Proteomic Profiling by Proteomic Profiling by Antibody Micro-ArrayAntibody Micro-Array

Kent J. Johnson M.D.

Roscoe L. Warner Ph.D.

Piezorray Non-Contact PrinterPiezorray Non-Contact Printer

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Micro-Array SystemMicro-Array System

Y YY Y Y YY

DyLight 650 NeutrAvidin

Target Antigens (Std. or Sample)

MFI Intensity of Standards

Used for Calculation of Unknowns

YYY Y

Y YY

Y YY YY Y

Y

Sandwich ELISA

Y Y YY

BiotinyledSecondary Antibody

Primary Antibodies

Y Y Y

Y YY

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Representative WellRepresentative Well

….. ….. ….. ….. …..

….. ….. ….. ….. …..

….. ….. ….. ….. …..

….. ….. ….. ….. …..Isotype Controls: Mouse, Rabbit, Human

Primary Antibodies

IL-2 IL-6 IL-17

IFN IP-10TNF

IL-8

GM-CSFIL-2R

IL-10

RANTES EGF MCP-1 MCP-3 MMP-7

KIM-1

1 9

2 10

3 11

4 12

5 13

6 14

7 15

8 16

Chip

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Each spot receives 325 picoL of antibody solution applied to Sialylated Chips as a Piezorray-static spray

Spot size: 120 m

Chip-Plate Technology using 5 chips Chip-Plate Technology using 5 chips

in an 80 well groupingin an 80 well grouping

Prototype Lab (UM: Medical Innovation Center)

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1 9 17 25 33 41 49 57 65

2 10 18 26 34 42 50 58 66

3 11 19 27 35 43 51 59 67

4 12 20 28 36 44 52 60 68

5 13 21 29 37 45 53 61 69

6 14 22 30 38 46 54 62 70

7 15 23 31 39 47 55 63 Ctrl.

8 16 24 32Zero pg/ml 40 48 56 64 Ctrl.

Chip-1 Chip-2 Chip-3 Chip-4 Chip-5

Sta

nd

ard

Cu

rve

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Cross-Reactivity Testing

DyLight 650 NeutrAvidin

Biotinylated Secondary Antibody

MFI Intensity of DyLight 550 and DyLight 650

Specific AntigenY

Y Y

YYYYY YYYYY YYYYYYYYYY YYYYY YYYYY YYYYY YYYYY YYYYYYYYYY YYYYY YYYYYYYYYY YYYYY YYYYYYYYYY YYYYY YYYYY

YYY

YY

YY

Second SeriesSecond Series First SeriesFirst Series

Biotinylated Secondary Antibody

DyLight 550 NeutrAvidin

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123456

123456

Specific Binding of Antigen

Non-Specific Bindingof Secondary Antibody

A. B. C.DeterminationsDeterminations

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Standards used as Log4 dilution of master mix plus a zero value.

Standard Curve generated for each antigen and equation of the line determined.

Antigen concentration of samples calculated from equation of the line.

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Development of Standard Curve Development of Standard Curve and Linear Rangeand Linear Range

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Detection Range 1.2 - 10,000 pg/mL

Regression 0.95 – 0.99

Recovery 86% - 108%

Intra-Assay Multiple Printings 6%

Inter-Assay Multiple Printings 10%

Human Micro-Array Chip System

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Performance of Micro-Array Methodology

Advantages of Custom Antibody Micro-ArrayAdvantages of Custom Antibody Micro-Array

1. Easily modified to accommodate unique target antigens.

2. Ability to custom develop chips for desired species, limited only by availability of purified antigen and antibodies.

3. Quantifiable approach to high throughput analysis of multiple antigens using small sample size.

4. Only One U.S. company to date makes Antibody Arrays.

5. Comparable technologies include ELISA, Bead-based assays,

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Micro-Array Findings

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Rat Micro-Array 45 AntibodiesProtein Range (pg/ml) Curve fit r2

