily. These problems involve the frequency characteristic and sensitivity of the impedance sensors, and how to design the dimension of electrodes for optimization of sensitivity or other specifi c features desired. No publications have thoroughly addressed these problems. In this section, we seek to provide a systemic analysis of frequency and sensitivity charac-teristics of IDEs.

At the electrode/liquid interface, an electric double layer is formed as soon as the metal is wetted, which can be treated as a function of the distance perpendicular to the surface. The charge distribution is described by the Gouy-Chapman model [18], which divides the charge distribution into two layers, a Stern layer (adsorbed layer) and a diffusive layer. The Stern layer includes an adsorbed layer of ions and

7.2 Principle 157

exists at a distance δ from the electrode surface. The distance corresponds roughly to the radius of a hydrated ion. This layer can be modeled as a normal capacitor, Cstern. The capacitance is about 20 μF/cm2, which is independent of the ion concen-trations. The diffusive layer is present because of the fi nite amount of charge that can be presented in the Stern layer and the need for charge neutrality of the double layer. The diffusive layer capacitance, Cdiff, depends on the total thickness of this layer, which is a function of the electrolyte concentration [19, 20]. In physiological electrolyte solutions, it is about 10 nm. The thinner the diffusive layer is, the higher the electrolyte concentration will be. Thus, the total capacitance of the double layer (CDl) is made up of Cstern and Cdiff in series. If the electrolyte is very dilute, Cstern >> Cdiff. On the contrary, when the solution is concentrated, Cstern << Cdiff. In real-world systems, some property may cause homogeneousness or some dispersion of the value of some physical property of the system. Therefore, CPE is a better model for the interfacial response to AC signals as an imperfect capacitor, where phase difference is given by −(90n)°,0 ≤ n ≤1. Its phase difference is remaining constant over frequency. The impedance of CPE is given by an empirical formula, as shown in Table 7.2, where Q0 and n are parameters that depend on the properties of the electrolytes and the electrodes. More detailed descriptions are available in the lit-erature [19]. Equivalent circuits are modeled and shown in Figure 7.3.

Based on the equivalent circuit shown in Figure 7.3(a), the total impedance can be expressed as a function of the known values of the components and frequencies [20]:

( ) 2

2 ,1para sol

cell Dl

xZ j R x R

j C j Cω

ω ω= + = +

+ (7.3)

At lower frequencies, the impedance will be dominated by the CDl until the im-pedance of this capacitor becomes lower than Rsol. Then the sensor impedance be-comes frequency-independent. The low cutoff frequency can be expressed as (7.4) and is a function of the solution resistance and the double layer capacitance.

1 11

2 22

lowsol Dl

para Dl para cell sol Dl

fR C

R C R C R Cπ≈ ≈

⎛ ⎞+ +⎜ ⎟⎝ ⎠

(7.4)

Figure 7.3 (a) Equivalent circuit of IDEs in electrolyte. (b) Schematic diagram of impedance- frequency plots divided into dominant components.


However, at a certain frequency the impedance of the Ccell becomes lower than the one of Rsol. Then the impedance will decrease with an increase of frequency. The high cutoff frequency can be expressed as (7.5) and is a function of the solution resistance and the cell capacitance.

1 1

22

highDl cell sol cell

solDl cell

fC C R CR

C Cπ

≈ ≈

+ (7.5)

A schematic diagram of total impedance is shown in Figure 7.3(c). There are three regions in the impedance spectrum, which correspond to the three types of components in equivalent circuit. As shown in Figure 7.3(a), there are parallel-ing branches. When the frequency is not lower than fhigh, the current cannot fl ow through the Ccell; thus, the CDl and Rsol in series are accounting for the total imped-ance. When the frequency increases from zero, CDl decreases but mainly contrib-utes to the total decreasing impedance until flow, where Rsol begins to dominate. Thus, in frequency range from flow and fhigh, the total impedance only depends on Rsol. Since fhigh is beyond the commonly used frequency range and the cell mem-brane becomes invisible in the radio frequency range, Ccell can be neglected in the following cell impedance modeling. The Rpara is also negligible in frequency char-acteristic analysis, because it is frequency independent and very small compared to other elements.

Cells attached on electrodes can be modeled as a capacitance (Ccells), which represents the dielectric property of the insulating cell membrane. The electrical resistance of the gaps between growing cells can be defi ned as Rcells. Rgap and Cgap stand for the capacitive property of the small gap between the underside of the attached cells and the substrate surface. Based on the equivalent circuit of cell-free electrodes in Figure 7.3, the cell-covered model is shown in Figure 7.4. Wang defi ned the fcutoff-low and fcutoff-high by simplifying the circuit to classical highpass circuits in a different frequency range [21]. And the flow was defi ned to be lower than one-fi fth of fcutoff-low, fhigh to be fi ve times of fcutoff-high.

( ) ( )11 1 1

1 1 1, 5

5 2 2low high

sol cells gap Dl sol Dl cells gap

f fR R R C R C C Cπ π

−− − −= × = ×

+ + + + (7.6)

The most appropriate frequency for cell impedance sensing is the frequency with the highest sensitivity, which is defi ned to be fmiddle, the root of (7.7).

( )( ) ( ) ( )( )covcell ered cell freed Z f Z fd Sensitivity f

df df

− −−= (7.7)

7.2 Principle 159

Based on experimental results and theoretical analysis, some rules are gener-ated for optimization of ECIS sensor design. The parameters of sensors based on IDEs include electrode width, gap, length, number, and total area. These param-eters infl uence the sensitivity and effective cell number in different ways.

The sensitivity increases with the reduction of electrode width and length, namely, minimizing the total area of the electrodes should increase the sensitivity. More cells are needed to respond to the same percentage of change in a large area compared to a small electrode area, in accordance with the analysis of the monopo-lar system. However, if the electrode width and length are reduced to increase the sensitivity, the number of effective cells would be reduced, which is not desirable. Therefore, a tradeoff of the sensitivity and the effective cell number has to be made in sensor design. Additionally, as the electrode width reduces, the impedance of each electrode branch will increase, which would cause a large electrical potential difference along the electrode. Cells adhered at different positions would contrib-ute differently to the total impedance [22]. Because cells attached to the electrode at any position contribute similar impedance signals, the electrode width should not be too narrow and the electrode length should not be too long. The fringe ef-fect cannot be avoided in planner electrode, which would cause particularly large impedance when a cell attaches on the edge of an electrode. The equivalent circuit model mentioned earlier is based on the premise that one cell cannot simultaneous-ly adhere to two adjacent electrodes, which is also not desirable in sensor design. To sum up, short electrode branch length and high branch number can provide a more uniform electric fi eld and higher sensitivity.

Figure 7.4 (a) Equivalent circuit model for two-branch cell-covered IDEs. (b) The impedance varies against frequency for cell-free and cell-covered IDEs. (c) The corresponding sensitivity varies with the sensing frequency. (From: [21]. Reproduced from Biosensors and Bioelectronics. © 2008, with permis-sion from Elsevier B.V.)



7.3.1 Device Fabrication

Some criteria for the design of chip-based biosensors are necessary to keep in mind: biocompatibility, maintenance of the physiochemical environment and sterility, methods of sample introduction, a transducer for monitoring the desired electrical signal, low signal path parasitic, and packaging, which facilitates insertion of the cell culture system into the measurement electronics while protecting the living sys-tem from the external environment. Comprehensive considerations are to be taken to achieve the best overall approach for specifi c application.

Typically, an impedance-based biosensor comprises an electrode, substrate, cul-ture plate, and packaging. The materials of the substrate could be glass, sapphire, silicone dioxide on silicon, ceramics, polymer, fi berglass (like those for printed cir-cuit boards), or other insulating materials. The electrode is usually fabricated by Au, Pt, and iridium tin oxide (ITO). A variety of microfabrication or microma-chining methods can be used to fabricate the microelectrode on the substrate. The makeup and fabrication of impedance-based biosensors share a lot with those of MEA; thus, some details can be referred to Chapter 4.

Here we present the fabrication process of our impedance chip, as shown in Figure 7.5. A single chip was made up of four channels, each comprised of eight mi-croelectrodes and a pair of IDEs. Each electrode was connected to a single periph-eral pad. This pad was later bonded to the PCB by metal clips. Pads were located at a distance of 500 μm from each other all around the chip edges. The layouts of the gold leads structure and the electrode openings of the whole wafer were drawn by CAD, separately. The drawings were transferred to chromium masks or photo emulsion masks. The wafer was then processed by a photolithographic procedure described later. The substrate, a 450-μm-thick, 4-inch glass wafer, was coated an adhesion layer of 10-nm Ti and an electrode layer of 200-nm Au by sputtering, respectively. Then the whole wafer was spin-coated with positive standard photore-sist and exposed to illumination through a darkfi eld mask. Subsequent developing released the unexposed areas. In the following step, the exposed photoresist was removed by solvent rinsing, leaving the structured glass substrate. The fabrication may be fi nished in some types of chips, if cells attach regardless of location. Oth-erwise, the wafer was then insulated with combination of SiO2/Si3N4/SiO2 (100 nm/500 nm/100 nm) passivation layers using plasma-enhanced chemical vapor deposition (PECVD). The insulating layer on the electrodes was removed following a second photolithographic step similar to the fi rst one, but reactive ion etching (RIE) was performed to etch away the insulation layer. Finally, the fi nished wafer was diced into 12 single chips of size 20 mm × 20 mm.

The PCB board sized 50 mm × 50 mm was structured with gold leads on both sides. A square aperture in the center was left for observation through micros-copy during cell culture. There are many methods for assembly of chip and PCB [22], such as fl ex circuit and metal clip. Figure 7.5 shows one type of metal clip used to connect chip and PCB. Metal clips connecting to chips were soldered to PCB. The peripheral pads on the glass chip were bonded to the gold lead termina-tions on the backside of the PCB, which offered a low ohmic electrical connection.


Finally, a hollow plastic well having a cylinder shape was bonded to the chip so that the electrode was exposed to the added sample. Here we glued a four-channel chamber made of polymethylmethacrylate (PMMA) to the glass chip to fi nish the encapsulation.

7.3.2 Bioimpedance Measurement System

Before presenting the measurement systems, we would like to have a brief review of measurement conditions and the methods of impedance measurement.

Linear1. : The impedance must be independent of either v or i. Both the prin-ciple of superposition and proportionality must hold. Most of the objects of our interest are not linear at DC, but they may have a linear amplitude range at AC.Passive2. : The energy delivered to the network must be positive for any exci-tation waveform, and all currents or voltages must be zero without excita-tion. However, cells and tissues do not fulfi ll the last requirement, because the cells have electrical activity for containing ionic pumps.Causal3. : There must be no response before an excitation has been applied.

The systems mentioned in this chapter are considered to meet all these condi-tions, if not otherwise noted. If the real part of a linear network function of fre-quency is known over the complete frequency spectrum, it is possible to calculate the imaginary part. There is a relationship between the real and imaginary parts of impedance, given by the Kramers-Kronig transforms (KKT). When the frequency range is limited and the number of measurement points is reduced, some errors are committed in applying this transform. In practical measurement, the modulus of impedance |Z| is commonly used in cell impedance measurement, since the phase is subject to measurement system error or perturbation from solution.

Figure 7.5 (a) Exploded view of typical IDEs chip. (b) Fabrication process. (c) Complete device. (d) Metal clip.


Impedance measurement may be carried out by various techniques: (1) phase sensitive detection (PSD); (2) frequency response analysis (FRA); (3) fast Fourier transform (FFT).

PSD is used in lock-in amplifi ers, which are interfaced with potentiostat. In this method, the measured signal, Em is multiplied by a square-wave signal (Es) of the same angular frequency ω. The resulting signal Em × Es equals

( ) ( )

( ) ( ) ( )

( ) ( ) ( ) ( ){ }

( ) ( )

( )

1 20

1 20

1 2 1 20

1 2 1 2

1 2

4 1sin sin 2 1

2 1

4sin sin 2 1

2 1

2cos 2 2 1 cos 2 2 2 1

2 1

cos cos 2

2 1 1cos 2 3 cos 4

3 3

m s man

ma

n

ma

n

ma

E E E t n tn

Et n t

n

En t n n t n

n

t

Et t

ω ϕ ω ϕπ

ω ϕ ω ϕπ

ω ϕ ϕ ω ϕ ϕπ

ϕ ϕ ω ϕ ϕ

ω ϕ ϕ ω ϕπ

∞

=

∞

=

∞

=

= + ⋅ ⎡ + + ⎤⎣ ⎦+

= + ⎡ + + ⎤⎣ ⎦+

= ⎡− + − + ⎤ − ⎡ + + + + ⎤⎣ ⎦ ⎣ ⎦+

− − + +

= + − + − − +

∑

∑

∑

( )

( ) ( )

1 2

1 2 1 2

3

1 1cos 4 5 cos 6 5

5 5t t

ϕ

ω ϕ ϕ ω ϕ ϕ

⎧ ⎫⎪ ⎪⎪ ⎪⎪ ⎪+⎨ ⎬⎪ ⎪⎪ ⎪+ − + − − + + +⎪ ⎪⎩ ⎭

(7.8)

It contains one time-independent component, depending on the phase differ-ence of two signals, and is proportional to the amplitude of the measured AC signal. It reaches a maximum when the phase difference of the two signals is zero. The output signal is subsequently applied to a lowpass fi lter, which averages the signal components with frequencies above the cutoff frequency of fi lter. It produces a DC signal proportional to the amplitude. Because the average value of periodic functions is equal to zero, the average value of EmEs equals

( ) ( )1 2

2cosma

m s

EAverage E E ϕ ϕ

π= − (7.9)

The disadvantage is that it retains contributions of the harmonic frequencies (2n+1)ωref, if they are present in the input signal (e.g., harmonics, noise), although their infl uence is attenuated by 1/3, 1/5, 1/7, and so on with increasing n. Lock-in amplifi ers operate in the frequency range from 0.5 (lower limit about 10 Hz, de-pending on the manufacturer) to ~105 Hz with a precision of 0.1% to 0.2%.

FRA is a method that determines the frequency response of a measured system, as shown in Figure 7.6(a). Their function is different from that of lock-in amplifi -ers, based on the correlation of the studied signal with the reference. The measured signal is multiplied by the reference signal of sine and cosine of the same frequency and then integrated during one or more wave periods. The calculation is presented as (7.10):


( ) ( ) ( ) ( )

( ) ( ) ( ) ( )

11 1 1 1

0

11 1 1 1

0

1Re sin sin cos

2

1Im sin cos sin

2

nTa

a

nTa

a

EE E t t dt

nT

EE E t t dt

nT

ω ϕ ω ϕ

ω ϕ ω ϕ

= + =

= + =

∫

∫

(7.10)

where T is a period of the ac wave, f is the frequency of the wave, and n is the num-ber of cycles included in the measurement/integration. The high-frequency harmon-ics are removed by digital fi ltering and the SNR is determined by n. The frequency range covers from a few hundred kilohertz to megahertz. Practically, multiple mea-surements are made to average the response signals to enhance the SNR.

FFT-based impedance measurement method applies Fourier transform to trans-fer the time domain signal into frequency domain. Thus, a complex value can be presented as amplitude and phase, which corresponds to the impedance. A wide-band white noise is applied on the sensor to get a response signal. The sensor output is converted to a digital signal by ADC block for FFT calculation in PC or digital signal processor (DSP). The schematic diagram of this system is shown in Figure 7.6(b). The impedances at all interested frequencies can be obtained simul-taneously, if the noise source is ideal. When only single or several frequencies are interested, a series of sinusoidal signals can be used as the stimulus. The FFT-based

Figure 7.6 Schematic diagrams of two types of impedance measurement systems based on differ-ent techniques: (a) FRA and (b) FFT.


method provides a speed boost because no integrator is used and all frequencies are measured simultaneously. The disadvantage is the requirements of high-speed ADC and mass data process.

Based on these measuring techniques, there are several solutions for imped-ance measurement. ECIS applied a 4,000-Hz AC signal with an amplitude of 1.0V from a wave generator through a large resistor and measured in-phase and out-of-phase voltage values by a lock-in amplifi er [5, 15, 23], shown in Figure 7.6. Ehret performed impedance spectroscopy with a Solatron MTC 1260A impedance/gain-phase analyzer and long-term measurement with a Stanford Research System SR 715 or SR 720 LCR meter [6]. Computer-controlled eight fold multiplex units were used to examine eight IDEs at the same time. An Agilent 4294A impedance analyzer could be also used for impedance spectroscopy [24], as the frequency ranges were performed from 40 Hz to 100 MHz. However, traditional impedance measurement instruments pose some problems, such as bulk mass, high costs, and lack of fl exibility. As digital signal processing develops, the methods of impedance measurement change a lot. Geisler et al. established a DSP-based integration system for simultaneously detecting cell impedance, pH, and O2 of 24 wells [25]. Although this integration hardware is highly automatic and fl exible, the development period may be a problem. Wang’s group provided a solution based on virtual instruments [21]. Measurements were carried out using a multifunctional data acquisition card NI DAQ PCI-6110 (National Instruments, Austin, Texas) controlled by a Lab-VIEW (National Instruments) program. The impedance was calculated, recorded, and displayed automatically in real time.

7.4 Theoretical Analysis

The current models for the interpretation of the electrical impedance spectral re-sponse of a cell monolayer cultured on electrodes can be divided into two distinct approaches: one is a lumped model or distributed circuit model, the other is an analytical model based on microscopic characteristics of the monolayer.

7.4.1 Lumped Model

The lumped model is given based on the assumptions that the microscopic charac-teristic of the system, such as the properties of cell membranes, tight junctions, and different morphologies create global electrical properties for the ensemble of cells. Many publications have presented the lumped models for data analysis [16, 26], which have been mentioned in Chapters 3 and 4. However, there is no optimal one for every application, and the differences among these models probably confuse new investigators. Complicated models give more comprehensive descriptions of true properties of cells, whereas redundant parameters retard the data fi t in practi-cal use. Upon the knowledge of some general rules, we can develop specifi c suitable models for particular applications.

Mostly, cell impedance is measured in cell culture media, which only contains electrolyte such as K+, Na+, Cl-, PO4

3-, and some biomolecules. Thus, the equivalent circuit and explanation of measured data can be simplifi ed by dismissing faradic


current. In the range of 1 Hz–100 kHz, CDl and Rsol mainly dominate the imped-ance if there is no adsorption and desorption of species at the electrode surface. Grounded on the earlier discussion, we consider the equivalent circuit of electrodes in electrolytes without redox couple to be a series connection of double layer and solution resistance. Cell membrane (5–10 nm) has been reported to have a ca-pacitance of 0.5–2.5 μF/cm2 and a resistance of 102–105 Ω·cm2. Either specifi c or nonspecifi c cell attachments on electrodes do not directly affect the double layer for that cell scale beyond the order of several angstroms [27]. Additionally, the aque-ous gap between cells and the electrode surface prevents the direct effect of the cell membrane on the impedance of electrodes. The contact area can be viewed as an aqueous compartment spanned by the polycationic segments of membrane macro-molecules [27]. Cells adhere to adjacent cells with a gap of 10−20 nm.

7.4.2 Analytical Model

When the cells grow, the measured impedance keeps increasing due to the increase of cell number and the growth of the cell-covered electrode area. Even when the cell layers become confl uent, the impedance continues to fl uctuate. The monolayer is more complicated than the single cell, as it includes the cell-cell interactions and cell micromotions. Giaever and Keese proposed a model for the monolayer to under-stand the mechanism involved [19]. Since this model has been presented in Chapter 3, here we present further discussion based on the following research [12, 23].

This model has three adjustable parameters: Rb, α, and Cb. Normalized resist-ance and capacitance under different parameters are shown in Figure 7.7. The curves of normalized resistance share a common trend where the resistance value increases with frequency until a turning point called middle frequency; then, it de-creases with frequency. Specifi cally, these three parameters contribute in different ways to the total impedance. The junction resistance Rb signifi cantly affects the re-sistance and capacitance in the range near middle frequency, while α does so below the middle frequency, and Cb does so above the middle frequency. Additionally, the shift in the peak results from the changes in these parameters. These distinct differ-ences create an opportunity for extracting lots of information from the impedance measurements, such as junction resistance and space between cells and substratum, as well as the morphology of apical membrane and basal membrane.

7.4.3 Data Calculation and Presentation

Measured data and model data supplement each other. It is necessary to distinguish measured “raw” data, calculated measured data, and derived data. The measur-ing setup determines what data is raw data, which can be present in some chosen form. The data can be acquired as a function of time or frequency. When the data is related to both frequency and time, a three-dimensional presentation is required. Calculated data is based on a simple mathematical operation that contains no ad-ditional information with respect to any disturbing infl uence like temperature, at-mospheric pressure, and so on. Therefore, impedance and admittance are just two different ways to present the measured data. The derived data may contain some nonelectrical variable derived from electrical variables. For example, cell number,


cell viability, tightness of cell-substrate adhesion, and cell-cell tight connection all have some interior relations with measured data. How do these variables infl uence each other in correlation with the measured electrical variable? This is a selectivity problem and a calibration problem. The interpretation of the measured data always relies on some kinds of models.

Figure 7.7 (a) Normalized resistance, and (b) normalized capacitance from model calculation for different junction resistances (Rb) corresponding to 20, 40, 60, and 80 Ω·cm2. The other parameters, α, Ca, and Cb, were set to be 20 Ω1/2·cm, 4 μF/cm2, and 3 μF/cm2, respectively; these values are close to the experimental results of MDCK cells. (c) Normalized resistance and (d) normalized capacitance from model calculation for different value of α corresponding to 10, 20, 30, and 40 Ω1/2·cm. The other parameters, Rb, Ca, and Cb, were set to be 60 Ω·cm2, 4 μF/cm2, and 3 μF/cm2, respectively. (e) Normalized resistance and (f) normalized capacitance from model calculation for different apical membrane capacitances (Ca) corresponding to 1, 2, 3, and 5 μF/cm2. The other parameters, Rb, α, and Cb, were set to be 60 Ω·cm2, 20 Ω1/2·cm, and 3 μF/cm2, respectively. (From: [12]. Reproduced from the Biophysical Journal. © 1995, with permission from Elsevier B.V.)

7.5 Applications 167

An index is the ratio between two measurements made under different condi-tions but on the same sample volume. It can be calculated from data measured under the same parameter but at different time points or frequencies. Furthermore, it may be calculated from the combination of measurement data and other param-eters. An index termed cell index (CI) was created by ACEA bioscience [10] and was derived to represent cell status based on the measured electrical resistance. CI is described by (7.11):

( )( )1,...,

0

CI max 1cell i

i Ni

R f

R f=

⎛ ⎞= −⎜ ⎟⎝ ⎠

(7.11)

where R0(f) and Rcell(f) are the frequency-dependent electrode resistance without cells and with cells measured at different applied frequencies, respectively. CI is re-lated to the cell number and status; thus, the change of cell number and status will refl ect on the CI. The effect of different drugs or chemicals may increase the value of CI by increasing cell spreading or decreasing it by causing cell death or retraction from the electrodes. The concentration response curve, such as EC50 or IC50, is the concentration at which the half-maximals increase or decrease in CI, respectively. They can be calculated from the measured CI. An index is usually valuable be-cause it provides a comparable relative parameter among a larger number of wells in high-throughput screening. Other indexes may be calculated following several mathematical operations if they are well correlated with practical judgments.

7.5 Applications

During the past 20 years, there have been remarkable changes in technology and applications regarding cell impedance. Some publications describe the applications of monitoring cellular events, such as cell adhesion and spreading on different sub-strate, barrier function of endothelial cells, cell micromotion, cell migration, cell morphology and cell shape change due to various factors, and cell responses to cy-totoxic compounds. The most widely covered application area relates to investigat-ing the effect of different chemicals on cells applied in pharmacology, cytotoxicity, and cell biology. Some examples of the different categories are discussed next, from basic to advanced.

7.5.1 Monitoring of Cell Adhesion, Spreading, Morphology, and Proliferation

Cell adhesion and motility depend strongly on the interactions between cells and extracellular matrix (ECM) substrates. When a cell in suspension encounters an adhesion surface, the cell fl attens and deforms at the fi rst stage, which is charac-terized by passive adhesion and spreading, likely as a liquid initially adheres to a surface sharing a common law [28]. Then, in the following active stage, the cell spreads further, involving the mechanisms of cell crawling such as organization of actin cytoskeleton and formation of focal adhesions [29]. The cell type, the spe-cifi c amounts of receptors and ligands on the cell and substrate, and the surface


energy and hydrophilic do affect the strength of adhesion and the time to complete spreading.

ECIS is constructed on the basis of the phenomenon of cell adhesion and spread-ing on the substrate. Thus, fi guring out the underlying interaction of cell substrates may promote the cell culture quality and cell assays in vitro. Efforts have been made to determine whether a certain type of cell can adhere to a specifi c adhesive substrate and specifi c reagents that can block the cell/ECM interaction or disrupt cell-signaling pathways or cytoskeletal architecture [8, 30].

The experimental methods, which are capable of exploring cell-substrate inter-actions quantitatively, are very limited. The most important physical property is the mechanics of cells on substrates, which can be investigated by force microscope. As the cells grow on the substrate, the viability and proliferation are related to the cell-substrate interactions, which are highly concerned. Some commonly used techniques, such as WST-1 assay, XTT/MTT assay, BrdU assay, and fl uorescence microscopy, have several limitations. However, the impedance sensing method pro-vides a solution to probe the kinetic aspects of this complex process.

The various ECM, such as fi bronectin (FN), collagens (CL), laminins (LM), poly-l-lysine (PLL), and vitronectin (VN), interact with different cells through dif-ferent integrins. Integrins recognize and bind to specifi c sites, thus mediating the cell adhesion and spreading as well as initiating an intracellular signaling cascade that directs cellular processes. Wegener suggested the high-frequency capacitance as the most sensitive parameter for monitoring the early attachment and spreading of MDCK cells [8]. They reported MDCK cells spread signifi cantly faster on FN-coated substrate than on any other proteins in the experiments, such as LM, VN, and BSA. The interaction of cells with FN is specifi c and mediated by integrin on the substrate and receptors on the cell surface [30]. Increasing the concentration of Arg-Gly-Asp-Ser (RGDS) in the medium may delay cell attachment and spread-ing. Seemingly on the nonadhesive substrate, a delay time is needed for the cells to modify the substrate by secreting self-produced extracellular matrix proteins.

