the plasma proteome: enabling a revolution in diagnostics feb 2004 web.pdf · ppi ©plasma proteome...

PPI© Plasma Proteome Institute

The Plasma Proteome:Enabling a Revolution in Diagnostics

Leigh Anderson Ph.D.Founder & CEO, Plasma Proteome Institute


Introduction to the PlasmaProteome

• Why the plasma proteome representssuch an enduring technical challenge

• What it takes to develop a comprehensivelist of candidate markers in plasma

• The next step: what we need to carry outsystematic validation of markers tosupport a revolution in diagnostics


Plasma is the largestand deepest version of the

human proteome

• Largest = Most proteins

• Deepest = Widest dynamic range


Major Components of thePlasma Proteome

Immunoglobulins

Cellular Proteins(Leakage)

“Plasma

Proteins”

• ~40,000 forms of proteinssecreted to function inplasma, most glycoproteins– Assume 500 gene products x 2

splice variants x 20 glycoforms x2 clip forms

• ~500,000 forms of tissueproteins– Essentially all tissue proteins x

splice and PTM variants

• ~10,000,000 clonal forms ofimmunoglobulin

Total: the largest version ofthe human proteome


Major Plasma Proteins99% of plasma protein mass

Albumin

IgG Total

Transferrin

Fibrinogen

IgA Total

Alpha-2-MacroglobulinIgM Total

Alpha-1-Antitrypsin

C3 Complement

Haptoglobin

Apolipoprotein A-1

Apolipoprotein B

Alpha-1-acid Glycoprotein

Lipoprotein(a)

Factor H

Ceruloplasmin

C4 Complement Complement Factor B

Prealbumin

C9 Complement

C1q Complement

C8 Complement

1%10%

0 - 90% 90 - 99%


Fac

tor

H

*He

mo

glo

bin

*A

lbum

inIg

G T

otal

Tra

nsfe

rrin

Fib

rino

gen

IgA

Tot

alA

lpha

-2-M

acro

glob

ulin

IgM

Tot

alA

lpha

-1-A

ntitr

ypsi

nC

3 C

ompl

emen

tH

apto

glob

inA

polip

opro

tein

A-1

Apo

lipop

rote

in B

Alp

ha-1

-aci

d G

lyco

pro

tLi

popr

otei

n(a)

Cer

ulop

lasm

inC

4 C

ompl

emen

tC

ompl

emen

t F

acto

r B

Pre

albu

min

C9

Com

plem

ent

C1q

Com

plem

ent

C8

Com

plem

ent

C5

Com

plem

ent

Pla

smin

og

en

IgD

C1

Inhi

bito

rC

6 C

ompl

emen

tC

7 C

ompl

emen

tC

ompl

emen

t F

acto

r I

Ret

inol

Bin

ding

Pro

tein

iC3b

Thy

roxi

n B

indi

ng G

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linC

2 C

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emen

t P

rote

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hrom

bus

Pre

curs

or P

rote

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-rea

ctiv

e P

rote

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b F

ragm

ent

C3a

Com

plem

ent

Pro

tein

Fer

ritin

Ran

tes

SC

5b-9

Com

plex

Myo

glob

inT

hyro

glob

ulin

TP

AC

5a C

ompl

emen

tN

euro

n-S

peci

fic E

no

lase

C-P

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eA

lph

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eto

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tein

TN

F-B

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ng P

rote

ins

Pro

stat

e-S

peci

fic A

ntig

enP

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atic

Aci

d P

ho

sph

ata

seC

EA

Mye

lin B

asic

Pro

tein

Tro

poni

n I

Inte

rleu

kin-

1ra

MIP

-1 b

eta

Tro

poni

n T

Inte

rleu

kin-

8M

IP-1

alp

haT

issu

e F

acto

rG

CS

FIn

terf

eron

Alp

haIn

terl

euki

n-2

Inte

rleu

kin-

4T

NF

-Alp

haIn

terf

eron

Gam

ma

Inte

rleu

kin-

1 B

eta

Inte

rleu

kin-

12In

terl

euki

n-10

Inte

rleu

kin-

5In

terl

euki

n-6

Proteins Measured Clinically in Plasma Span> 10 Orders of Magnitude in Abundance

(Human male 50yr - from Specialty Laboratories Books)

