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Calibration and Normalization of Protein Microarray Data Charlene Liang 1* , Virginia Espina 2 , Julia Wulfkuhle 2 , Emanuel F. Petricoin 3 III and Lance A. Liotta 2 , Yuexia Li 1 , Minzi Ruan 1 , 1 VigeneTech Inc, 2 National Cancer Institute, Center for Cancer Research, Laboratory of Pathology, FDA-NCI Clinical Proteomics Group, 3 Food and Drug Administration, Office of Cellular and Gene Therapy, Center for Biologic Evaluation and Research, FDA-NCI Clinical Proteomics Group, Bethesda, MD * Correspondence: [email protected]

Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

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Page 1: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Calibration and Normalizationof

Protein Microarray Data

Charlene Liang1*, Virginia Espina2, Julia Wulfkuhle2,

Emanuel F. Petricoin3 III and Lance A. Liotta2, Yuexia Li1, Minzi Ruan1,  

1VigeneTech Inc,2National Cancer Institute, Center for Cancer Research, Laboratory of Pathology,

FDA-NCI Clinical Proteomics Group, 3Food and Drug Administration, Office of Cellular and Gene Therapy, Center for

Biologic Evaluation and Research,

FDA-NCI Clinical Proteomics Group, Bethesda, MD* Correspondence: [email protected]

Page 2: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Abstract Reverse Phase Protein microarrays are a promising technology for

characterization of cellular protein signaling networks. This platform has been shown to have high sensitivity and good reproducibility when used with validated antibodies. There has been a need in clinical research to quantify individual analytes across patient samples, as well as comparison of analytes before, during and after treatment. In addition, there has been a need to develop a method of normalization and calibration for the microarray. We have developed a method of normalization based on total protein per microarray spot by using a total protein stain on the microarray. This allows normalization of each spot to a known analyte, maximizing reproducibility. We also developed a reference lysate of known composition for quantification of spots based on a common unit, which we termed the ‘reference standard unit’. This reference lysate serves as a standard for compensation of spot-spot and day-to-day variation.

Bioinformatic software capable of incorporating the normalization and calibration data is required for high throughput data analysis. We used MicroVigene software to quantify each analyte on the protein microarray, incorporating the reference lysate and total protein/spot data. A variety of automated curve fitting approaches are used to meet the coefficient of variation required for clinical trial research.

Page 3: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Coupling Laser Capture Microdissection With High Throughput Protein Arrays

Patient biopsy tissuecells are microdissected:

30,000 cells = 100 arrays

Each patient sample is arrayed in a miniature dilution curve:

Always in linear dynamic range of any antibody/ analyte pair

Arrays probed with labeledamplified antibody:

e.g. Ovarian cancer progressionFrom one patient probed with

Phospho-ERK antibody

Reverse Phase Protein Array

Page 4: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Application Areas of the Technology

Reserve Phase Protein Microarrays are applied to:1. Clinical Research – utilized in clinical trials for

assessing response to therapy and demonstrating protein molecular changes to therapy.

2. Disease Prognostics - utilized for determining which patient is likely to respond to a given therapy.

3. Personalized drug treatment – monitoring response to therapy before, during and after treatment.

Page 5: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Experimental Design

Experimental Design

Page 6: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Arrayer and Stainer used For Reverse-Phase Protein Arrays

GMS 417 pin and ring arrayerDakoCytomation Autostainer for protein detection/signal development

Page 7: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Reverse Phase Protein Microarray MethodologyTissue processing and microdissection

8.0 μm frozen sections of ovarian cancer tissues were placed on uncoated glass slides and stored at -80ºC prior to use. Ovarian tumor epithelial cells or other relevant cell populations were microdissected with a Pixcell II Laser Capture Microdissection system (Arturus). Approximately 5,000 LCM shots (20,000-25,000 cells) were microdissected for each case and stored on microdissection caps at -80ºC until lysed.

Cell lysis and cellular lysate arrayingMicrodissected cells were lysed directly from the microdissection caps into 50 μL of lysis buffer containing a 1:1 mixture of 2x Tris-Glycine SDS sample buffer (Invitrogen Life Technologies) and Tissue Protein Extraction Reagent (Pierce) plus 2.5% β-mercaptoethanol for 30 min at 75ºC. Positive control samples included A431 control and A431+EGF lysates (BD Pharmingen) at 1.0 mg/mL. Reference standard peptides specific for the pAkt and pERK antibodies (Cell Signaling Technology) were diluted in lysis buffer to 1.0 μg/mL. Immediately prior to arraying, lysates were loaded into a 384-well plate and serially diluted with lysis buffer into a 5-point dilution curve (ovarian samples and A431 controls) ranging from undiluted-1:16 or 12-point dilution curve (reference standard peptides) ranging from undiluted-1:16. Approximately 60 nL of each sample was spotted onto nitrocellulose-coated glass slides (Schleicher and Schuell Bioscience) with a GMS 417 microarrayer (Affymetrix). Slides were stored dessicated at -20ºC. For estimation of total protein amounts, selected arrays were stained with Sypro Ruby Protein Blot Stain (Molecular Probes) according to the manufacturer’s instructions and visualized on a Fluorchem™ imaging system (Alpha Innotech). One day prior to antibody staining, the lysate arrays were treated with Reblot antibody stripping solution (Chemicon) for 15 min at room temperature, washed 2 x 5 min in PBS, and then incubated overnight in blocking solution (1g I-block (Tropix), 0.1% Tween-20 in 500 mL PBS) at 4ºC with constant rocking.

