1. PRG2007 Research Study Advanced Quantitative Proteomics
ABRF PRG 2007

2. PRG Members Arnold Falick (Chair) – UC Berkeley HHMI
William Lane (EB Liason) – Harvard University
Kathryn Lilley (ad hoc) – University of Cambridge
Michael MacCoss – University of Washington
Brett Phinney – UC Davis Genome Center
Nicholas Sherman – University of Virginia
Susan Weintraub – Univ. Texas Heath Science Center
Ewa Witkowska – UC San Francisco
Nathan Yates – Merck Research Laboratories
ABRF PRG 2007

3. Past Research Studies
PRG2002: Identification of Proteins in a Simple Mixture
Task: Identify components of a 5 protein mixture
PRG2003: Phosphorylation Site Determination
Task: Identify 2 phosphopeptides and sites of phosphorylation
PRG2004: Differentiation of Protein Isoforms
Task: Discrimination of 3 closely related proteins
PRG2005: Sequencing Unknown Peptides
Task: De novo sequence analysis of 5 peptide mixture
PRG2006: Quantification of Proteins from a Simple Mixture
Task: Relative Abundance of 8 Proteins Between 2 Different Samples
ABRF PRG 2007

4. 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.
ABRF PRG 2007

5. PRG2007 Sample Design Identical Sample A Sample B Sample C
100 µg E. coli lysate
12 Total Protein Spikes
- 10 Non-E. coli proteins
- 2 E. coli proteins
100 µg E. coli lysate
12 Total Protein Spikes
- 10 Non-E. coli proteins
- 2 E. coli proteins
100 µg E. coli lysate
12 Total Protein Spikes
- 10 Non-E. coli proteins
- 2 E. coli proteins
Spikes at Different Levels and Ratios
ABRF PRG 2007

6. PRG2007 Study Tasks
Identify the proteins that had altered components between the samples
Determine the relative amounts of the proteins between samples
ABRF PRG 2007

7. Proteins in PRG2007 Sample * *
*E. coli Proteins
ABRF PRG 2007

8. Proteins in PRG2007 Sample
ABRF PRG 2007

9. Protein Sequence Database
>gi| |ref|NP_ | putative membrane protein [Escherichia coli K12]
MKTLIRKFSRTAITVVLVILAFIAIFNAWVYYTESPWTRDARFSADVVAIAPDVSGLITQVNVHDNQLVK
KGQILFTIDQPRYQKALEEAQADVAYYQVLAQEKRQEAGRRNRLGVQAMSREEIDQANNVLQTVLHQLAK
AQATRDLAKLDLERTVIRAPADGWVTNLNVYTGEFITRGSTAVALVKQNSFYVLAYMEETKLEGVRPGYR
AEITPLGSNKVLKGTVDSVAAGVTNASSTRDDKGMATIDSNLEWVRLAQRVPVRIRLDNQQENIWPAGTT
ATVVVTGKQDRDESQDSFFRKMAHRLREFG
Was converted to:
>PRG_seq_5 ABRF_PRG2007_Protein_5 MKTLIRKFSRTAITVVLVILAFIAIFNAWVYYTESPWTRDARFSADVVAIAPDVSGLITQVNVHDNQLVK
The file contains:
1) 4,346 protein sequences
2) common contaminants (e.g. keratins, trypsin, etc...)
3) an equal number of decoy sequences
ABRF PRG 2007

10. Samples Analyzed by 2D DIGE
Sample A Sample B Sample C
A pooled standard of all three samples was made and labelled with Cy5 (red). The samples were then labelled individually with Cy3 (green) and each gel was run with a single sample versus pooled standard.
ABRF PRG 2007

11. Samples by µLC-MS (1 µg on column)
Base Peak Chromatograms
Sample A
Sample B
ABRF PRG 2007

12. Demographics of the Participants
ABRF PRG 2007

13. Demographics of the Participants
Quantitative Data Returned = 35
Total Participants = 43
87 Labs Requested Samples: 49% Return Rate
ABRF PRG 2007

14. PRG2007 Abbreviations DIGE Differential In-Gel Electrophoresis
ICPL Isotope Coded Protein Label
iTRAQ isobaric Tags for Relative and Absolute
Quantitation
ICAT Isotope Coded Affinity Tag
18O Stable Oxygen Isotope Label
SRM Selected Reaction Monitoring
ABRF PRG 2007

15. 35 Participants Returned Methods Used
ABRF PRG 2007

16. Techniques Applied
ABRF PRG 2007

17. Results: True Positives vs False Positives17
ABRF PRG 2007
ABRF PRG 2007

18. Results: True Positives vs False Positives18
ABRF PRG 2007
ABRF PRG 2007

19. Quantitative Accuracy: Ubiquitin
2D Gels
Label Free
Stable Isotope Labeling
A = 5 pmol
B = 23 pmol 8
Anticipated Mole Ratio 4.6 6
B/A Ratio
Color Indicates Method Used
iTRAQ
ICPL
ICAT
18O Labeling
Label Free
Label Free + targeted SRM
2D-Gels (nonDIGE)
2D-DIGE 4 2
ABRF PRG 2007

20. Quantitative Accuracy: Myoglobin
2D Gels
Label Free
Stable Isotope Labeling
A = 0.5 pmol
B = 5 pmol 16 14 12
Anticipated Mole Ratio 10
B/A Ratio 10
Color Indicates Method Used
iTRAQ
ICPL
ICAT
18O Labeling
Label Free
Label Free + targeted SRM
2D-Gels (nonDIGE)
2D-DIGE 8 6 4 2
ABRF PRG 2007

