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Quantitative Proteomics: Approaches and Current Capabilities

Pathway Tools Workshop Chris BeckerPhysical Sciences DivisionOctober 27, 2010


There have been and can still be problems with large

scale genomic and metabolomic measurements.

What about proteomics?


Volume 359, Issue 9306, Pages 572 - 577, 16 February 2002

Use of proteomic patterns in serum to identify ovarian cancer

authors Emanuel F Petricoin … Lance A Liotta

What many/most scientists know about proteomics,

even if they don’t know about this publication.


How do researchers differentially quantify proteins?

• 2-D Gels

• Isotopic labeling

– iTraq (commercial reagent for tagging amine groups on lysine; read-out via

MS/MS)

– SILAC (stable isotope labeling with amino acids in cell culture)

• Label-free quantification


Label-Free Differential Profiling

• Two types of label-free quantification:

– Intensity based or MS1 or MS-only

– Spectral counting (some minor variations; must re-ID each sample)

• Our research group provided an early description of the approach of using signal

intensities of label-free peptides and metabolites for LC-MS for quantification, including

normalization.

– ASMS 2002 Meeting– Wang et al. Analytical Chemistry 75:4818-4826 (2003)

• Overcame a bias that only isotopic labeling or gel imaging could provide a quantification

basis. Worry was matrix effects; the answer was to use significant chromatography times

and comparing similar samples.


Label-Free Differential Profiling: easy to understand

Sample A

Sample B

What’s different between these two samples?


Label-Free Differential Profiling

Sample A

Sample B, more dilute and/or instrument losing some sensitivity over the course of a study


Typical spectral complexity: 1 sample in 2 minutes

Scans separated by 30 sec

050108-103

m/z400 405 410 415 420 425 430 435 440 445 450 455 460 465 470 475 480 485 490 495 500

%

0

100

%

0

100

%

0

100

%

4

LCT427_092208_IQC03 775 (25.846) Cm (770:775) TOF MS ES+ 396419.6420

407.1441 410.6188

431.6610

420.6443

438.7919 463.1574

443.1240

447.1326

456.1486

479.1483 492.1199

480.1478

491.6035

481.6388

492.6159

497.1432

LCT427_092208_IQC03 745 (24.846) Cm (745:750) TOF MS ES+ 704466.1288442.1834423.6487

418.0884402.1325 417.1749

438.1288425.8080

426.1444

431.6320

459.7661442.6830

447.4577

447.7927

448.1179

477.6205

467.1347

468.1115

478.1193

479.1177494.1365

LCT427_092208_IQC03 725 (24.178) Cm (720:725) TOF MS ES+ 753423.1121412.6123

401.6470

415.7778

450.1016428.6090431.1193

435.0929

435.5884

440.1098

450.6056

452.1194462.1057 484.7508

473.1123 483.1117

490.1487 497.1017

LCT427_092208_IQC03 705 (23.511) Cm (700:705) TOF MS ES+ 536472.4035401.1152 409.6191

408.6301456.4271423.1216410.1094

411.6197

426.6444449.0846

442.6046429.7860

456.7554

457.1138462.6264

476.6134

477.6103488.1094 493.6191

Narrow 100 m/z range


Association of Biomolecular Resource Facilities (ABRF)

Proteomics Research Group PRG2007 Study Objectives

• What methods are used in the community for assessing differences between complex mixtures?

• How well established are quantitative methodologies in the community?

• What is the accuracy of the quantitative data acquired in core facilities?

• We wanted to build upon last years study by providing samples that were more complicated, yet more realistic.

http://www.abrf.org/prg


Sample Design:

Sample A Sample B Sample C

100 µg E. coli lysate

12 Total Protein Spikes - 10 Non-E. coli proteins - 2 E. coli proteins





Spikes at Different Levels and Ratios

Identical


Techniques Applied

5 (14%)

5 (14%)

6 (17%)

2 (5.6%)

1 (2.8%)

1 (2.8%)

16 (44%)

iTRAQ ICPL ICAT 18O-Labeling Label Free 2D Gels (nonDIGE) 2D DIGE


Performance of Various Proteomics ApproachesResults from 36 Laboratories: True Positives vs False Positives

Note performance overall of label-free (yellow) results

Beckerlab


Performance of Various Proteomics ApproachesResults from 36 Laboratories: True Positives vs False Positives

Note performance overall of label-free (yellow) results


Quantitative Accuracy: Ubiquitin

Color Indicates Method UsediTRAQICPLICAT18O LabelingLabel FreeLabel Free + targeted SRM2D-Gels (nonDIGE)2D-DIGE

B/A

Ratio

8

2D Gels Label-FreeStable Isotope Labeling

Anticipated Mole Ratio 4.6

6

2

0

4

A = 5 pmolB = 23 pmol


Quantitative Accuracy: Glucose Oxidase

Color Indicates Method Used

iTRAQICPLICAT18O LabelingLabel FreeLabel Free + targeted SRM2D-Gels (nonDIGE)2D-DIGE

B/A

Ratio

1

2D Gels Label FreeStable Isotope Labeling

Anticipated Mole Ratio 0.67

0.8

0.4

0.2

0.6

0

A = 0.5 pmolB = 0.33 pmol


Reproducibility Testing:Process and Instrument Variation Workflow

Pooled human serum

1

2

3

4

5

n

Sample aliquots are processed

Processed samples are pooled beforeanalysis and replicates are run

IQC –Instrument QCVariation due to the LC and Mass Spec

Processed samples are run individually

PQC –Process QCVariation due to sampleprocessing in addition tothe LC and Mass Spec

Sample Processing LC-MS


6% median CV8% mean CV

14% median CV17% mean CV

IQC samplesInstrumentVariation

PQC samplesProcessing

plusInstrumentVariation

Proteome QC Report extracted from a 4-batch human plasma study

(~8000 components)

0

500

1000

1500

2000

2500

3000

3500

4000

0 5 10 15 20 25 30 35 40 45 50

CV (%)

Fre

qu

ency

0 5 10 15 20 25 30 35 40 45 500

500

1000

1500

2000

2500

3000

3500

4000

0 5 10 15 20 25 30 35 40 45 50

CV (%)

Fre

qu

ency

0 5 10 15 20 25 30 35 40 45 50

0

500

1000

1500

2000

2500

0 5 10 15 20 25 30 35 40 45 50

CV (%)

Fre

qu

ency

0 5 10 15 20 25 30 35 40 45 500

500

1000

1500

2000

2500

0 5 10 15 20 25 30 35 40 45 50

CV (%)

Fre

qu

ency

0 5 10 15 20 25 30 35 40 45 50


Example of quantification and the effect of a PTM, oxidation. In CSF.


Typical Metrics for Proteomics• Coefficients of variations ~ 20%

• Accuracy ~ 20%

• One-dimensional (1D) analysis

– Track, identify and quantify approximately 1,000 proteins.

• Two-dimensional (2D) analysis

– Track, identify and quantify approximately 2,000 proteins.

• False discovery rate < 1% for identification (decoy database)

• False discovery rate p-value < 0.01 for differential expression (Benjamini

Hochberg, Storey)

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coli lysate

coli proteinsspikes

coli proteins100 g

free peptides

quantitative proteomics

total protein spikes

sample asample bsample