Proteomics Standards Research Group (sPRG) Using Skyline to analyze the SPRG2013- 2014 Targeted...

Proteomics Standards Research Group (sPRG)

Using Skyline to analyze the SPRG2013-2014 Targeted Proteomics Assay (TPA) standard

Brett S. Phinney, Ph D.UC Davis Genome Center


What is the ABRF?

ABRF members are from over 300 international core laboratories in academia, government, and industry, in a broad spectrum of biomolecular technologies

The ABRF promotes the research, technology, communication and education

ABRF has Research Groups and Committees, Affiliates and Chapters, annual conferences with educational courses, a quarterly journal, a Newsletter, Research Group publications, and Listserves

The ABRF is unique for providing benchmarking studies by its Research Groups and has efficient mechanisms for networking and sharing


Proteomics Standards Research Group (sPRG) 2013-2014

Christopher Colangelo (Chair) Yale UniversityCraig Dufresne Thermo Fisher ScientificAlexander R. Ivanov (Ad hoc) Northeastern UniversityToni Koller Stony Brook UniversityBrett Phinney (EB Liaison) University of California, DavisKristie Rose Vanderbilt UniversityPaul Rudnick National Institute of Standards and TechnologyBrian Searle Proteome SoftwareScott A. Shaffer University of Massachusetts Medical SchoolBrendan Maclean University of Washington SOMDavid Hawke UT M.D. Anderson


• Design a comprehensive standard mixture of heavy-labeled tryptic peptides that can be used as internal standards for proteomics applications.

• Widely applicable across human, mouse, and rat proteomes.

• Proteotypic in nature.

• Utility towards both discovery and targeted proteomics applications.

Study Rationale / Design


• Heavy/light peptide ratios will allow for normalization and evaluation for both inter- and intra- sample comparisons.

• System suitability of LC-MS/MS performance including benchmarking and reproducibility of LC peptide elution profiles, evaluation of MS sensitivity and dynamic range.

• Provide additional control for longitudinal proteomics studies and can be used in interlaboratory studies.

• Peptides can be converted into absolute quantitation standards via cleanup, purification, and amino acid analysis.

Study Rationale / Benefits


• 1000 stable isotope labeled tryptic peptides conserved across Homo sapiens, Mus musculus and Rattus norvegicus.

• Peptide mixture will be spiked at fixed concentration to a HEK digest at 3 different dilutions (3 orders of magnitude).

• Participants will analyze the samples using LC-MS instrument platform of their choice, report ratios, return the questionnaire and raw data to the sPRG (via study anonymizer or possibly Panorama).

• Spectral library will be constructed, data searched, and peptide ratios determined for all data sets.

• Comparison of ratios, normalization of data, and peptide retention/elution order will be compared across data sets.

Study Design


Peptides selected via databases at Yale, NIST, Stony Brook

YPED; 115 LC-MS/MS HEK experiments• MASCOT SCORE > identity • BLAST search common to human, mouse, and rat• K or R tryptic cleavage (C-terminal)

6750 peptides, <1% to 67% observed in the samples• Removed peptides with upstream proline to K, R• Removed peptides with upstream KK, RR• Removed peptides with M• Removed peptides w/ miscleavages

1500 peptide candidates to send to JPT Peptide Technologies• JPT selected 1000 peptides for synthesis

Peptide Selection


1 53 1051572092613133654174695215736256777297818338859379890

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Charge State (NIST)Dynamic Range (YPED)observed spectral counts

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Peptides Map to 552 proteins

peptide number

# peptides mapping to each protein

Peptide Selection and Other Criteria


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 190

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Sample

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Peptide Identification in ABRF Labeled Peptide

Standard Mix


Using Skyline for AUC analysis

• 1000 peptides + 5 ug HEK lysate– We decided on AUC analysis as we did not have

enough time to develop a scheduled MRM method

– Still possible for someone out there to develop a scheduled MRM method if you want• Might be a good test for RT prediction algorithms


Using Skyline for AUC analysis

• 1 pmol peptide mix spiked into 5 ug HEK 293 lysate

• Set MS1 resolving power at 70K– Q-Exactive Data


Example PeptideGGSASVWSER (523.2505, 2+)


Example PeptideGGSASVWSER (523.2505, 2+)

