Proteomics pp 289-307 | Cite as

Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms

Part of the Methods in Molecular Biology book series (MIMB, volume 1550)


Targeted mass spectrometry comprises a set of methods able to quantify protein analytes in complex mixtures with high accuracy and sensitivity. These methods, e.g., Selected Reaction Monitoring (SRM) and SWATH MS, use specific mass spectrometric coordinates (assays) for reproducible detection and quantification of proteins. In this protocol, we describe how to analyze, in a targeted manner, data from a SWATH MS experiment aimed at monitoring thousands of proteins reproducibly over many samples. We present a standard SWATH MS analysis workflow, including manual data analysis for quality control (based on Skyline) as well as automated data analysis with appropriate control of error rates (based on the OpenSWATH workflow). We also discuss considerations to ensure maximal coverage, reproducibility, and quantitative accuracy.

Key words

Targeted proteomics SWATH SWATH MS SWATH acquisition Data-independent acquisition DIA OpenSWATH pyProphet TRIC aligner Skyline 


  1. 1.
    Domon B (2012) Considerations on selected reaction monitoring experiments: implications for the selectivity and accuracy of measurements. Proteomics Clin Appl 6:609–614. doi: 10.1002/prca.201200111 CrossRefPubMedGoogle Scholar
  2. 2.
    Picotti P, Aebersold R (2012) Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat Methods 9:555–566. doi: 10.1038/nmeth.2015 CrossRefPubMedGoogle Scholar
  3. 3.
    Picotti P, Bodenmiller B, Mueller LN et al (2009) Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138:795–806. doi: 10.1016/j.cell.2009.05.051 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Venable JD, Dong M-Q, Wohlschlegel J et al (2004) Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods 1:39–45. doi: 10.1038/nmeth705 CrossRefPubMedGoogle Scholar
  5. 5.
    Chapman JD, Goodlett DR, Masselon CD (2013) Multiplexed and data-independent tandem mass spectrometry for global proteome profiling. Mass Spectrom Rev. doi: 10.1002/mas.21400 PubMedGoogle Scholar
  6. 6.
    Gillet LC, Navarro P, Tate S et al (2012) 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 11:O111.016717. doi: 10.1074/mcp.O111.016717 CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Gallien S, Duriez E, Crone C et al (2012) Targeted proteomic quantification on quadrupole-orbitrap mass spectrometer. Mol Cell Proteomics 11:1709–1723. doi: 10.1074/mcp.O112.019802 CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Peterson AC, Russell JD, Bailey DJ et al (2012) Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol Cell Proteomics. doi: 10.1074/mcp.O112.020131 PubMedPubMedCentralGoogle Scholar
  9. 9.
    Röst HL, Rosenberger G, Navarro P et al (2014) OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat Biotechnol 32:219–223. doi: 10.1038/nbt.2841 CrossRefPubMedGoogle Scholar
  10. 10.
    Schubert OT, Gillet LC, Collins BC et al (2015) Building high-quality assay libraries for targeted analysis of SWATH MS data. Nat Protoc 10:426–441. doi: 10.1038/nprot.2015.015 CrossRefPubMedGoogle Scholar
  11. 11.
    Röst HL, Liu Y, D’Agostino G et al (2016) TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics. Nat Methods 13:777–783. doi: 10.1038/nmeth.3954 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    MacLean B, Tomazela DM, Shulman N et al (2010) Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26:966–968. doi: 10.1093/bioinformatics/btq054 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Schubert OT, Ludwig C, Kogadeeva M et al (2015) Absolute proteome composition and dynamics during dormancy and resuscitation of Mycobacterium tuberculosis. Cell Host Microbe. doi: 10.1016/j.chom.2015.06.001 Google Scholar
  14. 14.