Agrin 38.8 - 2,000 0.994

Annexin-V 2.4 - 10,000 0.990

B-NGF 3.9 - 1,000 0.970

B7-1 24.1 - 4,000 0.977

B7-2 51.8 - 2,000 0.973

CINC-1 4.8 - 1,000 0.973

CINC-2 4.8 - 2,000 0.974

CINC-3 73.4 - 2,000 0.970

Clusterin 2.5 - 10,000 0.990

CNTF 23.7 - 2,000 0.999

E-Selectin 6.2 - 2,000 0.980

EGF 3.9 - 1,000 0.960

EphA5 40.2 - 2,000 0.999

Fas 20.7 - 2,000 0.980

FGF-BP 31.5 - 2,000 0.980

Fractalkine 57.7 - 12,500 0.994

GFR-1 44.6 - 1,000 0.981

GM-CSF 46.2 - 1,000 0.999

GRO- 1.2 - 1,000 0.997

-GST 10.0 - 1,000 < 0.95

ICAM-1 64.2 - 5,000 0.999

IFNg 20.4 - 2,500 0.991

Protein Range (pg/ml) Curve fit r2

IL-1 12.6 - 1,000 0.999

IL-1 15.9 - 4,000 0.997

IL-2 26.2 - 4,000 0.992

IL-4 4.6 - 1,000 0.997

IL-6 71.6 - 8,000 0.997

IL-10 73.4 - 4,000 0.988

IL-12 23.4 - 2,000 0.997

IL-13 3.1 - 2,000 0.999

IL-18 7.8 - 2,000 0.990

Jagged-1 12.3 - 4,000 0.997

KIM-1 1.2 - 5,000 0.990

LIX 30.1 - 4,000 0.976

L-Selectin 11.7 - 4,000 0.997

MCP-1 13.1 - 2,000 0.970

Neuropilin-1 28.6 - 2,000 0.986

NGAL 1.0 - 2,000 0.980

Notch-2 14.1 - 2,000 0.993

PAI-1 9.77 - 10,000 0.987

PDGF-AA 63.2 - 2,000 0.991

RANTES 1.0 - 2,500 0.952

STAT-1 78.3 - 5,000 0.982

TNF 49.1 - 4,000 0.983

VEGF 52.7 - 1,000 0.992

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Rat Models

Dermal ArthusGlucanStreptozotocin Induced DiabetesGentamicinCyclosporin AFenoldopam

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Gentamicin InjurySerum Protein Levels

Pre-Immune

Gentamicin 80 mg/Kg Gentamicin 100 mg/Kg

Antigen (pg/ml) Mean SD Mean SD Mean SD

IFN 128.64 107.66 46.79* 0.00 46.79* 0.00

IL-1 116.07 33.23 99.46 0.00 99.46 0.00

IL-1 13.21 7.71 15.27 0.27 13.41 7.23

IL-2 1.06 0.00 1.06 0.00 1.06 0.00

IL-4 0.12 0.00 0.12 0.00 0.12 0.00

IL-6 477.52 242.66 1,924.46* 450.22 1,004.80 1,219.82

IL-10 1.26 2.19 0.18 0.02 1.83 2.35

IL-12 1,555.65 335.99 3,200.08* 1,044.83 2,661.43* 374.79

TNF 2.00 0.00 2.00 0.00 2.00 0.00

Neuropilin-1 0.35 0.00 0.55 0.28 10.81* 13.25

EGF 3.99 2.46 6.16 1.69 8.87* 2.50

NGAL 4,000.00 0.00 4,000.00 0.00 2,169.57 2,588.62

PAI-1 3.88 6.32 0.65 0.10 23.68* 26.28

RANTES 1,009.96 239.58 804.28 754.00 3,113.77* 2,667.54

ANNEXIN V 3,613.34 4,737.10 73.76* 71.67 1,628.23 2,267.44

Clusterin 20,000.00 0.00 20,000.00 0.00 20,000.00 0.00KIM-1 146.29 100.62 357.07* 77.89 220.72 15.70