Protein microarray staining Blocked arrays were stained with antibodies on an automated slide stainer (Dako Cytomation) using the Catalyzed Signal Amplification System kit according to the manufacturer’s recommendation (CSA; Dako Cytomation). Briefly, endogenous biotin was blocked for 10 min using the biotin blocking kit, followed by application of protein block for 5 min; primary antibodies were diluted in antibody diluent and incubated on slides for 30 min and biotinylated secondary antibodies were incubated for 15 min. Signal amplification involved incubation with a streptavidin-biotin-peroxidase complex provided in the CSA kit for 15 min, and amplification reagent, (biotinyl-tyramide/hydrogen peroxide, streptavidin-peroxidase) for 15 min each. Development was completed using diaminobenzadine/hydrogen peroxide as the chromogen/substrate. Slides were allowed to air dry following development.Primary antibodies used in these studies were: Akt 1:100 (Cell Signaling Technology); phosphoAkt S473 1:50 (Cell Signaling Technology); phosphoAkt T308 1:50 (Cell Signaling Technology); extracellular signal-regulated kinase (ERK) 1/2 1:200 (Cell Signaling Technology); phosphoERK1/2 T202/Y204 1:1000 (Cell Signaling Technology Secondary antibody and dilution used was biotinylated goat anti-rabbit IgG (H+L) at a 1:5000 dilution (Vector Laboratories).

Page 8: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Printing Arrays

Multiple samples may be printed on a single slide in the single pad format. Alternatively, one sample can be printed in six separate sectors.

Each slide contains patient samples, standards and controls.

Single Pad Format Sector Format

Page 9: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Normalization Using Total Protein

•Why normalization?It is essential to account for differences in total protein concentration between each

sample so that antibody staining between each tissue sample on the array can be compared directly.

•How do we do it?

One slide/printing is stained for total protein using a total protein stain such as Sypro Ruby™ blot stain or a colloidal gold stain. For estimation of total protein amounts, selected arrays were stained with Sypro Ruby™ Protein Blot Stain (Molecular Probes) according to the manufacturer’s instructions and visualized on a Fluorchem™ imaging system (Alpha Innotech).

•ReproducibilityNormalization is based on the total protein per microarray spot. Normalized intensity

values are calculated by dividing the measured intensity value of the antibody by the corresponding measured intensity value of the total protein. This allows normalization of sample to a known analyte, maximizing reproducibility.

Page 10: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Negative Control Slides

• Why negative slides? Serve as controls for non-specific binding of

the secondary antibody to the array

• How is it done? Arrays are probed with the labeled secondary

antibody (biotinylated anti-rabbit or anti-mouse IgG) in the absence of the primary antibody against the analyte of interest and processed as all other slides in the experiment

Page 11: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Cross Sample Calibration using Reference Standard

The reference standard is a pool of peptides. This pool is comprised of the peptides used as the immugen to produce the primary antibody.

The reference standard dilution curve is printed on each microarray slide. MicroVigene™ software automatically finds RSU measurements for all samples through curve fitting and other mapping algorithms.

Page 12: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Image Quantification and Data Analysis Using MicroVigene™

• Quality

• Reproducibility

• Automation

• Dust and Contamination Control

• Performance for High Throughput

Page 13: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Quality Images – Actual Spots Boundary, Regional Background Correction

Page 14: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Reproduce Same Results Same sample stained two months later produces over

90% correlation

Correlation of images stained in April and June

R2 = 0.9441

7

7.5

8

8.5

9

9.5

10

10.5

11

7 7.5 8 8.5 9 9.5 10 10.5 11

tERK June

tER

K A

pri

l

Correlation of images stained in April and June with Negative Control (4-image-correlation)

R2 = 0.9476

7

7.5

8

8.5

9

9.5

10

10.5

11

7 7.5 8 8.5 9 9.5 10 10.5 11

tERK June- Negative

tER

K A

pri

l -N

egat

ive

Page 15: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Automation - One Mouse Click Operation