Kataoka et al. combined ECIS and atomic force microscopy (AFM) to deter-mine the effect of the monocytes and endothelial cells interaction on endothelial cell micromotion and mechanical properties, which was essential for the late stage of atherosclerosis [31]. Monocytes may decrease the resistance between the en-dothelial cells and the substrate but not the cell-to-cell resistance.

Bouafsoun et al. investigated the effect of several adhesion promoter proteins such as FN, collagen IV, immunoglobulin G, and heparin adsorbed on the gold electrodes [32]. Using faradaic electrochemical impedance spectroscopy, charge transfer resistance (Rct) was measured to be in an increasing tendency for collagen IV, immunoglobulin G, FN, heparin, and albumin, whereas CPE changed depend-ing on the sign of the protein charges.

It is also important to keep in mind that there are some limitations with these technologies. Because their resolutions are mainly at the cellular level but not at the molecular level, any experiment using these technologies needs to be complemented with additional molecular, cellular, and biochemical experiments. Additionally, it is practically impossible to distinguish between the extent of adhesion and spread-ing of cells and the number of cells at an earlier stage. However, this defect can be partially eliminated by preventing cell division.


7.5.2 Monitoring of Cell Migration and Invasion

Cell migration plays an essential role in various complex processes such as em-bryonic development, homeostasis, immune response, wound healing, and cancer metastasis [33]. Migration is a cyclical process, which starts with polarizing and extending a protrusion in the direction of movement, fl owing by the formation of adhesion complexes. Then the cell contracts and releases the attachments at the rear, resulting in moving forward to complete the cycle. The basic assays include Boyden chamber assay, wound-healing assay, Dunn chemotaxis chamber assay, and time-lapse microscopy, which are important tools of investigating cell migration.

Using impedance sensing in wound healing was fi rst reported by Noiri et al. [34]. They electropermeabilized the confl uent cell monolayer to generate wounds on the electrode by applying a DC current. However, the DC signal was uncon-trolled, which might also cause some electrochemical reaction on the electrode, thus making the measurement uncertain. Keese et al. improved the wounds-generation methods by using high-frequency AC current in the milliampere range [35]. The wound was restricted to the small electrode, and no electrode damage was found. This method provided highly reproducible results comparable to that observed in traditional wound-healing experiments.

The duration and amplitude play important roles in complete wound genera-tion. First, a 2.5-V pulse stimulus with a duration of 200 ms at 40 kHz is used. The transient drop in impedance may be due to cell membrane poration, and then the reversion curve results from the repair of membrane and recovery of the injured cell. As the duration of the current increases, the drop in impedance seems more marked [Figure 7.8(e)]. When the duration reaches 5 seconds, the damage seems complete, as resistance values drop to the situation of cell-free electrode. As the duration reaches 10 seconds or 15 seconds, the curves are almost the same as the result of a 5-second pulse. The reversion curves, unlike the record in short dura-tion, are the response of the cell migration from the electrode edge, which require 5 hours. The duration and amplitude are to be determined by experiments on specifi c cells and electrodes. Wang modifi ed the wound-healing methods by using SAMs [36]. The SAMs were formed on the electrodes to inhibit cell adhesion, which could effectively mimic wounds in a cell monolayer. After a DC pulse was applied, the SAMs were destroyed and cells began to migrate (Figure 7.9).

Wound-healing assays can also be used to measure the velocity of cell migra-tion. BS-C-1 cells are incubated on electrodes of different sizes, namely, 50, 100, and 250 μm in diameter. After applying a wound-generation pulse, the impedances of different electrodes drop to different levels according to the electrode size. A lag time is needed before cell repopulation. Then the curves reach the initial level in time intervals, which depend on electrode sizes. Upon the assumption that the cells move forward in the same rate, an average cell migration rate is calculated to be 18 μm/h.

Two-dimensional impedance tomography images createopportunities for mea-suring the resistivity distribution of cells, which can be used to reveal cell migra-tion and epithelial stratifi cation [37]. They measured the impedance signal from cell migration on 16 microelectrodes strips (5 μm × 4 mm) and then inversed the apparent resistivities to fi nd the resistivity of the cells using commercial software


RES2DINV (GeoTomo Software, Penang, Malaysia). As the cells migrated, time-lapse impedance images could be obtained in this method, as shown in Figure 7.10. The tomography system could simultaneously measure the resistivity distribution and spiking by integrating with multielectrode arrays for extracellular recording.

7.5.3 Monitoring of Cellular Ligand-Receptor Interactions

The impedance-based method has been successfully applied to monitor the cellu-lar integrated response of ligand-receptor interactions, mainly including G-protein coupled receptor (GPCR) activation and protein tyrosine kinase (PTK) activation in living cells. This method could generate more physiologically relevant measures than pharmacological endpoints assays.

GPCRs represent the largest family of plasma membrane proteins involved in signal transduction across the plasma membrane. More than 50% of the current therapeutic agents on the market are targeted against GPCRs. GPCRs have been re-ported to modulate the actin cytoskeleton and cell morphology in a specifi c manner

Figure 7.8 (a–d) Phase contrast photomicrographs of cells before and after wounding. (a) ECIS electrode (250-μm diameter) with MDCK cells before wounding. (b) Immediately after wounding pulse. (c) Twenty hours after wounding. The wounding pulse of 2.5V at 40 kHz was applied for 30 seconds. (d) Vital staining of NRK cells on the ECIS electrode after the elevated fi eld pulse. (e) The response of confl uent BS-C-1 cells to different time exposures of an elevated fi eld (2.5V at 40 kHz) for a time of 0.20 second, 1.0 second, 5.0 sec-onds, 10 seconds, and 15 seconds, separately. One served as a control. The resistive portion of the impedance at 4 kHz is shown. The arrow marks the application of the elevated fi eld pulses. (f) Wound-healing assays on different size electrodes of 250-, 100-, and 50-μm diameter. The impedance measured at 4 kHz and normal-ized to its value at the start of data acquisition is plotted as a function of time. (From: [35]. Reproduced from Proceedings of the National Academy of Sciences. © 2004, with permission from National Academy of Sciences, U.S.A.)


depending on the Rho family GTPase of small GTPases. The traditional means of investigating GPCR function involve labeling the cells with radioactive precursors or fl uorescent reagents and measuring single molecular entity. The impedance-based method has been demonstrated to monitor receptor-specifi c activation on live cells [9, 38]. CHOK1 cells expressing the human H1 histamine receptor (H1/CHO) and 1321-N1 cells expressing the human vasopressin receptor (V1a/1321-N1) were treated by histamine and vasopressin, respectively (Figure 7.11). The curves all show an immediate and transient increase in resistance. The pictures taken for cells fi xed and stained with FITC-phalloidin and anti-paxillin mAb validate the fact that these changes in resistance correlate with modulation of the actin cytoskeleton and its associated proteins. Besides, dose-response curves can be obtained to ana-lyze and rank histamine receptor antagonists and inverse agonists quantitatively. Ciambrone classifi ed the impedance response relative to specifi c receptor-ligand interaction. However, GPCR and PTK signaling pathways overlap; thus, stimula-tion of members from each of these families may give similar cellular physiological outputs. In conclusion, impedance-based measurements of GPCR-mediated actin cytoskeleton dynamics and morphological changes are a promising readout for ligand-mediated GPCR activation.

Figure 7.9 (a, b) Photographs taken in the process of cell migration onto the electrodes. (c, d) Fluo-rescence images of the electrodes show cell viability after modifi cation of the SAMs and application of the DC current, respectively. (From: [36]. Reproduced from Lab on a Chip. © 2008, with permission from the Royal Society of Chemistry.)


7.5.4 Cytotoxicity Assays

Cells respond to cytoxins in the forms of loss of adhesion and cell rounding, mem-brane protrusions or blebbing, formation of apoptotic bodies, and ultimately en-gulfment of apoptotic bodies by phagocytosis. Such nonlinear dynamic cellular changes depend largely on cell types, compound properties and concentration, and compound exposure duration. These apopototic responses change cell adhesion and morphology, which ultimately induces decline of cell-substrate impedance in an acute or chronic manner. Keese et al. demonstrated the applicability of the ECIS system for toxicological testing utilizing fi broblasts and epithelial cells [39]. In vitro cytotoxicity assays based on impedance have the potential to be alternative meth-ods to animal tests. The traditional methods, such as MTT, NRU, ATP measure-ment, or growth assay like colony forming effi ciency (CFE), are based on single end points. Impedance-based assays provide multiple parameters in the same assay under dynamic conditions.

Some recommended reference chemicals for cytotoxicity tests, such as antipy-rine, trichlorfon, dimethyl formamide, and sodium dichromate, as well as some fa-miliar toxins, such as sodium arsenite [As(III)], mercury(II) chloride, enzalkonium chloride (BAK), Triton X100, sodium lauryl sulfate, cadmium chloride (CdCl2), 1,3,5-trinitrobenzene (TNB), and cycloheximide (CHX), and neutral red solution have been tested to determine the effects and presented as time-response and dose-response curves by ECIS and RT-CES systems with various types of cells [40–42].

Figure 7.12(a) shows a classical dynamic cytotoxicity pattern. Cells fused to-gether, forming huge multinuclear cell bodies after 3 hours of As(III) treatment, and began to dissociate into smaller multinucleated cells after 8 hours of compound ad-dition. Actually cells had undergone nuclear condensation as an indication of early stage of nuclear fragmentation. Most cells died after 23 hours of As(III) treatment through apoptosis. Figure 7.12(b) shows the time- and dose-dependent cytotoxic effect of Scriptaid on the cells. Some useful information can be obtained from real-

Figure 7.10 Time-lapse impedance imaging of cell migration from the left to the right over a time period of 23 hours. The cells are more resistive than the surrounding medium and therefore appear as dark regions in the image (From: [37]. Reproduced from Lab on a Chip. © 2008, with permission from the Royal Society of Chemistry.)

7.6 Development Trends 173

time cytotoxic data, such as rate and real-time dose response of the compound. Figure 7.12(c) shows that the dose response of Scriptaid can vary with time.

Industrial developments bring a great increase of environmental toxins in the air, water, and soil, which may impact human health and the global environment. There are a multitude of views on testing protocols and data interpretation for the environment health impact assessment. At the cellular level, impedance biosensors have been widely researched and used to profi le compound cytotoxicity.


7.6.1 High-Throughput Screening

The process of discovery and commercialization of a new drug is a very complex, time-consuming, and labor-intensive process. The molecular biology revolution of

Figure 7.11 Correlation of agonist-induced increases of CI and cell morphology dynamics. (a) H1/CHO cells were seeded on microelectrodes and stimulated with histamine (indicated by the arrow). (b) Contrast experiments were carried out on chamber slides and stimulated with 100 nM histamine for 5 minutes. After stimulation, the cells were fi xed and processed. The arrows indicate the site of membrane ruffl es and paxillin translocation as a result of histamine treatment. (c) V1a/1321-N1 cells were seeded on microelectrodes and stimulated 1-μM vasopressin. (d) Contrast experiments were carried out on chamber slides and stimulated with 1-μM vasopressin for 5 minutes. After stimula-tion, the cells were fi xed and processed. The arrows indicate the site of membrane ruffl es and paxillin translocation as a result of vasopressin treatment. (From: [38]. Reproduced from Analytical Chemistry. © 2006, with permission from the American Chemical Society.)


the 1990s and high-throughput screening (HTS) have brought a signifi cant shift in the traditional drug discovery paradigm. HTS has become the essential tool of drug discovery in most pharmaceutical companies and academic institutions [44]. HTS has the advantages of high productivity and low cost. The recent development of microfl uidics technologies has facilitated the development of high-density, low-volume assays. Due to the constraints with traditional microelectrode and encap-sulation, the current limitation of impedance systems for GPCR drug discovery is relatively low to moderate throughput. Therefore, these systems are probably better suited for secondary screening assays. It is inspiring to increase the throughput to 384- or 1,536-well, enabling the generation of >100K data points/day to screen compound libraries in excess of 1 million compounds.

Figure 7.12 Dynamic cytotoxicity pattern and its corresponding cellular morphological changes. (a) NIH 3T3 cells were seeded into the 16X device, and cell proliferation was monitored. Once the cells entered the exponential growth phase, 20 hours after seeding, the cells were treated with either As(III) at the concentration of 24.66 μM () or Triton X-100 at the concentration of 0.1% (v/v) ( ) (From: [41]. Reproduced from Chemical Research in Toxicology. © 2005, with permission from the American Chemical Society.) (b) Dynamic monitoring effect of HDAC inhibitor-Scriptaid on A549 cell. (c) Real-time data were used to generate real-time IC-50 values for Scriptaid. The inset shows the dose response of Scriptaid at a single time point. (From: [43]. Copyright © 2008, Biotechnology & Bioengineering. Reproduced with permission of John Wiley & Sons, Inc.)

7.7 Summary 175

7.6.2 Integrated Chip

Electrical impedance spectroscopy and chemical analysis techniques have been ap-plied to investigate bioelectrical properties of cell membranes and mechanisms of the excitable cell responses to chemical or electrical stimuli. Compared with con-ventional methods such as patch-clamping and fl uorescent microscopy, the elec-trochemical method has several advantages, which we have mentioned earlier. In particular, as microelectromechanical systems and nanoscale technologies mature, it creates great opportunities for simplifi ed automated and high-throughput ap-proaches for basic research in cells. Furthermore, these techniques can be incor-porated to simultaneously measure as many parameters as possible by integrating different methods into one chip. Consequently, time cost is thought to be reduced while more information about cells is obtained.

Dittami et al. designed and fabricated a platform for EIS of small regions of the cell membrane and the measurement of chemical concentration adjacent to the cell membrane. The neurotransmitter release was modulated in phase with the positive peak of the sine stimulus, which highlighted the potential of the device to spatially resolve the cell membrane’s electrical properties as well as its intracellular components [45].

We presented an idea of simultaneous measurement of cell impedance and ex-tracellular recording by the integration of two types of electrodes shown in Figure 7.13. It is a specifi c design for electric active cells. The cell viability probed by the impedance measurement indicates the best time for extracellular recording, which cannot be done in any existing commercial products.

7.7 Summary

In conclusion, impedance, one of the most important electroanalytical methods, is highly compatible with micro- and nano-machine technology and capable of real-time, label-free, noninvasive, dynamic cell analysis. The transducer system gathers the response signal from the whole cell and yields generic sensitivity that is a distin-guishing feature in comparison to other molecular biosensor. Moreover, it is benefi -cial to combine with optical probes and imaging techniques, which can largely help

Figure 7.13 Integration of microelectrode array and IDEs on single chip.


overcome the shortcoming of nonspecifi city. Numerous applications of impedance-based biosensors in areas ranging from pharmaceutical screening to environmental monitoring indicate a promising future in this fi eld. Novel and diverse designs surge up with a combination of other technologies, such as microfl uidic, SAMs, DEP, electroporation, and electroanalytical methods. Specifi c requirements from medical, environmental applications guide the designs of specifi c biosensors. It is inspiring to see more and more commercialized products come to reality. However, efforts should be made to improve the reproducibility and reliability.

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7.7 Summary 177

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179

C H A P T E R 8

Patch Clamp Chip as Cell-Based Biosensors

Peihua Chen, Qingjun Liu, and Ping Wang

8.1 Introduction

Ion channels are extremely important proteins that can be mainly classifi ed into voltage-gated ion channels, ligand-gated ion channels, and mechano-sensitive ion channels. They all play critical and important roles in cellular signal transduction, impulse conduction, cellular microenvironment balance, and so on. Therefore, the investigation of ion channels is particularly important in drug discovery nowadays. Lots of drugs are the agonist or antagonist of ion channels, especially cardiovas-cular drugs. The toxic effect of drugs on ion channels in cardiovascular systems is an essential parameter, before new drugs will be admitted to come into the market. Some conventional methods, like ligand binding assays, membrane potential assays, and ion fl ux assays, have the quality of high throughput—however, with low or medial information content. The gold standard method, patch clamp electrophysi-ology experiment, possesses large information content, but with low throughput, which hampers its application in drug discovery. As a benefi t from its advantage of high throughput and large information content, the emergence of the planar patch clamp will accelerate the development of drug discovery. In this chapter, we will introduce the patch clamp chip fabrication and its application in detail.

8.2 Theory

8.2.1 Conventional Patch Clamp

The patch clamp technique was invented by German cytophysiologists Neher and Sakman. Due to their contribution to electrophysiology, they were awarded the Nobel Prize in 1991. This technique was used to investigate the behavior of a single channel on a small membrane patch or the macroscopic current from the whole cel-lular membrane. With continuous improvement, the patch clamp has been widely used in various levels, such as molecular, cellular, and tissue level.

180 Patch Clamp Chip as Cell-Based Biosensors

The basic principle of this technique is to isolate a small membrane patch by using a fi re-polished glass pipette, which has a fi ne tip. The key step of the patch clamp technique is to achieve a giga-ohm seal resistance between the membrane patch and pipette, around 10–100 GΩ. This membrane patch is electrically isolated from the surroundings, which makes recording from the patch of membrane pos-sible. The confi guration of patch clamp is shown in Figure 8.1(a).

In the past several decades, the resolution of patch clamp recording has been greatly improved. It can obtain physiological and kinetics characteristics, such as the gating, permeability, selectivity, and voltage-sensitivity of ion channels and re-ceptors, at the molecular level, as well as the regulation of transmitters and second messengers on the channels. Patch clamp technique shows great potential in the research of neuroscience and electrophysiology.

To realize different experiment purposes, corresponding experimental systems can be constructed. The basic confi guration consists of mechanical, optical, and electrical components. The mechanical instruments, such as vibration-proof table, Faraday cage, and micromanipulator, ensure recording stability. The optical com-ponent contains the microscope, visual monitor, and homogeneous light generator. The electrical instrument is the most important part. It consists of patch clamp amplifi er, stimulator, data acquisition equipment, interface, computer, and power.

According to the purpose of the study, different modes of patch clamp recording were adopted. For single channel recording, cell-attached mode, inside-out mode, and outside-out mode were fi rst used. Later on, some new recording confi gura-tions, like open cell-attached/inside-out and perforated vesicle outside-out mode, were established. For whole-cell recording, whole-cell mode and perforated-patch mode were normally used.

Patch clamp work modes, including voltage clamp mode and current clamp mode, are normally used. Voltage clamp mode is used to record the membrane cur-rent when the membrane potential is held on to a certain level. The current clamp mode records the membrane potential changes upon a current stimulus or chemical stimulus.

Patch clamp is the golden standard in the research fi eld of electrophysiology. However, patch clamp technology has certain inevitable limitations, such as low

Figure 8.1 The confi guration of (a) the conventional patch clamp and (b) the planar patch clamp. (From: [1]. Reproduced from Progress in Natural Science. © 2008, with permission from Elsevier Ltd.)

8.2 Theory 181

throughput. In this respect, it can hardly be applied to the research of cellular communication in a neural network. Moreover, during the process of recording, the intracellular solution cannot be exchanged conveniently. Thus, a large number of experiments must be carried out. In addition, a highly skilled and experienced operator is needed to accomplish the suction, exchange of solution or drug, and perform the recordings under a microscope. Overall, this conventional patch clamp technology can hardly be applied to high throughput drug screening.

8.2.2 Patch Clamp Chip

Due to the intrinsic problems of the conventional patch clamp, patch clamp tech-nology is limited to research work in the laboratory. For its application in industry, scientists have made great efforts to improve conventional patch clamp technology by developing a new confi guration of the conventional microelectrode or a new generation of microelectrode.

Improvement of the electrode confi guration was fi rst achieved by Sophion Bio-science. Neuropatch tried to replace the operator in conventional patch clamp with a computer visual control micro-operator–based robot to posit the microelectrode on a cell automatically. Flyion put forward a technology called fl ip-tip and pro-duced a novel automatic patch clamp instrument, known as the Flyscreen 8500 sys-tem, which inverted the interface between cell and electrode [2]. Cells were placed inside the microelectrode, so that the cell reached the tip of the pipette and formed a seal from the inside. However, these systems were still based on a single micro-electrode and were diffi cult for high-throughput applications.

In the late 1990s, scientists set out to develop a patch clamp chip and raised the concept of guiding cells onto a microaperture, replacing the glass microelectrode with a planar structure [Figure 8.1(b)]. A negative pressure or static electricity fi eld was utilized to guide the cell onto the aperture. Then another negative pressure was applied to form a high seal resistance between cell and chip. The operation was more convenient and rapid. The multielectrode array patch clamp chip could record multiple cells simultaneously.

In electrophysiological recording, there are mainly two recording modes, cell-attached and whole-cell mode. The prerequisite for high-quality electrophysiologi-cal recording is a high-resistance seal. Several problems about cell preparation and patch clamp chip should be noted:

Cells should be in a good state. The cell density should reach a certain level 1. according to the purpose. When cells are isolated, enzymes can be used to keep the cell membrane clear. Bath solutions should be fi ltered rigorously to get rid of contamination. 2. Meanwhile, clear cell suspension is needed, and the debris should be removed.The materials of the planar aperture should be insulated and hydrophilic. 3. The aperture is required to be smooth and round, and have a diameter small enough (about 1–2 μm for cell recording) and thick enough to realize the characteristics of low noise.A method to position a cell on the aperture precisely is needed.4.


From this design of planar structure, on which cells are guided, there are many advantages. The capacitance of the chip (about 1 pF) should be less than that of a glass pipette (several pF). Likewise, the resistance is reduced, which results in a lower distributed RC noise and a higher resolution. The patch clamp chip can eas-ily be combined with optical measurements, like AFM and fl uorescence. The patch clamp array can achieve multicell, highly parallel recordings. The recording process would be easier, since a micromanipulator, optical instrument and variation-proof table are needed.

8.3 Sensor Device and System

8.3.1 Patch Clamp Chip Device

The critical technique is how to fabricate a smooth aperture on the chip with a diameter of 1 μm or even smaller. So far, some materials, such as silicon, quartz crystal, glass, and polymers, have been utilized to fabricate patch clamp chips. In this section, the fabrication of aperture on the planar structure will be discussed in detail.

8.3.1.1 Chip Based on Silicon

The initial attempt for patch clamp chip fabrication was to use silicon, which was chosen undoubtedly because of the convenient application of the standard semicon-ductor technology. Micro- and even nano-sized apertures can be easily processed on planar silicon substrates.

Generally, lithography, reactive ion etching (RIE), deep reactive ion etching (DRIE), pressure chemical vapor deposition (LPCVD), and thermal oxidation were adopted. One silicon-based method used by Matthews was introduced here, as shown in Figure 8.2 [3]:

Figure 8.2 The process of the fabrication with silicon-based substrate.

8.3 Sensor Device and System 183

The p-doped 1. <100> silicon wafer with a diameter of 100 mm is chosen, which is cleaned with 5:1 H2SO4: H2O2.Next, 200 nm of silicon nitride (Si2. 3N4) is deposited onto the silicon wafer with 40 sccm of SiH2Cl2 and 108 sccm of NH3 in the LPCVD furnace at 790°C and 33-Pa condition.Photolit hograthy is performed on the backside of the wafers using AZ5214 3. EIR photoresist. Openings in the backside of the silicon nitride layer are etched in RIE under the condition of 13 Pa, 200W, and 5:1 CF4:O2 for 3 minutes.Photoresist is striped from the wafers. Then, the wafers are submerged in 4. a 30% solution of KOH to form a 20-μm membrane by etching the single-crystal silicon defi ned by the backside Si3N4.Photolithography is performed using AZ5214EIR to defi ne the cell-patch 5. sites. The frontside Si3N4 is then etched using RIE process. DRIE is applied to create a hole through the membrane.Once the hole is formed, the wafers are cleaned to remove the fl uorine-6. based polymer deposited in the DIRE process.LPCVD or thermal oxidation is used to deposit a layer of SiO7. 2 about 2 μm thick.

The main advantages of this kind of chip are its convenient fabrication and low cost. Taking advantage of the simple fabrication, silicon still has great potential. Moreover, silicon-based patch clamp chip can be easily integrated with microfl uidic technology [3, 4]. However, the use of silicon is prone to several problems, such as that a high density of free charge carriers causes a transient parasitic current and that silicon-based chips have intensive photoelectric effect and have diffi culty in forming GΩ seal with cells. Nevertheless, when a certain voltage is applied to a silicon-based chip, a static electric fi eld is elicited around the aperture, which can be used for guiding cells (the mechanism will be interpreted in Section 8.3.2). Cur-rently, this kind of chip is mainly used for study of the ionic channels constructed in liposomes or the artifi cial lipid bilayers [5, 6].

8.3.1.2 Chips Based on Glass

Quartz crystal has excellent dielectric qualities. Adopting the standard planar pro-cess technology, μm- to sub-μm-sized apertures can be obtained. This kind of chip has excellent performance. However, the formed aperture is always a triangular shape, making it hard to form high seal resistance with cells.

Glass is used as the pipette electrode in conventional patch clamp. It is a kind of excellent insulating material with good mechanical and naturally hydrophilic properties, which attract the attention of many researchers. However, an obvious disadvantage of glass is the lack of standard fabrication method. Fertig et al. uti-lized ion-track etching techniques to process a smooth aperture with a diameter of 1 μm or smaller [7, 8]. The detailed methods from Fertig’s group are described next and shown in Figure 8.3(a):


Amorphous quartz with a thickness of 200 1. μm was used as the substrate. The quartz was locally thinned down with the standard planar processing techniques. The remaining thickness was around 20 μm. A 200-nm-thick Au layer was deposited on both sides of the substrate by using a thermal evaporation chamber. Another 5 nm of NiCr were deposited below the Au mask, as an adhesive layer. A thin (1 μm) layer of photoresist (Microposit S1813, Shipley, United Kingdom) was deposited with a programmable spin coater operated at 3,500 rpm. The photoresist was baked at 90°C for 20 minutes. Using lithography, a 500-μm diameter and round etch mask was defi ned in a mask aligner (Karl Suess, Munich, Germany). The etch masks in the photo resist were transferred into the Au layer in a wet-etching step with HCl:HNO3 (1:2). In order to thin the quartz, it could also be achieved chemically with fl uoric acid (10% distilled in water at room temperature), where the structured Au layer served as the etch mask. The quartz membranes were penetrated by a single, highly accelerated 2. gold ion (11.5 MeV/nucleon, available at the linear accelerator UNILAC, Darmstadt, Germany). This process leaves a cylindrical zone damaged in the substrate, called the ion track [9]. To avoid the quartz membrane being exposed to multiple ions, a detector was used to monitor the penetrating ions, which activated a shutter to shield the sample accordingly. The latent track in the quartz was etched open using fl uoric acid, resulting 3. in a small aperture. The etching was done only from the prethinned side of the chip, since the chip surface was protected by an etch mask. In this way, a conical-shaped etch groove is formed along the latent track. The etching process is performed under a certain temperature and well-defi ned concen-tration control, which assures the reliable stability. The etching time can be estimated according to a given quartz membrane thickness. The size of the micro apertures can be fabricated reproducibly. The remaining Au layer was then stripped off the chips using HCl: HNO4. 3 (1:2).

