Oxygen Sensor Developments

for Real-time Monitoring of

Single Cells in Restricted

Volumes

Lloyd W. Burgess

CPAC SI 2010

Abstract: Platinum porphyrin based oxygen sensors are utilized to

measure oxygen consumption rates of single mammalian cells

diffusionally isolated in micro-wells, with volumes restricted to 300-400

picoliters. The challenges of this measurement will be discussed and

current methods to overcome these challenges will be presented.

• Measure multiple parameters in individual living cells in real-time to correlate cellular events with genomic information.

• Develop modular, affordable microsystems for analyzing complex cellular processes.

• Apply these systems to specific biological problems:

• Proteomics

• Metabolic Networks

• Remove the walls of separation from the various sciences and create a very interdisciplinary environment

Microscale Life Sciences Center

(MLSC)Comprehensive Understanding of

Complex Cellular Processes

• Challenge

– Inherent heterogeneity of cell populations [Raser & O’Shea 2005]

• Gene expression

• Phenotype

– Heterogeneity at cellular level underlies transitions to disease

states

• Cancer

• Inflammatory response-linked diseases

• Approach

– Microscale technology for dynamic, real-time, multi-parameter

analysis of single cells

– Apply this technology to fundamental problems of biology and

health for early diagnosis and treatment

MLSC Goal: Understanding, Predicting,

and Diagnosing Cell Function/Dysfunction

Two model systems:

– Pro-inflammatory cell death or pyroptosis (mouse

macrophage)

– Neoplastic progression of Barrett’s esophagus (human

epithelial cells)

Approach

Single-cell & cell-cell analysis to dissectmechanisms involved in live/die decisions

Barrett’s Esophagus:

a model system for heterogeneity

• Pre-malignant neoplasia,

preceded by episodes of gastric

reflux

• Morphologic progression:

• Genetic progression: p16/p14ARF -> p53 -> tetraploid ->

aneuploid

B

ESquam

ous

ulcer

low-grade

dysplasia

high-grade

dysplasia

Esophageal

Adenocarcinoma

BE metaplasia

w/o dysplasiasquamous

Having an abnormal DNA content means a person is

19 times more likely to develop esophageal cancer

Stroma

Buried

epithelium

Surface epithelium

StromaStroma

Buried

epithelium

Buried

epithelium

Surface epitheliumSurface epithelium

Experiments in Progress

• Characterize the single-cell and inter-cell line

heterogeneity in energy pathways (glycolysis

and oxidative phosphorylation) and the

Warburg effect in squamous, BE and EA cell

lines.

• Determine the effects of single-cell and inter-

cell line heterogeneity on the response to

hypoxic stress in squamous, BE and EA cell

lines.

• Measure mitochondrial mass and activity in

cells using JC-1 dye

• Synchronize cells when seeding to

determine effects of cell cycle on

heterogeneity in O2 consumption

• Measure heterogeneity in apoptosis

response to hypoxic stress for each cell line

Experiments in Progress

• Objective: Development of a system that

enables one to make real-time

measurements of metabolic processes at

the level of a single cell in order to correlate

cellular events with genomic information.

• Approach: Integrate reporter chemistry, cell

localization strategy, and readout signal

train into micro fluidic system.

Challenge

Overview of Method

Microwell

Oxygen

Sensor

Eukaryotic

Cell

Microscope

Objective

Piston

Glass Lid

Glass Chip

Quartz

Window

100 μm

1 cm

Glass tip

PDMS

donut

Steel

shaft

Former

piston

style

New piston

style

3µm

lips

Glass barrier

Piston Actuator

Fringe Confirmation of Sealing

• Squeegee (wet or dry)

• Spin coat

• Direct deposit1. Piezo injection

2. Pico-Injector

Sensor Deposition Approaches

* Alternative - Sensors on/in lid

Oxygen SensingPhosphoresence Time Decay Measurement

Phosphor-embedded Beads

Platinum-tetra(pentafluorophenyl)porphyrin

– PtTFPP –

Pt-Octaethylporphine

(PtOEP)

Oxygen consumption rate

measurements on mouse

macrophages

Zeiss System

Graphs showing an

example of bulk well

drawdown from 7ppm to 0

ppm (left-hand graph) and

a subsequent 0 ppm seal

(right-hand graph) of 20

minutes

Adaptation for Bulk Measurements

Cell Test 6/26/06

-0.05

0

0.05

0.1

0.15

0.2

0.25

0 1 2 3

# of cells

de

lta

O2

(p

pm

/min

)d

elta

O2

(pp

m/m

in)

DO

2(p

pm

/min

)

• 2 Locations (3X3)

• 1 chip (81 wells)

