Case Center for Proteomics and Bioinformatics Workshop Seriesproteomics.case.edu/pdfs/proteomicsworkshop_feb.pdf · Case Center for Proteomics and Bioinformatics Workshop Series

Case Center for Proteomics and Bioinformatics Workshop SeriesMarch 5: Masaru Miyagi and Chao Yuan Will Present:

“Quantitative proteomic analysis using stable isotopic labeling”

April 2: Elizabeth Yohannes Will Present:“2D-DIGE workflow: An applied quantitative proteomics for molecular signature identification for diabetic bladder and erectile dysfunction”

We will continue the workshop series in the Fall with the following topics:

Structural Mass SpectrometryPosttranslational modifications of proteins and peptidesInteraction ProteomicsBioinformatics

CWRU has a license for Ingenuity software. The software conducts pathways analysis from your supplied targets. If you are interested in utilizing this software, send an e-mail request to [email protected]

mailto:[email protected]

Global Proteome ProfilingUsing Label Free Technology in Human Clinical Studies and Animal Models of

DiseaseDaniela Schlatzer and Chao Yuan, Ph. D

Case Western Reserve UniversityExpression Proteomics Workshop

February 5, 2009

OutlineOverview of Quantitative Proteomics

Discuss Label Free Expression Platform

Section 1 – Solution Based AnalysisExample Center Clinical Proteomics Projects

DiabetesCardiovascularCancer

Example Proteomic Techniques for Validation of Targets/and or Biomarkers

Multiple Reaction Monitoring – Absolute QuantificationMS Western Technique – Relative Quantification

Section 2 - Gel Based AnalysisExample of Tissue Analysis in Animal Models

Cardiovascular

Quantitative ProteomicsDetermine changes in a protein’s abundance and/or expression due to a biological challenge (disease,drug)

Better understand biological function and pathophysiology of diseaseBiomarker Discovery

Disease preventionTreatment

Quantitative Proteomics – Analysis Platforms

‘Top Down’ ProteomicsQuantify in tact proteins

2-Dimensional gel electrophoresis2D-DIGE, SELDI-TOF

‘Bottom Up’ ProteomicsQuantify peptides via enzymatic cleavage of one or many proteins

Isotope Coding – iTRAQ, SILAC, O18

Direct Quantification – Label free expressionTargeted Quantification – MS Western, Isotope Dilution Tandem Mass Spectrometry

Label Free ExpressionAdvantages

Can accommodate complex experimental designsCan provide increase proteome coverageEasy transition to a ‘bottom up’ validation analysisAmendable to low sample concentration (600 nanogramsfor LC/MS/MS)

DisadvantagesDifficulty quantifying post translational modificationsHigh degree of sample complexity for the mass spectrometer

Anderson, N. L. (2002) Mol. Cell. Proteomics 1: 845-867

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

Sample Preparation and Fractionation Strategies

DepletionPlasma, Serum and CSF

Affinity depletionAlbumin only7 or 14 most abundant plasma proteins

FractionationStrong cation exchange chromatography (peptide)One dimensional gel (protein)

Label Free Analysis WorkflowC T

C TBiomarker Discovery

Stats.Analysis

Diffset N

(… = Mass, Time, Intensity and Sequence)

…T…C

Digestion

Raw Data AcquisitionLC/MS/MS Data Processing

NetworkAnalysis

Diffset 1

450 500 550 600 650 700 750 800 850 90m/z

0

5

10

15

20

25

30

35

40

45

50

55

60

Rel

ativ

e A

bund

ance

440.54470.27

586.82

682.29

821.37509.27

660.30754.55

522.26497.93 655.97620.76

801.78548.51 703.39581.79

787.36 845.94 890.7

732.312 Mo DM

1 Mo DM

3 Day DM

C:\FT_MS Raw Data\...\Dani15_LF_1 8/7/2007 10:28:51 PM

RT: 0.00 - 110.01

0 10 20 30 40 50 60 70 80 90 100 110Time (min)