Figure 8.3 Glass-based patch clamp chip. (a) The process of the patch clamp chip with single ion track etching method. (b) The scanning electron micrographs of the aperture fabricated with single ion track etching method. (From: [10]. Reprinted with permission from Physical Review E. © 2001 by the American Physical Society.)


The aperture fabricated with the ion-track etching technique is round and smooth, which is suitable for the formation of high seal resistance [Figure 8.3(b)]. This kind of patch clamp chip signifi cantly reduces the capacitance of the device to below 1 pF and exhibits very low series resistance of only about several hundred kilo-ohms. Glass-based chips greatly improve the distributed RC and enhance the behavior of patch clamp chip. Therefore, it has been utilized to perform whole-cell and single channel current recordings [11, 12]. In addition, a nice seal can be formed. Glass-based chips are transparent and convenient for observation. From this point, this kind of material is easy to be combined with optical measurements (e.g., fl uorescence). However, the noise of glass inherently exists. An alternative strategy is to use quartz. Nevertheless, this kind of material processes a high melt-ing point, which requires the usage of CO2 laser, making the fabrication more expensive.

8.3.1.3 Chips Based on PDMS

Polydimethylsiloxane (PDMS) is well known as Sylgard, which is widely applied in micromodels and microfl uidics. It is an attractive material because of its insulating quality and low dielectric loss. In conventional single-channel patch clamp mode, the tip of a glass pipette is coated with a layer of Sylgard to decrease the capacitance between the intrapipette solution and bath solution.

Sigworth et al. from Yale University succeeded in developing a simple and prac-tical method. The patch-clamp chip consists of two components, a primary support with a micro-sized hole and a secondary support. The fabrication process is shown in Figure 8.4 [13].

PDMS was produced from Dow Corning Sylgard 184 resin. The resin was mixed at a 10:1 ratio of silicone base to curing agent by weight. After mixing, the PDMS was placed into a vacuum chamber at about 15 Torr for 15 minutes to al-low air trapped in the mixing process to escape, ensuring a void-free material after curing.

There are three steps to fabricate the PDMS electrode:

Fabricating the support structures;1. Micromolding the aperture in the primary support;2. Mounting the primary onto the secondary support.3.

A primary support involves a sheet of PDMS that is made by pouring the un-cured PDMS into a 150-mm culture dish to cover the bottom surface about 0.5 mm thick. Then, the sheet of PDMS is cured in an 85°C condition for 2 hours. The pri-mary support was made by punching through the cured PDMS. A sharpened brass tube (4-mm diameter, sharpened using a lathe) is pressed onto the PDMS sheet to defi ne small disks of PDMS. Next, a blunt-tipped hypodermic needle (18 gauge, with a sharpened edge) is pressed onto the center of each of these disks to make a fi nal washer-like structure that has a 3.2-mm outer diameter, a 0.4-mm hole, and is ~0.5 mm thick.


A secondary support structure is used as a carrier for the primary support to permit easy handling and mounting into the recording chamber. It is also produced from a sheet of cured PDMS about a 1 cm thick. In this case, the sheet of PDMS is made by pouring uncured PDMS to fi ll a 75-mm culture dish and heat curing in an 85ºC oven for 2 hours, after which the sheet is sliced into 1-cm square chunks with a scalpel. Then, a 1.4-mm hole is punched into the center of each chunk, using a sharpened brass tube (2-mm diameter) to form a fi nal support structure that is ~1 cm on a side with a 1.4-mm hole in the center.

The aperture microfabrication mold consists of a metal plate (1-μm diameter) sealed over a Delrin chamber that is pressurized using nitrogen at ~40 psi. The met-al plate composed of nickel alloy is used to defi ne the micron-sized stream of air. The metal plate is ~150 μm thick. A tapered through-hole has a 2-μm opening on

Figure 8.4 The process of the fabrication of the PDMS-based patch clamp chip. (From: [13]. Repro-duced with permission from Pfl ügers Archiv European Journal of Physiology. © 2004, Springer-Verlag GmbH.)


the fl at side, however, which opens to nearly 300 μm on the backside. The primary support is coated with a thin layer of uncured PDMS, using a paintbrush (contain-ing only 1–2 hairs) and placed onto the metal plate. After creating an aperture, the metal plate is heated to cure the PDMS layer.

Finally, the primary support is mounted onto the secondary support.Another micromolding method for a single aperture was also invented by the

group of Sigworth [14]. The aperture size was large, typically 4–20 μm, which was not suitable for small-sized cells. In order to realize multisite recording simultane-ously, an array based on PDMS was introduced by the Sigworth group [14].

By comparison, the fabrication of a patch clamp chip based on PDMS is much easier as well as cheaper. Micromolding is expected to be a good method for fabri-cating apertures at the micrometer level. The hydrophobic nature of the surface can be modifi ed by oxygen plasma, enabling the chip to form a high seal resistance with cells easily. The transparent property also makes it easy to be combined with opti-cal measurement. Currently, the smallest size of the aperture that can be achieved in PDMS substrate is 2 μm in diameter. So the means to create smaller apertures and achieve mass production should be resolved.

8.3.1.5 Chips Based on Polyimide

Polyimide is a kind of attractive organic polymer, bearing the properties of good biocompatibility, transparence, excellent insulation, and low dielectric loss. The hydrophobic nature of the surface can be modifi ed by covering a layer of Si3N4. Focused ion beam (FIB) technique is used to process the aperture with a diameter of 2 μm. The fabrication process is shown as follows [15]:

A polyimide layer with a thickness of 6.5 1. μm was deposited on a glass sub-strate by spinning.A thin layer of gold was deposited on the polyimide fi lm to avoid the charg-2. ing of the ion beam.A beam of gallium ions, with energy of 30 keV and beam currents between 3. 20 pA and 1 nA, was focused on the polyimide fi lm with a spot diameter less than 10 nm. The ion beam was focused on the fi lm and moved in a circular manner. Under the control of software, the maximum external di-ameter was confi ned to 2 μm. After the formation of the microaperture, the gold layer was removed by 4. argon etching. Then a layer of 100-nm-thick Si3N4 was deposited on the polyimide by using rf-sputtering Si in Ar/N2 plasma.The fi lm was stripped from the glass substrate with the help of phosphate 5. buffered saline (PBS) solution.

The micromachined aperture based on polyimide was fabricated with a diameter of 2 μm [15]. Through experiments, they found that this kind of chip could hardly form giga high seal resistance. The reason might be the positioning of the cells on apertures by a suction (a negative pressure) and the diameter of the aperture. The


walls of the openings are always rough. Therefore, until now, this polyimide-based patch clamp chip was just applied in loose patch recording.

Other types of materials were also be used in the fabrication of the patch-clamp chip, like amorphous Tefl on [16]. We will not give more detailed description here, though.

8.3.2 Patch Clamp Chip System

Due to the concept of guiding cells onto a microaperture of patch clamp chip, some instruments, like micromanipulator, optical instrument, and variation-proof table, which are essential in conventional patch clamp experiments, are not needed here in the patch clamp chip system anymore. Compared with the conventional patch clamp, a great and important difference is the process of forming a seal. Once the cell is trapped by the microaperture, a negative pressure is applied to form a high resistance seal. The current or voltage signals enter the headstage, amplifi er, A/D, D/A converter, and computer. Figure 8.5 shows the data recording and experiment control process.

8.3.2.1 Guiding Cells

A big challenge in planar patch clamp technique is how to guide the cells onto a mi-crosized aperture precisely, further making them form the giga high seal resistance. In the experiment, in case that the cells or debris form a seal with the aperture, it is hard to get rid of both of the cells and debris. In the design of the patch clamp chip, cells are expected to form a giga seal with the aperture successfully after contacting with the recording spot. Therefore, an important factor to judge the design of patch clamp chip is the successful rate of the formation of giga seal resistance. Here, we introduce several methods for guiding cells.

Figure 8.5 The data fl ow and control fl ow in the whole system.


8.3.2.2 Suction/Pressure

This method is most widely used in conventional patch clamps. The principle is the same as that of the giga seal formed between the pipette and cells. After the cell suspension is settled, a positive pressure about 250 mbar is applied to the chip. This outfl ow prevents the debris around the aperture. Then, a negative pressure about 200–600 mbar is applied to direct a cell onto the aperture. As to the time needed for this process, it depends on the cell density, the distance between the cell and aperture, the magnitude of the pressure, and the cell type. Once a cell is on the ap-erture, this negative pressure is reduced to form a giga seal [7, 17]. For a whole-cell recording, an intensive negative pressure is applied to break up the cell membrane. The successful rate of this technique is comparatively higher. Currently, most com-mercial patch clamp chip systems, such as QPatch system and Cytopatch system, use this method.

8.3.2.3 Electrical Field/Dielectrophoretic Force

Electrode structures are arranged surrounding the microapertures for automated positioning of cells at the desired recording spot. Because the surface of most cell membranes have electrical charges, appropriate electrical fi elds can be used to di-rect the cells. When an AC potential is applied to the electrodes, an inhomogeneous electrical fi eld can be generated around the apertures. Before the application of AC potential, cells lie randomly on the chip [Figure 8.6(a)]. Afterward, cells move toward the lowest electric fi eld and fi nally onto the apertures, as shown in Figure 8.6(b) [17]. Moreover, it is found that when applying a voltage around several hun-dred minivolts on the Si3N4 layer of silicon-based patch clamp chip, an electrical fi eld is created. This electrophoresis can be used to guide the vesicles in the bilayers onto the microaperture [18].

8.3.2.4 Microfl uidics

This technique is used to direct the cells onto the microapertures for trapping cells and providing a rapid extracellular solution exchange [Figure 8.7(a)] [3, 19–21]. The commercial patch clamp chip system of Cytocentrics and Sophion are integrated

Figure 8.6 (a) Dielectrophoretic forces are utilized to guide the cells. (b) The cell is guided to a de-sired spot using dielectrophoretic forces compared without the electric fi eld application in (a). (From: [17]. Reproduced from Solid-State Electronics. © 2004, with permission from Elsevier Ltd.)


with microfl uidics. Once the cell is trapped onto the aperture, a negative pressure can be applied to enable a giga-ohm seal. Taking advantage of microfl uidics, a novel kind of patch clamp array chip was designed. Cells are trapped by the micro-openings [Figure 8.7(b)] on the lateral wall of a main fl uidic channel, as shown in Figure 8.7(c) [22]. The whole setup is shown in Figure 8.7(d).

Another special method is not to form giga-ohm seal. Several hundred MΩ seal resistance is enough. Although a large leak current exists, it is acceptable if the desired ionic currents are large enough. The Ion Works HT system of Molecular Device adopted this method. When performing whole cell recording, the cell mem-brane can also be perforated by amphotericin B, the same as the perforated patch mode in conventional patch clamp.

A commercial patch clamp, the CytoPatch chip, uses cytocentering technique. The processes of guiding the cell and rupturing the membrane both use the pressure and suction method [23]. But the pressures for guiding the cell and breaking down the cellular membrane come from different channels and apertures, as shown in Figure 8.8.

Figure 8.7 The patch clamp chip integrated with microfl uidic system. (a) An overview of a patch clamp chip with a microfl uidic system for trapping cell and exchanging solution. (From: [19]. Repro-duced by permission of the Royal Society of Chemistry.) (b) The phase-contrast micrograph of the patch clamp chip with cell trapping in (a). (c) A design of a patch clamp array chip utilizing a lateral cell-trapping structure. (From: [22]. Reprinted with permission from Applied Physics Letters. © 2004 by the American Physical Society.) (d) The micro-opening. (e) The whole confi guration of the design with the lateral micro-openings.


8.3.2.5 Patch Clamp Amplifi er

In electrophysiological experiments, the amplitude of ionic currents ranges from pA to nA. Thus, an amplifi er is needed to perform signal amplifi cation, compensation, and fi ltering.

A patch clamp amplifi er consists of three components, including headstage, signal processing and amplifi er, which implement measurement and clamp. Almost all the patch clamp amplifi ers bear the following features:

Current clamp/voltage clamp mode;1. Compensation for the offset;2. Compensation for the capacitive current;3. Compensation for the series resistance;4. Amplifi cation gain.5.

The headstage, the interface between amplifi er and biological preparation, is also the probe. The Ag/AgCl electrode is connected with the headstage directly. Besides, high input resistance is required. Two distinct circuits are included in the headstage, the feedback I-V converter in voltage clamp mode and the voltage fol-lower in current clamp mode. In some types of headstages, voltage clamp mode and current clamp mode utilize the same basic circuit.

Here follows a brief introduction of a kind of resistive feedback I-V converter. The gain of the headstage depends on the value of feedback resistance. The larg-er the feedback resistance (Rf) is, the larger the gain will be—and the higher the

Figure 8.8 The prototype of the CytoPatch chip (www.cytocentrics.com). (a) The contact channel is used for guiding cells, while the suction channel is used for rupturing the cell membrane. Scale bar, 2 μm. (b) The focused ion beam (FIB) fi gure of the cross section of the structure in (a).


sensitivity will be and the smaller the noise will be. Generally, when performing single channel recording, such as cell-attached, inside-out, and outside-out, larger Rf should be chosen. When performing whole cell recordings, we can choose Rf with a smaller value.

In current clamp mode, the voltage follower is used to obtain the recorded volt-age signals. The operation amplifi er with infi nite input resistance, unity gain, and the summing amplifi er for injecting current into cells are utilized. The current can be given by I = Vcmd/Rf, where Rf should match the resistive load of the cells.

A fi lter is used for data processing before storage. If the recorded currents or voltages have low SNR, the offl ine data analysis will not work well correspond-ingly. Thus, fi ltering is essential before data storage. The frequency of bioelectrical signals is low. However, the frequency range of noise is much wilder. So, the low-pass fi lter is usually used. Meanwhile, a notch fi lter is used for blocking the signals in a frequency range selectively, especially to fi lter power noise. There are various kinds of low-pass fi lters. Two most commonly used are the Bessel fi lter and the But-terworth fi lter. We will not discuss them in detail here.

8.3.2.6 Compensation

Due to the contact between patch clamp apertures in the substrate and cell mem-brane, and the fi lling of intracellular solution, electrode capacitance Cp should exist in the input terminal of the patch clamp amplifi er. This capacitance is small, usually around several pF. When applying a voltage step, the capacitance of the electrode is charged. The time constant is small, so this capacitance of electrode is called fast ca-pacitance. When working in the whole-cell patch clamp mode, the series resistance Rseries and the capacitance of cell membrane Cm form an RC circuit. When a voltage step is applied, the capacitance of the cell Cm is charged. The time constant is large, so we call it slow capacitance. These two types of charging currents fl ow through the feedback resistance Rf in the feedback circuit, resulting in the dynamic error of the output voltage Vo. Meanwhile, this may saturate the amplifi er and distort the interesting signals. In order to correct the errors, the corresponding compensation should be adopted.

The compensation is thought to inject a current into the headstage, which di-rectly charges the electrode or the cell membrane, bypassing the voltage clamp circuit. Thus, the charging current of the electrode and cell membrane is invisible to the experimenter. As a result, the compensation is corrected. The principle of fast capacitance compensation is introduced here. When the command voltage Vp changes, Ip fl ows through and charges the electrode capacitance Cp to make it hold onto a new voltage level. When setting appropriately to make the current fl owing through compensation capacitance equal to Ip, there will be no transient capacitive current in the output terminal. The principle of slow capacitance compensation is similar to that of fast capacitance. Adjusting the gain of the operational amplifi er and resistance appropriately, the injecting current from the compensation capaci-tance equals the transient charging current to the cell membrane—hence solving the slow capacitance compensation problem.

In voltage clamp mode, series resistance should also be compensated, which is the sum of the contact resistance and aperture resistance. When applying a clamped


voltage, an unnecessary voltage would be generated between the headstage and cell membrane. Thus, the command voltage is not equivalent to the cell membrane potential. Moreover, the series RC circuit comprised of series resistance Rseries and cell membrane capacitance Cm, generates a delay when the command voltage Vp changes. This leads to a slow voltage clamping, which may cover the activation, inactivation, or desensitization of the currents. Especially when performing fast current recordings such as sodium current, current signals will be distorted. The se-ries resistance and cell membrane capacitance form an RC fi lter with a single pole, which will decrease the bandwidth. After the series resistance is compensated, cells can be well clamped. Generally, slow resistance Cs and series resistance Rseries are compensated during the whole-cell voltage patch clamp.

In current patch mode, when injecting a current into the cell, there is a voltage drop if the aperture resistance is not compensated. The compensation principle is interpreted briefl y. The real cell membrane potential Vm equals the value of the re-corded membrane potential Vp minus current I multiplying the aperture resistance Ra.

8.3.2.7 Faraday Cage

Due to the small magnitude of the whole cell current or single channel current, high input resistant is needed. Any electromagnetic interference can be easily recorded and will interrupt the experimental results. The main circuit that needs the shield-ing is between the headstage and the preparation. In the conventional patch clamp system, a large, copper, and grounded cage is used for the shielding, which is called Faraday cage. It covers the whole worktable and some instruments to shield the electromagnetic interference. In some of the patch clamp chip systems, Faraday cages are still used. In others, no Faraday cage is needed; however, good experiment data can still be obtained, such as a Port a Patch system.

8.3.3 Cells Preparation

It is a big challenge to make a GΩ seal resistance between cells and chip substrate. The cell preparation for patch clamp chip recording is highly demanded. Thus, it is essential to choose some types of cells that are easy to make a seal with a chip. In conventional patch clamp recordings, the preparation used normally are primary cells, cultured cells, ionic channels in lipid membrane, and reconstituted ionic chan-nels in artifi cial bilayer lipid membrane.

Adult cells1. : Enzymes such as collagenase and protease are used to get rid of the extracellular matrix. The surface of the cell should be clear.Cultured cells2. : It is convenient to perform electrophysiological experiments if the extracellular matrix is little. All kinds of cell lines are widely used, such as Chinese hamster ovary (CHO), oocyte, and human embryonic kid-ney (HEK) cells.Ionic channels in a lipid membrane3. : The microsome is frozen and melted. Then the process dehydrates/hydrates or loads the cell with KCl and water, and treats with protease.


Reconstituted ionic channels in an artifi cial bilayer lipid membrane4. : It is very useful to investigate the ion channels in some special membrane fragments.

In patch clamp chip experiments for ionic channels, electrophysiology and drug screening, CHO, HEK, and oocyte cells are normally used. Due to the lack of endogenous voltage-gated ionic channels in CHO and HEK cells and their large diameter of around 15–50 μm, they are used as the cell models to evaluate the suc-cessful rate of forming the seal resistance between cells and chip substrate. Ionic channels like hERG channels are transfected into these cells for drug screening.

The diameter of oocyte cell is large, around 0.7 mm. This kind of cell is used when the aperture on the substrate is large. But some problems exist. First, as the cell surface is large, so is the cell membrane capacitance. It takes a long time to clamp the cells. Then, the currents are large in these cells. A large voltage drop will be generated. Therefore, the current should be kept on the level of μA.

8.4 Biomedical Application

Ionic channels play important roles, like impulse conduction in nervous system and pace making in cardiac system. Dysfunction in ionic channels leads to disease. Therefore, interest in ionic channels comes not only from the basic theory study about their biophysics properties, but also from drug research about their regula-tion on ionic channels in biotechnology and pharmaceutical industry.

Many ionic channels are the targets of drugs. Based on the structure, ionic channels can be mainly categorized into three types: ligand-gated channels, volt-age-gated channels, and mechano-sensitive ion channels. Until now, ligand-gated and voltage-gated channels consisted of around 61 and 123 subunits, respectively. However, in the pharmaceutical industry now, only a small amount of ionic chan-nels can be used as the drug target.

The process of drug discovery and detection is greatly affected by the rational-ity and screening ability of the conventional methods. At present, a high through-put screening technique is defi cient, and it hinders the discovery of the massive unknown drug target. The advent of the parallel, automated, and high throughput patch clamp chip system is expected to change the present status.

8.4.1 Ionic Channels Research

Molecular Devices, Axon Instruments, Sophion Bioscience, Cytocentrics, and Nan-ion Technologies have already launched their planar patch clamp systems separately. In each case, the planar patch clamp systems is combined with high throughput, the automatic formation of high seal resistance, voltage and current clamps, whole cell and single channel recordings, drug perfusion, and data analysis.

The novel automated patch clamp chip system, such as IonWork HT and PatchXpress 7000A, can perform high throughput screening and reduce labor in-tensity. These instruments are the platform for drug or ion screening based on the planar array chip. IonWork HT is the fi rst planar patch clamp system to realize whole cell patch clamp automatically. Under the bottom of every micro aperture of

8.4 Biomedical Application 195

the 16-384 patchplate, there is a recording electrode connected with an amplifi er. It is potentially powerful for parallel and high throughput screening—greatly dif-ferent from the conventional patch clamp, which can fi nish just one cell recording once. The human genome project has already identifi ed 300 ionic channel genes. The high throughput electrophysiological method is expected to speed up the re-search of new ionic channels.

Nanion Technologies launched a miniature product, the NPC1 Port-a-Patch, adopting the fabrication methods of Fertig as mentioned in Section 8.3.1 and shown in Figure 8.9. It needs semi-automated operation for each cell. The chip substrate is made of glass, which is glued onto a small cap, as shown in Figure 8.9(a). Cell suspension is added onto the chip manually, which is then mounted onto the instru-ment shown in Figure 8.9(b). The whole planar patch clamp electrophysiological recording setup is shown in Figure 8.9(c). High resistance seal can be formed under software control. The seal resistance is larger than 1 GΩ. Chip resistance is 2–5 MΩ, while the chip capacitance is less than 10 pF [24].

Stable, low-noise, and long-term whole-cell recordings can be obtained in Port-a-Patch. The throughput reaches 50 data points per day (8 hours). The throughput of the multichannel NPC16s “sequential” system reaches up to 200 data points, while the NPC16p “parallel” can get up to 2,000 data points, which will promote the work effi ciency greatly. With this system, whole cell voltage-clamp recordings were performed on HEK cells expressing rNav1.2a. When sodium channels were blocked with TTX (tetrodotoxin), IC50 was 14.9±5.3 nM (12 nM in other litera-ture), demonstrating its feasibility in fast-gated channels [24, 25]. When recording the endogenous potassium channels (hKv1.3) of Jurkat cells, fi ve concentrations of quinidine were applied. The whole-cell recordings were shown in Figure 8.10(a). The Hill fi t is shown in Figure 8.10(b) with IC50=7.8±3.8 μM (www.nanion.de).

Figure 8.9 Port-a-Patch system (www.nanion.de). (a) The chip is glued onto small caps, which are mounted in the Port-a-Patch system in (b). (b) The chip-mounting station (in front) and suction control unit (in black). (c) The whole Port-a-Patch setup consisting of the chip holder and suction control, an HEKA EPC-10 amplifi er and a computer.


The formation of a high seal resistance also assured its low noise recording and application in single channel recordings. Holding the membrane patch on a con-stant voltage, the single channel activities can be easily identifi ed [26]. Bruggemann et al. performed cell-attached mode on CHO cells expressing BK channels under various holding potential, like 60 mV, 40 mV, and 0 mV. BK channels had differ-ent open and close probabilities. The recording was also performed on primary cultured erythrocytes [26].

The recordings of single channel activities formed by the peptailbol alamethi-cin, which is constructed in bilayers from giant unilamellar vesicles (GUVs), were realized by Sondermann et al. using the Port-a-Patch system [27]. Lipid bilayers were positioned onto the chip aperture by application of a negative pressure. The typical pattern of different nonequidistant conductance states of a single alamethi-cin channel can be clearly resolved. The histogram of all current points from the data of 220 rums can be obtained. The nonconducting state is denoted by “C,” while the conducting single channels states are denoted by O1–6. The total noise current was 5 pA (rms).

On the other hand, compared with the conventional patch clamp, a potential advantage of the patch clamp chip system is the easy exchange of intracellular solu-tions. A kind of potassium channel, which can be blocked by cesium ions internally, is expressed in Jurkat cells. In control conditions with no Cs in the intracellular solution, potassium currents were recorded. After the exchange of pipette solutions with Cs containing solution, no potassium currents can be recorded. Then, after washout of the Cs solution, the potassium currents were obtained again [26].

The Ionworks Quattro system by Molecular Devices adopted the population patch clamp technology to make multicell recording simultaneously possible [28]. The principle is shown in Figure 8.11(a). Multicells are voltage clamped in the mean time. A normal patch clamp amplifi er is used to record the ensemble cur-rents from all the clamped cells. The substrate [Figure 8.11(b)] consists of multiple wells, which contain multiple microapertures. The intracellular chamber is com-mon to all the wells. Approximately 7,000–10,000 cells are added to each well. This system can be used in the fast analysis of the structure and function of massive mutant ionic channel. Using 32 apertures, 384 wells patch clamp, it takes 1 hour to perform the measurement of 12 kinds of clones. The throughput reaches 3,000

Figure 8.10 Whole-cell patch clamp recordings on Jurkat cells of the endogenous hKv1.3 potas-sium channels. (a) The recordings upon fi ve different concentrations of quinidine. (b) The inhibition effi ciency of quinidine (www.nanion.de).


data points in one day. As a benefi t of the consistency of the recorded currents, the dynamics of massive mutant ionic channels can be obtained. Using an 8-microap-erture patch plate for recordings 1 hour, the measurements of 48 different mutant heterogeneities can be achieved.

Whole-cell recordings of noninactivated channels hKv1.5 from conventional patch clamp [Figure 8.11(c)] are compared with those from IonWorks HT [Figure 8.11(d)] [29]. In voltage clamp mode, the holing potential is set to –70 mV. The depolarization pulses are applied as shown in the lower panel of Figure 8.11(d). I-V curves from both systems are shown in Figure 8.11(e). Meanwhile, 4-AP, a kind of block reagent of the potassium channels, is applied to hKv1.5 expressed in CHO cells. The effect of 4-AP on hKv1.5 can also be studied using IonWorks HT com-pared with the conventional patch clamp. The results from the patch clamp chip system are reliable and reproducible with almost equivalent IC50 values.

