Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Kohlbacher O, Reinert K, Gröpl C et al (2007) TOPP--the OpenMS proteomics pipeline. Bioinformatics 23:e191–7. doi:10.1093/bioinformatics/btl299
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media LLC
About this protocol
Cite this protocol
Röst, H.L., Aebersold, R., Schubert, O.T. (2017). Automated SWATH Data Analysis Using Targeted Extraction of Ion Chromatograms. In: Comai, L., Katz, J., Mallick, P. (eds) Proteomics. Methods in Molecular Biology, vol 1550. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6747-6_20
Download citation
DOI: https://doi.org/10.1007/978-1-4939-6747-6_20
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-6745-2
Online ISBN: 978-1-4939-6747-6
eBook Packages: Springer Protocols