    Escher C, Reiter L, MacLean B et al (2012) Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics 12:1111–1121. doi: 10.1002/pmic.201100463 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Kohlbacher O, Reinert K, Gröpl C et al (2007) TOPP--the OpenMS proteomics pipeline. Bioinformatics 23:e191–7. doi: 10.1093/bioinformatics/btl299 CrossRefPubMedGoogle Scholar
  16. 16.
    Sturm M, Bertsch A, Gröpl C et al (2008) OpenMS – an open-source software framework for mass spectrometry. BMC Bioinformatics 9:163. doi: 10.1186/1471-2105-9-163 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Röst HL, Schmitt U, Aebersold R, Malmström L (2014) pyOpenMS: a Python-based interface to the OpenMS mass-spectrometry algorithm library. Proteomics 14:74–77. doi: 10.1002/pmic.201300246 CrossRefPubMedGoogle Scholar
  18. 18.
    Röst HL, Schmitt U, Aebersold R, Malmström L (2015) Fast and efficient XML data access for next-generation mass spectrometry. PLoS One 10:e0125108. doi: 10.1371/journal.pone.0125108 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Junker J, Bielow C, Bertsch A et al (2012) TOPPAS: a graphical workflow editor for the analysis of high-throughput proteomics data. J Proteome Res 11:3914–3920. doi: 10.1021/pr300187f CrossRefPubMedGoogle Scholar
  20. 20.
    Aiche S, Sachsenberg T, Kenar E et al (2015) Workflows for automated downstream data analysis and visualization in large-scale computational mass spectrometry. Proteomics 15:1443–1447. doi: 10.1002/pmic.201400391 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Teleman J, Röst HL, Rosenberger G et al (2014) DIANA-algorithmic improvements for analysis of data-independent acquisition MS data. Bioinformatics. Oxford, England. doi: 10.1093/bioinformatics/btu686 Google Scholar
  22. 22.
    Reiter L, Rinner O, Picotti P et al (2011) mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nat Methods 8:430–435. doi: 10.1038/nmeth.1584 CrossRefPubMedGoogle Scholar
  23. 23.
    Röst HL, Rosenberger G, Aebersold R, Malmström L (2015) Efficient visualization of high-throughput targeted proteomics experiments: TAPIR. Bioinformatics. Oxford, England. doi: 10.1093/bioinformatics/btv152 Google Scholar
  24. 24.
    Malmström L, Bakochi A, Svensson G et al (2015) Quantitative proteogenomics of human pathogens using DIA-MS. Proteomics 129:98–107. doi: 10.1016/j.jprot.2015.09.012 CrossRefPubMedGoogle Scholar
  25. 25.
    Bruderer R, Bernhardt OM, Gandhi T et al (2015) Extending the limits of quantitative proteome profiling with data-independent acquisition and application to acetaminophen treated 3D liver microtissues. Mol Cell Proteomics. doi: 10.1074/mcp.M114.044305 PubMedPubMedCentralGoogle Scholar
  26. 26.
    Egertson JD, Kuehn A, Merrihew GE et al (2013) Multiplexed MS/MS for improved data-independent acquisition. Nat Methods 10:744–746. doi: 10.1038/nmeth.2528 CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Parker SJ, Röst HL, Rosenberger G et al (2015) Identification of a set of conserved eukaryotic internal retention time standards for data-independent acquisition mass spectrometry. Mol Cell Proteomics 14:2800–2813. doi: 10.1074/mcp.O114.042267 CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Rosenberger G, Koh CC, Guo T et al (2014) A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci Data 1:140031. doi: 10.1038/sdata.2014.31 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Selevsek N, Chang C-Y, Gillet LC et al (2015) Reproducible and consistent quantification of the Saccharomyces cerevisiae proteome by SWATH-MS. Mol Cell Proteomics 14:739–749. doi: 10.1074/mcp.M113.035550 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Institute of Molecular Systems BiologyETH ZurichZurichSwitzerland
  2. 2.Department of GeneticsStanford UniversityStanfordUSA
  3. 3.Faculty of ScienceUniversity of ZurichZurichSwitzerland
  4. 4.Department of Human GeneticsUniversity of California Los AngelesLos AngelesUSA

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