Nor

mal

Ran

ge A

bund

ance

sLo

g10

Con

cent

ratio

n in

pg/

mL

0

2

4

6

8

10

12

1

3

5

7

9

11

Interleukins & Cytokines

Classical Plasma ProteinsTissue Leakage

Other

Dynamic range of2-D gels or LC/MS

Total Cost: $10,695per sample (clinical

assays)

From:The human plasma proteome: history, character,and diagnostic prospects. Anderson, N. L.Anderson, N. G., Mol Cell Proteomics (2002) 1:845-67.


Nucleic Acids Exist Free in Plasma• Genomic DNA is present in plasma (~1ug/ml)

– Released by apoptosis & necrosis, e.g., by tumorcells

– Mutations (e.g., in P53) can be detected

• mRNA can be detected in plasma– Fetal mRNA can be found free in maternal circulation

• Current and likely future utility confined toqualitative tests (genotyping)– mRNA concentrations are very poor indicators of the

amount of protein products

• Proteins remain the focus of the search fordisease and response markers


Genetic Component of Variation inAbundance of 15 Proteins in Plasma

0%

20%

40%

60%

80%

100%

C-pe

ptide

Facto

r XIIa

* Ins

ulin-

like

grow

th fa

ctor I

Lept

in

Pros

tate

-spe

cific

antig

en

IGFB

P-3

von

Wille

bran

d fa

ctor

Insu

lin-lik

e gr

owth

facto

r II

Histi

dine-

rich

glyco

prot

ein

Gc (g

roup

-spe

cific

com

pone

nt)

IgE

Facto

r XIII

acti

vity

* Ins

ulin-

like

grow

th fa

ctor-I

PAI-1

ant

igen

Lipop

rote

in(a)P

rop

ort

ion

of

Var

iati

on

Att

rib

ute

d t

o G

enet

ics

(Published values from many sources*= two discordant studies)

Genotype

Phenotype


Plasma Proteomics Began in SwedenSvedberg, Tiselius, Laurell, et al

Year

1

10

100

1000

10000

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Lo

g10

Nu

mb

er o

f R

eso

lved

S

pec

ies Number of Zones/Spots

Sequence Identified Unique Proteins

“1-D”



The Second Phase of Plasma ExplorationBegan with 2-D Gels (c. 1976)

2-D Electrophoresis

300+ resolved spots

40 identified proteins

Anderson,L., Anderson,N. G. High resolution two-dimensionalelectrophoresis of human plasma proteins. (1977) PNAS 74, 5421-5


2-D Electrophoresis Continued the Growth inNumber of “Spots” but Not Many New Proteins

Year

1

10

100

1000

10000

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Lo

g10

Nu

mb

er o

f R

eso

lved

S

pec



“1-D”

“2-D”

40*49+ 60†


* L. Anderson and N. G. Anderson. (1977) PNAS 74,5421-5.+ G. J. Hughes, et al (1992) Electrophoresis 13, 707-14.† Swiss 2DPage website


>2 Dimensions of Separation RadicallyIncreases the Number of Detected Proteins

Year

1

10

100

1000

10000

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Lo

g10

Nu

mb

er o

f R

eso

lved

S

pec



“1-D”

“3+-D”

“2-D”

4049 60

341b607a


a. J. N. Adkins, et al, Mol Cell Proteomics 1, 947-55.b. R. S. Tirumalai, et al, Mol Cell Proteomics 2, 1096-103.c. R. Pieper, et al, Proteomics 3, 422-32.d. H_Plasma_NR-v2

319c

1175d


Serum

High Abundance Proteins in Plasma or SerumLimit Detection of Minor Components

A: Albumin

B: Transferrin

C: Apo A-I lipoprotein

D: Haptoglobin β-chain

E: α1-Antitrypsin

AE

D

C

B


Fibrinogen-β

Fibrinogen-α

Fibrinogen-γ

pH 7.5

Mr 200 kD

10 kD

pH 4

Polyclonal Antibodies Specifically and RepeatablyRemove Target Proteins from Serum/Plasma

Proteins Bound from Plasma by Affinity-Purified anti-Fibrinogen Ab


A: anti-albumin, anti-transferrin,anti-haptoglobin, anti-α-1-antitrypsin, anti-α-1-acidglycoprotein, anti-α-2-HSglycoprotein, anti-hemopexin,anti-transthyretin, anti-antithrombin-III.

