Label-Free Quantification by Data Independent Acquisition Mass Spectrometry to Map Cardiovascular Proteomes

  • Sarah J. Parker
  • Ronald J. Holewinski
  • Irina Tchernyshyov
  • Vidya Venkatraman
  • Laurie Parker
  • Jennifer E. Van Eyk
Chapter

Abstract

The large-scale identification and quantification of proteins by liquid chromatography mass spectrometry (LC MS) can be achieved by at least three general methods, categorized into targeted, data independent (DIA), and data dependent (DDA) acquisition modes. Each acquisition strategy has its own set of benefits and drawbacks, and the methods serve complementary purposes for the study of protein quantification in biological samples. While not specific to research in cardiovascular physiology, a long-standing but recently popularized proteomic approach, termed Data Independent Acquisition Mass Spectrometry (DIA-MS), promises unique strengths to complement and extend the existing capabilities of traditional “discovery” proteomic profiling by combining development of a peptide library and DIA-MS. In this chapter we will provide background on the DIA-MS technique, highlighting its fundamental differences relative to other mass spectrometry methods, and discuss important considerations for researchers interested in implementing this technique for their proteomic experiments.

Keywords

Data independent acquisition mass spectrometry Label-free Quantitation Peptide library Targeted analysis Untargeted analysis Proteomics 

References

  1. 1.
    Michalski A, Cox J, Mann M. More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J Proteome Res. 2011;10(4):1785–93.CrossRefPubMedGoogle Scholar
  2. 2.
    Liu H, Sadygov RG, Yates 3rd JR. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal Chem. 2004;76(14):4193–201.CrossRefPubMedGoogle Scholar
  3. 3.
    Lambert JP, Ivosev G, Couzens AL, Larsen B, Taipale M, Lin ZY, et al. Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition. Nat Methods. 2013;10(12):1239–45. Pubmed Central PMCID: 3882083.CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Bateman NW, Goulding SP, Shulman NJ, Gadok AK, Szumlinski KK, MacCoss MJ, et al. Maximizing peptide identification events in proteomic workflows using data-dependent acquisition (DDA). Mol Cell Proteomics MCP. 2014;13(1):329–38. Pubmed Central PMCID: 3879624.CrossRefPubMedGoogle Scholar
  5. 5.
    Venable JD, Dong MQ, Wohlschlegel J, Dillin A, Yates JR. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods. 2004;1(1):39–45.CrossRefPubMedGoogle Scholar
  6. 6.
    Bilbao A, Varesio E, Luban J, Strambio-De-Castillia C, Hopfgartner G, Muller M, et al. Processing strategies and software solutions for data-independent acquisition in mass spectrometry. Proteomics. 2015;15(5–6):964–80.CrossRefPubMedGoogle Scholar
  7. 7.
    Gillet LC, Navarro P, Tate S, Rost H, Selevsek N, Reiter L, et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics MCP. 2012;11(6):O111016717-1-17. Pubmed Central PMCID: 3433915.Google Scholar
  8. 8.
    Tsou CC, Avtonomov D, Larsen B, Tucholska M, Choi H, Gingras AC, et al. DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods. 2015;12(3):258–64, 7 p following 64. Pubmed Central PMCID: 4399776.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Gillet LC, Navarro P, Tate S, Rost H, Selevsek N, Reiter L, et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics MCP. 2012;11(6):O111 016717. Pubmed Central PMCID: 3433915.Google Scholar
  10. 10.
    Guo T, Kouvonen P, Koh CC, Gillet LC, Wolski WE, Rost HL, et al. Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps. Nat Med. 2015;21(4):407–13. Pubmed Central PMCID: 4390165.CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Schubert OT, Gillet LC, Collins BC, Navarro P, Rosenberger G, Wolski WE, et al. Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc. 2015;10(3):426–41.CrossRefPubMedGoogle Scholar
  12. 12.
    Parker SJ, Roest H, Rosenberger G, Collins BC, Malstroem L, Amodei D, et al. Identification of a set of conserved eukaryotic internal retention time standards for data-independent acquisition mass spectrometry. Mol Cell Proteomics MCP. 2015;14(10):2800–13.Google Scholar
  13. 13.
    Toprak UH, Gillet LC, Maiolica A, Navarro P, Leitner A, Aebersold R. Conserved peptide fragmentation as a benchmarking tool for mass spectrometers and a discriminating feature for targeted proteomics. Mol Cell Proteomics MCP. 2014;13(8):2056–71. Pubmed Central PMCID: 4125737.CrossRefPubMedGoogle Scholar
  14. 14.
    Escher C, Reiter L, MacLean B, Ossola R, Herzog F, Chilton J, et al. Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics. 2012;12(8):1111–21. Pubmed Central PMCID: 3918884.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Egertson JD, Kuehn A, Merrihew GE, Bateman NW, MacLean BX, Ting YS, et al. Multiplexed MS/MS for improved data-independent acquisition. Nat Methods. 2013;10(8):744–6. Pubmed Central PMCID: 3881977.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics. 2010;26(7):966–8. Pubmed Central PMCID: 2844992.CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Zhang Y, Bilbao A, Bruderer T, Luban J, Strambio-De-Castillia C, Lisacek F, et al. The use of variable Q1 isolation windows improves selectivity in LC-SWATH-MS acquisition. J Proteome Res. 2015;14(10):4359–71.CrossRefPubMedGoogle Scholar
  18. 18.
    Teo G, Kim S, Tsou CC, Collins B, Gingras AC, Nesvizhskii AI, et al. mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry. J Proteomics. 2015;129:108–20. Pubmed Central PMCID: 4630088.CrossRefPubMedGoogle Scholar
  19. 19.
    Collins BC, Gillet LC, Rosenberger G, Rost HL, Vichalkovski A, Gstaiger M, et al. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system. Nat Methods. 2013;10(12):1246–53.CrossRefPubMedGoogle Scholar
  20. 20.
    Choi M, Chang CY, Clough T, Broudy D, Killeen T, MacLean B, et al. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics. 2014;30(17):2524–6.CrossRefPubMedGoogle Scholar
  21. 21.
    Keller A, Bader SL, Shteynberg D, Hood L, Moritz RL. Automated validation of results and removal of fragment Ion interferences in targeted analysis of data-independent acquisition mass spectrometry (MS) using SWATHProphet. Mol Cell Proteomics MCP. 2015;14(5):1411–8. Pubmed Central PMCID: 4424409.CrossRefPubMedGoogle Scholar
  22. 22.
    Rardin MJ, Schilling B, Cheng LY, MacLean BX, Sorensen DJ, Sahu AK, et al. MS1 peptide ion intensity chromatograms in MS2 (SWATH) data independent acquisitions. Improving post acquisition analysis of proteomic experiments. Mol Cell Proteomics MCP. 2015;14(9):2405–19.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sarah J. Parker
    • 1
  • Ronald J. Holewinski
    • 1
  • Irina Tchernyshyov
    • 1
  • Vidya Venkatraman
    • 1
  • Laurie Parker
    • 2
  • Jennifer E. Van Eyk
    • 1
    • 3
  1. 1.The Advanced Clinical Biosystems Research Institute, Heart InstituteCedars Sinai Medical CenterLos AngelesUSA
  2. 2.Department of Biochemistry, Molecular Biology, and BiophysicsUniversity of MinnesotaMinneapolisUSA
  3. 3.Department of MedicineJohns Hopkins University School of MedicineBaltimoreUSA

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