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Retention Time

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precursor - 523.2505++ precursor [M+1] - 523.7519++ precursor [M+2] - 524.2532++

26.0(idotp 0.19)

22.5(idotp 0.84)

30.7(idotp 0.52)

29.8

26.2(idotp 0.81)

26.0(idotp 0.81)

23.8(idotp 0.88)

23.2(idotp 0.96)

21.8(idotp 0.98)

Isotope Dot Product idot = 0.81

Isotope Dot Productidot = 0.96

Retention Time from

Library

Proteomics Standards Research Group (sPRG)Lets look at raw data

RT: 0.00 - 120.00

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

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25.5128.28

23.5720.89

20.36

18.17

31.8516.45

35.41

38.4615.64

71.59

39.81

60.40

44.7749.37 51.09

41.2695.2860.02 63.47

95.07 95.4553.4014.78

55.2763.68 91.06

66.21

13.94

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69.95107.83

12.46 110.91 112.7784.15 102.0486.0575.49 77.6012.06 79.707.793.56

NL:5.92E8Base Peak MS qe2_20120108_6SPRGheavy


XIC523.25-523.27 m/z

RT: 15.50 - 33.54

16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33Time (min)

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19.52

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18.74

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26.3326.0721.78

21.70

26.3631.4521.6416.32

26.0321.61 24.06 31.31 31.5518.5116.25 20.56 32.2325.9321.56 26.44 29.3422.09 32.8528.26 31.2525.01 29.4017.80 27.8617.73

NL: 1.70E8Base Peak m/z= 523.25-523.27 F: FTMS + p NSI Full ms [400.00-1600.00] MS qe2_20120108_6SPRGheavy


MS Spectra (522-527 m/z)Peak at 23 min

qe2_20120108_6SPRGheavy #6572 RT: 23.20 AV: 1 NL: 1.87E8T: FTMS + p NSI Full ms [400.00-1600.00]

522.5 523.0 523.5 524.0 524.5 525.0 525.5 526.0 526.5 527.0m/z

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522.75525z=2

523.25641z=2

524.79266z=2

523.75702z=2

525.29376z=2522.30762

z=? 524.25757z=2 525.79553

z=2522.91669z=?

526.29596z=2

526.96307z=?

523.25641A+1 isotopeidotp = 0.96


MS Spectra (522-527 m/z)Peak at 26 minutes

qe2_20120108_6SPRGheavy #7683 RT: 26.16 AV: 1 NL: 1.11E8T: FTMS + p NSI Full ms [400.00-1600.00]

522.5 523.0 523.5 524.0 524.5 525.0 525.5 526.0 526.5 527.0m/z

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523.25079z=2

523.75232z=2

525.29523z=?

526.79504z=2

524.25360z=2 525.79706

z=2524.79248

z=?522.76361

z=? 526.29883z=2

522.25598z=?

526.91724z=?

525.71985z=?

523.25079Correct isotopeidotp = 0.81

Proteomics Standards Research Group (sPRG)Precautions

• Skyline’s Isotope Dot Product (idotp) is actually better for the wrong analyte– This can get a bit tricky when you are analyzing

1000 peptides– Probably best to not rely on just idotp– We have found that getting the RT’s correctly in

the library really is critical


Making a library that has proper RT’s

• This has been very finicky in our hands• Some workflows work, some don’t

– Different combinations of software all seem to affect this• Peak List generator (mzML…mzXML….)• Searching software (x!tandem, Mascot...)• Meta file has to have the same name as the raw

data file (except for the extension)


Generating libraries with correct RT’s (our workflow)

• Generate mzML using proteome discoverer• Combine all peptides into an artificial

protein• Search with x!tandem (turning on the

skyline option!)• Rename the x!tandem output files to

extension *.xtan.xml• Make Library in skyline

Proteomics Standards Research Group (sPRG)Correct library

File Name

RT


HEK lysate Non-Co-eluting labeled peptides??