B: 50% anti-IgA and 50% anti-IgMC: anti-α-2-macroglobulin

D: anti-apolipoprotein A1

Scout: switching valve 1

Scout: switching valve 2

A B C D

Multicolumn Implementation ofPlasma Immunosubtraction


Serum Bound Unbound

Multi-Component Immunoaffinity Subtraction Chromatography, AnInnovative Step Towards A Comprehensive Survey Of The Human PlasmaProteome, Rembert Pieper, et al, Proteomics, (2003) Proteomics 3, 422-32.

Antibody Affinity Subtraction of10 High Abundance Proteins from Serum

SubtractedSubtractedAlbuminAlbuminIgGIgGIgA IgA αα11 antitrypsin antitrypsinαα11 acid glycoprotein acid glycoproteinαα22 HS glycoprotein HS glycoproteinHemopexinHemopexinAntithrombin Antithrombin IIIIIITransferrinTransferrinHaptoglobinHaptoglobinTransthyretinTransthyretinIgMIgMαα22 macroglobulin macroglobulin

PPI© Plasma Proteome InstituteThe Human Serum Proteome: Display Of Approximately 3,500 Chromatographically

Separated Distinct Protein Spots On 2-DE Gels And Identification Of 307 UniquelyAnnotated Proteins, Rembert Pieper, et al, Proteomics 3(7): 1345-64. (2003)

The Current Phase of Plasma ProteomicsEmploys Multi-Dimensional (>2D) Approaches

(e.g., 3-D Chromatography + 2-DE + LC/MS)

66 2-D gels

wholeserum

2 4,5

IASC-serum

6X 11 SEC-serumfractions each

6 AEC-serumfractions

1 3D:

>300 distinct proteingene products


F4F3

F2 F1

100

100100

50

100

5050

25

50

25

25

25

6.5

6.5

6.5

6.5

5

5

5

5


So How Many Proteins Are Therein Plasma?

• Different methods yield different sets of proteins– Reasons include physicochemical biases of techniques, and

statistics of peptide choice in MS/MS

• The most comprehensive approach is therefore tocombine data from different approaches– We used four input datasets:

• Base list of ~450 proteins reported in “non-proteomics” literatureas measured/detected in plasma or serum

• Three sets of 300-600 proteins each from proteomics surveys (2-Dgels + MS/MS; LC/LC-MS/MS)

• Combined data can be made non-redundant usingmethods of genomics– Redundancy definition: >95% homology over >=15 amino acid

subsequence• Results from: The Human Plasma Proteome: A Non-Redundant List

Developed by Combination of Four Separate Sources, Anderson etal, Molec. Cell Proteomics, in press


HAPTOGLOBIN

Achieving Non-Redundancy: MS Identifications ofHaptoglobin in Plasma Proteome Datasets

LCMS1

Fragment

Splice Variant

SwissProtAccessionNumberP00737

P00738

Related Protein P00739

NCBI GI Accession

NumberGI123507GI123510

GI4826762

RefSeqNP_005134

LCMS1LIT

LIT

LIT

2DEMS

2DEMS

2DEMS

LCMS2

LCMS1

From: The Human Plasma Proteome: A Non-Redundant List Developed by Combinationof Four Separate Sources, N. L. Anderson et al, Molec. Cell Proteomics, in press


Sources of H_Plasma_NR_v2Lit LCMS1 LCMS2 2DEMS Total

Beginning Accessions 468 607 341 319 1735Minus non-human 458 580 330 312 1680Minus intra-source

redundancy and nonhuman accessions

433 475 318 283 1509

Unique to source in NR 284 334 221 141 980Total combined NR list - - - - 1175

• Lit: N.L. Anderson and M. Polanski, result of literature search for proteins detected in plasma or serum.