• 4 sequential repetitions

• Mouse macrophage

• Live: Calcein AM

• Dead: Sytox Orange

• 10x objective

Sequential Drawdowns

Cell DrawdownHistogram

0

2

4

6

8

10

12

14

0 0.02 0.04 0.06 0.07 0.09 0.11 0.13 0.15

Bin

Fre

qu

en

cy

Δ O2 (ppm/min)

Fre

qu

en

cy

Histogram

Single Cell Drawdown

# of cells

Cell Test 6/26/06 1

-0.05

0

0.05

0.1

0.15

0.2

1 2 3 4 5 6 7 8 9

w ell #

de

lta

O2

(p

pm

/min

)

1

2

3

4

Average

2

1

0

2

1

0 00 0

de

lta

O2

(pp

m/m

in)

Location 1Cell Test 6/26/06 1

-0.05

0

0.05

0.1

0.15

0.2

1 2 3 4 5 6 7 8 9

w ell #

de

lta

O2

(p

pm

/min

)

1

2

3

4

Average

2

1

0

2

1

0 00 0

de

lta

O2

(pp

m/m

in)

Location 1 Cell Test 6/26/06 2

-0.05

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9

w ell #

de

lta

O2

(p

pm

/min

)

1

2

3

4

Average

2

1

0

1 1 1

0 0 0

Location 2

delt

a O

2(p

pm

/min

)

Cell Test 6/26/06 2

-0.05

0

0.05

0.1

0.15

0.2

0.25

1 2 3 4 5 6 7 8 9

w ell #

de

lta

O2

(p

pm

/min

)

1

2

3

4

Average

2

1

0

1 1 1

0 0 0

Location 2

delt

a O

2(p

pm

/min

)

Major Issues with the

Sensor System

•Induction effect (aka mystery effect!)

•Uniformity of distribution

•Measurement precision

•Multiple analyte capability

10x10 Binning A=10 B=60

-1

0

1

2

3

4

5

6

7

8

9

0 50 100 150

Data Points

O2

(p

pm

)

Wells Drawdown in the Absence of Cells!

Aqueous O

xygen C

onc. (p

pm

)

Time (min.)

Poor Precision in Calibration

Data The “Induction Effect”

(oxygen consumption in

the absence of cells)

Problems Encountered

0 20 40 60 80 100 120 140 160Distance (µm)

0

50

100

150

200

250

Intensity

Intensity ChD

Consistent microwell volumes are required for accurate comparison

of oxygen consumption rates well-to-well. Wells are etched into

fused silica using a Si3N4 mask and reactive ion dry etching. This

wafer material and fabrication process now allow us to achieve

uniform microwell volumes with less than 0.4% standard deviation

in well volume across an entire wafer.

Micro-Well Fabrication

Pico-Injector and MM Workstation

20 micron i.d., 150 micron o.d.

Fused silica capillary, tapered

20 micron i.d., 150 micron o.d.

Fused silica capillary, tapered50 micron i.d., 75 micron o.d.

Polyimide capillary

Commercial tip

2 micron tip i.d.

20 micron i.d., 150 micron o.d.

Fused silica capillary, tapered50 micron i.d., 75 micron o.d.

Polyimide capillary

Commercial tip

2 micron tip i.d.

Solutions in Progress

Optimized Ratiometric Lifetime Determination

10x10 Binning A=10 B=60

-1

0

1

2

3

4

5

6

7

8

9

0 50 100 150

Data PointsO

2 (

pp

m)

1x1 Binning A=10 B=60

-1

0

1

2

3

4

5

6

7

8

9

0 50 100 150

Data Points

O2

(p

pm

)

Sensors are Now

“Picoinjected”

Software Modifications Improve SNR

Solutions in Progress II

A response curve is shown where the data is so closely spaced for the nine different microwells of

an array, individual data points are difficult to discern. The bottom portion of the Figure depicts the

relative standard deviations (RSD) for each data point in expanded scale to see the variations. Note

that all RSDs are less than 0.5%.

Sensor Material Developments

Molecular Orbital Overlap of O2 with PtE.S Dy and H. Kasai, e-J. Surf. Sci. Nanotech., 3, 2005, 473-5.

Platinum octaethylporphyrin

Platinum tetra(pentafluorophenyl)porphyrin

= Sites for bulky functional groups

Layered Microspheres

Micelle Structure can Encapsulate a

Variety of Sensor Materials

Oxygen permeable layer

Dye core

Water soluble coating

Advantage of modified PtTFPP

Preventing self-quenching

Long lifetime

High phosphorescence QY

Dual Sensors (Oxygen &

pH)

Self-assembly in water

hydrophobic block

hydrophilic block

pH-sensitive chromophores

oxygen sensor

Well-dispersed solution in THF

Micelles with oxygen sensor in the core and pH sensor on the shell

1. Deposition and

evaporation

2. Crosslinking

Stable sensor spots in the wells

Solution of an ATP sensor under 370 nm excitation with increasing concentration of ATP

500 550 600 650 700 750

Flu

ore

scence Inte

nsity (

a.u

.)