ReplicatesSpot levelCurve levelProtein Level

Background correction

Regional & Negative Ctrl

Normalization

Internal & Total Protein

Dilution Curve fittingLinear non_linearDynamic switch

MeasurementLinear Range RSU IndexBest Linear Point

Reverse Phase Protein Microarray

Quality ControlOutliers Bad Curves

Image AnalysisGrid &Spot Quantification

Sample & Control Images

OutputMeasurementsError barsCurve fitting quality flag

Page 16: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Find the Linear Range through Robust Curve Fitting

Nonlinear 4-parameter fitting

Line represent linear range

Bad spot

Automatically remove the spot

linear fitting

MicroVigene™ starts with a nonlinear logistic model. If the number of reliable points outliers is less than five after removing, the program will automatically switch to a linear model. The end results are the optimal fitting of either linear or nonlinear model.

Page 17: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Choosing the Right Linear Range

Smallest error and largest linear range, graph with outliers

51%

linea

r ran

ge

Chosen:Linear range lineLarger slop & longest linear range

Not chosen:Small slop orshorter linear range

Nonlinear regression fitting

Page 18: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Best Linear Point Measurements

Taking Y intensity measurements at X0-average provides the least error due to extrapolation and offers means for sample comparison.

X0-avg

Y

Page 19: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Reference Standard Unit Mapping

Sample

rsu

x

Y0(100)

rsu= 100 ex

First we find the best intensity measurement Y0, then find the point on the rsu dilution curve, whether linear range line or the actual curve fitting line, the corresponding x is the sample concentration (or dilution) measured in the reference standard unit.

Y0(100)

Page 20: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Measurements for Control Types

A431 and A431+EGF lysates are printed on each slide as process controls. Expected results are relative elevation of pERK and pAKT in the A431+EGF lysate as compared to the A431 lysate.

0

2000

4000

6000

8000

10000

12000

14000

pERK totERK pAkt totAkt

A431 A431+EGF

Page 21: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Results: Relative Signal Intensity

All Sample Average

0

2000

4000

6000

8000

10000

12000

14000

pERK totERK pAkt totAkt

Page 22: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Relative Signal Intensity by Disease Stage

Measurement by Disease Stage

0

2000

4000

6000

8000

10000

12000

pERK totERK pAkt totAkt

Stage 1 Stage 3 Stage 4

Page 23: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Using Reference Standard Unit (RSU)

Y0 and RSU Correlation

-0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

0 0.2 0.4 0.6 0.8 1

Y0

RS

U

pERK pAkt Linear (pAkt) Power (pERK)

It is important to make measurements in the linear range of protein concentration. In order to be able to compare results across samples and periods, we have introduced RSU measurements. We are showing here that correlation between Y0 and RSU is proportional.

Page 24: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

Results: Reference Standard Unit (RSU)

120

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pERK pAkt

RS

U

0

1000

2000

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6000

Y0

RSU

Y0

RSU and Y0 give close relative measurements between antibodies.

Page 25: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

ConclusionThe detection of changes in the activity of various signaling pathways innormal and tumor tissue in a patient is essential for understanding diseaseprogression, appropriate treatment selection, and monitoring treatmentefficacy. Reverse Phase Protein Microarray technology provides a means to detect, in a highly multiplex way, these changes. With the standardization on the total protein normalization and the incorporation of a reference standard, Reverse Phase Protein Microarrays make data comparison among different studies and clinical trials possible.

This analysis is performed using MicroVigene™ software employingadvanced algorithms for image analysis and novel data analysis processes for high quality, highly reproducible, end-to-end Reverse Phase ProteinMicroarray analysis solutions. MicroVigene™ also automates theCalibration, normalization and background correction analysis steps of the method.

Page 26: Calibration and Normalization of Protein Microarray Data Charlene Liang 1*, Virginia Espina 2, Julia Wulfkuhle 2, Emanuel F. Petricoin 3 III and Lance

ABOUT US• The goal of the FDA-NCI Clinical Proteomics Program is to invent

and apply proteomics technology to patient care. New proteomics research technology is now being used for clinical studies ranging from cancer to cardiovascular disease and organ transplant. Researchers within the program are searching for proteins in the blood, urine, and diseased tissue that can be used as early biomarkers of disease, predict response to therapy, or the likelihood of relapse after treatment, or serve as new targets for therapy itself.

• About VigeneTech, Inc.: VigeneTech provides novel scientific software, customized solutions and online services in the areas of image analysis, automation, and instrumentation. VigeneTech’s MicroVigeneTM for microarray image analysis delivers 100% reproducible, operator independent results; it is robust to handle various shifted and noisy images; and supports unattended batch process. For more information about VigeneTech, please visit

http://www.vigenetech.com.