• LCMS1: J. N. Adkins, S. M. Varnum, K. J. Auberry, R. J. Moore, N. H. Angell, R. D. Smith, D. L.Springer and J. G. Pounds. (2002) Toward a human blood serum proteome: Analysis bymultidimensional separation coupled with mass spectrometry. Mol Cell Proteomics 1, 947-55.

• LCMS2: R. S. Tirumalai, K. C. Chan, D. A. Prieto, H. J. Issaq, T. P. Conrads and T. D. Veenstra. (2003)Characterization of the low molecular weight human serum proteome. Mol Cell Proteomics 2, 1096-103.

• 2DEMS: R. Pieper, Q. Su, C. L. Gatlin, S. T. Huang, N. L. Anderson and S. Steiner. (2003) Multi-component immunoaffinity subtraction chromatography: An innovative step towards a comprehensivesurvey of the human plasma proteome. Proteomics 3, 422-32.



Overlap of Four Plasma Proteome Datasets(Number of NR proteins)

284

141

221

46

5

17

17

7

32

21

32

3

5

334

10

LCMS2

LCMS1Lit

2DEMS• 46 proteins in

all four lists

• 195 proteins in2 or more lists

• 1175 NRproteins total



Some Proteins of H_Plasma_195• adiponectin (involved in the control of fat metabolism and insulin

sensitivity),

• atrial natriuretic factor (a potent vasoactive substance synthesized inmammalian atria and thought to play a key role in cardiovascularhomeostasis),

• various cathepsins (D, L, S),

• centromere protein F (involved in chromosome segregation duringmitosis),

• creatine kinase M chain (an abundant muscle enzyme),

• glial fibrilary acid protein (GFAP: distinguishes astrocytes from otherglial cells),

• psoriasin (S-100 family, highly up-regulated in psoriatic epidermis),

• interferon-induced viral-resistance protein MxA (confers resistance toinfluenza virus and vesicular stomatitis virus),

• melanoma-associated antigen p97 (a proposed cancer marker alsoexpressed in multiple normal tissues),

• mismatch repair protein MSH2 (involved in post-replication mismatchrepair, and whose defective forms are the cause of hereditary non-polyposis colorectal cancer type 1),



Some Proteins of H_Plasma_195

• oxygen regulated protein (which plays a pivotal role in cytoprotectivecellular mechanisms triggered by oxygen deprivation),

• peroxisome proliferator-activated receptor (PPAR) binding protein(which plays a role in transcriptional coactivation),

• prostate-specific antigen (a protease involved in the liquefaction of theseminal coagulum, and one of the few successful cancer diagnostics),

• selenoprotein P (contains selenocyteines encoded by the opal codon,UGA),

• signal recognition particle receptor alpha subunit (an integralmembrane protein ensuring, in conjunction with srp, the correcttargeting of the nascent secretory proteins to the endoplasmic reticulummembrane system),

• squamous cell carcinoma antigen 1 (which may act as a proteaseinhibitor to modulate the host immune response against tumor cells),

• V-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (thereceptor for stem cell factor).



Signal Sequences in the Plasma Proteome

2DEMS

LCMS2

LCMS1

Lit

no possiblesignal

signal signalconfident

Num

ber o

f Occ

urre

nces

0

200

400

0

400

no possiblesignal

signal signalconfident

Signal Prediction

H_Plasma_195

H_Plasma_NR



Predicted TransmembraneSegments

0 4 8 12 16 200

50100150200250300350400

0

10

20

30

40

B

Occ

urre

nces

(num

ber,

0-1

Segm

ents

)

Occ

urre

nces

(num

ber,

2-21

Seg

men

ts)

A

0

20

40

60

80

100

120

140

0

5

10

15

Transmembrane Segments (number)