Wavelength (nm)

ATP Conc.

0 mM

0.2 mM

0.4 mM

0.6 mM

0.8 mM

1.0 mM

0.0 0.2 0.4 0.6 0.8 1.0-2

0

2

4

6

8

10

12

14

(F-F

0)/

F0

ATP Concentration (mM)

Before and after

addition of ATP

ATP Sensors

Accurately measuring ATP levels, in conjunction with lactate

production and oxygen consumption rate provide a more

comprehensive picture of the relative contributions of oxidative

phosphorylation and glycolysis to overall energy production in

genetically defined cell lines.

A complementary extracellular ATP assay based on increasing

emission intensity (14 times at 1 mM of ATP) of supramolecular

self assembly of ATP and sensor material with aggregation-

induced emission (AIE) property are being developed.

Requirements for Sensor

Characterization•Deposition: must be uniformly deposited into the cells with surface contamination kept as

minimal as possible. Deposition characteristics must be stable enough to form “coffee rings”

in repeatable fashion with daily fluctuations in local temperature, pressure, and humidity

conditions.

•Lifetimes: must be short enough to allow biological experiments to proceed in an acceptable

time frame and long enough to allow good dynamic range with the available data acquisition

system. Lifetime characterizations must be performed in the same manner and under the

same physical conditions as an actual experiment will be performed.

•Dynamic Range: this must be assessed over two ranges; zero to midpoint and midpoint to

span. An assessment of linearity (or non-linearity) should be reported over each range.

•Background: the background signal must be evaluated with the ORLD algorithm under LED

illumination. The uniformity across microwell arrays should be especially examined.

•Blanks: blank measurements (i.e. drawdown measurements with no cells present) for

several microwell arrays should be measured in order to characterize the induction effect.

•Excitation/emission: these spectra should be collected and compared for the polymer with

cross-linker present, then again for the polymer, cross-linker, and sensor material mixed

together.

•Surface adhesion: characterization should include an assessment of how well the sensor

sticks to microwells after typical handling and cleaning procedures. This assessment must

also include how effective potential cross-linking procedures are, such as by examining wash

solution for monomer via an analytical method such as HPLC.

•Toxicity: persons familiar with such procedures should assess cell toxicity.

•Photobleaching: the extent of photobleaching must be examined not only for typical

experimental conditions, but also for any photo-polymerization that may be employed for

sensor deposition.

Cell Seeding/Manipulation

•Random seeding

•Aspirate and deposit

•Laser trapping

•Other approaches

Current experimentation is being carried out on a Nikon Eclipse Ti-E platform (A). The system has two stacked filter

turrets with six positions for excitation/emission which are easily configured to accept laser, LED, or white light input.

The bottom turret has an external shutter, and the upper turret can be configured to switch between modulated sources,

or to act as an additional emission port.

A B

C

Current Nikon System

• Oxygen measurements in highly restricted

volumes present new challenges for an old

problem

• High data variability has been reduced by

controlling sensor deposition, implementing

improved measurement methods, and by

improving image processing methods

• Sensor materials are being developed to limit

the induction effect, improve signal-to-noise, and

allow for intercellular or intracellular delivery of a

multitude of sensors

Summary

AcknowledgmentsNIH NHGRI CEGS Grant www.life-on-a-chip.org

Arizona State UniversityDeirdre Meldrum

Mark Holl

Joseph Chao

Jeff Houkal

Michael Zhang

Ying Tian

Cody Young

Pahnit Seriburi (UW)

Clement Sun (UW)

Patrick McVittie (UW)

Barry Lutz (UW)

Fred Hutchinson Cancer Research CenterBrian Reid

Tom Paulson

Carissa Sanchez

Martin Suchorolski

Jesse Salk

Brandeis UniversityLawrence Wangh

Cristina Hartshorn

University of WashingtonPeter Rabinovitch

Judy Anderson

David Hockenbery

Jan McClure

University of Washington (cont.)Mary Lidstrom

Tim Strovas

Linda Sauter

Sarah McQuaide

Tim Molter

Shawn McGuire

Babak Parviz

Sam Kim

Ethan Saeedi

James Etzkorn

Lloyd Burgess

Noel Fitzgerald

Joe Dragavon

Brad Cookson

Susan Fink

Meng Zhang

Norm Dovichi

Kimia Sobhani

D Michels

Alex Jen

Sei-Hum Jang

Karl Bohringer

Ashutosh Sastry