Proteomics Standards Research Group (sPRG)More non co-eluting pairs

Proteomics Standards Research Group (sPRG)And more

Proteomics Standards Research Group (sPRG)Lets look at this one

Proteomics Standards Research Group (sPRG)MS spectra very complex

qe2_20120108_10SPRGheavyHEK #13810 RT: 37.50 AV: 1 NL: 3.97E7T: FTMS + p NSI Full ms [400.00-1600.00]

400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600m/z

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521.31274z=2

555.31287z=2

461.28021z=2

608.81256z=2

659.97528z=3

709.84430z=2

756.42395z=2

864.37903z=2 921.55170

z=1 989.45789z=2

1109.61719z=1

794.92877z=2

1216.61597z=1 1296.76257

z=?1527.34009

z=?1410.73596

z=?

Proteomics Standards Research Group (sPRG)Very complex


705 706 707 708 709 710 711 712 713 714 715 716 717 718m/z

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709.84430z=2

710.34546z=2

714.35980z=2

712.33911z=?

714.86041z=2

710.84686z=2

712.84064z=?

711.33990z=2

706.39160z=2

711.84100z=2706.89484

z=2715.36218

z=2713.34332z=?705.34259

z=?709.34143

z=? 715.86237z=2

713.88672z=?

707.39459z=2705.84265

z=?708.35510

z=? 717.34222z=?

716.36603z=2

Heavy Peptide

Light Peptide?

Proteomics Standards Research Group (sPRG)Light HEK peptide?


704.5 705.0 705.5 706.0 706.5 707.0 707.5 708.0 708.5m/z

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706.39160z=2

706.89484z=2

705.34259z=?

704.83911z=?

707.39459z=2

705.84265z=?

708.35510z=?

707.67383z=?

m/z =704.83911Thero m/z =704.8408Delta = 2.39 ppm

Proteomics Standards Research Group (sPRG)MS/MS spectra

• None Acquired for light peptide

• Is this the correct light peptide?– Probably not

• Next steps– Need to acquire with an isolation list or a MRM

or Data Independent method??? Maybe use a much longer gradient or LC-LC-MS/MS

Proteomics Standards Research Group (sPRG)Skyline analysis

• Can take a lot of analysis time to go over 1000 peptides in skyline in full scan mode

• Need to consider adding additional data metrics to reduce need to manually check each integration

• Figuring out if the light peptide of a heavy/light pair is real or interference is a real problem. Especially the more complex your data is.

• sPRG sample is probably too complex to analyze by Full scan in one 2 hour LC-MS/MS run


•Progress has been made for the 1st year in development of an innovative proteomics standard.

•Study sample is designed to represent proteins spanning three orders of magnitude in concentration.

•1,000 isotope labeled peptides were synthesized and analyzed. Greater than 99% of the peptides were identified during the validation runs.

•In peptide dilution experiments, the majority of peptides behaved as expected with a linear response; however, a few peptides had a non-linear response.

•The peptides were spiked into a HEK digest and showed minimal variation in both retention time and peak area.

•The sPRG is hopeful that the designed formulation will become a valuable resource in various mass spectrometry-based proteomic applications, including quantitative and differential protein profiling, interlaboratory studies, as well as general LC-MS/MS benchmarking.

Conclusions

Proteomics Standards Research Group (sPRG)How to Get a sample

• E-mail – [email protected]

• Samples are limited so be certain you can analyze the sample if you ask for one.

mailto:[email protected]

Proteomics Standards Research Group (sPRG)Acknowledgments

• SPRGCurrent Membership

Dr. Christopher Colangelo (Chair) - Yale University Dr. Craig P. Dufresne - Thermo Fisher Scientific Dr. Alexander R. Ivanov - Northeastern University Dr. Antonius Koller - Stony Brook University Brendan MacLean - University of Washington Dr. Kristie L. Rose - Vanderbilt University Medical Center Dr. Paul A Rudnick - NIST Brian C. Searle - Proteome Software Inc. Dr. Scott A. Shaffer - University of Massachusetts Medical School Dr. Brett S Phinney (EB Liaison) - Proteomics Core UC Davis Genome CenterDr. David Hawke – MD Anderson Caner Center

http://www.abrf.org/index.cfm/dir.wpDetail?entID=4360














Proteomics Standards Research Group (sPRG)Acknowledgments part 2

• JPT Peptide• ABRF (funding) www.abrf.org• Dr. Rich Eigenheer UC Davis (UC Davis Data)

Proteomics Standards Research Group (sPRG) Using Skyline to analyze the SPRG2013- 2014 Targeted...

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