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Urine Protein Levels

Pre-Immune Gentamicin 80 mg/Kg Gentamicin 100 mg/Kg

Antigen (pg/ml) Mean SD Mean SD Mean SD

IFN 395.48 63.04 2,157.80* 1,708.60 134.87 172.28

IL-1 ND ND ND ND ND ND

IL-1 1.78 1.48 2.95 4.17 1.06 1.50

IL-2 96.01 90.87 1,549.28* 1,947.12 119.55 153.47

IL-4 7.88 6.30 29.63* 39.08 3.82 4.27

IL-6 23.72 0.00 1,447.21* 509.33 ND ND

IL-10 265.84 120.85 1,309.28* 1,392.05 258.60 344.71

IL-12 228.73 152.26 1,239.04* 139.55 636.19* 414.58

TNF 161.36 162.39 637.86* 880.93 14.94 0.00

Neuropilin-1 46.27 38.79 165.31* 220.36 42.53 47.02

EGF 4,000.00 0.00 2,109.60 2,673.43 2,036.86 2,776.31

NGAL 11,105.13 5,439.76 17,618.36* 3,368.15 11,904.37 1,877.06

PAI-1 0.24 0.01 10.64* 5.66 6.99* 6.54

RANTES ND ND ND ND ND ND

ANNEXIN V 3,054.48 873.28 6,766.21* 1,911.34 4,456.25* 1,520.17

Clusterin 703.07 1,570.96 1,231.00* 983.08 939.35 760.30

KIM-1 395.48 544.97 2,157.80* 1,708.60 134.87 172.28

N.D. - Not Detected

Gentamicin Injury

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Urine Serum  

Control CsA 80 mg/Kg Control CsA 80 mg/KgProteins (pg/mL) Mean SD Mean SD Mean SD Mean SD

ICAM-1 1,565 1,649 2,060 2,139 2,121 805 5367 * 3,033

IL-1 4.2 8.6 30.7 61.9 5.3 1.8 21.27 * 12.3

Fas 767 374 508 209 96 42 167 * 38

IL-12 1,336 2,063 832 1,601 1,907 353 928 * 409

Notch-2 0.28 0.34 8.7 8.8 2,466 845 3649 * 470

EGF 1,335 1,030 1,178 908 0.58 0.62 18.3 * 11

Clusterin 169 663 3551 * 1,296 19,880 1,782 17,533 6,042

KIM-1 171 118 592 * 376 93 52 178.9 * 56

*Significantly Different (p < 0.05), Mean +/- SD of Cyclosporin treated rats (n=10) and normal healthy (n=10) control animals .

Cyclosporin A Injury

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Human Array Findings

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Human Micro-Array 108 AntibodiesACE Fc γ RIIB/C IL-2 MIF Survivin (IC)

Activin A FGF basic IL-3 MIP-1α TARC

ADAM8 FGF-4 IL-4 MIP-1β TGF-α

Adiponectin FGF-6 IL-5 MIP-1δ TGF-B1

ALCAM Fractalkine IL-6 MIP-3α TGF-B2

BCAM G-CSF IL-6 sR MIP-3β TGF-B3

β-NGF GM-CSF IL-8 MMP-3 TIMP-1

BLC/BCA 1 GRO α IL-10 MMP-9 TIMP-2

Cathepsin S HGF IL-12 p70 MMP-13 TIMP-4

Complement D HSP27 (IC) IL-13 OSM TNF-α

CTGF HSP70 (IC) IL-15 P-Cadherin UPAR

Dectin 1 ICAM-3 IL-18 BPa PDGF-AB VCAM-1

E-Cadherin IFN-γ IP-10 PDGF-BB VEGF

EGF IGF-BP2 Leptin pro-Cathepsin S VEGF-D

Endostatin IGF-BP3 L-Selectin P-Selectin VEGF R2 (IC)

Eotaxin IL-α MCP-1 RANTES PAI-I

Epo R IL-β MCP-2 Resistin IL-18

E-Selectin IL-1 RI MCP-3 sICAM-1 KIM-1

Fas Ligand IL-1 sRII MCP-4 sTNF RI DAF

Fetuin A/AHSG IL-2 R Osteopontin TNF RII Lactoferrin

IL-2 sR Clusterin NGAL Fibronectin Annexin V

Cystatin C Lymphotoxin R Lymphotoxin

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.