H_Plasma_195

H_Plasma_NR 2DEMSLCMS2

LCMS1

Lit



GO Component Annotations forSubsets of the Human Proteome

Extracellular7%

Membrane39%

Cytoskeletal3%

Golgi2%

Mitochondrial4%

Nuclear30%

Kinesin complex0%

Intracellular8%

Endoplasmic Reticulum

3%

Lysosomal1%

Cytoplasmic3%

Extracellular27%

Membrane27%Intracellular

2%

Cytoplasmic13%

Cytoskeletal4%


3%

Golgi2%

Lysosomal2%

Mitochondrial3%

Kinesin complex6%

Nuclear11%

Extracellular

Membrane

Intracellular

Cytoplasmic

Cytoskeletal


Golgi

Lysosomal

Mitochondrial

Kinesin complex

Nuclear

Extracellular

Membrane

Intracellular

Cytoplasmic

Cytoskeletal


GolgiLysosomal

Mitochondrial

Kinesin complex

Nuclear

Plasma4 of 4 (46)

Plasma2 of 4 (195)

Plasma 1 of 4 (1,175)

Human Proteome (10,911)



GO Component Annotations forFour Data Sources

Extracellular

Membrane

Kinesin complex

Intracellular


Lysosomal

Cytoplasmic

Nuclear

Mitochondrial

Golgi

CytoskeletalExtracellular

Membrane

Intracellular

Cytoplasmic

Cytoskeletal


Golgi

Lysosomal

Mitochondrial

Kinesin complex

Nuclear

Extracellular

MembraneIntracellular

Cytoplasmic

Cytoskeletal


Golgi

Lysosomal

Mitochondrial

Kinesin complex

NuclearExtracellular

Membrane

IntracellularCytoplasmic

Cytoskeletal


Golgi

Lysosomal

Mitochondrial

Kinesin complex

Nuclear

LCMS2

Lit 2DEMS

LCMS1



GO Functional Categories of Proteins inH_Plasma_NR

0

50

100

150

200

250

Calc

ium

bin

ding

Cyto

kine

DN

A bi

ndin

g

Hor

mon

e

Inhi

bito

r

Prot

ease

Rec

epto

r

Tran

spor

t

Num

ber o

f Occ

urre

nces

Go:Function Assignments

2DEMS

LCMS2

LCMS1

Lit



Reasons for Optimism re Discoveryof Protein Disease Markers

• Discovery-type proteomics can now detect 500-1,000distinct proteins in serum– Steady improvements in LC-LC/MS-MS

• Many of these proteins fall in classes likely to beinformative re tissue status– Secreted proteins, extracellular domains of plasma

membrane proteins, leaked intracellular proteins

• An open-source database of proteins of proteinsobserved in plasma is emerging

• Single protein markers are not required– Disease associated panels exist where no accurate single-

protein disease marker is available

• A series of measurements over time detects changemore accurately than comparison with simplereference interval


0

1

2

3

4

1992 1994 1996 1998 2000 2002 2004

Year

Nu

mb

er o

f N

ew P

rote

ins

in P

lasm

a/Y

r Year

1

10

100

1000

10000

1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Lo

g10

Nu

mb

er o

f R

eso

lved

Sp

ecie

s

Number of Zones/SpotsSequence Identified Unique Proteins

“1-D”

“3+-D”

“2-D”

4049 60

305*490+

More New Proteins =Fewer New Diagnostics?

• The gap between what canbe measured on a lab scaleand what can be usedeffectively in clinicaldiagnostics is widening

• The number of tests for newproteins in plasma approvedby US FDA over the lastdecade has declined, andnow approaches zero

• Problems include– Technology mismatch– Regulatory costs– Demonstration of medical

value– Medical economics


Challenges Facing Marker/DiagnosticProteomics: Translation into

Diagnostic Tests

• Lack of protein measurement platforms gearedto validation (high-throughput, low-cost)

• Access to large, well-organized sample sets forvalidation

• Falling rate of new protein tests over last decade

• Low expectation of diagnostic profitability impairscommercial investment

• Potential IP traffic jam


A Major Technology Gap Exists BetweenDiscovery and Routine Diagnostic Proteomics

Routine IVDDiscovery

100-10,000# samples2-50~15 mintime required4-52 wks

3-5%CV25-50%$2-100$ per analysis$1,000-$10M1-20# proteins50-700

?