21. Quantitative Accuracy: Serum Albumin
2D Gels
Label Free
Stable Isotope Labeling
A = 5 pmol
B = 3.3 pmol
3.5 3
2.5
Anticipated Mole Ratio 0.67
B/A Ratio 2
Color Indicates Method Used
iTRAQ
ICPL
ICAT
18O Labeling
Label Free
Label Free + targeted SRM
2D-Gels (nonDIGE)
2D-DIGE
1.5 1
0.5
ABRF PRG 2007

22. Quantitative Accuracy: Carbonic Anhydrase I
2D Gels
Label Free
Stable Isotope Labeling
A = 2.5 pmol
B = 1.14 pmol
1.8
1.6
1.4
Anticipated Mole Ratio 0.45
1.2
B/A Ratio 1
Color Indicates Method Used
iTRAQ
ICPL
ICAT
18O Labeling
Label Free
Label Free + targeted SRM
2D-Gels (nonDIGE)
2D-DIGE
0.8
0.6
0.4
0.2
ABRF PRG 2007

23. Quantitative Accuracy: Glucose Oxidase
2D Gels
Label Free
Stable Isotope Labeling
A = 0.5 pmol
B = 0.33 pmol 1
0.8
Anticipated Mole Ratio 0.67
0.6
B/A Ratio
Color Indicates Method Used
iTRAQ
ICPL
ICAT
18O Labeling
Label Free
Label Free + targeted SRM
2D-Gels (nonDIGE)
2D-DIGE
0.4
0.2
ABRF PRG 2007

24. Quantitative Accuracy: Hexokinase
2D Gels
Label Free
Stable Isotope Labeling
A = 0.5 pmol
B = 0.16 pmol
2.5 2
Anticipated Mole Ratio 0.31
B/A Ratio
1.5
Color Indicates Method Used
iTRAQ
ICPL
ICAT
18O Labeling
Label Free
Label Free + targeted SRM
2D-Gels (nonDIGE)
2D-DIGE 1
0.5
ABRF PRG 2007

25. Quantitative Accuracy: Tryptophanase*
2D Gels
Label Free
Stable Isotope Labeling
A = 5 pmol
B = 1.56 pmol 10 8 6
Anticipated Mole Ratio from 1 to 0.31 4
B/A Ratio 2
Color Indicates Method Used
iTRAQ
ICPL
ICAT
18O Labeling
Label Free
Label Free + targeted SRM
2D-Gels (nonDIGE)
2D-DIGE
ABRF PRG 2007

26. Biggest Challenges Reported – Summary
Complexity of the proteolytic digest. Long calculation times at several analytical steps
To find the resources: spent more than $1000 on [the study] and had one technician busy for more than a week and a scientist for 2-3 days
Finding the time
No automation software available - too much hands-on work.
Sample solubilization
The ABRF fasta database: several search algorithms had problems.
Number of replicates possible, making it difficult to determine a reasonable error rate, making it difficult to determine whether a protein is actually differentially expressed
The MS identification of low abundance differential spots
ABRF PRG 2007

27. Selected Comments
The study was very good for researchers new to the proteomics field.
This was an excellent learning experience. This study highlighted my facility's capabilities (peptide fractionation and MS) and weaknesses (chemical labeling of proteins and peptides and quant. analysis).
This years study was a much more realistic sample that imitates real proteomic samples (without the dynamic range issue from serum/plasma samples).
Very interesting study because it addresses a 'real world' issue which is the relative quantitation of a small number of proteins in a very complex mixture.
We didn't have enough time...
The protein amount of these samples is small and so it is difficult to have confident results.
ABRF PRG 2007

28. Selected Comments -- Continued
More sample, more time. We would have run these in at least triplicate as per our routine operation if we had had more sample and time.
Make sure the solubilisation is as good as possible: I did not obtain any useful data from the samples, probably because I was not able to solubilise the sample completely.
not fun!!!
Overall peak intensity of the samples was not as high as the expected intensity for the amount of protein specified (100 µg) in the study.
Liked it, because we could evaluate ourselves. For regular samples (500 µg on gel) I always am able to confidently assign most proteins. That was not so with the concentrations here.
ABRF PRG 2007

29. Would you do this sort of study again?
Other Responses:
Yes, learned a lot, but need to watch resources
Yes, but time issue
Maybe
Yes, but it was not fun
ABRF PRG 2007

30. Conclusions
Quantitative proteomics experiments are complex and require many factors for success
A handful of participants reported excellent results indicating that quantitative results are achievable
Participants using similar techniques did not obtain similar performance and suggests that expertise is a key factor
Head to head comparisons of different approaches is not possible because of the high dependence on expertise
Interest in this area is high and many labs appear to be developing these capabilities
ABRF PRG 2007

31. Acknowledgements Kevin Hakala (UTHSCSA) Michelle Salemi (UC Davis)
Rich Eigenheer (UC Davis)
Matthew Russell (University of Cambridge)
Ekaterina Deyanova (Merck Research Laboratories)
ABRF PRG 2007

32. A huge thanks to all the labs that participated in this year’s study!
Acknowledgements
A huge thanks to all the labs that participated in this year’s study!
ABRF PRG 2007