Proteins Normal (pg/mL) Wegener’s Fold Granulomatosis change

(pg/mL) .

 ACE-1 3,290.1 (+/- 214.3) 5,927.1 (+/- 283.0)* + 1.8 IFN- 23.4 (+/- 3.4) 151.4 (+/- 19.5)* + 6.5 IL-8 1.7.3 (+/- 9.0) 1,294.0 (+/- 55.3)* + 12.1 s-ICAM 6,195.2 (+/- 533.7) 12,679.4 (+/- 870.7)* + 2 s-VCAM 120.7 (+/- 26.5) 674.3 (+/- 28.8)* + 5.6

.

The quantified values along with standard error of the mean (in parenthesis) are shown and allow for comparison of normal (n=30) and WG serum (n=26) samples. The fold change shows the difference between the normal and affected patients with positive (+) indicating an increase in WG patient serum.

Analytes of Wegener’s Granulomatosis PatientsAnalytes of Wegener’s Granulomatosis Patients

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RAVE Clinical TrialRAVE Clinical Trial

Rituximab in ANCA-Associated Vasculitis (RAVE) trial

Long-term program to identify markers that are clinically useful in staging vasculitis activity, distinguishing vasculitis from other inflammatory diseases such as infections, and predicting response to treatment and risk of relapse.

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Of the 186 subjects evaluated at screening,

139 had been diagnosed with GPA and 46 with MPA;

124 were positive for anti-PR3 and 62 for anti-MPO

93 had active glomerulonephritis

90 had a new diagnosis of AAV

96 had established diagnoses and were experiencing relapses.

At screening:

92 patients were receiving glucocorticoids, and

104 were receiving some immune-suppressive drug (glucocorticoids, other drugs, or both).

The 68 healthy controls included 28 males and 40 females, median age 41.

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Use of Multiple Markers to Better Distinguish Severe or Mild Vasculitis from Remission

Four markers (CXCL13/BCA-1, G-CSF, IL-15, and TIMP-1) were

significantly higher at month 6, after adjustment for multiple comparisons, in

the 25 subjects with active disease than in the 137 subjects in remission,

Five additional markers (IFN, CXCL8/IL-8, sIL-2R, CCL5/RANTES) might

be higher based on unadjusted P values of < 0.05.

Discrimination between mild disease and remission at month 6 was limited,

with all AUC < 0.7

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Based on logistic regression models with active disease vs. remission as the dichotomous outcome, the set of markers that best distinguished active AAV from remission in the 137 subjects with paired samples was:

ACE (negatively), GM-CSF, MMP-3, TIMP-1, and ESR, with AUC=0.96.

Odds ratios (for active AAV vs. remission in these 137 subjects) associated with 2-fold changes in these markers. When these 5 markers were used to model data limited to month 6, comparing 25 subjects with milder active AAV to 137 in remission, AUC=0.78.

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Biomarkers In Patients with Biopsy Proven Biomarkers In Patients with Biopsy Proven Rejection of Renal Allografts Rejection of Renal Allografts

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Our findings demonstrate that the protein signature of healthy subjects is distinctly different from renal transplant patients with good allograft function and no previous history of rejection.

We have identified 10 proteins that can reliably differentiate stable renal transplant recipients from healthy subjects in both the training and validation cohorts.

Cystatin-C, EGF, GM-CSF, IL-1 R1, IL-5, KIM-1, MCP-1, MCP-3, MIF, TIMP-4

In addition, 17 proteins were identified that can differentiate rejecting renal transplant recipients from stable renal transplant patients.

TGF-β2, E-Cadherin, GROα, TGF-β1, IL-6, IL-1 R1, EGF, MIP-3α, TNF-RII, KIM-1, Osteopontin, VEGF-R2, Epo-R, MIF, IL-12p70, MCP-1, GM-CSF

The ultimate goal of the protein array is be to monitor non-invasively, renal transplant patients over time in order to detect subclinical changes before they would be detected by conventional methods, ie., change in serum creatinine levels, with the intent to alter long-term graft outcome.

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Micro-Array Chip and ProPlate MaskMicro-Array Chip and ProPlate MaskTM

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