“The appealing notion that research advancestravel from bench to bedside is laudable, butconceptually flawed. Even though the U.S.

Congress fully anticipates that funding to theNational Institutes of Health (NIH) will result in

advances in clinical medicine and that otherforces, presumably non-governmental, willtranslate the latest in exciting science intohealth technologies, under the system of

healthcare we have today, this advancement isnot likely to happen.”

Floyd BloomPresident, AAAS

Science 300:1680-1685 (2003)


Towards a Flexible, High-ThroughputQuantitative MS Platform

• Goals– Assay pre-selected proteins, i.e., identified

candidate disease markers for validationstudies

– Combine specific enrichment with MSquantitation

– Increase speed and throughput bydecreasing reliance on gradient LC

– Avoid method bias towards a class ofproteins


SISCAPA*: A New Method Combining The Specificity ofMS Detection with Sensitivity of Antibody Capture

(SISCAPA = Stable Isotope Standards with Capture by Anti-Peptide Antibody)

* patent pending


Selection of Peptides and Anti-Peptide Antibodies

Identification code

Protein Peptide Immunogen RabbitAntibody

Interleukin-6 (IL-6) EALAENNLNLPKGSGC IMM2 Ab 2

Hemopexin (Hx) NFPSPVDAAFRGSGC IMM3 Ab 3

α1-Antichymotrypsin (AAC) EIGELYLPKGSGC IMM4 Ab 4

Tumor necrosis factor alpha(TNFα)

DLSLISPLAQAVRGSGC IMM5 Ab 5

Tumor necrosis factor alpha(TNFα)

CGSGDLSLISPLAQAVR IMM6 Ab 6

• 10,203 peptides generated in silico from 237 known plasmasequences

• Monitor peptides selected based on physical/antigenicityparameters. >80% of proteins had 1 or more “good” peptides.

• Selected peptides synthesized, coupled to albumin carrier, usedin 38-day rabbit immunization protocol. Ab’s affinity purified onsame peptide immobilized on agarose.


Flow cytometric detection of rabbit anti-peptideantibodies coupled to POROS® –Protein G beads

• A: Black profile; POROS® protein Gbeads incubated with biotinylatedprotein L and fluorescein-labeledstreptavidin (negative control).Green profile; POROS® Streptavidinbeads incubated with biotinylatedProtein L and detected withfluorescein-conjugated streptavidin(positive control).

• B: Detection of covalently coupledrabbit anti-peptide antibodies onPOROS® Protein G beads. Beadscovalently coupled with 5 rabbitaffinity-purified Abs incubated firstwith biotinylated Protein L, followedby detection with fluorescein-conjugated streptavidin.


Relative binding of Alexa Fluor®488-labeled peptides tofive affinity purified anti-peptide antibodies

• The binding of fourpeptides, ALX 2-6, byPOROS® affinity matricescontaining either theirhomologous (specific) orheterologous (non-specific)antibodies was analysed.The values for eachantibody are normalized tothe maximum fluorescenceintensity for that antibody.Each value is the medianfluorescence intensity for1200 flow cytometerevents. Ab’s 5 and 6 areagainst opposite ends ofthe same peptide.


SISCAPA: Initial LC-MS Experiments

• 100nl Ab-POROS columns(100u ID x 1cm)

• Acid-eluted peptide (fromAb column) captured onC18 and eluted into MSby gradient LC

• N. L. Anderson et al,Mass SpectrometricQuantitation of Peptidesand Proteins Using StableIsotope Standards andCapture by Anti-PeptideAntibodies (SISCAPA), J.Proteome Research, inpress


Time, min

31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52

SIS2666.34

SIS3615.79 SIS5

694.90

554.78

SIS4534.33

0.0

5000.0

1.0e4

0.0

5000.0

1.0e4

A

B

C

Intensity, cps

3.5e4

4.0e4

4.5e4

5.0e4

5.5e4

6.0e4

6.5e4

7.0e4

Ab Capture from SIS Peptide Mixturewith Selected Ion Monitoring


)

40 41 42 43 44 45 46 47 48 49 50 51 52

Time, min

950.46

610.83767.92 472.23

507.28871.97

516.28.

767.9

610.8

615.8

A

B

C D

959.5233

1220.66091107.6035

862.4561775.4235

959.5220

775.4199862.4642

969.5465

785.4437

872.4988

750 850 950 1050 1150 1250m/z, amu

819.4783

932.5742

A

B

C

D

SIS3: NFPSPVDAAFR

Nat3: NFPSPVDAAFR

Nat3+: WKNFPSPVDAAFR

Apo A-I: QGLLPVLESFK

Enrichment and Quantitation of a HemopexinMonitor Peptide by SISCAPA


Ab2Ab3

Ab4Ab5

SIS2

SIS3

SIS4

SIS5

0

0.2

0.4

0.6

0.8

1

Relative Quantities of Four SIS Peptides Bound by Four Anti-Peptide Antibodies, Using Two-stage MS Selection (SRM)

Average Peptide Enrichment by Ab > 100-fold

The signals (vertical axis) for each antibody are normalized to the largest signal for that antibody


Cycle 1Cycle 2

Cycle 3Cycle 4

Cycle 5

SIS2

SIS3

SIS4

SIS5

0.0

2,000.0

4,000.0

6,000.0

8,000.0

10,000.0

12,000.0

SRM integrated ion current measurements of four SIS peptides eluted on five successive cycles from Ab 3.

Rabbit Polyclonal Anti-Peptide Ab’sCan Be Recycled After Acid Elution


Targeted Proteomics (e.g., SISCAPA)Enables Marker Validation

• Focus onmeasurement ofidentified proteins

• Accepts input fromboth proteomicsand genomics

• Bridges gapbetween discoveryand routinediagnostic use

• Hybrid methods,e.g., SISCAPA


PPI’s Plasma Marker Validation Project

• Identify marker protein candidates from anysource (literature, proteomics, genomics)

• Develop high-throughput MS-based validationassays

• Assay candidates in large well-characterizedplasma/serum sample sets (disease vs controland population studies)

• Place validation data in public domain• Advance protein measurement technology for

plasma


Acknowledgements

• SISCAPA Experiments– Bob Olafson, Derryl Hardy, UVIC-Genome B.C.

Proteomics Centre– Terry Pearson, Lee Haines, Department of

Biochemistry and Microbiology, University of Victoria,B.C, Canada

– John Rush, Cell Signaling

• Plasma Proteome Database– Malu Polanski (PPI)– Richard Fagan, Anna Lobley, Inpharmatica Ltd.,

London– Rembert Pieper, Tina Gatlin, present address: The

Institute for Genomic Research – Radhakrishna S. Tirumalai, Timothy D. Veenstra, Mass

Spectrometry Center, U. S. National Cancer Institute– Joshua N. Adkins, Joel G. Pounds, Biological Sciences

Department, Pacific Northwest National Laboratory

• Graphics– Arkitek Studios, Seattle

Ahmed, NAnderson, N GAponte, AAponte, JBraatz, JBrooke, EDas, TDavis, TEidbo, EEsquer, RField, EGatlin, CGoodman, JHardin, RHofmann, J-PHuang, S-TJett, G

Kho, KKiersarsky, KLennon, JLove, CMakusky, JMatthews, JMcGrath, AMcCrea, CMichael, SMiller, SMondal, MMontgomery, RMyers, TNorouzi, TParmar, PPiasecki, M

Russo, PSchatz, CSeonarain, MSims, CSteiner, SStewart, DSu, QSun, QTaylor, ATaylor, JTran, MVizzi, BWallgren, LWang, FWannberg, SZhou, J

• LSBC Proteomics Alumni

the plasma proteome: enabling a revolution in diagnostics feb 2004 web.pdf · ppi ©plasma proteome...

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