High throughput screening (HTS) technology was developed in the 1990s and is widely used in drug discovery and drug screening. Because of the inherent fast

Figure 8.11 Population planar patch clamp technique (www.moleculardevices.com). (a) The prin-ciple of the population patch clamp, where n denotes the number of apertures. (b) The patch plate. The comparison of the whole-cell recordings from CHO cells expressed hKv1.5 potassium channels using a (c) conventional patch clamp, (d) IonWorks HT system, and (e) peak current at each test potential. (From: [29]. Reproduced from Journal of Biomolecular Screening. © 2003 by Society for Biomolecular Sciences.)


and highly effi cient features of this technology, it has attracted attention from most of the international medicine research organizations. In recent decades, it has be-come accepted and used as the major technology in drug discovery.

High-throughput drug screening technology combines the computer control, automated operation, high sensitivity measurement, and automatic data acquisi-tion. Based on the molecular and cellular tests, the microplate is used as the ex-periment tool, which can be operated automatically. The data from thousands of samples can be measured simultaneously. The corresponding database supports the whole process running normally. There are fi ve parts in high-throughput screening: (1) plenty of samples; (2) specifi c screening models on the level of molecular and cellular in vivo and in vitro; (3) data acquisition and processes; (4) high sensitivity; and (5) automated operation. In addition, computer-aided design (CAD), combi-natorial chemistry, high effi ciency, and natural compounds extraction methods can be used. Aland, a British scholar, suggested that in a lab using the conventional method and 20 kinds of drug targets, 75,000 samples can be fi nished screening in a year. However, in 1997, at the initial stage of the development of high-throughput screening, 100,000 samples could be screened every day, using about 100 drug targets. In 1999, due to the improvement of this technology, 100,000 compounds could be screened every day. However, nowadays, high-throughput screening main-ly performs the initial fast screening with large amount of samples but with low information content.

Currently, the ionic screening methods are as follows:

Electrophysiology experiments (voltage clamp and patch clamp)1. : The in-formation content is high but with low throughput. Patch clamp is the gold standard for direct measurement of the ion channel properties and pharmacology. Nevertheless, the low throughput limits its application for drug discovery and drug safety.Ligand biding assay2. : The information content is low but with high through-put. This is a kind of indirect measurement of ion channel activities, but not a functional assay.Membrane potential assay3. : This includes voltage ion probe reader (VIPR) and fl uorescent imaging plate reader (FLIPR). Both of these have low-medium information content but with high throughput. They are indirect measurements of ion channel activities. However, the disadvantages are that the voltage is not controlled with low sensitivity. It takes a long time to fi nish the whole process.Ion fl ux assay4. : 86Rb, 45Ca, fl uo-3/4, fura-2 are used. This method has low information content with high throughput. The disadvantages are that the voltage is not controllable and the sensitivity is low.

The ideal drug screening method should be sensitive, real-time, and linear for the measurement of ionic channels. The data should not be contaminated by the other ionic channels. It should be high throughput, simple in technique, highly re-producible, and cheap. Currently, planar patch clamp systems based on array chips have been applied in drug screening and made delightful progress.


8.4.2 Drug Discovery

Drug research is closely related to human health. The discovery stage of a novel drug includes four aspects: elucidation of the molecular and cellular mechanism of disease prevention and treatment, drug target, discovery, and optimization of the molecular structure of the leading compound. The basis of the research and exploi-tation of a novel drug is to choose an appropriate screening model to fi nd some featured drugs with excellent effects. To discover new drugs based on optimization, we need to modify the known structure. Then, massive compounds having differ-ent structures, but with the same mother nucleus, can be obtained. Comparison and screening are employed based on pharmacology method. Finally, drugs with the best effects are obtained. In addition, some drugs with known pharmacological effects can be extracted and screened. During this process, the more the compounds are synthesized and separated, the greater the opportunity to fi nd a novel excellent drug. For some drugs with known function mechanisms, screening methods should fi rst be constructed. Then, the active screening should be performed on different kinds of compounds. It might be possible to obtain better compounds and novel drugs.

The function of the drug is testifi ed through its effects with the target molecules in vivo. According to the statistics, there are 483 effective targets of the therapeu-tic drugs. With the research development of human genome and protein, lots of genes that are related to the diseases will be found. It is predicted that until 2010, the drug effects targets will increase to 500 kinds. Among the known drug targets, ionic channels take 5%. However, genomics and proteomics research indicate that ionic channels bear variety and complexity. In around 5,000 potential drug-effect targets, ionic channels should take 15%. Therefore, the breakthrough of the ionic channel drug screening is a step that should be leaped over.

In drug discovery, the measurement of the ionic channels is limited to the con-ventional patch clamp or the single aperture planar patch clamp. The problems are that the diversity of the ionic channel expressing leads to the inconsistency of the current data, and the low resistance seal leads to the discarding of data points. The planar patch clamp based on the array chip can solve these problems, with the IonWorks Quattro system for an example. The signifi cant advantage is the consist-ency in the compound screening. It does not require repetitive sampling; however, it can screen most compounds in the database reliably. It is also benefi cial to study the pharmacological quality of the compounds.

The inhibitory effect of the drug tedisamil on hKv channels were studied us-ing Ionworks HT with IC50 73±7 μM. The throughput can reach 2,000–3,000 patchplate in one day. Some 200–400 compounds with two or three different con-centrations can be screened. The pharmacological research can reach up to 40 kinds of drugs with 10 dose-dependent points. For conventional patch clamps, the throughput is about 15–25 cells. Some 5–10 compounds with one or two different concentrations can be screened. One kind of drug with 3–5 dose-dependent points can be obtained. Therefore, the work effi ciency has been greatly improved.

PatchXpress (Axon Instruments) is a 16-channel planar patch clamp system. It can be used in high-throughput ionic channel screening. When performing hERG channels screening, the throughput is improved by fourfold, reaching 2,000 data


points per day. Tao et al. studied the effects of small molecules on hERG channels on the platform of PatchXpress [30]. Reliable data points can be obtained. With its high throughput, it can satisfy the drug test.

Qpatch 16 (Sophion Bioscience) is a 16-channel automated planar patch clamp screening system. The chip includes the electrode array based on silicon and mi-crofl uidic channels based on glass (www.sophion.dk/Qpatch/qplate). The drug ex-change can be performed more than 4 hours with no operator. When performing whole-cell recordings on hERG channels expressed in CHO cells, the throughput can reach 250–1,200 data points. The successful rate of GΩ seal and more than 4 hours whole cell recording is 80%. This system has been applied in the screening of potassium channel antagonists.

Kutchinsky et al. screened the hERG and KCNQ4 channels expressed in CHO and HEK cells, respectively, based on the Qpatch 16 [31]. The successful rate of gi-ga-ohm seal is 40%–90%. About 67% of cells can sustain whole-cell recordings for more than 20 minutes. The blocking effects of iberiotoxin (the block reagent for BK channels) and linopirdine (a KCNQ channel block reagent) on KCNQ4 are tested. The data points denote the activated steady-state currents at 40 mV. It can be found that 0.1-μM IBTX has no effects on KCNQ4 currents. However, 10-μM linpoirdine can block this channel effectively. The IC50 (1.7±0.7 μM) is a bit higher than that in other literature. Three kinds of compounds, NS1619, IBTX, and verapamil were used for screening of hERG channels expressed in CHO cells. It can be obtained that verapamil has obvious inhibitory effect, while NS1619 and IBTX do not.

The blocking effects of verapamil and rBeKm-1 on hERG potassium channels expressed in CHO cells has been studied [31]. Holding potential is –90 mV. Depo-larization pulses are stepped to 10 mV, then hyperpolarized to –120 mV. The data from Qpatch 16 are compared with conventional patch clamps under six different concentrations of verapamil. The IC50 is comparable (0.44 and 0.45, respectively). The IC50 of rBeKm-1 is 0.94 nM, which is close to the value reported in other literature. In order to improve the throughput, Qpatch 96 is designed, which has 96 parallel channels. The throughput can get to 1,500–7,000 data points. Asimd et al. fi nished a compound database screening automatically based on the platform of Qpatch 96.

The effects of eight known hERG blockers, including asterrizde, cisapride, pimozide, fl unarizine, quinidine, terfenadine, dofeide, and imipramine were tested using IonWorks Quattro from IonWorks. The channel activities upon the blockers are shown in Figure 8.12. The data from PatchPlate with single hole and PatchPlate PPC substrate were comparable (www.moleculardevices.com).

8.4.3 Drug Safety

The purpose of drug safety is to study the adverse effect of some drugs on the physi-ological function. It mainly focuses on adverse effects on the cardiovascular system, respiratory system, and nervous system. For the evaluation of the cardiovascular system, blood pressure, rhythm, electrocardiogram, cardiac output, ventricular contractibility, vascular resistance, and the response of the cardiovascular system are tested. For the evaluation of the respiratory system, some parameters such as tidal volume, compliance, and pulmonary artery pressure should be studied. For the


nervous system, the evaluation includes motor activity, behavior changes, sense/mo-tor refl ex response, body temperature, learning rate and memory, neurochemistry, and vision and hearing.

According to a report from the Journal of American Pharmacists Association in 2001, the incidence and death rates related to drugs take more than $177 billion in 2000 in the United States. Hospitalization expense takes about 70% of the total expenditure. According to statistics, during 1980–2000, about 25 kinds of drugs were withdrawn from the market. The reasons included liver intoxication, hyper-sensitive response, drug interaction, stroke, anemia, hypoglycemia, and prolonged QT wave.

For example, the inhibition of drugs on hERG channels leads to arrhythmia, prolonged QT wave, and the duration of action potential of cardiac cell prolong-ing. Since late 1990, some drugs that lead to a prolonged QT wave had already been withdrawn. IKr and IKs, which are the fast and slow components of potassium currents, respectively, have great effects on the duration of AP and QT wave inter-val. The mechanism of prolonged QT wave is owing to the inhibition of delayed rectifi er potassium channel. The Committee for Proprietary Medicinal Products (CPMP) suggests studying the drug effects on hERG channels in a nonclinical way, like with cells. The International Committee on Harmonization (ICH) demands that drugs on the market should be screened and tested on hERG channels.

With the exploitation of the new technologies, drug safety can be evaluated by fl ow cytometry (FC), laser scanning confocal microscopy (LSCM), laser capture microdissection technology, and biochips. Benefi ting from the high throughput and high information content, planar patch clamp technology will play an important role in drug safety. In the following description, we will take the drug safety on hERG channels as an example and introduce the application of planar patch clamp in drug safety.

Some cardiac drugs with inhibitory effects on hERG channels are as follows:

Cisapride has antiemetic effects and treats gastrointestinal motility disorders. •However, it has serious cardiovascular side effects, which leads to arrhythmia and sudden death.

Figure 8.12 The effects of known blockers on hERG channels using IonWorks Quattro (www.mo-leculardevices.com). Comparison of the data from PatchPlate with (a) a single hole and (b) the PatchPlate PPC substrate.


d-Sotalol, the blocking reagent for • β adremaline, is used in arrhythmia and arterial hypertension treatment. It will increase the death rate after myocar-dial infarction.

Terfenadine is a kind of antihistamine. If it is not used appropriately, it will •initiate heart abnormality.

Quinidine is a drug for arrhythmia treatment. The side effect are the initia- •tion of hypotension, inhibition of myocardial contraction, and serious ven-tricular arrhythmia.

Imipramine is an antidepressant. The side effects will lead to nervous disor- •der, myocardial damage, and so on.

MK499 is a drug for arrhythmia treatment. •

The IonWorks HT planar patch clamp system was applied for the screening of hERG channels expressed in CHO cells using quinidine [32]. When increasing the concentration of quinidine, the hERG currents were inhibited. Drugs such as cisapride, MK499, imipramine, and d-Sotalol on hERG channels were also stud-ied. The results were compared with those of conventional patch clamps. The IC50 values are similar. However, in order to obtain these values, conventional patch clamps take 160 hours. While using IonWorks HT, it just takes half an hour, which greatly improves the effi ciency.


The high throughput of multichannel patch clamp chips will accelerate the develop-ment of drug discovery and drug safety. Another promising application will be in basic scientifi c research.

In vivo, neural networks are formed by millions of synaptically connected neu-rons. The ion channel activities in the synaptically connected cells are still not well known. Patch clamp array chips should be a new tool in this fi eld. The cells should be guided to position on the micro apertures and form cell networks (for more details, see Chapter 2).

Once a cell was attached to the microaperture to form the network, negative pressure was applied to make a giga-ohm seal. Then, the cell-attach confi guration and whole-cell confi guration can be obtained [33]. The single-channel activities and whole-cell currents can both be recorded in the neuronal network. The design of this concept is shown in Figure 8.13.

Figure 8.13 Schematic of the synaptically connected neuronal networks on patch clamp chip.

8.6 Summary 203

Until now, the planar patch clamp has not been applied to neural networks practically, but efforts have recently been made in this area. Mealing et al. [34] tried PDMS substrate–based patch clamp array chips, which permitted cell-attached and whole-cell confi guration recording. They also solved the problems of cell network growth on substrate and seal formation between cells and microapetures [35]. The patch clamp chip overcomes the spatial problems of the conventional patch clamp, which makes the neuron genesis, synaptic transmission, and cell differentiation possible and promising. Moreover, the investigation of molecules on neural net-work formation and long-term modulation will be feasible.

As for the instrument itself, miniaturization, portability, automation, and high-er throughput are important and should be improved potentially.

8.6 Summary

The emergence of a planar patch clamp technology makes the highly parallel and automatic electrophysiology recording of ionic channels possible. This novel chip can record many cells simultaneously and can be combined with multiple measure-ment methods easily. Planar patch clamp technology will be a potential and effec-tive approach in the study of ion channels and drug discovery. However, at present, the successful rate of whole-cell confi guration is about 60%–70%. Much higher throughput is needed to satisfy applications in drug discovery and screening. Now clear cell suspension is required and cell line is mainly used. More improvements on the successful rate and primary cells recordings are needed.

References

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8.6 Summary 205

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207

C H A P T E R 9

Other Cell-Based BiosensorsQingjun Liu, Chunsheng Wu, and Ping Wang

9.1 Quartz Crystal Microbalance (QCM) as Cell-Based Biosensors

9.1.1 Introduction

The quartz crystal microbalance (QCM) is a very sensitive sensor to detect mass changes based on the piezoelectric effect, which was discovered by the Curie broth-ers in 1880 [1]. In the 1950s, Sauerbrey fi rst revealed the linear relationship be-tween the mass changes at the QCM surface and the oscillation frequency changes in quartz crystal, namely the Sauerbrey equation, and later demonstrated the ex-tremely sensitive nature of QCM toward mass changes on its surface [2]. For many years, QCMs were regarded as just gas-phase detectors—until the 1980s, when Nomura and Okuhara designed oscillator circuits that could apply this sensor not only in a vacuum or air, but also in liquids [3]. This opened a new world for QCM systems applications—it helped to make the QCM a useful tool in the fi eld of bio-technology and biosensors.

QCM has many advantages over the traditional sensing methods, such as high sensitivity (up to nanograms level), noninvasiveness, long-term measurements, and free labels. And it has a wide detection range from a monolayer of small molecules to much greater masses—even complex arrays of whole cells.

Over the past few decades, QCM has been well applied to develop cell-based biosensors as secondary sensors to deliver functional information of cells (e.g., the pharmaceutical effect of an analyte on a living cell, which is often critical in drug discovery and drug analysis). Some properties of cultured cells had been success-fully monitored with QCM, such as the cell attachment, proliferation, and cell-substrate interaction under different conditions. Additionally, QCM can measure the exocytosis, vesicle retrieval of cell populations, and cell-microparticle interac-tion in real time.

Compared to conventional techniques, a cell-based sensing process can provide detailed physiological information of cells, biomedical applications of these cell-based biosensors in cellular analysis, the possibility of characterizing cell adhesion processes in situ without labels, and more details of the initial cellular adhesion

208 Other Cell-Based Biosensors

steps. So, QCM as a cell-based biosensor will be a more attractive and promising analytical technique and can be widely applied in the fi eld of biomedicine.

9.1.2 Principle of QCM

QCM is a very sensitive mass measuring device as its oscillation frequency decreases while the mass is bound on the crystal surface (Figure 9.1).

The basic principle of QCM is that tiny mass changes can cause pressure chang-es on the crystal surface and subsequently lead to the oscillation frequency shifts of crystal. An increase of mass bound to the quartz crystal surface causes a decrease of oscillation frequency. According to it, QCM can respond to the mass changes of coated material on the crystal surface. Applying a DC voltage leads to the genera-tion of a material wave with a certain oscillation frequency propagating in the me-dium above the quartz disc. In liquids, the wavelength of material wave decreases to approximately 250 nm. If rigid masses are bound tightly to the resonator and in an ideal case on the assumption that the material properties of quartz and ad-sorbed masses are equal, the wavelength of material wave will increase to satisfy the resonance conditions. However, the velocity of material wave propagating in the same medium remains the same; therefore, the oscillation frequency decreases.

Figure 9.1 (a) Typical QCM device. (b) Scheme of a flow cell for piezoelectric crystals in liquid media, including the oscillator circuit and frequency counter. (c) Impedance analysis is based on conductance curve. The central parameters of measurement are the oscillation frequency fres and the bandwidth w. The variation in quartz crystal oscillation frequency is a function of time. (d) (f1) is natural oscillation frequency, (f2) is oscillation frequency after analyte addition, and masses bound to QCM induces frequency change (Δf ).

9.1 Quartz Crystal Microbalance (QCM) as Cell-Based Biosensors 209

The relationship between oscillation frequency change and the mass bound to the crystal surface can be described by the Sauerbrey equation [2]:

2 2

0 02 2

q q q q

mf ff m

A Aρ μ ρ μ

−Δ = = − Δ (9.1)

where

Δf = frequency change (Hz);

Δm = mass change (g);

f0 = national oscillation frequency (Hz);

A = piezoelectrically active crystal area (area between QCM electrodes, m2);

ρq = density of quartz (ρq = 2.648 g/cm3);

μq = shear modulus of quartz for AT-cut crystal (μq = 2.947 × 1,011 g/cm·s2);

νq = transverse wave velocity in quartz (m/s).

According to the Sauerbrey equation, the oscillation frequency decrease is pro-portional to the mass changes. Generally, a resolution of 0.1 μg can be achieved by the commercial available QCM device; therefore, sometimes QCM is termed the quartz crystal nanobalance.

The relation of frequency shift and mass adsorption makes the QCM a valu-able tool for characterizing adsorption reactions, the covalent adsorption of pep-tides and proteins onto surfaces, the evolution of material fi lms (including indirect thickness measurements), and the determination of ligand-receptor interactions ki-netics. However, the Sauerbray equation is only valid for small elastic masses added to the crystal surface. If the masses are greater than about 2% of the crystal mass, the equation becomes inaccurate.

In the case of QCM as cell-based biosensors, the living cells do not behave as elastic masses on the QCM surface, so the Sauerbray equation does not apply (Fig-ure 9.2). Here, how to calculate corresponding mass binding quantities based on decreases observed in the crystal oscillation frequency magnitude becomes a prob-lem. In the case of liquid-phase measurements, QCM can provide more valuable information about reactions and conditions at the liquid-solid interface than about mass changes on the crystal surface. The resonant resistance (R) of quartz crystal combined with the oscillation frequency (f) is also used to characterize the viscoe-lastic properties of deposited inelastic masses on the surface of the crystal, whose values are affected by the medium density and viscosity. The f decrease is due to both mass binding and viscoelastic energy dissipation behavior while R increases. When solution and crystal surface contact tightly without relative sliding, the oscil-lation frequency shift can be quantitatively calculated by the Kanazawa equation [4]. The equation is as follows:

13 22 L

q

f fρ η

πμρ

⎛ ⎞Δ = − ⎜ ⎟

⎝ ⎠ (9.2)


where ρL is the density of liquid and η is the viscosity of liquid. The resonant resistance change can be quantitatively calculated by the Muram-

atsu equation [5]. The equation is as follows:

( )12

22 L

AR f

kπ ρ ηΔ = (9.3)

where k is the electromechanical coupling factor. When QCM operates in solution, the total f changes may result from a bound mass as well as the solution contribu-tion. Since both Δf and ΔR are proportional to (ρLη)1/2, the Δf − ΔR plot is a straight line for liquids with different density and viscosity.

The behaviors of specifi c systems under investigation can be graphically ana-lyzed by the Δf − ΔR plot. The viscoelastic properties and the energy-dissipating properties of surface with masses attached, such as living cells, can be evaluated relative to density-viscosity changes of a liquid solution. According to these equa-tions, the QCM technique has the sensitive quantitative capability to investigate the behavior of adherent cells in response to chemical, biological, or physical changes in the environment. Because of simplicity, low cost, high sensitivity, rapid response, and real-time dynamic detection, the QCM technique seems to be an ideal and powerful technique for the aim of characterizing some important properties of liv-ing cells. In particular, the properties related to the mass and viscoelasticity of cells can be studied well by the QCM technique.

9.1.3 QCM Sensors and Measurement System

The signal transduction mechanism of the QCM technique relies on the piezoelec-tric effect in quartz crystal. When an alternating electric fi eld is applied to the quartz crystal through upper and lower mental electrodes covering the quartz surface, a mechanical oscillation of characteristic frequency is produced in the crystal. In com-mercial QCM applications, most QCM devices are operated in the range of 5–10 MHz, and the QCM technique relies on circular quartz crystals operating in the thickness shear mode of oscillation.

The quartz crystal is a few tenths of a millimeter in thickness and cut in the AT form, which can provide a stable oscillation with almost no temperature fl uctuation

Figure 9.2 The process of cells attachment and spreading onto a quartz crystal surface. The oscilla-tion frequency shift and energy dissipation, which are due to mass changes and medium viscoelastic properties, give quantitative information about the cell attachment process.


in frequency at room temperature. The electrodes of QCM can be deposited with metals on the upper and lower quartz surfaces such as Au and Pt. The electrodes are then connected to an oscillator circuit by wire leads attached to the electrodes. Typically, QCM works at continuous resonance mode and the relative shift in crys-tal frequency is measured. Many QCM devices are commercially provided by the vendors such as Elchema, EG&G, QCM Research, Maxtek, and Universal Sensors [6, 7].

For example, commercially available Q-Sense instruments—tracking changes on the surface—are based on the patented QCM-D technique (http://www.q-sense.com). The instruments measure the mass of molecular layers that are deposited on the quartz sensor in real time. Simultaneously, the viscoelastic properties of such molecular layers are monitored, which enables distinction between two similar in-teractions and observation of a phase transition in deposited layers. Measurements of both mass and viscoelastic properties give a thorough understanding of binding events such as molecular adsorption and interaction. Their newly designed four-sensor systems could acquire measurement data within a second with preserved sensitivity. The chamber has four removable fl ow modules, each holding one sen-sor. The fl ow modules can be used in any serial or parallel confi guration to suit dif-ferent measurement needs. In addition, compared to optical mass measurements, the QCM-D technique makes it possible to extract the water content of molecular layers and allows several kinds of substrates such as metals, polymers, and func-tionalized coatings to control the studies of interactions.

To apply QCM as cell-based biosensors, a cell culture incubator must be in-tegrated into the QCM device to provide an appropriate environment for the cell culture. The cell-culture medium or liquid samples can be delivered directly to the QCM electrode surface via syringe, micropipette, or fl ow injection analysis systems.

9.1.4 Biomedical Application

9.1.4.1 Drug Discovery and Drug Analysis

The traditional process of drug discovery and drug analysis is a time-consuming, complex, and multistage course with low success probability and high cost. The main reason may be the lack of powerful high-throughput or information-rich content screening tools. This is also the major driving force for the pharmaceuti-cal industry to create novel measurement techniques to evaluate the effect of drug compounds on specifi c pharmaceutical targets and ultimately improve the overall effi ciency of drug discovery and development.

The emergence of biosensors may provide some novel tools for monitoring the interactions between drug compounds and biological components. One of the most promising biosensors is the QCM biosensors integrated with living cells as sensitive elements, which have been used in a number of studies for drug discovery and drug analysis. The whole cell–based biosensors are suitable for drug detec-tion because they can detect the magnitude and kinetics of a whole-cell biological response, which is much closer to the type of information pharmaceutical com-panies require as drug testing output. The cell-based QCM biosensors can also


provide information on changes in the viscosity properties of cells in addition to mass changes. Furthermore, QCM is more sensitive to changes than optical sensors in fl uid environment.

One recent interesting example concerns the use of living cells attached to the QCM for sensing the actions of nocodazole, which is a known microtubule-binding drug that can alter the cytoskeletal properties of living cells. Marx et al. developed a cell-based biosensor on the QCM platform to detect the effects of varying concen-trations of nocodazole, in which sensitive elements were the living endothelial cells (ECs) adhering to the gold QCM surface under in vitro growth conditions [8].

Figure 9.3 shows the schematics of the entire cell QCM biosensor measure-ment system, EC QCM biosensor quartz surface and signal transduction pathway elements, and microtubules interacting with nocodazole destabilized to monomers, and those with taxol are hyperstabilized. By the measurement of shift values in the QCM steady state frequency (Δf) and motional resistance (ΔR), the cell-based QCM biosensor can monitor the process of cell microtubules disruption by the addition of nocodazole. Figure 9.3(d) shows time-dependent behavior of the EC QCM biosensor in response to either: 6 μM nocodazole or 10 μM taxol. The re-sponses of cell-based QCM biosensors to nocodazole in Δf is quite different from that of taxol, which has a microtubule hyperstabilization effect. It is indicated that the cell-based QCM biosensor can be used to detect and study cytoskeleton altera-tions and dynamics. The cell-based QCM biosensor holds the potential of real-time identifi cation or screening of biologically active drug compounds that affect cel-lular attachment and cellular spreading, regardless of their molecular mechanism of action.