0

20

40

60

80

1000

20

40

60

80

100

Rel

ativ

e A

bund

ance

0

20

40

60

80

10011.50

107.74

21.56 27.31 33.10 44.76

68.93

15.41 63.9553.2440.1320.57 37.39 106.7848.69 77.8711.03 92.5862.51 104.066.79 74.26 89.33 94.07

107.48

33.8528.4122.33 45.8834.14

43.7038.59 64.7225.3115.73 54.36

14.7111.65 78.6317.63 63.4410.93 106.5452.646.75 62.61 69.74 92.3375.30 103.1280.80 86.686.17

107.58

44.5232.0426.7120.93 32.34 36.78

25.83 64.3353.8042.3413.52

106.6912.43 18.11 92.8875.0962.98 78.82 104.0968.8252.056.72 82.6658.86 102.366.29

NL:5.81E6Base Peak F: FTMS + p NSI Full ms [400.00-1600.00] MS Dani15_LF_1

NL:4.14E6Base Peak F: FTMS + p NSI Full ms [400.00-1600.00] MS dani15_lf_11


Retention Time

Label Free Mass Spectrometry Data

Quantification of 1,000 of peptides

Under Accurate Mass Conditions there are 160,000 units on the Y axis

m/z 400 - 1,200 (+ / - 0.005)

< 5 ppm mass error

Can be thought of an ‘array’technology

Data AnalysisProcess Raw Data

Intensities vs. SamplesPeak list generation (Mass)

Chromatographic Peaks Peptide sequence assignment

Data QCFilter poor quality peptides

Replace missing valuesNormalization

Statistical AnalysisANOVA

PCAFDR

Raw Imputed

NormalizedpeptideN

(… = Mass, Time, Intensity and Sequence)

…SN…S1

peptidet1

Jean eudes Dazard, Rob Ewing

Bioinformatic AnalysisData mining to enable biological interpretation of high dimensional datasets

Molecular network relevant to disease pathway understudy

Software tools which we use:Ingenuity Pathway Analysis (IPA)

Curated literature and protein interaction databasesPathway Studio

Automated literature search and protein interaction databases

MetaCoreProtein interaction databases

Example 1 – DiabetesMark Chance Ph.d – Center for Proteomics

Urine Biomarker for diabetic complications (CAD and DM nephropathy)

20 million people in the US have Type I or II diabetes; 150 million worldwideEnd Stage Renal Disease (ESRD) and Coronary Artery Disease are important disease complications

DM is leading cause of kidney failure Approximately 60,000 with ESRD die yearly

CAD leading cause of mortality in the US with a 3-10 fold increase risk in those with diabetes

Biomarkers of DM Complications-Limitations

Increased urinary albumin excretion is a current biomarker for both diabetic nephropathy and coronary artery diseaseMeasurement can be non linear over time and high incidence of remission is observed, questions as to sensitivity

Perkins et al, Current Diabetes Reports 5:455-463, ‘05

Frequency of micro-albuminuria remission and progression

Patients with micro-

albuminuria, N

Mean follow-up, yr % Remission % Progression

(to proteinuria)

Joslin study 386 6 59 (54-64)* 22 (18-27)EURO-DIAB 352 7 50.6† 13.9

Proteomic Components of Urine: Potential Biomarkers

Sources of Urinary ProteinsSoluble Proteins

Glomerular filtration of plasma proteinsEpithelial cell secretion of soluble proteins

Solid Phase ComponentEpithelial cells – whole cell sheddingExosome secretion

Uro-genital ComplicationsChanges in GFRDefects in proximal tube re-absorptionDefects in lysosomal processingChanges in protein expression in relevant tissues

