Skip to main content
SpringerLink
Log in

Quantification of Changes in Protein Expression Using SWATH Proteomics

  • Protocol
  • First Online:
Proteomics Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2361))

Abstract

Sequential Window Acquisition of all THeoretical fragment ion spectra (SWATH) is a data independent acquisition mode used to accurately quantify thousands of proteins in a biological sample in a single run. It exploits fast scanning hybrid mass spectrometers to combine accuracy, reproducibility and sensitivity. This method requires the use of ion libraries, a sort of databases of spectral and chromatographic information about the proteins to be quantified. In this chapter, a typical workflow of SWATH experiment is described, from the sample preparation to the analysis of proteomics data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. 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 11:1–17. https://doi.org/10.1074/mcp.O111.016717

    Article  CAS  Google Scholar 

  2. Krasny L, Bland P, Kogata N et al (2018) SWATH mass spectrometry as a tool for quantitative profiling of the matrisome. J Proteome 189:11–22. https://doi.org/10.1016/j.jprot.2018.02.026

    Article  CAS  Google Scholar 

  3. Rosenberger G, Koh CC, Guo T et al (2014) A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci Data 1:1–15. https://doi.org/10.1038/sdata.2014.31

    Article  CAS  Google Scholar 

  4. Braccia C, Espinal MP, Pini M et al (2018) A new SWATH ion library for mouse adult hippocampal neural stem cells. Data Brief 18:1–8. https://doi.org/10.1016/j.dib.2018.02.062

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. 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. https://doi.org/10.1038/nbt.2841

    Article  CAS  PubMed  Google Scholar 

  6. Yang Y, Liu X, Shen C et al (2020) In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics. Nat Commun 11:146. https://doi.org/10.1038/s41467-019-13866-z

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Searle BC, Pino LK, Egertson JD et al (2018) Chromatogram libraries improve peptide detection and quantification by data independent acquisition mass spectrometry. Nat Commun 9:5128. https://doi.org/10.1038/s41467-018-07454-w

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Phung TK, Zacchi LF, Schulz BL (2020) DIALib: an automated ion library generator for data independent acquisition mass spectrometry analysis of peptides and glycopeptides. Mol Omics 16:100–112. https://doi.org/10.1039/c9mo00125e

    Article  CAS  PubMed  Google Scholar 

  9. Muntel J, Kirkpatrick J, Bruderer R et al (2019) Comparison of protein quantification in a complex background by DIA and TMT workflows with fixed instrument time. J Proteome Res 18:1340–1351. https://doi.org/10.1021/acs.jproteome.8b00898

    Article  CAS  PubMed  Google Scholar 

  10. Sondo E, Tomati V, Caci E et al (2011) Rescue of the mutant CFTR chloride channel by pharmacological correctors and low temperature analyzed by gene expression profiling. Am J Physiol Cell Physiol 301:872–885. https://doi.org/10.1152/ajpcell.00507.2010

    Article  Google Scholar 

  11. Braccia C, Tomati V, Caci E et al (2019) SWATH label-free proteomics for cystic fibrosis research. J Cyst Fibros 18:501–506. https://doi.org/10.1016/j.jcf.2018.10.004

    Article  CAS  PubMed  Google Scholar 

  12. Pang Z, Chong J, Li S, Xia J (2020) Metaboanalystr 3.0: toward an optimized workflow for global metabolomics. Meta 10:186. https://doi.org/10.3390/metabo10050186

    Article  CAS  Google Scholar 

  13. Zi J, Zhang S, Zhou R et al (2014) Expansion of the ion library for mining SWATH-MS data through fractionation proteomics. Anal Chem 86:7242–7246. https://doi.org/10.1021/ac501828a

    Article  CAS  PubMed  Google Scholar 

  14. Deutsch EW, Lam H, Aebersold R (2008) PeptideAtlas: A resource for target selection for emerging targeted proteomics workflows. EMBO Rep 9:429–434. https://doi.org/10.1038/embor.2008.56