Likewise, the cell-based QCM biosensor was proven to be useful for real-time screening of cell lines and their sensitivity to anticancer drugs. It can be used to reli-ably predict tumor responses to drugs prior to therapy or before resistance or hy-persensitivity develops. For example, taxanes are fi rst- and second-line chemothera-peutics for cancers. In the course of treatment, many patients develop resistance or hypersensitivity to some form of taxanes and require a different form of taxane to regain the therapeutic benefi t. Braunhut et al. used QCM to study responses of hu-man mammary epithelial tumor cells to taxanes [9]. After the achievement of stable frequency and resistance levels, cells in the QCM were treated with taxanes. The cell responses monitored in real time via frequency and resistance changes refl ect alterations of cell mass distribution and viscoelastic properties. Distinct shifts in frequency and resistance accurately predicted apoptosis or resistance to treatment. QCM analysis accurately predicted which cell line is more resistant to taxanes. It is indicated that cell-based QCM biosensors can also be used to predict therapy outcome prior to treatment for initial therapy or to rescue therapy effi cacy.

9.1.4.2 Cell Adhesion Monitoring

Measurement of cell adhesion to a surface typically is performed by enumerating the cells that are attached or that are in suspension during the time course of the ex-periment [10] or by microscopic observation. The traditional methods to evaluate the cytopathic effects are generally using microscopic observation or chemical assay by the introduction of a cytopathic agent to adherent cells. However, these methods


are often time consuming and tedious. Since QCM measurement is allowed to per-form directly in liquid media, the QCM technique has become an attractive method for the study of surface adherent cells due to its mass- and viscoelasticity-sensing features. Actually, QCM can monitor whole cells that are bound to the electrode surface of the QCM. The cell-based QCM biosensors may represent one effec-tive functional biosensing approach for the detection or monitoring of medically relevant cell behaviors under certain biological conditions. The QCM responses are sensitive to very small amounts (a few hundreds) of cells and highly specifi c

Figure 9.3 Schematics of (a) the entire cell QCM biosensor measurement system, (b) EC QCM biosensor quartz surface and signal transduction pathway elements, and (c) microtubules interact-ing with nocodazole destabilized to monomers, and those with taxol are hyperstabilized. (d) Time-dependent behavior of the EC QCM biosensor (20,000 ECs) in response to either: 6 μM nocodazole or 10 μM taxol (at arrowhead). (From: [8]. Reproduced from Analytical and Biochemistry. © 2007, with permission from Elsevier B.V.)


to surface chemical properties. In a cell-based QCM biosensor, cells can interact with QCM directly, and the sensor itself has a capability to monitor cell-pertinent interfacial processes such as attachment, contraction, and proliferation in real time. Due to its characteristics of no-invasion, sensitivity, and time resolution, the QCM is also a powerful tool for quantitatively studying various aspects of cell-substrate interactions. A number of surface-adherent cell types have been investigated, which include human patellar tendon fi broblasts (HPTFs), epithelial cells, endothelial cells, NIH 3T3-EGFP fi broblasts, lung melanoma cells (B16F10), and MCF-7 cells.

The research of Wegener and his colleagues have provided deep insight into the underlying mechanisms that determine the QCM signal for a particular cell type [11]. Different experimental approaches were designed to explore the particu-lar contributions of various subcellular compartments to the overall QCM signal. The signal contributions of many subcellular compartments were explored by the use of AC impedance analysis in a frequency range, which closely embraces the resonators’ fundamental frequency, including the extracellular matrix, the actin cytoskeleton, the medium that overlays the cell layer, as well as the liquid compart-ment that is known to exist between the basal plasma membrane and the culture substrate. It is indicated that the QCM technique is only sensitive to those parts of the cellular body that are involved in cell substrate adhesion and are therefore close to the resonator surface.

In a recent research, a cell-based QCM biosensor was developed to monitor the adhesion and growth of rat epithelial cells (WB F344) and lung melanoma cells (B16F10) on the gold surface of the QCM in real time [12]. This biosensor can continuously monitor the process of cell attachment and spreading on the gold sur-face by measuring changes in the oscillation frequency Δf and resistance ΔR values of the piezoelectric resonators. The initial phase of cell attachment and spreading induced a decrease of frequency and an increase of resistance relating to the viscoe-lastic properties of the cell monolayer on the sensing surface. The steady state of both shifts can be achieved after a few hours. Compared to the results obtained by fl uorescent microscopy, it is indicated that the cell-based QCM biosensor is suitable for monitoring the cell adhesion processes.

QCM coated with indium-tin oxide (ITO) has also been used to develop cell-based biosensors for monitoring the behavior of human patellar tendon fi broblasts (HPTFs) [13]. One prominent advantage of using transparent ITO as resonator elec-trodes is that it enables the combination of the device with a microscope to form a sensor platform. The time-dependent cell behavioral information was achieved by monitoring the characteristic frequency shift Δf and the motional resistance change ΔR in real time. The HPTFs morphology was also monitored simultaneously to correlate with the quantitative sensor responses. The study implies that the cell-based QCM biosensor system with a combination of appropriate biocompatible interface, living cells, mini-incubator, and optical microscopy may provide a fast yet quantitative functional biosensing approach for cell behaviors under controlled biological conditions in real time.

A QCM whose gold electrode surface was modifi ed with polyporphyrin fi lm has been applied to the development of cell-based QCM biosensors for real-time


monitoring of MCF-7 cell growth and assessment of chemical cytotoxicity [14]. The polymerized fi lm on the gold surface can be removed completely and easily, which greatly improves the reproducibility of the quartz crystal gold electrode. The proliferation and responses of cells to chemical cytotoxicity were effi ciently moni-tored by QCM and confi rmed by fl uorescence microscopic observation, which were consistent with each other. Additionally, by combining with impedance analysis, the cell-based QCM biosensors can be a completely new approach for determining electrical and viscoelastic properties of the epithelial and endothelial cell monolay-ers that form controlled barriers in vivo [15]. It is also indicated that cell-based QCM biosensors have potential for identifi cation and screening of biologically ac-tive drugs and other biomolecules affecting cellular shape and attachment.

QCM with dissipation (QCM-D) is a novel and attractive technique for in vitro real-time characterization of cell attachment and spreading on surfaces, which is commercially provided by Q-Sense AB (Sweden). It can monitor cell adhesion and spreading, as it allows in situ real-time measurements. There has been increasing interest in using the QCM-D technique for cell-monitoring applications. This tech-nique can simultaneously measure the oscillation frequency, f, and the dissipation energy, D, of the oscillating system. The combined measurement of f and D by the QCM-D technique may provide more useful information about cell-surface interactions for biomaterials. Lord et al. investigated the potential of the QCM-D technique to characterize the initial adhesion and spreading of cells in contact with protein precoated biocompatible surfaces [16]. The QCM-D was applied to moni-tor the adhesion of NIH 3T3-EGFP fi broblasts to tantalum (Ta) and oxidized poly-styrene (PSox) surfaces precoated with serum proteins. The QCM-D measurements shows that cells can adhere and spread on the fi bronectin- and serum-coated sur-faces, while hardly do so on the albumin-coated surfaces. Time-lapse photography shows that cells adhered to albumin-coated surfaces show round morphology. The response differences between fi bronectin- and serum-precoated surfaces indicate that the process of cell adhesion and spreading elicits different responses result-ing from both the protein coating composition and the infl uence of the underlying substrate. The different responses may be due to extracellular matrix remodeling as well as cytoskeletal changes. Frequency (f) and dissipation (D) changes associated with cell adhesion may provide more detailed information on the process of cell adhesion and the viscoelastic properties of the cells.

9.1.4.3 Measurement of Exocytosis and Vesicle Retrieval of Cell Populations

In cell biology, it is a very important issue to explore the molecular physiology and mechanisms of vesicular release (exocytosis) and retrieval machinery (endocytosis). The detailed mechanisms of exocytosis and vesicle recycling may contribute to un-derstanding many biological processes such as cell-to-cell communication, biologi-cal information processing, and other pertinent clinical issues. These mechanisms are very complex because they may involve membrane phase, shape transitions, and many interactions such as protein-protein and protein-lipid. Several methods for studying these mechanisms are currently available, which include microelectrode-


based detection, patch pipet-based capacitance techniques, confocal fl uorescent microscopy, evanescent wave fl uorescence microscopy, and various combinations of these techniques. However, these methods, which cannot study the exo- and endocytosis in uniform populations of cells all treat the single cell as the object of investigation. Cell-based QCM biosensors may be a new complement technique to the single-cell methods mentioned here. By using QCM-D technique, QCM can not only be used for sensitive measurements attributed to changes in total cell mass during exocytosis and endocytosis, but also can provide information on structural rearrangements of the cells during these and later occurring events.

Cans et al. used QCM-D to monitor the mass change and rigidity of populations of excitable cells during exocytosis and subsequent retrieval of dense-core vesicles [17]. Figure 9.4(a) shows a schematic diagram of a cell grown on the surface of QCM and the layered confi guration of the device. Two cell lines, NG 108-15 and PC 12, were investigated by QCM-D to demonstrate their differences in release and retrieval with cells of different morphology, sizes, and numbers of dense-core vesi-cles by being cultured on piezoelectric quartz crystals. As shown in Figure 9.4(b), stimulating the cells to exocytosis with media containing an elevated potassium concentration resulted in an increase in the frequency response corresponding to loss of mass from the cells owing to release of vesicles. In Ca2+-free media, the re-sponse was completely abolished. The amplitude and peak area in the frequency response corresponding to mass change with stimulated release were greater for PC 12 cells than for NG 108-15 cells, whereas the initial rate constants for the frequency responses were similar. These results demonstrate that measurements of complex dynamic processes relating to dense-core vesicle release and retrieval can be simultaneously accomplished using the QCM-D technique.

9.1.4.4 Real-Time Detection of Oral Epithelial Cell-Microparticle Interaction

Recent applications of quartz crystal resonant sensor technology to monitor the process of cell and specifi c ligand interactions have triggered the development of a new category of cell-based QCM biosensors. Elsom et al. developed a cell-based QCM biosensor to monitor human oral epithelial cell (H376) responses to micro-spheres in real time [18]. The cell-based QCM biosensor was able to follow cell-microsphere interactions in real time and under conditions of fl ow as would occur in the oral cavity (Figure 9.5).

In response to the microspheres, unique frequency profi les were generated, which may be due to phases of mass addition and altered cellular rigidity. Support-ing microscopic evidence demonstrated that the unique frequency responses ob-tained from these interactions were partly due to binding between the cell surface and the microspheres. Furthermore, the cell-based biosensors can not only detect a cellular uptake process in response to microsphere loading, but also identify the infl uence of the microsphere on the rigidity of the cellular cytoskeleton through the frequency responses obtained. It is suggested that this cell-based QCM biosensor provides a novel method to detect the responses of viable human oral epithelial cells to microspheres in a dynamic and noninvasive way.

9.2 Surface Plasmon Resonance (SPR) as Cell-Based Biosensors 217

9.2 Surface Plasmon Resonance (SPR) as Cell-Based Biosensors

9.2.1 Introduction

Conventional chemical analytical techniques such as optical spectroscopy, nuclear magnetic resonance, or electrochemical measurements can hardly resolve the small

Figure 9.4 (a) Schematic representation showing a cell grown on an oxidized polystyrene-coated piezoelectric quartz crystal and outlining the layered configuration of the device. (b) Detection of fre-quency changes in populations of cells following high potassium stimulation. Changes in frequency (Δf) were recorded with the QCM for (1) NG 108-15 cells grown on polystyrene-coated quartz crys-tals that were exposed to high-K+ media at t = 0, (2) a control experiment in which the NG 108-15 cells were rinsed with physiological buffer only, (3) a control experiment in which NG 108-15 cells were depolarized in the presence of 3 mM EGTA, (4) PC12 cells exposed to high-K+ media, and (5) a control experiment in which PC12 cells were stimulated in the presence of 3 mM EGTA. The stimula-tion buffer was 90 mM K+ for all stimulations with 2 mM Ca2+ added (1, 2, 4). (From: [17]. Repro-duced from Analytical Chemistry. © 2001, with permission from the American Chemical Society.)


signal change from background noise or discriminate nonspecifi c binding of other components in the sample. Interest has tended to focus on alternative methods of transducing changes in physicochemical properties of the solution-surface interface, such as refractive index and dielectric constant. The surface plasmon resonance (SPR) is an optical technique that uses the evanescent wave phenomenon to mea-sure changes in the refractive index near the sensor surface. In recent years, the SPR-based biosensors have been widely used to study biomolecular interactions.

As a signifi cant breakthrough in molecular sensor, SPR-based biosensor systems (i.e., BIAcore, Eco Chemie, the Netherlands, www.biacore.com; AutoLAB, Biacore International AB, Sweden, www.autolab-instruments.com) have been developed and are commercially available. This technology has made it possible to perform a real-time analysis of the amounts of bound ligand and the rates of association and dissociation of biomolecules with high precision and without label [19]. It has been used in studying molecular interactions over a wide range of analyte molecular weights and for biological membrane events. Contrary to biosensors using pure biomolecules, cell-based biosensors, which use whole cells as the biorecognition el-ements, can detect agents functionally. Recently, the SPR system has also been used for the detection and analysis of interactions between living cells and molecules related to cells.

Hide et al. fi rst reported the SPR signals generated from IgE-sensitized cells stimulated by an antigen [20]. Then, Lee et al. fabricated an olfactory cell biosen-sor using SPR, which can be used to characterize molecular interactions between olfactory receptors and odorant molecules [21]. However, the evanescent fi eld on the SPR surface is in the range of several hundreds nanometers, while the thickness of the cell body is several micrometers. This means that cells cultured on the surface are out of the range of the detectable evanescent fi eld. Therefore, the researches considered that the SPR signals are derived from intracellular signaling rather than

Figure 9.5 Representative trace demonstrating the frequency response of a cell-coated crystal to three injections of microsphere suspension. Inset: representative photomicrograph of the H376 cell-coated sensor surface after exposure to the microspheres (magnification × 200). (From: [18]. Repro-duced from Biosensors and Bioelectronics. © 2008, with permission from Elsevier B.V.)


simple binding kinetics between the ligands and their receptors on the cell surface. Recent experimental results also demonstrated that the change in the area of cell adhesion to the sensor chip and cell membrane structure is insuffi cient to explain the entire angle of resonance change in response to the activation of living cells [22, 23]. Thus, SPR systems can be more and more widely spread to develop cell-based biosensors, with mechanisms revealed step by step.

9.2.2 The Principle of SPR

SPR is an optical detection process that occurs when a polarized light hits a prism covered by a thin (gold) metal layer (Figure 9.6) [24]. Under certain conditions (wavelength, polarization, and incidence angle), free electrons at the surface of the gold layer absorb incident light photons and convert them into surface plasmon waves. A drop in refl ectivity of the light is seen under these SPR conditions. Per-turbations at the gold surface of the biochip, such as an interaction between probe molecules immobilized on the chip and captured target molecules, induce a modifi -cation of resonance conditions that are, in turn, seen as a change in refl ectivity that can be measured. This is the basis for molecule SPR measurements.

For cell-based biosensors, cells are often directly cultured on the sensor surface as sensitive elements for probing target molecules. Researchers have studied spher-oid cells with diameters of about 10 μm, such as basophils and B cells, as well as those of adhering cells cultured on the gold fi lm of SPR [20, 22]. However, evanes-cence waves could only penetrate to just a few hundred nanometers from the gold fi lm surface. So, the authors considered that cellular events detected by the SPR should be those on and/or just above the plasma membrane (approximately 10-nm thickness), rather than those a few hundred nanometers away from the membrane (Figure 9.7).

Recently, Mozsolits et al. studied biomolecular bindings to the lipid bilayer containing diacylglycerol and phosphatidylserine spread on a sensor chip of SPR

Figure 9.6 SPR detects changes in the refractive index in the immediate vicinity of the surface layer of a sensor chip. (a) SPR is observed as a sharp shadow in the reflected light from the surface at an angle that is dependent on the mass of material at the surface. (b) The SPR angle shifts (from I to II in the lower left-hand diagram) when biomolecules bind to the surface and change the mass of the surface layer. (c) This change in resonant angle can be monitored noninvasively in real time as a plot of resonance signal (proportional to mass change) versus time. (From: [24]. Reproduced from Nature Reviews Drug Discovery. © 2002, with permission from Nature Publishing Group.)


[25]. SPR may refl ect the amount of proteins or other biochemical molecules bound to and/or dissociated from the plasma membrane. Changes in the angle of reso-nance also refl ect the level of conformational change of proteins in the evanescence fi eld [26]. Therefore, conformational changes of individual proteins in or near the plasma membrane on the sensor chip may also contribute to the change of reso-nance angle.

At the same time, when cells were stimulated by extracellular stimuli via recep-tors, they initiated various intracellular events, such as phosphorylation or dephos-phorylation of membrane-associated proteins, translocation of cytosolic proteins to the plasma membrane and/or other organelles, release of membrane proteins into the cytosol, and changes in pH, temperature, and membrane potentials. All of these local microenvironment changes may be affecting factors of angle changes.

9.2.3 SPR Sensors and Measurement System

The SPR measurement is expressed in light incident angles (millidegrees). As the shift in angle is proportional to the mass increase on the gold surface, in the direct measurement there are limitations to the size of the molecule to be detected. Mol-ecules smaller than 1,000 Dalton are not big enough to change the refractive index

Figure 9.7 The principle of the SPR-based biosensor for living cell reactions. The cell is cultured on the surface of the gold layer coated on a glass plate. The cell has different receptors (FceRI, IgE, and A3) illustrated on its surface. The reaction medium containing antigens is flowing in the direction indicated by the open arrow. The laser beam is directed toward the cell and reflected there to the monitor, which detects the shift in the resonance angle. The reflection of the laser beam is affected by the amount of substances in the evanescent field, a few hundred nanometers from the surface. (From: [20]. Reproduced from Analytical and Biochemistry. © 2002, with permission from Elsevier B.V.)


on a surface with limited binding sites and, consequently, are diffi cult to detect with suffi cient accuracy. This limitation can be overcome by increasing the ligand density on the disk. Of course, the several micrometer thickness of the cell body is also beyond the evanescent fi eld on the SPR surface. However, the gold surface of SPR is well suited for cell cultures, with very good electrochemical behaviors and excellent biocompatibilities.

The AutoLAB SPR assay (Eco Chemie, the Netherlands) works with a laser diode fi xed at a wavelength of 670 nm, using a vibrating mirror to modulate the angle of incidence of the p-polarized light beam on the SPR substrate. An increase of the plasmon resonance angle of 120 millidegrees corresponds to an average material layer growth of 100 ng/cm2. The laser spot has an area of 2 mm2 on the sensor disk. The instrument is equipped with a cuvette. A gold sensor disk (17 mm in diameter) was mounted on the hemicylindrical lens (with index-matching oil) to form the base of cuvette. An O-ring (4-mm inner diameter) between the cuvette and disk prevents leakage. An auto-sampler with controllable aspirating-dispensing-mixing pipette was used to add samples into the cuvette and provide constant mixture by aspirating and dispensing during measurements.

The Biacore 2000 and the Biacore 3000 (Biacore, Sweden) instruments use the same sensor chip technology: a thin gold fi lm (approximately 50 nm) on a glass slide is divided into four channels, or fl ow cells, to which probe molecules can be immobilized by several methods, including amine, nickel-histidine, or streptavidin-biotin coupling. Both instruments can simultaneously record signals from four fl ow cells, allowing real-time reference subtraction and measurement of probe mole-cules binding to three different ligands. The Biacore 3000 has better signal-to-noise characteristics than the 2000 device, making it suitable for measuring binding of smaller analytes (as small as 180 Da), while the 2000 device has versatile sample recovery functions enabling downstream processing of analytes that have bound to the ligand.

9.2.4 Biomedical Application

9.2.4.1 Cell-Based Measurement of Odorant Molecules

The biological olfactory system allows the detection and perception of a large num-ber of odorant molecules. The initial events in olfaction occur in the olfactory epithe-lium, where the olfactory receptor neurons located. Those sensory cells are bipolar neurons with a dendritic process ending with several cilia and an axon projecting to the olfactory bulb. Odorant molecules bind to odorant receptors in the ciliary membrane of olfactory receptor neurons, initiating the olfactory signal transduction through cAMP or IP3 pathway, and fi nally contribute to cell depolarization.

As shown in Figure 9.8, Lee et al. fabricated a cell-based olfactory biosensor using SPR, which can be used to characterize molecular interactions between ol-factory receptors and odorant molecules [21]. The bindings of the receptors and ligands could trigger signal transduction cascades. Subsequently, the changes of intracellular components, such as infl ows of ions, occur in cells, and these changes can be detected by SPR.


In the experiment, they expressed olfactory receptors of Caenorhabditis el-egans (ODR-10) on the plasma membrane of human embryonic kidney (HEK)-293 cells. Then, after cells were cultured and adhered to the gold surface of the sensor chip, the specifi c odorant molecule of ODR-10(diacetyl) was used as a stimulus. Their results show exposing cells to 0.1-mmol/L diacetyl induced an SPR signal from the HEK-293 cells expressing ODR-10, while no SPR signal was detected from the control HEK-293 cells. Besides, the intensity of the induced signal was dependent on the dose of diacetyl. Based on these results, they concluded that the hybrid system, which integrates SPR and cells expressing different olfactory recep-tors, can be used to identify the odorant molecules specifi c to each olfactory recep-tor effi ciently.

9.2.4.2 Cells Adhesion on Gold Surface

Since SPR refl ects the events in the fi eld of evanescence, in previous studies cells needed to be fi xed on the sensor chip, so as to be placed in the fi eld of evanescence, for cell-based biosensors using SPR [20]. Recently, researchers also have developed

Figure 9.8 Principle of cell-based measurement of odorant molecules using SPR. A HEK-293 cell ex-pressing ODR-10 adhered to the gold surface of SPR and activated by diacetyl. The specific binding of diacetyl to ODR-10 triggers signal transduction through the IP3 pathway. Activated ODR-10 subse-quently activates phospholipase C (PLC), which converts the phosphatidyl inositol 4,5-bisphosphate (PIP2) into the inositol 1,4,5-triphosphate (IP3). The IP3 opens the Ca2+ channel on the surface of en-doplasmic reticulum (ER), which increases cytosolic Ca2+ ions. The laser beam is irradiated toward the cell and reflected, and the consequent shift in the resonance angle is detected by SPR. The amount of substance in the evanescent field affects the reflectance of the beam. (From: [21]. Reproduced from Enzyme and Microbial Technology. © 2006, with permission from Elsevier B.V.)


methods to fi x living cells, especially the nonadherent cells, on a gold surface with their functions preserved for SPR analysis [22].

In their studies, human basophils cells were used as an example of the non-adherent cells. When basophils were isolated from human blood, and incubated in a fl oating condition, they specifi cally released histamine in response to anti-IgE antibody (23% of the total cellular histamine). On the other hand, no or only marginal increase of resonance angle was observed when they were located on the sensor chip of SPR without any treatment, although some of them were observed by microscopy to have adhered to the sensor chip. To determine a suitable method of fi xing fl oating cells on the sensor chip and to measure SPR signals, they exam-ined different types of molecules to anchor cells: (1) the biocompatible anchor for cell membranes (BAM: SUNBRIGHT OE-040CS), which can be inserted into cell membranes without causing any damage to cells; (2) amino-alkanethiol (cysteam-ine or 8-aminoalkanethiol) that has an amino group (positively charged) with elec-trical affi nity to connect cell membranes (negatively charged); and (3) dithiobis[succinimydylpropionate] (DSP) that reacts with primary amines and forms covalent amide bonds.

As shown in Figure 9.9, when cells were exposed to anti-IgE antibodies, a rapid increase of resonance angle was observed. The increase of resonance angle

Figure 9.9 Human basophils cause the increase of angle of resonance (AR) in response to an anti-IgE antibody on the sensor chip treated by molecules that can anchor cells. An SPR sensor chip was treated with (a) cysteamine, (b) 8-amino-octanethiol, (c) DSP, or (d) BAM before cell attachment. Human basophils were then placed on the sensor chip and perfused with or without anti-IgE anti-bodies (670 ng/ml). Horizontal bars show the period of anti-IgE perfusion. Inlets show the surface of sensor chips at the beginning of the perfusion. (From: [22]. Reproduced from Biosensors and Bioelec-tronics. © 2007, with permission from Elsevier B.V.)


continued after the removal of anti-IgE antibodies from the perfusion buffer. The reaction time (time of maximal increase was 10–30 minutes) and the degree of resonance angle increase (0.2–0.4 angle) was variable among donors (more than three different donors). However, regardless of the method of fi xation, the overall shapes of the reaction curves were the same and similar to those observed, when RBL-2H3 mast cells were stimulated on an ordinary SPR sensor chip.

9.2.4.3 Relationship Between SPR Signals and Intracellular Signaling

Studies discussed earlier have demonstrated that an SPR sensor readily detects reac-tions of spheroid cells with diameters of 8–10 μm, such as basophils and B cells, as well as those of adhering cells. Taking into account that evanescence waves may penetrate to only a few hundred nanometers from a gold fi lm surface, cellular events detected by the SPR sensor should be those on and/or just above the plasma mem-brane (approximately 10-nm thickness), rather than those a few hundred nanome-ters away from the membrane. Therefore, further studies of these receptors with amino acid mutations with gain and/or loss functions should clarify the precise relationship between SPR signals and intracellular signaling.

Recently, studies demonstrated that RBL-2H3 rat mast cells and PAM212 mouse keratinocytes can also induce large changes in the angle of resonance of SPR, when stimulated by epidermal growth factor (EGF) [23]. They explored these changes due to intracellular reactions, through the relationship between the angle of resonance and the area of cell adhesion using confocal microscopy (Figure 9.10). The effect of mycalolide B and toxin B, inhibitors for cell motility, on the angle of resonance was observed using RBL-2H3 cells. Measurement in the presence of various numbers of nonstimulated cells demonstrated that angle and cell density were proportional to each other. However, the angle increase in response to antigen was 35% higher than that expected by solely an increase of the cell adhesion area. Moreover, the angle with PAM212 cells decreased following a transient increase in response to EGF, while the area of cell adhesion remained at an increased level. Furthermore, the treatment of RBL-2H3 cells with either mycalolide B or toxin B slightly inhibited, but never abolished, the angle increase induced by antigen. These treatments abolished all morphological changes, including ruffl ing and the increase of cell adhesion area observed by light microscopy. These results suggest that angle of resonance changes refl ects intracellular events rather than changes in the size of the area to which cells adhere.

As analyzed in Chapter 7, both cell-based impedance biosensors and even QCM-based biosensors have been successfully used to detect the number, attach-ment, and spreading of the cells on sensor surfaces in real time without labeling. However, these techniques do not directly refl ect intracellular signal transduction events. Thus, with mechanisms illuminated step by step, SPR cell–based biosensors may be utilized as unique and potent tools to detect intracellular activities of living cells in real time without labeling.

9.3 Immune Cell-Based Biosensors 225

9.3 Immune Cell-Based Biosensors

9.3.1 Introduction

As one of the most complex biological systems, the immune system protects against disease by identifying and killing pathogens and tumor cells. In order to function properly, the immune system is exquisitely sensitive in detecting a wide variety of agents, from viruses to parasitic worms, and can distinguish them from the organ-ism’s own healthy cells and tissues. Immune cells play a very important role in im-mune mechanisms. Different classes of immune cells carry out specialized functions, including cells that prompt, cells that alert, cells that facilitate, cells that activate, cells that surround, cells that kill, and even cells that clean up. Many immune cells also synthesize and secrete special molecules that act as messengers, regulators, or helpers in the process of defending against invaders. Immune cells can recognize and respond to antigens with very high sensitivity and specifi city. These features make immune cells ideal candidates served as sensitive elements in cell-based bio-sensors for antigens or pathogens detection. A number of immune cells have been investigated to explore the feasibility of being used as sensitive elements in cell-based biosensors such as mast cells and B cells.

Figure 9.10 The relationship between the angle of resonance and the area of cell adhesion of RBL-2H3 cells and PAM212 cells. (a) Confocal slices of PAM212 cells stained with phalloidin-TRITC at each time point demonstrated that spreading started 10 minutes following stimulation. (b) The area of RBL-2H3 cells adherence measured under differential interference contrast microscopy and confocal laser microscopy increased up to about 1.6 times that of the original area in response to the antigen. And, a dotted line shows the change of the angle of resonance to SPR. (c) The area of PAM212 cells adherence measured by means of confocal slices increased up to 1.45 times that of the resting level in response to EGF. A dotted line shows the change of the angle of resonance to SPR. (From: [23]. Reproduced from Biosensors and Bioelectronics. © 2007, with permission from Elsevier B.V.)


9.3.2 Mast Cell–Based Biosensors

Mast cells, an important type of immune cell, can recognize many specifi c antigens. Therefore, they can be integrated into cell-based biosensors. The sensitization of mast cells to specifi c antigens can be initialized by the binding of IgE antibody to surface membrane Fc receptors. The interactions between antigen and antibody on the cell surface can lead to signifi cant cellular activation responses, characterized by morphological changes, increased metabolism rate, and extracellular release of biochemical mediators.

A schematic representation of the essential steps involved in mast cell sensiti-zation [Figure 9.11(a, 1)], activation, and degranulation is shown in Figure 9.11. Activation [Figure 9.11(a, 2)], a cation-dependent process, results in an exocytotic process known as degranulation involving the expulsion of potent intracellular chemical mediators [Figure 9.11(a, 3)]. Physically, degranulation is characterized by a marked transformation of the mast cell from a resting state to a very excited state in which intracellular granules are set into rapid motion and followed by the development of deep clefts that extend from the surface to deep into the cytoplasm, as depicted in this sequence [27]. The response time of mast cell activation is rela-tively fast, beginning almost immediately after exposure to an antigen or a chemical activator, such as calcium ionophore A23187. Mast cell activation, degranulation,

Figure 9.11 (a) Illustrations of the key events of mast cell activation: (1) initial antibody binding event preceeding mast cell activation; and (2) antigen-antibody binding triggering mast cell activa-tion; and (3) mast cell degranulation resulting in the release of chemical mediators. (b) Thermogram from MC/9 mast cells (2.6 × 106 viable cells) showing heat output at basal and activated states. Ther-mal spikes are due to calcium ionophore injection artifacts. (From: [27]. Reproduced from Biosensors and Bioelectronics. © 1997, with permission from Elsevier B.V.)

9.3 Immune Cell-Based Biosensors 227

and recovery are metabolically driven energy requiring events that exceed the ba-sal energy levels required to maintain cell viability derived from increases in both aerobic respiration and glycolysis.

A mast cell-based biosensor has been developed in which mast cells were trans-formed into unique biotransducer couples by engineering their molecular recogni-tion for preselected antigens of clinical interest [27]. The basic principle of this biosensor is based on the energetics of mast cell activation. Mast cell antigen rec-ognition can generate metabolic changes, which then lead to a signifi cant increase in exothermic heat relative to basal metabolic conditions. The mast cell-based bio-sensor is developed by combining mast cells with a microfabricated thermoelectric device via the use of biomolecular linkages. Mast cell activation processes induced by antigen can be monitored in real time. Mast cell activation and degranulation can be discriminated thermally from basal metabolic activity. Cultured mast cells (MC/9 mucosal-like mast cell line), and harvested mast cells (rat peritoneal mast cells) have detectable increases in heat output (~3 ± 0.5 pW/cell, mean peak out-put) immediately following cell activation. A typical thermogram of mast cell heat output is shown in Figure 9.11(b). Page et al. then investigated the use of enzyme amplifi cation systems to enhance the direct transduction of immune cell responses to analyte [28]. It was found that with appropriate enzymes, peak outputs occurred within ~5 minutes (4–20 times faster than without enzymes) and peak response magnitudes were up to ninefold greater than without enzymes.

It is indicated that this mast cell–based biosensor may greatly extend the ca-pability for selective, rapid, on-site antigen detection for a wide range of clinically relevant antigens and offer new approaches for diagnostics in vitro.

9.3.3 Dendritic Cell–Based Biosensors

Dendritic cells are also immune cells strategically located at the environmental in-terface, serving as immunological sentinel and tissue-resident antigen presenting cells. Their main function is to process antigen material and present it on the surface to other cells of the immune system, functioning as antigen-presenting cells.

Dendritic cells are present in small quantities in tissues that are in contact with the external environment, mainly the skin (where there is a specialized dendritic cell type called Langerhans cells) and the inner lining of the nose, lungs, stomach, and intestines. They can also be found in an immature state in the blood. Once activated, they migrate to the lymphoid tissues where they interact with T cells and B cells to initiate and shape the adaptive immune response. At certain development stages, they grow branched projections, so the cells are called dendritic cells. How-ever, these do not have any special relation with neurons, which also possess similar appendages. Immature dendritic cells are also called veiled cells, in which case they possess large cytoplasmic “veils” rather than dendrites.

Recently, researches reported the development and validation of a dendritic cell–based biosensor system as a highly sensitive, high throughput–compatible screening platform for the discovery of dendritic cell–targeted immunostimulants [29]. They chose to employ a stable dendritic cell line XS106 established from the epidermis of newborn mice, which can respond to diverse stimuli by altering the phenotypes and functions. For example, they have observed TLR ligands (LPS


and CpG ODNs), purinergic type 2 receptor (P2R) ligands (extracellular ATP and ADP), necrotic keratinocytes, and a proinfl ammatory chemical, CrO, differentially regulated the expression of MHC II and CD86 as well as the production of tumor necrosis factor-α (TNF-α), IL-6, IL-12, vascular endothelial growth factor (VEGF), and macrophage infl ammatory protein-1α (MIP-1α) by XS106 cells. The results showed that IL-1β mRNA was commonly induced by all tested stimuli [Figure 9.12(a)]. Thus, they engineered XS106 cells to stably express the promoter-driven yellow fl uorescent protein (YFP) gene under the control of the IL-1β promoter, which served as a universal readout to indicate the activation status of dendritic cells. Using this method, Figure 9.12(b) was a representative dendritic cell biosensor clone, termed the XS106-pIL1-YFP, expressed YFP in response to LPS stimulation in dose- and time-dependent fashions. YFP expression became detectable within 120 minutes after LPS treatment, indicating rapid responsiveness [Figure 9.12(c)].

The dendritic cell–based biosensor system is a novel platform for time- and cost-effi cient discovery of DC-stimulatory drugs. Despite the growing interests in academia and industry in the clinical use of immunostimulants as therapeutics for cancer and infectious diseases and also as vaccine adjuvants, no systematic effort has been reported in the literature toward discovery of newer immunostimulants. The detected concentration 100- to 300-fold was lower than ordinary detection limits with phenotypic or functional assays, with the advantages of responding to a wide variety of biologic and pharmacologic agents.

Figure 9.12 Development and characterization of dendritic cell biosensor system. (a) XS106 den-dritic cells were incubated for 6 hours with the indicated agents, including necrotic keratinocyte preparations (Nec KC), and then examined for cytokine mRNA profiles by RNase protection assay. (b) The XS106-pIL1-YFP dendritic cell biosensor clone was incubated for 16 hours with LPS at the in-dicated concentrations and then examined for the mean fluorescence intensity (MFI) for YFP signals. (c) The same clone was incubated with 30 ng/mL LPS or PBS alone for the indicated periods and then examined for YFP expression. (From: [29]. Reproduced from Blood. © 2005, with permission from the American Society of Hematology.)

9.4 Summary 229

9.3.4 B Cell–Based Biosensors

The development of microarray technologies and high-throughput analysis meth-ods is rapidly leading to the development of cell array–based biosensors. The cell array–based biosensors may provide new levels of detection sensitivity and the abil-ity to rapidly quantify the response of millions to billions of cells to chemical librar-ies or complex analytes. Arrays of lymphocytes could be particularly useful for controlling the cell-cell contacts that dictate immune cell function while excluding infl uences from interactions between the neighboring cells of the same type. Arrays of lymphocytes also present new possibilities for ultrasensitive and rapid-detection biosensors.

Kim et al. developed a reliable method to produce isolated B cell arrays uti-lizing nonlithographic microscopic patterning over large areas and a novel func-tionalizable, protein adsorption–resistant copolymer [30]. Figure 9.13 shows the biotinylated antibody and B cell array fabrication. Templates for B cell arrays were generated by modifying the characteristics of the patterned surface via introduc-tion of surface biotinylation and specifi c protein adsorption. It is suggested that the controlling factor in the fabrication of clean and regular arrays of immobilized lymphocytes over large areas is the binding strength between nonadherent B cells and the template surface. It may provide a novel method for nonadherent B lym-phocytes to be patterned in arbitrary arrays over large surface areas within domains composed of single cells, using simple seeding and washing steps. It is also applica-ble to pattern other nonadherent cell types on the surface of appropriate devices for the development of cell array–based biosensors. Combined with high-throughput technologies, this cell array–based biosensor can be used in fundamental studies of multicellular interactions in the immune system and other areas of cell biology.

9.4 Summary

In the fi eld of biosensors, analytical techniques of QCM and SPR have become known as prevalent methods suitable for the detection of biomolecular interac-tions. In particular, the different commercially available instruments of QCM and SPR have greatly promoted the research and development of the biosensor industry.

Figure 9.13 Fabrication of biotinylated antibody and B cell array. (a) Patterned array of fluorescence labeled antibody fabricated by biotin-streptavidin conjugation. (b) B cell array fabricated from pat-terned array of biotinylated antibody. (From: [30]. Reproduced from Biomacromolecules. © 2004, with permission from the American Chemical Society.)


They represented signifi cant breakthroughs of molecular sensors. For cell-based biosensors, the sizes of cells, at least several micrometers, often fall beyond the detecting available fi eld (about a hundred nanometers) of QCM and SPR. It is the main drawback to using them for whole-cell assays. However, recent experimental results have demonstrated that the changes in the area of cell adhesion to the sensor chip and that of membrane receptors binding to stimulative ligands could introduce the changes of response in QCM or SPR.

At the same time, in addition to QCM and SPR technique, immune cells are also employed in the development of cell-based biosensors. Because the immune system is exquisitely sensitive in detecting a wide variety of agents, these novel biosensors have shown promising applications in many pathogens detection. These biosensors could report on specifi c molecular events in living immune cells by fl uo-rescent labeling. Thus, cell-based biosensors of immune cells are mainly evolved from in vitro fl uorescence spectroscopy and fl uorescent analogue cytochemistry. The fast-developing fl uorescent probe design and computerized optical instrumen-tations will allow the biosensor engineering of endogenous cellular components that localize and function as reporters of their activities. Therefore, researches of the immune biosensors will also lead to innovations of the whole cell–based bio-sensors. In any case, the commercially available QCM and SPR systems, including immune cells, will see more and more widespread use in detecting cell-based bio-sensors, with mechanisms illuminated step by step.

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9.4 Summary 231

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233

C H A P T E R 1 0

Developments of Cell-Based BiosensorsPing Wang, Qingjun Liu, and Lidan Xiao

10.1 Introduction

In the future, biosensors will develop much faster by using biological organs, cells, and molecules and such active materials as sensing elements combined with micro-fabrication technology to realize the true biomimetic sensor chips. Such biosen-sors have super high sensitivity and selectivity to the surrounding environment and measured objects like human and animals. Cell-based biosensors using living cells can detect the measured information qualitatively and quantitatively to determine the existence of some substances, their concentration, and so on. What’s more, cell-based biosensors can detect functional information of an external physical or chemical stimulant. Besides, cell-based biosensors as true biomimetic sensors will also get practical application in the medical fi elds of repairing and substituting of human organs and so on.

The future development of cell-based biosensors will also combine the other up-to-date technology in science and engineering (e.g., microelectronics, nanotech-nology, and molecular biology) to fabricate the integrated, multifunctional, intelli-gent, or smart sensor chips. In this chapter, we will introduce the novel and possible progress in cell-based biosensors in recent years and in the near future.

10.2 Cell-Based Biosensors with Integrated Chips

Integrated cell-based biosensors can provide either high throughput information or different functional parameters of living cells. In recent years, the integration of electronic sensors with extracellular cells for biomedical applications has drawn considerable interest from researchers in relative fi elds.

A rigorous preselection of identifi ed compounds by in vitro cellular screen-ing is necessary prior to using the drug candidates for the further time-consuming and expensive stage (e.g., in animal models). So nowadays research is focused on the parallel development, adaptation, and integration of different microelectronic sensors into miniaturized biochips for a multiparametric, functional, online analy-sis of living cells in physiological environments and extracellular action potential

234 Developments of Cell-Based Biosensors

monitoring. The monitoring of extracellular parameters can be greatly accelerated by high-throughput screening platforms. It is a different approach in functional on-line analysis of living cells in physiologically controlled environments for extended periods of time.

Usually, there are three types of integrated sensors. Sensor array is an integra-tion of the same or similar sensors with the same or similar functions. Multisensors involve different sensor elements with different functions. Multifunction chips can monitor the different parameters in different detecting environment.

10.2.1 Integration Chip of the Same or Similar Functional Sensors

Different techniques can be used to get more information about living cells. The research group of Wolf designed several types of integrated chips these past years [1]. Baumann et al. have integrated interdigital electrodes (IDES) and photodiodes on a silicon sensor chip with ISFETs and temperature diodes [2], as shown in Figure 10.1 Such a miniaturized system needs only a small amount of cells to get suffi cient information from cellular biochemical plants. The various cell monitoring systems (CMSs) developed in their group allow parallel, online, and noninvasive measure-ment of different parameters of cellular signaling. Henning et al. used the integrated sensor chip for tumor chemosensitivity assays (TCA) [3, 4]. A series of experiments has been performed on cell lines and human tumor explants.

Brischwein et al. have designed multiparametric silicon sensor chips mounted into biocompatible cell culture units, which are used for investigations on cellular microphysiological patterns. Potentiometric, amperometric, and impedance mi-crosensors are combined on a common cell culture surface on the chip with an area of about 29 mm2 [5], as shown in Figure 10.2. Extracellular acidifi cation rates (with pH-sensitive fi eld effect transistors, ISFETs), cellular oxygen consump-tion rates (with amperometric electrode structures), and cell morphological altera-

Figure 10.1 Layout of the silicon sensor chip with integrated ISFETs, IDESs, temperature, and light sensors. (From: [2]. Reproduced from Sensors and Actuators B: Chemical. © 1999, with permission from Elsevier Science S.A.)

10.2 Cell-Based Biosensors with Integrated Chips 235

tions (with impedimetric electrode structures, IDES) are monitored on a single chip simultaneously for up to several days.

10.2.2 Multisensors Involve Sensing Elements with Different Functions

Otto et al. have developed a multiparametric test system using microsensor chips that can detect online microphysiological changes in living cells [6]. Tumor cells were grown directly on glass- or silicon-based electronic sensor chips. Changes in extracellular pH and pO2, reflecting metabolic activities, and changes in imped-ance, refl ecting morphological properties, were monitored. Results show that this microphysiological monitoring is a versatile tool for chemosensitivity testing of tumor cells.

Geisler et al. integrated multiparametric, bioelectric, and biochemical sensors for the analytical monitoring of intra- and extracellular parameters and an auto-mated imaging microscope for high-content screening into a single embedded plat-form [7, 8], as shown in Figure 10.3. The concept is highly modular, and its design-inherent versatility allows a multitude of platform confi gurations suiting widely differing user requirements. A systems analysis approach gives an idea of how the biological component of these hybrid structures works [1]. This is exemplifi ed by the putative role of the microenvironmental pH as a parameter of the utmost im-portance for the malignant “mode” of tumor cells, which can be monitored and modeled on such hybrid structures.

Integrated cell-based biosensors with sensor array are most commonly devel-oped, such as MEA (see Chapter 4), and FET array (see Chapter 5). Cell-based biosensor arrays can provide extracellular information about living cells cultured

Figure 10.2 Layout of the sensor chip used for potentiometric, amperometric, and impedance measurement. The chip size is 7 × 14 mm. (From: [5]. Reproduced from Lab on a Chip. © 2003, with permission from the Royal Society of Chemistry.)


on different detecting sites. With defi ned neuron networks created on the chip, an intimate communication of network dynamics and computation can be envis-aged. Our group has paid great attention to the realization of integrated cell-based biosensors using the LAPS array and has established a novel cell-based biosensor array for extracellular microenvironment detection [9], including concentration of different metabolic substances (see Chapter 6).

10.2.3 Multifunctional Chip Monitoring Different Parameters

Integrated cell-based biosensors with multisensors have drawn much attention these days [7]. Compared to sensor array, multisensors can provide information of different properties. This means that while constructing cell-based biosensors, multisensors are more likely to offer more precise monitoring of cell condition in different aspects.

Therefore, our efforts are directed at the parallel development, the fabrication and integration of different microelectronic sensors into miniaturized biochips for a multiparametric cellular monitoring with the multiparametric chip. Parallel and online acquisition of data related to different cellular targets will be required by advanced stages of drug screening, and the chip includes three main units, as shown in Figure 10.4: the interdigitated array (IDA) with the impedance measurement of cells for attachment evaluation; MEA with the voltage measurement of cells for extracellular action potential measurement; the LAPS with the sensitive ions (e.g., H+, K+, Ca2+) for cellular metabolites in a microenvironment. In our chip, the trans-ducer element LAPS for records of extracellular pH changes due to the adoption of extracellular acidifi cation. Interdigitated electrode array structure was applied for the detection of changes in cellular adhesion and morphology. Microelectrode array was used to detect the extracellular voltages of different cellular action po-tentials. These three sensor functions are to be incorporated into a multiparametric chip. After living cells are cultured on corresponding areas, both chemical and elec-trical parameters can be obtained. Living condition of the target cells, including the

Figure 10.3 Glass plate with sensor arrangement for 12 wells. Two plates are needed to equip a 24-well plate. (From: [8]. Reproduced from IFMBE Proceedings. © 2008, with permission from Springer-Verlag GmbH.)

10.3 Cell-Based Biosensors Using Nanotechnology 237

attachment evaluation and cell metabolism, can be analyzed from the data of IDA and LAPS elements. Then, functional response with or without certain treatment of cells can be acquired as a potential signal recorded by the MEA element.

Multifunctional cell-based biosensors are also an important improvement. Dif-ferent from the multisensors, it’s constructed with identical or similar sensor ele-ments. LAPS is one of the typical multifunction sensors. While constructing cell-based biosensors with LAPS, as mentioned before, they depend on the cell class and the detecting environment. Our group has also developed a multifunction cell-based biosensor with LAPS array [10, 11], which can detect both the metabolism and the extracellular potential signals of living cells on several different detecting parts under different detecting conditions. This integration can greatly simplify the fabrication and reduce the size of biosensors compared to the multisensors, which makes the miniaturization possible.

Higher and higher level of integration is the most important feature of the requirement of integrated cell-based biosensors. Most integrated cell-based biosen-sors involve all the three types of integrated sensors at the same time. High-level integration can provide more comprehensive information to give precise determi-nation of cell functioning. Therefore, more precise and rigorous preselection of identifi ed compounds can be achieved with these integrated cell-based biosensors.

10.3 Cell-Based Biosensors Using Nanotechnology

In the past decade, nanotechnologies have greatly changed the state of science and technology. Nanotechnology involves the study, creation, manipulation, and use of materials, devices, and systems typically with dimensions smaller than 100 nm. The most commonly used nanomaterials include nanowire, nanotube, nanocapsule, nanopartical, nanochannel array, and nanoporous membrane. Now, nanotechnol-ogy is also playing an increasingly important role in the development of biosensors. Sensitivity and performance of biosensors can be improved by using nanomaterials. Nanomaterials display unique physical and chemical features because of effects such as the quantum size effect, mini size effect, surface effect, and macro-quantum tunnel effect. Based on their submicron dimensions, nanobiosensors have allowed

Figure 10.4 Schematic view of the integrated cell-based biosensors with multisensors.


simple and rapid analyses in vivo. Even portable instruments capable of analyzing multiple components are becoming available.

Lots of works have reviewed the status of the various nanotechnology-based biosensors, especially at the molecular level [12]. However, the application of nan-otechnology to biosensor design and fabrication is also promising to revolutionize diagnostics and therapy at the cellular level. The convergence of nanotechnology, biology, and photonics makes it possible to detect and manipulate atoms and mol-ecules by using a new class of nanoprobes and nanosensors for a wide variety of medical uses at the cellular level. Nanosensors have the potential for monitoring in vivo biological processes within/without a single living cell (e.g., the capacity to sense individual chemical species in specifi c locations of a cell), which will greatly improve our understanding of cellular function, thereby revolutionizing cell biol-ogy. In this chapter, we will give some examples, such as nanowires, nanoporous, and even nano-micropatterned cell cultures, of cell-based nanobiosensor studies.

10.3.1 Nano-Micropatterned Cell Cultures

The micropatterned cell cultures have become very popular due to its importance in the development of biosensors, microarrays, tissue engineering, and cellular studies in recent years. Several in vitro and in vivo experiments have shown that nanostruc-tured materials, which mimic the nanometer topography of the native tissues, can improve biocompatible responses and result in better tissue integration in medical implants. Understanding various aspects of nanotopography is extremely important for better designs of these devices. Some articles [13–15] have reviewed the prospect of integrated designing of nano-interface to probe living cells. In those studies, the micro/nanoparticles are assembled into patterns and form the substrate for pro-teins and cell attachment. The assembled particles create a micro- or nanotopogra-phy, depending on the size of the particles employed. The nonplanar structure can increase the surface area for biomolecules attachment and therefore enhance the sensitivity of biosensors. Furthermore, a nanostructured substrate can infl uence the conformation and functionality of protein attached to it, while cellular response in terms of morphology, adhesion, proliferation, differentiation, and so on can be af-fected by a surface expressing micro- or nanoscale structures.

For example, the ability to position cells on a substrate with well-controlled size and spatial arrangements could facilitate fundamental studies of cellular re-search. Micropatterned cell cultures are ideal to study fundamental interactions like cell-cell interaction and cell-substrate interaction.

Researchers [16] describe an approach to manipulate and measure mechanical interactions between cells and their underlying substrates (PDMS on silicone) by using microfabricated arrays of elastomeric, microneedle-like posts (Figure 10.5). By controlling the geometry of the posts, they varied the compliance of the sub-strate while holding other surface properties constant. Cells attached to, spread across, and defl ected multiple posts. The defl ections of the posts occurred inde-pendently of neighboring posts and, therefore, directly reported the subcellular distribution of traction forces. Two classes of force-supporting adhesions exhibited distinct force-size relationships. Force increased with size of adhesions for adhe-sions larger than 1 μm2, whereas no such correlation existed for smaller adhesions.

10.3 Cell-Based Biosensors Using Nanotechnology 239

By controlling cell adhesion on these micromechanical sensors, cell morphology regulates the magnitude of traction force generated by cells. These fi ndings dem-onstrate a coordination of biochemical and mechanical signals to regulate cell ad-hesion and mechanics. They introduce the use of arrays of mechanically isolated sensors to manipulate and measure the mechanical interactions of cells.

10.3.2 Nanoporous-Based Biosensor

The very high surface area–to–volume ratio is probably the most important prop-erty of nanoporous materials. The increased pores’ surface is able to interact either with adsorbents, particles embedded into the pores, or the fl ow passing through the materials. The control on pore size, morphology, and specifi c distribution directly combined with the interaction surface enables enhanced absorption, and ultimately leads to substantial improvements on functional applications such as catalysis, membranes, electrodes, chromatography, separation, and sensing element.

Among the nanoporous materials used in bioengineering fi elds as well as other research fi elds, nanoporous anodic aluminum oxide (AAO), also known as alu-mina, is of particular interest because of its excellent biocompatibility as well as the well-established fabrication process (Figure 10.6) [17, 18]. Alumina has already been extensively used as a substrate for tissue constructs. To achieve successful tissue engineering applications, the cell response to surface topography is one of the most crucial factors. Osteoblast response to alumina surfaces has been widely investigated in different labs. Alumina surfaces show signifi cant biointegration and cell ingrowth, and the cell response can be improved with nanoscale architecture.

Tejal at Boston University have been doing research on the osteoblast cultured in nanoporous alumina membranes for about 3 years. In one of their papers, a two-step anodization process was optimized for the fabrication of nanoporous alu-mina membranes with uniform pore dimension and distribution. The impact of the nanoscale pores on osteoblast response was studied by evaluating cell adhesion,

Figure 10.5 Smooth muscle cell lying on a bed of microneedles. (a) With the appropriate surface density of vertical posts positioned on a substrate, a cell should spread across multiple posts as de-picted. (b) A uniform array of posts. (c) SEM of the cell deforming the posts during adhesion. (From: [16]. Reproduced from Proceedings of the National Academy of Sciences of the United States of America. © 2003, with permission from National Academy of Sciences, U.S.A.)


morphology, and matrix production via different methods. This research combined the advantages of alumina and incorporated porous features on the nanoscale, which have been reported to improve osteoblast response.

Our laboratory has successfully fabricated ordered AAO using the two-step anodization procedure, with pores sized between 50 nm and 120 nm. The charac-teristic of the AAO surface was observed by SEM. A biosensor system was set up with PDMS to take the cell culture and impedance measurement procedure [19]. Different cells are cultured on the AAO surface, and their responses to specifi c drugs have been observed. In our new analytical system based on this, the mor-phology changes of cancer cells after treatment of specifi c anticancer drugs can be found in multifrequency electrical impedance spectra. We hope in the future it can substrate the traditional time-consuming microscopic techniques in anticancer drug screening procedures.

10.3.3 Nanoprobes to Intracellular Nanosensors

With the development of the nanotechnology, devices of nanoscale dimensions be-came capable to probe the innerspace of single living cells, leading to new infor-mation on the inner workings of the entire cell. As a novel approach for system biology research, it can greatly improve our understanding of cellular function. Those nanosensors could be fabricated to have extremely small sizes, which make them suitable for sensing intracellular/intercellular physiological and biological pa-rameters in microenvironments.

Zinc oxide (ZnO) has received considerable attention because of its unique op-tical, semiconducting, piezoelectric, and magnetic properties. ZnO nanostructures exhibit interesting properties, including high catalytic effi ciency and strong adsorp-tion ability. Recently, interest has been focused toward the application of ZnO in biosensing because of its high isoelectric point, biocompatibility, and fast electron transfer kinetics. Such properties indicate the potential applications of ZnO as one of the promising materials for biosensor applications [20].

Figure 10.6 HepG2 cells on self-supporting aluminum oxide membrane (pore diameter is about 75 nm). (a) Overview and (b) magnifi cation of a cell border. (From: [18]. Reproduced from Acta Bio-materialia. © 2007, with permission from Elsevier B.V.)

10.4 Cell-Based Biosensors with Microfl uidic Chips 241

The focus of the current biosensor study is the fabrication of nanostructure ZnO nanorods suitable for intracellular pH sensing. Some authors have reported ZnO nanorods as an intracellular sensor for pH measurements [21]. Their main effort has been directed toward the construction of tips capable of penetrating the cell membrane as well as optimization of the electrochemical properties. In this study, ZnO nanorods with diameter of 80 nm and length of 700 nm, grown on the tip of a borosilicate glass capillary (0.7 μm in diameter), were used to cre-ate a highly sensitive pH sensor for monitoring [H+] within single cells. The ZnO nanorods, functionalized by proton H3O+ and hydroxyl OH- groups, exhibit a pH-dependent electrochemical potential difference versus an Ag/AgCl microelec-trode (Figure 10.7). The potential difference was linear over a large range, which could be understood in terms of the change in surface charge during protonation and deprotonation. Therefore, the nanoelectrode devices have the ability to enable analytical measurements in single living cells and the capability to sense individual chemical species in specifi c locations within a cell.

Besides intracellular detecting, some groups have also reported nanosensors for extracellular studies. For example, Patolsky et al. have used FET arrays of silicon nanowire to record neuronal signals [22]. In their studies, hybrid structures of na-nowire arrays integrated with the individual axons and dendrites of live mamma-lian neurons. And, they think those nanoscale junctions can be well used as biosen-sors for highly sensitive detection, stimulation, and/or inhibition of neuronal signal propagation, with simultaneous measurement of the rate, amplitude, and shape of signals propagating along axons and dendrites. It was also a very important and successful application of nanotechnology for cell-based biosensor research.

10.4 Cell-Based Biosensors with Microfl uidic Chips

To be highly integrated and miniaturized is one of the developmental trends of cel-lular analysis systems. The lab-on-a-chip (LOC), which is called micro total analysis systems (μTAS) as well, is a type of device that integrated one or more lab processes on a single microchip. It assembles the microfl uidics to mechanically control activ-ity of biological samples with control devices like pumps, valves, or sensors.

Figure 10.7 Optical image and schematic diagram illustrating (a) intracellular pH measurements performed in a single human fat cell using ZnO nanorods as a working electrode with (b) Ag/AgCl reference microelectrode. (From: [21]. Reproduced from the Journal of Applied Physics. © 2007, with permission from the American Institute of Physics.)


Usually the LOCs are the size of only millimeters to a few square centimeters. It could handle cellular components in the extreme small fl uid volumes down to less than pico liters. Compared with the traditional cellular analysis systems, it provides some advantages:

A small volume of sample is required. It causes low consumption and low •contamination.

It could reduce the reaction response time and improve the effi ciency of cel- •lular analysis.

It could precisely control the cellular activity. •

It could easily complete the high-throughout analysis for massive paralleliza- •tion due to its compactness.

With mass production due to the microfabrication technique, the cost for the •chips is low.

However, the technology for LOCs is still under development. Even though it has precise geometry, it may not reach the precision of the traditional analysis sys-tem. In small-scale assays, the condition could be more easily affected by complex physical and chemical factors.

Cell-based LOCs are usually designed to analyze cellular activity and structure from the cellular level to the molecular level. They cover all the steps from cell culture and growth, surface treatment, selection, cellular lysis, and separation, to componential analysis [23]. In general, the techniques commonly applied in the cel-lular microsystems include the microfl uidic fl ow, soft lithography, and electric force like dielectrophoresis (DEP).

10.4.1 Microfl uidic Flow

The microfl udic channels are fabricated on the substrate of silicon, glass, and poly-mer such as PDMS. The motion of cells is driven by the continuous liquid fl ow through microfl uidic channel. The fl ow is actuated by the external or integrated pumps or other electrokinetic mechanism.

In theory, the Reynolds number (Re) is a dimensionless number defi ned as the ratio of inertial force and the viscous force. By quantifying these two forces, Re could give the fl ow conditions. When Re < 2,000, the fl ow is defi ned as the lami-nar fl ow, which means that the parelleled fl ow layers will not disrupt each other. With this characteristic, it makes the microfl uidic channel based on laminar fl ow a widely used platform in cellular and subcellular study.

While actuated by laminar fl ow, the concentration gradient distributed in “Christmas tree” microfl uidic networks is spatially and temporally constant and can easily be maintained for hours [24]. This consequence is useful in the research of cellular biology and fi eld of drug screening. The technique PARTCELL [25] used the microfl uidic structure (Figure 10.8) to deliver small-molecule reagents to cells and remove them from cells with subcellular resolution. In Figure 10.8(c–e), the cell in the combined fl uid fl ow generates two spatially localized populations: one part of the cell is treated with the Mitotracker Green FM, a molecule that


labels mitochondria irreversibly, and another part is unlabeled. In this process, it does not require a micromanipulator and it is easily to handle.

By the soft lithography technique, the elements in microfl uidics network such as channel, pump, actuator, and valve could be molded in the same substrate (spe-cial on PDMS). Hence, the microfl uidics network could be integrated similarly to the integrated circuit [26, 27]. It provides the platform to combine the multisteps of cellular biological assays onto the single chip. Balagadde and his coworkers imple-mented a microfl uidic bioreactor (Figure 10.9) adopting a series of micromechani-cal valves and peristaltic pump [28]. It enables long-term culture and monitoring population of bacteria with single-cell resolution.

10.4.2 Soft Lithography

Soft lithography is a microfabrication technique used to construct features mea-sured on the micrometer to nanometer scale with elastomeric material such as PDMS [29, 30]. It mainly includes the technologies of microcontact printing (μCP), replica molding (REM), microtransfer molding (μCP), micromolding in capillaries

Figure 10.8 (a) The top view of the PARTCELL microfl uidic channel, which is made by PDMS. The gray channel is the Mitotracker Green FM solution (3 μM in media). (b) The inlet channels combine into one main channel. Different streams of fl uids were allowed to fl ow over different regions of a single cell. The fl uorescence image was taken after (c) 5 minutes, (d) 11 minutes, and (e) 35 minutes, respectively, of the continuous fl ow of Mitotracker Green FM solution over the left portion of the cell. The white dotted line indicates the position of the interface between the fl ows with and without green solution. (From: [25]. Reproduced from Chemistry and Biology. © 2003, with permission from Elsevier Science Ltd.)


(MIMIC), and solvent-assisted micromolding (SAMIM). By complexly patterning chemical agents or proteins, or changing the physical structure of the surface, it can be used to study the spreading, growth, lysis, or death of populations of cells, even a single cell.

Figure 10.10(a, b) shows a 3D perfusion microfl uidic cell culture array for high-throughput cell-based assays [31]. It binds PDMS multilayers together and forms the perfusion channels to complete the whole process of cell loading, cell culture, cell-based assays, and cell passaging. The 3D microfl uidic technology also could help to deposit the cells and proteins onto the surface and culture two differ-ent cells onto a single surface in a certain pattern [32].

Figure 10.9 The view of microfl uidic channels with six microchemostats operating in parallel on a single chip. The channels are loaded with food dyes for visualization. The coin is 18 mm in diameter. (From: [28]. Reproduced from Science. © 2005, with permission from the American Association for the Advancement of Science.)

Figure 10.10 (a) The photography of 10 × 10 microfl uidic cell culture array. (b) The SEM picture of a single culture unit. Each one has four fl uidic access paths for perfusion inlet (left), perfusion outlet (right), loading (top), and waste (bottom). (From: [31]. Reproduced from Biotechnology and Bioengineering. © 2005, with permission from Wiley-VCH Verlag GmbH & Co. KGaA.) (c) The ECM contact areas are on the small islands with various diameters for cell spreading. (d) Phase-contrast micrograph of cells spread on single 20-μm or 50-μm diameter circles or multiple 5-μm circles shown in (c). (From: [33]. Reproduced from Science. © 1997, with permission from the American Association for the Advancement of Science.) (e) The SEM picture of the microcontact stamp with 2-μm-wide lines and 10-μm nodes. (f) The neuronal network cultured on the stamped protein. (From: [34]. Re-produced from Soft Matter. © 2007, with permission from the Royal Society of Chemistry.)


Soft lithography is driving the development of surface engineering. It is found that the cell shape or integrin binding changes the state of the cells. When the human and bovine capillary endothelial cells cultured on the micropatterned sub-strate contained extracellular matrix-coated adhesive islands [Figure 10.10(c, d)], they were switched from growth to apoptosis with the islands’ area decreased to restrict cell extension [33].

Microcontact printing is one of the most widely used soft lithography technol-ogies. It can fabricate some complex self-assembled monolayers (SAM) or protein topography [in Figure 10.10(e, f)] on the surface to guide the cell, such as the neu-rons growing and forming the required network to study the cell-cell communica-tion [34]. The information about this part can be referred to Chapter 2.

10.4.3 Dielectrophoresis

The electrokinetically driving force could combine or overcome the viscous force to change the activity of cells in the fl ow. Dielectrophoresis (DEP) force is one of the electrokinetic forces and it is exerted on the noncharged dielectric particles when they are subjected to a nonuniform electric fi eld. With magnitude and phase variation of the electric fi eld between electrodes with various confi guration and ge-ometry, it could arouse different kinds of DEP methods, such as the common dielec-trophoresis (DEP), electrorotation, and traveling-wave dielectrophoresis (TWDEP) [35].

A series of DEP devices were developed and widely used to manipulate, sort, and trap different cells in the liquid. The DEP-activated cell-sorting (DACs) device in Figure 10.11(a, b) with sloped electrode could generate a resulted force of DEP force and hydrodynamic force in a continuous fl ow [36]. The rare targeted cells are labeled with particles to differ in polarization response and then be refl ected by the resulting force into the collection channel. Besides applying the AC electric fi eld onto the metallic electrode, the insulator-based DEP (iDEP) in the DC electric fi eld could also generate a nonuniform electric fi eld. In Figure 10.11(d, e), the insulative post arrays change the distribution of uniform DC electric fi eld, which forms elec-tric gradient between the posts. It is a potential platform to selectively concentrate or separate different species of bacteria [37] or viable and unviable bacteria [38].

DEP force could also manipulate the activity of a single cell or populations of cells and trap them to a desired position. It makes sense in controlling the cellular microenvironment such as cell-matrix or cell-cell interaction and enhances the ef-fi ciency in cell-based biosensors. Joel Voldman and his corworkers designed the cylindrical gold electrodes applied with AC fi eld to trap the cells in the confi ned fi eld cage in Figure 10.11(c) [39]. It is like an “electrical tweezer” and can trap the particles with various scales and separate the cells by the viable property or their size.

In addition, the DEP is also used at the subcellular level, such as DNA and protein. For more effi ciency and precision, the DEP technique is usually realized by resorting to microfl uidic and physical structure by soft lithography. And they are all the fundamental tools in the high-throughput cell-based biosensor.


10.5 Biomimetic Olfactory and Gustatory Cell-Based Biosensors

The biological olfactory and gustatory systems can recognize and discriminate a large number of chemicals. The sensation processes are initiated by the interaction of chemical molecules and corresponding receptors. Subsequently, through the cel-lular signaling pathways, chemical signals are transduced to electrical signals, and the electrical signals propagate along the axons of neurons to the upper organs, where the signals are processed and encoded, and then the output signals are trans-mitted to the brain. At last, the brain can decode the signal and discriminate the corresponding olfaction and taste.

Since olfactory and gustatory systems play important roles in detecting envi-ronmental conditions, a lot of olfactory and gustatory research has been carried out due to potential commercial applications [40]. Electronic nose and electronic tongue belong to these technologies, which mimic animals’ olfactory and gustatory systems to detect smell and taste by exploiting sensitive materials. The detection ability of these devices mainly depends on the absorbability or catalysis of sensitive materials to special chemicals. Although great achievements have been made, this

Figure 10.11 (a) In the DACS system, the cells labeled with dielectrophoretically responsive label are defl ected into the collection stream. (b) The view of the device with channels and electrodes. (From: [36]. Reproduced from Proceedings of the National Academy of Sciences of the United States of America. © 2003, with permission from National Academy of Sciences, U.S.A.) (c) Pseudocolored scanning electron micrograph (SEM) of traps consisting of four electroplated gold electrodes ar-ranged trapezoidally along with the substrate interconnects. The small right picture shows a single trap. (From: [39]. Reproduced from Analytical Chemistry. © 2002, with permission from the American Chemical Society.) (d) The schematic of iDEP device with insulative post arrays for obstructions to change the DC electric fi eld distribution. (e) The live bacteria are separated from the live group in the electric fi eld gradient. (From: [38]. Reproduced from Analytical Chemistry. © 2004, with permission from the American Chemical Society.)

10.5 Biomimetic Olfactory and Gustatory Cell-Based Biosensors 247

method still has limitations in sensitivity and specifi city, compared with the biology binding of specifi c odorants or tastants to the olfactory receptor neurons or taste receptor cells [41].

10.5.1 Bioelectronic Nose and Bioelectronic Tongue

Göpel and his colleague fi rst proposed utilizing olfactory neurons as sensitive ma-terials to develop a bioelectronic nose [42–44]. They suggested the biomolecular function units can be used to develop highly sensitive sensors (like the dog’s nose to sense drugs or explosives) as an independent trend for an electronic nose or tongue chip. For example, they designed two different approaches for olfactory cell–based biosensors to be used (Figure 10.12). In the fi rst approach, biochemical sensors could be used to measure metabolic products of cultured cells in the medium. As a variant the mechanical contact between cells and between cells and substrates could be measured via conductivity measurements in which the cells act as resistor. In the second approach, the direct electrical response of a neural cell or a neural cell net-work could be measured. They think the fi rst approach has two main advantages: established sensors and cell lines can be used. Studies of neuronal cells on the other hand are diffi cult to perform because of several reasons: neuronal cells cannot be produced in continuous cell lines and they appear more fastidious in their choice of substrate. However, the unique advantage of high specifi city through receptor interaction can be used in this approach, and receptors may be incorporated inten-tionally for fi ne-tuning. And, all of these principles of different cell-based biosensors have been introduced in our book.

10.5.2 Olfactory and Gustatory Biosensors with Special Receptors

In recent years, some researchers have tried to express olfactory receptors in heter-ologous cells (i.e., HEK-293 cells and Escherichia coli) and then coupled them to

Figure 10.12 Schematic representation of possible setup to utilize electrogenic cells as sensor ele-ments for electronic nose or tongue chip. (From: [43]. Reproduced from Biosensors and Bioelectronics. © 1998, with permission from Elsevier B.V.)


some transducers (i.e., QCM and SPR). Ko and Park have expressed the olfactory receptor of rats, I7, on the membrane of human embryonic kidney (HEK)-293 cells successfully [45]. The expressed olfactory receptors were extracted and then coated on the QCM electrode surface. The results showed that compared with other odor-ants tested, the olfactory receptor I7 can interact with octyl aldehyde (octanal) strongly and differently, which is a specifi c odorant of I7 receptor. Sung et al. have also expressed a kind of Caenorhabditis elegans olfactory receptor in Escherichia coli as a fusion protein [46]. After confi rming that the olfactory receptor ODR-10 was expressed functionally in the host Escherichia coli, they extracted the crude membrane protein containing ODR-10 and coated the extracts on the electrode surface of QCM. Diacetyl, which is a natural ligand for the ODR-10 receptor, in-teracted more strongly with the olfactory receptor than other odorants tested. This hybrid biosensor can be used to detect ways that the olfactory receptors interact with different odorant. The research also indicated a dose-dependent response of olfactory receptor to the specifi c odorant molecules in certain ranges and had a low detection limit. This kind of olfactory-based QCM biosensor provided an effective method to identify specifi c odorant molecules of olfactory receptors.

10.5.3 Olfactory and Gustatory Cell-Based Biosensors

When olfactory receptors were expressed on the membrane of a heterologous cell system, the binding of olfactory receptors with specifi c odorant molecules could be detected by QCM or SPR. However, these cells were not excitable, so the action po-tentials produced by the interaction of receptors and glands could not be detected. Here, we will present the olfactory and taste cell-based biosensors in our laboratory. The implementation of the olfactory and taste cell sensors system based on LAPS with artifi cial olfactory and artifi cial taste sensor system for odor and ion sensor array are described [47–49].

The mechanism of signal detection and transduction in biological olfaction sensation is a lectrophysiological process, which fi rst takes place in olfactory epi-thelium neurons and their corresponding mitral cells of the olfactory bulb. Then the output signals are transferred to the olfactory cortex. The primary taste sensa-tions commonly are categorized as sweet, acid, bitter, salty, and umami. The taste sensation is initiated by the interaction of tastants with receptors in the apical mi-crovilli of taste receptor cells. Subsequently, the gustatory signals are processed and transduced gradually into the brain.

A dissociated culture of olfactory epithelium and olfactory bulb was prepared according to the basic method of the olfactory cells. For taste receptor cells, taste buds were isolated from the epithelium of the rat tongue. Figure 10.13 shows olfac-tory neurons and taste receptor cells growing on the surface of LAPS. After 7 days, some neuronal networks have been formed among the olfactory cells. After 3 or 4 days, taste receptor cells spread on glass coverslips in a bipolar shape, of which gustatory hairs on the top can be seen clearly. After 5 or 7 days, taste receptor cells tend to be more spheral without the sustentation of the tissue.

In order to primarily testify the feasibility of odorants detection, different concentrations (1 μM, 25 μM, 50 μM) of acetic acid, which is a kind of organic acid with a distinctive pungent odor, are taken as stimulant to olfactory receptor


neurons [Figure 10.14(a)]. The typical peaks were obtained as those of mitral cells, stained in the whole course of the acetic acid’s stimulation to the receptor cells. The result proved the excitability and desensitization of Glu to mitral cells more convincingly. With FFT analysis, we also found that olfactory receptor neurons showed a specifi c appearance of 24 Hz occurred repeatedly to the stimulant. The amplitude of the frequency increased in a concentration-dependent manner and disappeared along with the stopping of the odors stimulation 10 minutes later [Fig-ure 10.14(b)]. Thereafter, we used acetic acid as stimulation to mitral cells. Neither potential signals nor frequency signals were found. The results convince us that the frequency signal represented the binding of the odor to the receptor neurons, and only the receptor neurons gained odor sensitivity.

There are more than 2,000 distinct olfactory receptor neurons in the animal olfactory epithelium. Cultured rat olfactory neurons are excitable and can respond to odors. Although there is a large family of odor receptor neuron types (approxi-mately 1,000), each receptor cell class responds to many different odors. Thus, any particular odor activates a substantial subset of these receptors (on the order of hundreds of receptor types). The great variety, exquisite specifi city, high sensitivity, and fast response of olfactory receptor neurons make them an ideal candidate for olfactory cell–based biosensors.

At the same time, we selected tastant mixture to foliate and vallate taste buds isolated from adult SD rats. The mixture includes NaCl 0.1mol/L, HCl 0.01 mol/L, sucrose 0.5 mol/L, and MgSO4 0.03 mol/L, which represented salty, sour, sweet, and bitter, respectively. As shown in Figure 10.14(c), compared with control

Figure 10.13 Neurons cultivated on the LAPS for 7 days. (a) Triangle mitral cell of olfactory bulb. (b) Bipolar olfactory receptor neuron of olfactory epithelium. (c) Cultured taste buds on LAPS. (d) Taste receptor cells. (From: [47]. Reproduced from Biosensors and Bioelectronics. © 2006, with permis-sion from Elsevier B.V. From: [49]. Reproduced from Sensors and Actuators B: Chemical. © 2008, with permission from Elsevier B.V.)


conditions, there were representative fi ring spikes upon taste mixture stimuli. Two kinds of basic taste stimuli, salty and sour, were also applied respectively. In 10 tested taste cells, more than half showed intensive response to salty stimuli. The fi r-ing frequency increased, obviously. About 50% of the taste cells responded to sour stimuli. The responses under acidic conditions were found to be signifi cantly dif-ferent from control conditions in the frequency domain—a characteristic frequency around 16–17 Hz, as in Figure 10.14(d).

Currently, there are about 40 gustatory receptors playing a key role in bitter, sweet, and umami taste sensations. Cultured rat receptor cells are excitable and re-sponsive to taste stimuli. A particular tastant can activate a subset of taste receptor cells. The specifi c and sensitive features of gustatory receptor cells make them an ideal candidate for gustatory cell-based biosensors.

Anyway, utilizing olfactory and gustatory cells as sensitive materials to develop a bioelectronic nose or tongue chip is one of the independent trends in the research and development of electronic nose and electronic tongue, which makes use of bio-molecular function units to develop highly sensitive cell-based biosensors.

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Figure 10.14 Extracellular recording of olfactory cells and taste cells. (a) Odor-elicited extracellular potential of the olfactory receptor cells before (b) the odorant uniquely and consistently elicits strong frequency com-ponent extracellular potential. (c) Tastants elicited extracellular potential of the taste cells. (d) The tastants elicit strong frequency component. (From: [47]. Reproduced from Biosensors and Bioelectronics. © 2006, with permission from Elsevier B.V. From: [48]. Reproduced from Biosensors and Bioelectronics. © 2007, with permis-sion from Elsevier B.V.)


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255

Glossary

11-MUA 11-Mercaptopropionic acid3-MPA 3-Mercaptopropionic4-AP 4-Aminopyridine5-HT 5-hydroxytryptamineAl2O3 Aluminum oxideAAO Anodic aluminum oxideAC Alternating currentAFM Atomic force microscopy AM Amplitude modulationAng II Angiotensin IIANN Artifi cial neural networkAP Action potentialAPDMS Aminopropyldimethysilane As(III) Sodium arseniteATP Adenosine triphosphateATPase Adenosine triphosphataseAu AurumBAK Enzalkonium chlorideBCI Brain-computer interfaceBDMS ButyldimethysilaneBK BradykininBLM Bilayer lipid membraneBoNT Botulinum toxinBr BromineCa2+ Calcium ionCAD Computer-aided designCaM Ca2+/calmodulincAMP Cyclic adenosine monophosphateCARB CarbamylcholineCF Condition factorCFE Colony forming efficiencyCH=CH2 VinylCH3 MethylCHEMFET Chemically sensitive FETCHO Chinese hamster ovaryCHX Cycloheximide

256 Glossary

CI Cell indexCL CollagenCMS Cell monitoring systemCO2 Carbon dioxideCoA Coenzyme ACOOH CarboxylCPE Constant phase elementCPMP Committee for Proprietary Medicinal ProductsCROM CromakalimCSD Current source densityCSG Cell surface glycoproteinCSM Confocal scanning microscopeDC Direct currentDEP DielectrophoresisDETA DiethylenetriamineDG Dentate gyrusDIP Dual in-line packageDMSO DimethylsulfoxideDRIE Deep reactive ion etchingDSP Dithiobis succinimydylpropionateEC Entorhinal cortexECAR Extracellular acidifi cation rateECG ElectrocardiogramECIS Electric cell-subtrate impedance sensorECM Extracellular matrixECs Endothelial cellsEDAC 1-ethyl-3-(3-dimethylamino-propyl) carbodiimideEDL Electric double layerEGF Epidermal growth factorEIS Electrolyte-insulator-semiconductorEMI Electromagnetic interferenceENFET Enzyme modifi ed FETEOMOS Electrolyte-oxide-metal-oxide-semiconductorEOSFET Electrolyte-oxide-semiconductor FETEPSP Excitatory postsynaptic potentialERG ElectroretinogramERK Extracellular signal-regulated protein kinaseFC Flow cytometryFET Field effect transistorFFT Fast Fourier transformFIB Focused ion beamFLIPR Fluorescent imaging plate readerFM Frequency modulationFN FibronectinFRA Frequency response analysisGL Granular layerGM-CSF Granulocyte-macrophage colony stimulating factor

Glossary 257

GPCR G-protein coupled receptorGUVs Giant unilamellar vesicles H+ Hydrogen ionHCO3

− BicarbonateHEK Human embryonic kidneyhERG Human ether-á-go-go-related geneHF Hydro-fl uoric acidH-H model Hodgkin-Huxley modelHPTFs Human patellar tendon fi broblastsHTS High throughput screeningIC Integrated circuitICH International Committee on HarmonizationIDA Interdigitated arrayIDEs Interdigitated electrodesIGFET Insulated-gate FETIHP Inner Helmholtz planeIMFET Immune-reaction based FETIP3 Inositol 1,4,5-trisphosphateIr IridiumIS Impedance spectroscopyISFET Ion-selective fi eld effect transistorISI Interspike intervalISO IsoproterenolITO Iridium tin oxideJak2 Janus kinase 2K+ Potassium ionKATP channels ATP-sensitive potassium channelsKCOs Potassium channel openersKKT Kramers-Kronig transformsLAPS Light addressable potentiometric sensorLM LamininLOC Lab-on-a-chipLPCVD Low pressure chemical vapor depositionLSCM Laser scanning confocal microscopyLTD Long-term depressionLTP Long-term potentiationLVET Low volume eye testME MicroelectrodeMEA Microelectrode arrayMf Mossy fi bermicro-ERG MicroelectroretinogramMIMIC Micromolding in capillariesMIP-1α Macrophage infl ammatory protein-1αMIS Metal-insulator-semiconductorML Molecular layerMLAPS Multi-LAPSMOS Metal-oxide-semiconductor

258 Glossary

MOSFET Metal-oxide-semiconductor FETMRD50 50% decrease in the rateMSC Multichannel systemsMTA Multitransistor arrayNa+ Sodium ionnAChR Nicotinic acetylcholine receptorsNADH Nicotinamide adenine dinucleotideNGF Nerve growth factorNH2 AmineNH4

+ AmmoniumNHE Na+/H+ exchangeNHS N-hydroxy succinimideOH HydroxylOHP Outer Helmholtz planeP2R Purinergic type 2 receptorPAB Potentiometric alternating biosensor systemPBS Phosphate buffered salinePCL Purkinje cell layerPCM Physiocontrol microsystemPDL Poly-D-lamininPDMS Polydimethyl siloxane PECVD Plasma-enhanced chemical vapor depositionPEG Polyethylene glycol pH Potential of hydrogenPIN PinacidilPLC Phospholipase CPLL Poly-L-lysinePSD Phase sensitive detectionPSTH Post-stimulus time histogramPt PlatinumPTK Protein tyrosine kinasePVC Poly(vinyl chloride)QCM Quartz crystal microbalanceRASM Rat aortic smooth muscleRC Resistant and capacitanceRGDS Arg-Gly-Asp-SerRIE Reactive ion etching RPE Retinal pigment epitheliumRRA Radio receptor assaySAM Self-assembled monolayerSAMIM Solvent-assisted micromoldingSDG Secoisolariciresinol diglucosideSEM Scanning electronic microscopeSi3N4 Silicon nitrideSiO2 Silicon dioxideSNR Signal-noise ratioSPR Surface plasmon resonance

Glossary 259

Ta2O5 Tantalum oxideTCA Tricarboxylic acidTEP Transductive extracellular potentialTHF TetrahydrofuranTiN Titanium nitriteTMPP Trimethylopropane phosphateTMT Trimethyltin chlorideTNB 1,3,5-trinitrobenzeneTNF-α Tumor necrosis factor-αTTX TetrodotoxinUTP Uridine 5’-triphosphateVEGF Vascular endothelial growth factorVGCC Voltage-gated calcium channelVIPR Voltage ion probe reader VN VitronectinYFP Yellow fl uorescent proteinZnO Zinc oxideμTAS Micro total analysis systems

261

About the Editors

Ping Wang received a B.S., an M.S., and a Ph.D. from the Department of Electri-cal Engineering of Harbin Institute of Technology, Harbin, China, in 1984, 1987, and 1992, respectively. From 1992 to 1994 he was a postdoctoral fellow in the Biosensor National Special Laboratory, Department of Biomedical Engineering at Zhejiang University, Hangzhou, China. Presently, he is a professor of biomedical engineering of Zhejiang University, the deputy director of the Biosensors National Special Laboratory, and the vice-director of the Key Lab of Biomedical Engineer-ing of National Education Ministry of China, Zhejiang University. Dr. Wang is also the director of the Biomedical Measurement Technique Society of China, the vice-director of the Ion Sensors & Biosensor Society, and the vice-director of the Biomedical Sensors Technique Society of China. In addition, he has been a visiting scholar at Edison Sensors Laboratory of Case Western Reserve University and Bio-sensors and Bioinstrumentation Laboratory of University of Arkansas, in 2002 and 2005, respectively. He can be reached at [email protected].

Qingjun Liu received a B.S. in medicine from Gansu College of Traditional Chi-nese Medicine, Lanzhou, China, in 1999, an M.S. in medicine from Zhejiang Uni-versity of Traditional Chinese Medicine, Hangzhou, China, in 2002, and a Ph.D. in biomedical engineering from Zhejiang University, Hangzhou, China, in 2006. From 2006 to 2008, Dr. Liu was a postdoctoral fellow with the Department of Biomedical Engineering at Zhejiang University. In 2008 his Ph.D. dissertation was nominated for the National Excellent Doctoral Dissertations in China. Dr. Liu is currently an associate professor in the Biosensor National Special Laboratory at Zhejiang University. He is the director member of the Chinese Sensors Society, a guest researcher in State Key Laboratory of Transducer Technology at the Chinese Academy of Sciences in Shanghai, China, and a visiting scholar in the Department of Health Technology and Informatics at the Hong Kong Polytechnic University in Hong Kong, China. He can be reached at [email protected].

262 List of Contributors

List of Contributors

Hua CaiBiosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang UniversityZhejiang, Hangzhou, 310027Chinae-mail: [email protected]

Peihua ChenBiosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang UniversityZhejiang, Hangzhou, 310027Chinae-mail: [email protected]

Qingmei ChenBiosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang UniversityZhejiang, Hangzhou, 310027Chinae-mail: [email protected];

Zhaoying HuBiosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang UniversityZhejiang, Hangzhou, 310027Chinae-mail: [email protected]

Qingjun LiuBiosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang UniversityZhejiang, Hangzhou, 310027Chinae-mail: [email protected]

Ping WangBiosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang UniversityZhejiang, Hangzhou, 310027Chinae-mail: [email protected]

Chunsheng WuBiosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang UniversityZhejiang, Hangzhou, 310027Chinae-mail: [email protected]

Lidan Xiao Biosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang UniversityZhejiang, Hangzhou, 310027Chinae-mail: [email protected]

Hui YuBiosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang Univers ityZhejiang, Hangzhou, 310027Chinae-mail: [email protected]

Wei ZhangBiosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang UniversityZhejiang, Hangzhou, 310027Chinae-mail: [email protected]

Jun ZhouBiosensor National Special LaboratoryDepartment of Biomedical EngineeringZhejiang UniversityZhejiang, Hangzhou, 310027Chinae-mail: [email protected]

263263

Index

Acidifi cation detection, 127–29Action potential (AP), 45–47 defi ned, 45 Hodgkin-Huxley (H-H) model, 46–47Action potential measurements, 44–56 cell-electrode interface model, 52–54 cell-silicon interface model, 54–55 secondary transducers, 55–56 solid-electrolyte interface, 47–52Aminopropyldimethysilane (APDMS), 19Analytical model (ECIS), 165Anodic aluminum oxide (AAO), 239, 240Artifi cial neural network (ANN), 92AutoLAB SPR assay, 221

B cell-based biosensors, 229Biacore 2000/3000, 221Bilayer lipid membrane (BLM), 154Bioelectronic nose, 247Bioelectronic tongue, 247Bioimpedance, 151, 154

measurements, 151 measurement system, 161–64 technology, 154

Biological noise, 63Boltzmann constant, 81Boltzmann equation, 42Butyldimethysilane (BDMS), 19

Carbon sources, 38–39Cardiomyocytes, pharmacological

application on, 90–91Cell adherence and spreading, 14Cell adhesion, 52

ECIS, 167–68 on gold surface, 222–24 monitoring, 167–68, 212–15 QCM, 212–15

SPR, 222–24Cell-based biosensors action potential measurements, 44–56 advantages, 3–4 characteristics of, 3–4 common problems, 3 coupling, 2 defi ned, 1 developments, 233–50 disadvantages, 4 electric cell-substrate impedance

sensors (ECIS), 4, 7–8, 151–76 external stimulation, 2 gustatory, 246–50 immune, 10, 225–29 impedance measurements, 56–62 integrated, 233–37 light addressable potentiometric

sensors (LAPS), 4, 7, 119–46 mechanisms, 37–64 metabolic measurements, 38–44 microelectrode arrays (MEA), 4–5,

65–93 with microfl uidic chips, 241–46 multifunctional, 236–37 noise sources, 62–63 olfactory, 246–50 patch clamp chips, 4, 8–9, 179–203 potential uses, 3 quartz crystal microbalance (QCM),

4, 9, 207–30 as research hotspot, 1 schematic diagram, 2 surface plasmon resonance (SPR), 4,

10, 217–25 types of, 4–10Cell-based FET biosensors, 4, 5–7, 97–116 application, 106–13

264 Index

Cell-based FET biosensors (continued) cell culture, 99 cell-integrated extracellular system,

106 cell metabolism, 99 cell microenvironment monitoring,

112–13 development trends, 114–15 electrophysiological recording, 106–8 encapsulated array chip, 101 enhancement type n-channel

MOSFET, 99 extracellular potential measurement, 99 fabrication chart, 100 fabrication of, 100–102 gate insulators, 115 neuronal network study, 109–11 principle, 98–100 silicon chip and neuron

communication, 108–9 summary, 115–16 system, 102–3 theoretical analysis, 103–6Cell culture in vitro, 16Cell culture on chips, 13–33 cell immobilization factors, 14–20 fast ink-jet printing, 26–27 methods, 20–32 microcontact printing, 24–26 microfl uidic technology, 30–32 neurons, 22 perforated microelectrode, 27–28 physical structure, 22–24 self-assembled monolayer (SAM),

29–30 surface modifi cation rules, 16–20Cell-extracellular matrix (ECM), 13Cell immobilization factors, 14–16 biological factors, 15–16 chemical factors, 15 physical factors, 14–15Cell index (CI), 167Cell metabolism, 38–40Cell microenvironment monitoring,

112–13Cell monitoring systems (CMSs), 6, 113,

234

Cells cytoxin response, 172 dendritic, 227 dissociated, 18–19 electrical activity in, 44 electrode-attached, 158 extracellular acidifi cation of, 39 guiding, 188 mast, 226 neuronal, culturing, 109 patterning, 16 preparation, 193–94Cell-semiconductor hybrid LAPS devices,

141–43Cell-silicon interface model, 54–55Cell-substrate impedance, 154–56Charge transfer resistance, 50Chemical coating, 18–20Chemically sensitive FET (CHEMFET), 97Chip surface modifi cation rules, 16–20 roughness, 14CMOS-MEA, 76Coating protein, 81Combinatorial chemistry, 198Compensation, patch clamp system,

192–93Complementary metal oxide

semiconductor (CMOS), 111Computer-aided design (CAD), 198Confocal scanning microscope (CSM), 23Cultured cell coupling, 53Current source density (CSD), 86CytoPatch chip, 190–91Cytotoxicity assays, 172–73

Debye length, 49Deep reactive ion etching (DRIE), 182Dendritic cell-based biosensors, 227–28Dendritic cells, 227Dielectrophoresis (DEP), 245–46Dimethylsulfoxide (DMSO), 139Drug analysis, 137–43 cell semiconductor hybrid LAPS device

for, 141–43 with microphysiometer, 138–41

Index 265

quartz crystal microbalance (QCM), 211–12

Drug discovery, 199–200 ionic channel measurement, 199 quartz crystal microbalance (QCM),

211–12 stage aspects, 199Dual in-line package (DIP), 101–102

Electrical fi elds/dielectrophoretic force, patch clamp system, 189

Electrical impedance spectroscopy, 175Electric cell-substrate impedance sensors

(ECIS), 4, 7–8, 151–76 AFM combination, 168 analytical model, 165 applications, 167–73 bioimpedance measurement system,

161–64 cell adhesion, 167–68 cell migration and invasion, 169–70 cell substrate impedance basic, 154–56 cellular ligand-receptor interactions,

170–71 cytotoxicity assays, 172–73 data calculation and presentation,

165–67 defi ned, 7–8, 151–52 development trends, 173–75 device fabrication, 160–61 electrochemical impedance, 152–54 high-throughput screening, 173–74 integrated chip, 175 introduction to, 151–52 lumped model, 164–65 morphology, 167–68 platform, 8 principle, 152–59 proliferation, 167–68 in real-time cell monitoring, 62 spreading, 167–68 summary, 175–76 theoretical analysis, 164–67Electrocardiogram (ECG), 66Electrochemical impedance, 152–54Electrode/liquid interface, 156Electrode noise, 62–63

Electrodepositing, 81Electrolyte-insulator-semiconductor (EIS),

121Electrolyte-oxide-metal-oxide-

semiconductor (EOMOS), 111Electrolyte-oxide-semiconductor (EOS)

transistors, 111Electrolyte/oxide/semiconductor FET

(EOSFET), 111Electromagnetic interference (EMI), 63Electrophysiological recording, 106–8Electrophysiology experiments, 198Electrorotation, 245Enzyme modifi ed FET (ENFET), 97Epidermal growth factor (EGF), 224Equivalent circuits of IDEs, 157 model, 79–80Extracellular acidifi cation rate (ECAR),

136 detection, 127 measurements, 138Extracellular matrix (ECM) substrates,

167, 168Extracellular metabolite sensing, 43–44Extracellular pH monitoring, 40–43 defi ned, 40 site-binding theory, 40–43Extracellular recording, 250Extracellular signal analysis, 82–83Extracellular voltage, 53

Faraday cage, 193Fast Fourier transform (FFT), 162 analysis, 249 impedance measurement, 163Fast ink-jet printing, 26–27Field-effect transistors (FETs), 4, 5–7, 21 array of, 102 chemically sensitive (CHEMFET), 97 enzyme modifi ed (ENFET), 97 fabrication, 5 high-density arrays, 97 immune-reaction based (IMFET), 97 insulated gate (IGFET), 5, 55, 98 ion-sensitive (ISFET), 44, 103 metal free gate, 97

266 Index

Field-effect transistors (continued) See also Cell-based FET biosensorsFlow cytometry (FC), 201Fluorometric imaging plate reader (FLIPR),

133, 198Frequency response analysis (FRA), 162

Giaever and Keese model, 60Giant unilamellar vesicles (GUVs), 196Glass, 15, 69–70 chips based on, 183–85 etching process, 74 as pipette electrode, 183 Pyrex, 74 in 3D MEA, 72Gouy-Chapman model capacitance, 49 charge distribution, 156G-protein coupled receptor (GPCR), 170,

171, 174Granulocyte-macrophage colony

stimulating factor (GM-CSF), 134Guiding cells, 188Gustatory cell-based biosensors, 246–50 defi ned, 246 extracellular recording, 250 receptors, 248 with special receptors, 247–48

Helmholtz capacitance, 49hERG blockers, 200, 201HexaMEA, 75High-throughput screening (HTS), 173–74,

174, 197–98 ideal, 198 methods, 198Hodgkin-Huxley (H-H) model, 46–47, 52 defi ned, 46 illustrated, 79Hydrophilicity, 17–18

Iconic screening, 198Immune cell-based biosensors, 10, 225–29 B, 229 dendritic, 227–28 introduction to, 225 mast, 226–27

Immune cells, 10, 225 classes of, 225 mast, 226 number of, 225Immune-reaction based FET (IMFET), 97Impedance bioimpedance, 151, 154 cell-substrate, 154–56 as complex value, 153 electrical spectroscopy, 175 electrochemical, 152–54 FFT-based measurement, 163 membrane, 56–57 properties analysis, 80–82 sensing, 155–56 time-lapse imaging, 172 total, schematic diagram, 157, 158 Warburg, 50–51, 153Impedance-based biosensors, 152Impedance measurements, 56–62 membrane impedance, 56–57 of population of cells, 59–61 secondary transducers, 61–62 of single cell, 57–59Impedance spectroscopy (IS), 80Indium-tin-oxide (ITO), 214Inner Helmholtz plane (IHP), 48Insulated gate FETs (IGFETs), 5, 55 D-type, 98 E-type, 98Integrated cell-based biosensors, 233–37 chip of same/similar functional

sensors, 234–35 defi ned, 233 multifunctional chip monitoring

different parameters, 236–37 multisensors, 235–36 with nanotechnology, 237–41 with sensor array, 235Interdigitated array (IDA), 236Interdigitated electrodes (IDEs), 152 AC frequency, 156–59 diverse schemes, 156 equivalent circuit of, 157 illustrated, 155 sensitivity characteristics, 156, 156–59Interfacial capacitance, 49

Index 267

Intracellular detecting, 241Intracellular recording techniques, 45Ion channels, 179Ion fl ux assay, 198Ionic channels research, 194–98Ion-sensitive fi eld-effect transistor

(ISFET), 44 cell interspace, 113 encapsulation, 103 illustrated, 113 invention of, 112–113 pH sensitive, 112 sensor system based on, 102–3 with/without cells, 113Ion-sensitive membrane based

chalcogenide glass, 44IonWorks HT planar patch clamp system,

202IonWorks Quattro system, 196–97

Johnson-Nyquist formula, 80Junction resistances, 166

Kramers-Kronig transforms (KKT), 161

Lab-on-chips (LOCs), 30, 92 cell-based, 242 size, 242LabVIEW, 131Laser capture microdissection technology,

201Laser scanning confocal microscopy

(LSCM), 201Leakage current, 46Ligand biding assays, 198Ligand/receptor binding, 134–36 identifi cation of, 136–37 interactions, monitoring, 170–71 radio receptor assay (RRA), 136Light-addressable MEA chip, 76Light addressable potentiometric sensors

(LAPS), 4, 7, 44, 56, 119–46 advantages, 132 application, 132–43 for array sensing application, 120 for biomedical applications, 121

cell-semiconductor hybrid, 129–32, 132

in cell-semiconductor hybrid research, 120

characteristic I-V curves, 121 defi ned, 7 detecting system, 129–32 developing trend, 143–46 device, 124–25 disadvantage, 143 drug analysis, 137–43 electrolyte-insulator-semiconductor

(EIS), 121 equivalent circuit, 124 fabrication, 125 fundamental principle, 121–22 heterostructure, 119 identifi cation of ligand/receptor,

136–37 integration of, 120 introduction, 119–46 ligand/receptor binding, 134–36 measurement rate, 144 metal-insulator-semiconductor (MIS),

121 microphysiometer system, 126–29 multifunctional, 145–46 multi- (MLAPS), 120, 141, 145 numerical analysis, 122–24 for parallel detecting, 144–45 physiocontrol microsystem (PCM),

120 potentiometric alternating biosensor

system (PAB), 120 principle, 121–24 realizations, 7 schematic diagram, 7 signaling mechanism study, 133–34 spatial resolution, 125 summary, 146 as surface potential detector, 122 surface potential system, 130 working principle illustration, 121Long-term depression (LTD), 86Long-term potential (LTP), 86Low-pressure chemical vapor deposition

(LPCVD), 182

268 Index

Low volume eye test (LVET), 138Lumped model, 164–65

Mast cell-based biosensors, 226–27Mast cells, 226Membrane impedance, 56–57Membrane potential assay, 198Metabolic measurements, 38–44 cell metabolism, 38–40 extracellular metabolite sensing,

43–44 extracellular pH monitoring, 40–43 secondary transducers, 44Metal-insulator-semiconductor (MIS), 121Michigan probe, 76Microcontact printing, 24–26, 245 in patterning neural cells, 26 process illustration, 25 as soft lithography technique, 24Microelectrode arrays (MEA), 4–5, 55–56,

61 application, 66 basic network study, 84–85 chips, 74–77 CMOS, 76 commercial products, 67 conformal, 75 for current source density (CSD), 86 design, 71–72 development, 66 development trends, 92–93 electrodes on, 27 equivalent circuit model, 79–80 fabrication, 4, 66, 69–74 fabrication illustration, 72 with fi ve subchambers, 85 HexaMEA, 75 impedance properties analysis, 80–82 key technologies, 5 lab on a chip, 92 layout and structure, 74–75 light-addressable chip, 76 long-term monitoring, 84 measurement setup, 77–79 metallic layer, 70 passivation layer, 70 pharmacological application, 89–91

planar, 69–72 platform, 67 portable system, 92 preamplifi er connection, 78 principle, 68–69 retina on, 88–89 “sandwich” structure, 69 schematic diagram, 5 signaling process, 77, 78 signal process, 79–83 slice on, 86–87 summary, 93 surface morphology, 71 3D, 72Microfl uidic cell-based biosensors, 241–46 microfl uidic fl ow, 242–43 soft lithography, 243–45Microfl uidic fl ow, 242–43Microfl uidics patch clamp system, 189–90 technology, 30–32Micromolding in capillaries (MIMIC),

243–44Microphysiometer, 7, 126–29 acidifi cation detection, 127–29 drug analysis with, 138–41 fl ow chamber, 126, 127 objectives, 126 schematic, 126 studies based on multi-LAPS, 141 testing chamber, 126, 127Micro total analysis systems (μTAS), 30,

241Microtransfer molding (μCP), 243Monopolar electrodes, 155–56Multifunctional cell-based biosensors,

236–37Multifunctional LAPS system, 145–46Multi-LAPS (MLAPS), 120 microphysiometer studies based on,

141 signal generator, 145

Nanobioelectronics, 114–15Nanomaterials, 237Nano-micropatterned cell cultures, 238–39Nanoporous-based biosensors, 239–40

Index 269

Nanoprobes to intracellular nanosensors, 240–41

Nanosensors, 238Nanotechnology cell-based biosensors with, 237–41 defi ned, 237Nerve growth factor (NGF), 134Neural networks artifi cial (ANNs), 92 pharmacological application on, 91Neural wall, 22Neuronal network study, 109–12Neurons extracellular potential, 82 immobilization, 110 intracellular potential, 82 two-way silicon communication,

108–9Noise biological, 63 electrode, 62–63 electromagnetic interference (EMI), 63 sources, 62–63NPC1 Port-a-Patch, 195

Olfactory cell-based biosensors, 246–50 defi ned, 246 extracellular recording, 250 with special receptors, 247–48Outer Helmholtz plane (OHP), 48, 49

Parallel detecting, 144–45PARTCELL technique, 242, 243Patch clamp limitations, 180–81 principle, 180 technique, 179–81 whole-cell recordings, 196 work modes, 180Patch clamp amplifi er, 191–92 components, 191 current clamp mode, 192 features, 191Patch clamp chips, 4, 8–9, 55, 179–203 amplifi er, 191–92 based on glass, 183–85 based on PDMS, 185–87

based on polyimide, 187–88 based on silicon, 182–83 biomedical application, 194–203 capacitance, 182 cells preparation, 193–94 compensation, 192–93 defi ned, 181 development trends, 202–3 device, 182–88 drug discovery, 199–200 drug safety, 200–202 electrical fi eld/dielectrophoretic force,

189 electrode confi guration, 9 Faraday cage, 193 guiding cells, 188 introduction to, 179 ionic channels research, 194–98 key technique, 9 microfl uidics, 189–90 parallel recordings, 182 problems, 181 resistance, 182 schematic diagram, 8 suction/pressure, 189 summary, 203 theory, 179–82Patch-to-FET sealing, 54PatchXpress, 199–200Pharmacological application (MEA),

89–91 on cardiomyocytes, 90–91 on neural networks, 91Pharmacology testing, 139Phase sensitive detection (PSD), 162Physiocontrol microsystem (PCM), 120Planar electrodes interdigitated system, 156 monopolar system, 155–56Plasma-enhanced chemical vapor

deposition (PECVD), 160Polydimethyl siloxane (PDMS), 22, 24 chips based on, 185–87 membrane fabrication, 32 primary support, 185 secondary support, 186Polyimide, 187

270 Index

Polyimide-based chips, 187–88Portable MEA system, 92Port-a-patch system, 195Post-stimulus time histogram (PSTH), 85Potassium channel openers (KCOs), 90Potentiometric alternating biosensor

system (PAB), 120Probe arrays, 76

Qpatch 16, 200Quartz crystal microbalance (QCM), 4, 9,

207–30 advantages, 207 biomedical application, 211–17 cell adhesion monitoring, 212–15 cell attachment and spreading, 210 cell interaction, 214 coated with ITO, 214 defi ned, 9, 207 device illustration, 208 devices, 211 with dissipation (QCM-D), 215 drug analysis, 211–12 drug discovery, 211–12 exocytosis measurement, 215–16 frequency shift/mass adsorption

relationship, 209 gold electrode surface, 214 introduction to, 207–30 measurements, 213 oral epithelial cell-microparticle

interaction, 216 principle of, 208–10 schematic diagram, 9 sensitivity, 208 sensors and measurement system,

210–11 signals, 214 system schematic, 213 vesicle retrieval measurement, 215–16Quartz membranes, 184

Radio receptor assay (RRA), 136Reactive ion etching (RIE), 160, 182Replica molding (REM), 243Retina on MEA, 88–89Roughness, 18

Scanning electron microscope, 23Secondary transducers action potential measurements, 55–56 array of FET, 56 ECIS, 62 impedance measurements, 61–62 ISFET, 44 LAPS, 44, 56 MEA, 55, 61 metabolic measurements, 44 patch clamp, 55Self-assembled monolayers (SAM), 24,

29–30, 245Signaling mechanism study (LAPS),

133–34Signal-noise ratio (SNR), 76Signal process (MEA), 79–83 equivalent circuit model, 79–80 extracellular signal analysis, 82–83 impedance properties analysis, 80–82 waveform analysis, 83Silicon, 18 cell culture on, 23 corrosion of, 97 needles, 103 patch clamp chip based on, 182–83 as semiconductor material, 69 in 3D MEA, 72 two-way neuron communication,

108–9Silicon grasses, 18Silicon-neuron junction, 108Site-binding theory, 40–43Soft lithography, 24, 243–45Solid-electrolyte interface, 47–52 charge transfer resistance, 50 diffusion and Warburg impedance,

50–51 illustrated, 48 inner Helmholtz plane (IHP), 48 interfacial capacitance, 49 outer Helmholtz plane (OHP), 48, 49 spreading resistance, 51–52Solvent-assisted micromolding (SAMIM),

244Spreading resistance, 51–52Suction/pressure, patch clamp system, 189

Index 271

Surface plasmon resonance (SPR), 4, 10, 217–25

biomedical application, 221–25 cell adhesion on gold surface, 222–24 cell-based measurement of odorant

molecules, 221–22 defi ned, 10, 218 evanescent fi eld, 218 introduction to, 217–19 optical detection process, 219 principle, 219–20 sensors and measurement system,

220–21 signal relationship with intracellular

signaling, 224–25 uses, 10

3D microelectrode arrays (MEA) cerebellar vermis on, 87 electrode tips, 74 fabrication, 72

fabrication steps, 73 glass etching process, 74 microelectrode shaping, 75 process fl ow, 73 See also Microelectrode arrays (MEA)Transductive extracellular potential (TEP),

103Transmembrane potential, 81Traveling-wave dielectrophoresis

(TWDEP), 245Tumor chemosensitivity assays (TCA), 234

Utah probe, 76

Voltage ion probe reader (VIPR), 198

Warburg impedance, 50–51, 153Whole-cell recordings, 197

Zinc oxide (ZnO), 240

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Cell Based Bio Sensors