Urinary Proteomics Discovery in STZ induced Diabetes Complications Rat Model

3 day 10 day 1 month 2 monthControl 1 3 2 3

STZ Treated 3 4 4 3

1 2 3 4 6 8

Development Progression of DiseaseEnd Organ Alterations

# Months after STZ treatment

Digested Peptides from Urine: LC/MS Chromatograms

INTENSITY

450 500 550 600 650 700 750 800 850 90m/z

0

5

10

15

20

25

30

35

40

45

50

55

60

Rel

ativ

e A

bund

ance

440.54470.27

586.82

682.29

821.37509.27

660.30754.55

522.26497.93 655.97620.76

801.78548.51 703.39581.79

787.36 845.94 890.7

732.312 Mo DM

1 Mo DM

3 Day DM

C:\FT_MS Raw Data\...\Dani15_LF_1 8/7/2007 10:28:51 PM

RT: 0.00 - 110.01

0 10 20 30 40 50 60 70 80 90 100 110Time (min)

0

20

40

60

80

1000

20

40

60

80

100

Rel

ativ

e A

bund

ance

0

20

40

60

80

10011.50

107.74

21.56 27.31 33.10 44.76

68.93

15.41 63.9553.2440.1320.57 37.39 106.7848.69 77.8711.03 92.5862.51 104.066.79 74.26 89.33 94.07

107.48

33.8528.4122.33 45.8834.14

43.7038.59 64.7225.3115.73 54.36

14.7111.65 78.6317.63 63.4410.93 106.5452.646.75 62.61 69.74 92.3375.30 103.1280.80 86.686.17

107.58

44.5232.0426.7120.93 32.34 36.78

25.83 64.3353.8042.3413.52

106.6912.43 18.11 92.8875.0962.98 78.82 104.0968.8252.056.72 82.6658.86 102.366.29

NL:5.81E6Base Peak F: FTMS + p NSI Full ms [400.00-1600.00] MS Dani15_LF_1



Retention Time

Summary Statistics for Diabetic ComplicationProteomics Discovery

2868 chromatographic peaks were generated812 of these received a peptide assignment 494 peptides passed QC filter and were analyzed by ANOVAOut of the 494 peptides, 64 of these had p-values of <0.05 for interaction (time & treatment)These peptides represent approximately 30 different proteins that:showed progression upon treatment

Specific Biomarker Candidates

Down –Regulated Protein (Hypoinsulinemia)

Up-Regulated Proteins (Hyperglycemia)

Major Urinary Protein Precursor Collagen alpha 2(1) chain precursor

Biomarker Discovery – Pilot Human Study

Goal – To transfer rat urine proteomic analysis protocols to human urine and to discriminate between sample types within pilot study

Coronary Artery Calcification in Type I Diabetes Study (CACTI)

Optimized protocol for 4 mL human urine3 controls, 3 Diabetics, 3 Diabetics with macro-albuminuriaAge matched (33-55 years)

Digested Peptides-LC/MS

DM - alb

DM

Control

RT: 0.00 - 121.99

0 10 20 30 40 50 60 70 80 90 100 110 120Time (min)

0

20

40

60

80

1000

20

40

60

80

100

Rel

ativ

e A

bund

ance

0

20

40

60

80

100 53.57

46.1346.30

26.8280.37

67.0234.2056.38 60.9144.7424.71 30.6619.12

84.1579.7773.98 102.8293.2987.1917.43 104.37 120.2114.826.2758.0153.80

44.97

46.3630.91

26.8647.12 67.0760.9239.01

80.4261.21 79.7034.3125.66 49.10 73.0018.9793.3084.7515.30 114.5775.45 102.87

107.10 121.427.48 9.945.1957.99

44.90

30.82

66.9049.7637.76

79.5861.1133.2825.6615.2993.2369.14 75.36 80.29

88.30 101.34 102.97 119.0913.907.80

Retention Time

INTENSITY

Summary Statistics

7446 diffsets were generated1165 of these received a peptide assignment 826 peptides passed QC filter and were analyzed by ANOVA

Contrast Significant Peptides (up or down regulated)

Adj P-value <0.05

Control vs DM 27

DM vs DM -ALB 377

Control vs DM -ALB 435

PCA of Label Free CACTI Analysis

DM-Alb

Control

DM

Changes in Protein Levels

Albumin Transferrin

CD 59 Glycoprotein PrecursorUromodulin

ConclusionsDeveloped and optimized an label free expression protocol for urine samplesBiologically significant changes in urinary proteins are observed in rat model across treatment and timePilot study shows differences in human data consistent with rat modelValidation study to to define predictors of CAD and ESRD underway

Example 2 – CardiovascularDaniel Simon MD – Cardiovascular Medicine

Pathway discovery for understanding the molecular basis of restenosis and drug eluting stents

Restonsis is a healing response to mechanical injury

Inflammation plays a critical role in neointimal thickening

Inflammatory cells contribute to the bulk within the intimaIncrease growth and chemotactic factorsProduction of enzymes which can facilitate cell migrationLocal inflammatory signals release the bone marrow derived stem cells that participate in the healing response of the blood

SMC and endothelial cells proliferation

Simon, DI et al. Circulation. 2005;111:2257-2273

Simon, DI et al. Circulation. 2007;115:548-549

Restenosis(DysregulatedHealing)

▪ Smooth Muscle Cell Proliferation▪ Matrix Growth

Neointimal Thickening

Reendothelialization(Normal Healing)

Inflammation▪ Activation and Cell-to-cell Interaction of Leukocytes,

platelets and Vascular Endothelial Cells

▪ Cytokines, Growth Factors, Radicals, Proteolytic Enzymes

Drug Eluting Stent

x

x

x

x

xxx

Vascular InjuryBare Metal Stent

Promotion

Inhibition

Daniel Simon

20% restenosis

1% vascular thrombosis/death

4% restenosis

Proteomic Analysis

Goal -To identify proteins that are involved in regulation of bone marrow derived stem cells in response to stenting of human blood vessels

20 plasma samples were analysed from patients pre and post (48hrs) stenting of blood vessel (s)

bare metal stents and developed restenosis after 6 monthsdrug eluting stents (sirolimus) and did not develop restenosis.

Sample Prep Depletion – 7 most abundant plasma proteinsFractionation – SCX of pooled digests

Results – Fractionation – 678 Non Redundant Proteins Id (75 ugrams total protein)

0

20

40

60

80

100

120

140

160

180

200

6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26Fraction

Num

ber o

f Pro

tein

s

Series2

• Angiopoeitn-related protein 3

• Synaptotagmin• Plastin 2• CSF1R• Brain derived

neurotrophic factor• FGF – 6• Pregnancy zone protein• S100 A9• Hepatocyte growth

factor activator• Hepatocyte growth

factor like protein

PCA

Label Free Analysis – Summary Statistics

Contrast Significant Peptides (up or down regulated) Adj P-value <0.10

Drug stent vs bare metal 48 hours 280 (56 Proteins)


Drug/Bare Metal at 48 hours

Green

Red

Follow Up

Two animal models to evaluate importance of CRP and complement pathway in recruitment of stem cell progenitors to vessel healing

Femoral hind limb artery ischemiaCRP knock in mice – increase recruitmentComplement knock out mice – decrease recruitment

Example 3 - CancerKenneth Cooke – Pediatric Oncology/Hematology

Biomarker discovery to understand the immunologic mechanisms that contribute to the development of idiopathic pneumonia syndrome (IPS) following allogeneic stem cell transplantation (SCT).

IPS is a major complication of SCT

3-15% of all SCT recipients60-80% mortalityEvidence from rodent models suggest lung is a target immunologically mediated damage

Inflammtory effectors of TNFαDonor derived T cell effectors

Cooke,KR et al. Bone Marrow Transplantation. 2004;34:753-765

Cytokine Plasma Measurements in BMT Patients

TNFα

Cont NL

Cont BMT

Pre-BMT

IPS Dx

0

50

100

150

**

*****

**

**

pg/m

L

IL-6

Cont NL

Cont BMT

0

5

10

500

1000

1500 ***

**

******

Pre-BMT

IPS Dx

pg/m

L

Cooke and Yanik et al. Blood. 2008;112 (8):3073-81

Proteomic Analysis

Goal -To identify proteins that are involved in the development of IPS in SCT patients

24 plasma samples were analysed from SCT patients at BMT and post (14 days) BMT or Dx of IPS

6 Non-progressors6 IPS patients

Sample Prep Depletion – 7 most abundant plasma proteinsFractionation – SCX of pooled digests

PCA

IPS dx

IPS d0

C d 0

C d 14

Label Free Analysis – Summary Statistics


Contrast Significant Peptides (up or down regulated)

Adj P-value <0.10

Interaction 552

Treatment 47

Time 294

Proteins – Expanding Disease PathwayAttractin Lumican

Secreted protein involved in the initial immune cell clustering during inflammatory responses and may regulate the chemotactic activity of chemokines.

LPS sensing by the Toll like receptor (TLR4) and innate immune responses are regulated by lumican

Lumican knock out mice are hypo responsive to LPS induced septic shock – poor induction of TNF-alpha, IL –beta and IL6 in serum

Individual Peptide Intensities for Lumican

Lumican Abundance at Day 0

0

2,000,000

4,000,000

6,000,000

8,000,000

10,000,000

12,000,000

14,000,000

16,000,000

1_0 2_0 3_0 4_0 5_0 7_0 8_0 9_0 10_0 11_0 12_0

IPS and Non IPS at Day 0

Ave

rage

Inte

nsity

Acr

oss

All L

umic

an

Pept

ides

Proteins – Early BiomarkersAHSG Alpha-2-HS-glycoprotein ORM1 Alpha-1-acid glycoprotein 1

A positive acute phase protein – TNF-alpha and IL-6 may up regulate the expression in the liver of this protein

A negative acute phase protein – TNF-alpha and IL-6 may down regulate the expression in the liver of this protein

Footprint of IL-6 and TNF-α signalingInteraction effect – disease over timeGreen Red

Follow Up

Validation of Lumican by WesternInvestigate knock out of rodent model of disease

Additional network analysis of using Pathway Studio for further data miningAnalysis including day 7 samples to detect potential early biomarkers id’d in initial analysis

Inte

nsity

MS/MSRP Column

Concentration

Biomarker Validation Strategy: Isotope Dilution Tandem Mass Spectrometry

High sensitivity, high specificity, absolute quantification

Quantification of Pro Alpha (2) 1 Collagen Peptide (GEPGSVGAQGPPGPSGEEGK)

ProalphacollagenY = -13012+155553*X R̂ 2 = 0.9956 W: Equal

5 10 15fmole

0

500000

1000000

1500000

2000000

2500000

Are

a

LOQ = 250 attamoles with S/N 303

Femtomoles

INTENSITY

Retention Time

Standard Curve

Extraction of Specific Peptide Signal

Poster : THPV -521

Quantization of Pro Alpha (2) 1 Collagen Peptide

Label Free Quantification - Proalpha(2) 1 collagen Peptide

0200,000400,000600,000800,000

1,000,0001,200,0001,400,0001,600,0001,800,0002,000,000

C 3Day

DM3Day

C 1Mo DM1Mo

C 2Mo DM2Mo

Time point

Inte

nsity low

medianhigh

SRM - Proalpha(2) 1 collagen Peptide

2 2.7

5.95 6.2

23.3

1.6 2.4

5.454.13.5

11.5

6.35

24.65

3.93.12.4

0

5

10

15

20

25

30

C 3D DM 3D C 1Mo DM 1Mo C 2Mo DM 2Mo

Time pointC

onc.

(fm

ole/

60ng

tota

l pro

tein

)

medianmin.max

Label Free Discovery Targeted Analysis

Quantization of Extra-cellular Matrix Related ProteinHuman_Proalpah

Y = -381.273+10602.8*X R̂2 = 0.9984 W: Equal

0 2 4 6 8 100

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

110000

Are

a

Area

Concentration (fmoles)

0

2

4

6

8

10

12

14

16

Control DM DM_Alb

Treatment

Con

cent

ratio

n (fm

oles

)

HighLow Mean

DM-AlbControl DM

AcknowledgementsMark Chance Ph.D Director Center for Bioinformatics and Proteomics

CollaboratorsDaniel Simon M.D.Ken Cooke M.D.CACTI

Marian Rewers, David Maahs and Janet Snell-Bergeon

Andrea Romani Ph.D

All Center Staff and Faculty

Jean Eudes Dazard Ph.DBiostatistics and experimental design

Rob M. Ewing Ph.DBioinformatics

Infochromics software

Label-Free Mass Spectrometry Analysis of Diabetic Cardiomyopathy

Chao Yuan02/05/2009

Tissue Proteomics

Sample Preparation:Protein ExtractionProtein FractionationProtease Digestion

Will briefly discuss mass spectrometry and data analysis

Consistency

Animal Preparation

Control (n=4)

Buffer

Isolate Hearts

Diabetic (n=4)

STZ

Isolate Hearts

Insulin (n=4)

STZ

Insulin

Isolate Hearts

Week 0

Week 4

Week 6

PI: Andrea Romani (Physiology, CWRU)

Protein ExtractionUnlike body fluids, tissue has significant amounts of structural and membrane proteins which are difficult to be extracted.In addition to mechanical homogenization and sonication, detergents are often used to facilitate the extraction of protein.Good detergents must

Have high/consistent extraction efficiencyBe compatible with digestion & LC/MS

Micro-dissected breast carcinoma tissue

Benefits of 1D-Gel

Compatible with most extraction buffers.

RemoveDetergentsAbundant protein speciesLow molecular weight contaminates

Protein fractionation

Fractionation

Divide whole proteome into multiple fractionsGoal: to increase proteome coverage

5X

Two Extreme Examples

DepletionRemove unwanted abundant species

Enrichmente.g. Phosphorylated proteins/peptides

Simplified proteome /Increased Sensitivity/Reduced cost

Fractionation

Subcellular levelNucleus, mitochondria, microsome, etcAble to obtain cellular localization

Protein level- Molecular weights, pI, hydrophobicity, etcPeptide level

Hydrophobicity, pI, charge etc

Combination of Different Fractionation Techniques

2D gel (Protein, pI, MW)PF2D (Protein, pI, MW, in solution)MudPIT (Peptide, charge, hydrophobicity)

Increased fractionation comes at the cost of inconsistency and $.Sufficient fractionation/High reproducibility/Economical

The Consistency of 1D Gel fractionation

C1 D1 T1 C2 D2 T2 C3 D3 T3 C4 D4 T4 P1 P2 P3 P4

C: control; D: Diabetic; T: Insulin; P: Pool

200 KDa

10 KDa

The Consistency of 1D Gel fractionation

Increased Proteome Coverage1

2

3

4

5

6

7

8Next: Overlapping between fractions

4X

Appearance of Identified Proteins

•Titin•MHC•Trypsin•4 Keratins

Total = 820

Majority (>90%) are found in one band or in adjacent bands.

Next: MW distribution of proteins found in one band.

MW of identified proteins showed strong correlation (0.88) with band position

Degradation products can be easily identified

Digestion

Protein->Peptides“Top-down” & “Bottom-up” proteomics

Two concerns regarding in-gel-digestion:-Digestion efficiency-Extraction efficiencyUse an improved in-gel-digestion protocolPulverize gel pieces -> increased exposure to trypsinExtract 5 times with shaking/sonication-> max. recovery

Next: Miss-cleavage

Miss-Cleavage

Maximum peptide recovery does not mean maximum digestion efficiency.Protein might have partial 2nd structure, or be protected from gel matrix that prevent full tryptic digestion.Some chemical properties of peptides also induce miss-cleavageThese larger peptides with miss-cleavages can be successfully extracted because of our vigorous extraction method.Miss-cleavage is usually not a problem for protein ID.But it poses serious challenges for protein quantification as peptides mixtures with different miss-cleavage rates can hardly be compared, and exclusion of these peptides will dramatically reduce the number of quantifiable peptides.Therefore, we used a two step digestion protocol.

Comparison of two digestion protocols

Band #6 (Pool)

In-gel-digestion

extraction

2nd digestion

LC/MS

Peptide/Protein ID

Calculate miss-cleavage rate

One-Step Two-Step

Total unique peptides: 806 (two step); 846 (one step)

Comparison of Miss-cleavage Rates

50%

Miss-cleavage Rates-Band 6 of 12 Samples

Average CV2 miss cleavages 1.6% 16%

1 miss cleavage 10.4% 14%

Total miss-cleaved 12.0% 13%

Next: properties of miss-cleavage sites

Nature of Miss-cleavage

R/K E/D or phosphorylation

Distribution of Miss-cleavages

Further reduction of miss cleavage rates using an bacterial trypsin

1.7% of total

Miss cleaved peptides from all band 6

A Promising Bacterial Trypsin (SET)•Streptomyces griseus trypsin (SET) was known to be resistant to autolysis.•We tested SET for digestion efficiency.•Porcine trypsin and SET were used to digest equal amounts of BSA for 5 hours.

Pep

tide

coun

t

Next: LC/MS analysis

71

LC/MS

Mass SpectrometerRP Column

HPLC

Care was taken to ensure the reproducible performance of LC/MS.Duration: 8 days; RT: < 2min; Mass Accuracy: < 2 ppm

72

MS of intact peptide (precursor ions)

m/z

Intensity

73

MS of fragment ions

P E P T I D E

P E P T I D Eb5 y3

y4

b3

b2

b1

y2

y3

y5y6b4

b5

R

RHe

………………………………………………………………

CID

Label Free MS Data Analysis

Peptide M

Intensity

Sample NAll Bands…

Sample 1All Bands

Peptide 1

LC/MS & Data Analysis

4106 unique peptides and 820 unique proteins were were identified with high confidence.Data are subjected to label free data analysis using a commercial software.Some changes can be observed even at chromatographic level.

Control 1

Diabetic 1

Insulin 1

Control 2

Diabetic 2

Insulin 2

Control 3

Diabetic 3

Insulin 3

Control 4

Diabetic 4

Insulin 4

Summary

1D GelProtein extraction “bottom-up” proteomics

•Compatible with most detergents•High Reproducibility•Economical and efficient fractionation tool•Optimized in-gel-digestion protocol

Next Workshop:

Quantitative proteomic analysis using stable isotopic labelingMasaru Miyagi and Chao Yuan

March 5, 2009

Key to label free MS analysis: Reproducibility

AcknowledgementsMark Chance Ph.D Director Center for Bioinformatics and Proteomics

CollaboratorsDaniel Simon M.D.Ken Cooke M.D.CACTI

Marian Rewers, David Maahsand Janet Snell-Bergeon

Andrea Romani Ph.D

All Center Staff and Faculty

Jean Eudes Dazard Ph.DBiostatistics and experimental design

Rob M. Ewing Ph.DBioinformatics

Infochromics software

Documents

Case Center for Proteomics and Bioinformatics Workshop Seriesproteomics.case.edu/pdfs/proteomicsworkshop_feb.pdf · Case Center for Proteomics and Bioinformatics Workshop Series