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Välikangas T, Suomi T, Elo LL (2018) A systematic evaluation of normalization methods in quantitative label-free proteomics. Brief Bioinform 19:1–11. https://doi.org/10.1093/bib/bbw095

    Article  CAS  PubMed  Google Scholar 

  16. van den Berg RA, Hoefsloot HCJ, Westerhuis JA et al (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7:1–15. https://doi.org/10.1186/1471-2164-7-142

    Article  CAS  Google Scholar 

  17. Ting L, Cowley MJ, Hoon SL et al (2009) Normalization and statistical analysis of quantitative proteomics data generated by metabolic labeling. Mol Cell Proteomics 8:2227–2242. https://doi.org/10.1074/mcp.M800462-MCP200

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Whetton AD, Preston GW, Abubeker S, Geifman N (2020) Proteomics and informatics for understanding phases and identifying biomarkers in COVID-19 disease. J Proteome Res 19(11):4219–4232. https://doi.org/10.1021/acs.jproteome.0c00326

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Chaousis S, Leusch FDL, Nouwens A et al (2020) Changes in global protein expression in sea turtle cells exposed to common contaminants indicates new biomarkers of chemical exposure. Sci Total Environ 751:141680. https://doi.org/10.1016/j.scitotenv.2020.141680

    Article  CAS  PubMed  Google Scholar 

  20. Zhu FY, Song YC, Zhang KL et al (2020) Quantifying plant dynamic proteomes by SWATH-based mass spectrometry. Trends Plant Sci 25(11):1171–1172. https://doi.org/10.1016/j.tplants.2020.07.014

    Article  CAS  PubMed  Google Scholar 

  21. Kaminskyy V, Zhivotovsky B (2012) Proteases in autophagy. Biochim Biophys Acta 1824:44–50. https://doi.org/10.1016/j.bbapap.2011.05.013

    Article  CAS  PubMed  Google Scholar 

  22. Chen EI, Cociorva D, Norris JL, Yates JR (2007) Optimization of mass spectrometry-compatible surfactants for shotgun proteomics. J Proteome Res 6:2529–2538. https://doi.org/10.1021/pr060682a

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Narasimhan M, Kannan S, Chawade A et al (2019) Clinical biomarker discovery by SWATH-MS based label-free quantitative proteomics: impact of criteria for identification of differentiators and data normalization method. J Transl Med 17:184. https://doi.org/10.1186/s12967-019-1937-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zauber H, Schüler V, Schulze W (2013) Systematic evaluation of reference protein normalization in proteomic experiments. Front Plant Sci 4:25. https://doi.org/10.3389/fpls.2013.00025

    Article  PubMed  PubMed Central  Google Scholar 

  25. Vizcaíno JA, Côté R, Reisinger F et al (2009) A guide to the proteomics identifications database proteomics data repository. Proteomics 9:4276–4283. https://doi.org/10.1002/pmic.200900402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The help of Dr. Valeria Tomati, from Gaslini Children Hospital, Genova, is gratefully acknowledged. This work was supported by Fondazione Italiana Ricerca Fibrosi Cistica (grant FFC#1-2019 to AA).

Author information

Authors and Affiliations

  1. D3 Pharmachemistry, Istituto Italiano di Tecnologia, Genova, Italy

    Clarissa Braccia

  2. Analytical Chemistry Lab, Istituto Italiano di Tecnologia, Genova, Italy

    Nara Liessi & Andrea Armirotti

Authors
  1. Clarissa Braccia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nara Liessi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrea Armirotti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andrea Armirotti .

Editor information

Editors and Affiliations

  1. Department of Biotechnology, University of Verona, VERONA, Verona, Italy

    Daniela Cecconi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Braccia, C., Liessi, N., Armirotti, A. (2021). Quantification of Changes in Protein Expression Using SWATH Proteomics. In: Cecconi, D. (eds) Proteomics Data Analysis. Methods in Molecular Biology, vol 2361. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1641-3_5

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-1641-3_5

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1640-6

  • Online ISBN: 978-1-0716-1641-3

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation