Skip to main content
SpringerLink
Log in

Data-Independent Acquisition Peptidomics

  • Protocol
  • First Online:
Peptidomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2758))

  • 343 Accesses

Abstract

In recent years, data-independent acquisition (DIA) has emerged as a powerful analysis method in biological mass spectrometry (MS). Compared to the previously predominant data-dependent acquisition (DDA), it offers a way to achieve greater reproducibility, sensitivity, and dynamic range in MS measurements. To make DIA accessible to non-expert users, a multifunctional, automated high-throughput pipeline DIAproteomics was implemented in the computational workflow framework “Nextflow” (https://nextflow.io). This allows high-throughput processing of proteomics and peptidomics DIA datasets on diverse computing infrastructures. This chapter provides a short summary and usage protocol guide for the most important modes of operation of this pipeline regarding the analysis of peptidomics datasets using the command line. In brief, DIAproteomics is a wrapper around the OpenSwathWorkflow and relies on either existing or ad-hoc generated spectral libraries from matching DDA runs. The OpenSwathWorkflow extracts chromatograms from the DIA runs and performs chromatographic peak-picking. Further downstream of the pipeline, these peaks are scored, aligned, and statistically evaluated for qualitative and quantitative differences across conditions depending on the user’s interest. DIAproteomics is open-source and available under a permissive license. We encourage the scientific community to use or modify the pipeline to meet their specific requirements.

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 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 299.99
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:O111.016717. https://doi.org/10.1074/mcp.O111.016717

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Doerr A (2015) DIA mass spectrometry. Nat Methods 12:35–35. https://doi.org/10.1038/nmeth.3234

    Article  CAS  Google Scholar 

  3. Hu A, Noble WS, Wolf-Yadlin A (2016) Technical advances in proteomics: new developments in data-independent acquisition. F1000Res 5:10.12688/f1000research.7042.1

    Article  Google Scholar 

  4. Poulos RC, Hains PG, Shah R et al (2020) Strategies to enable large-scale proteomics for reproducible research. Nat Commun 11:3793. https://doi.org/10.1038/s41467-020-17641-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Krasny L, Huang PH (2021) Data-independent acquisition mass spectrometry (DIA-MS) for proteomic applications in oncology. Mol Omics 17:29–42. https://doi.org/10.1039/D0MO00072H

    Article  CAS  PubMed  Google Scholar 

  6. Caron E, Espona L, Kowalewski DJ et al (2015) An open-source computational and data resource to analyze digital maps of immunopeptidomes. elife 4. https://doi.org/10.7554/eLife.07661

  7. Ritz D, Kinzi J, Neri D, Fugmann T (2017) Data-independent acquisition of HLA class I peptidomes on the Q exactive mass spectrometer platform. Proteomics 17. https://doi.org/10.1002/pmic.201700177

  8. Pak H, Michaux J, Huber F et al (2021) Sensitive immunopeptidomics by leveraging available large-scale multi-HLA spectral libraries, data-independent acquisition and MS/MS prediction. Mol Cell Proteomics 0. https://doi.org/10.1016/j.mcpro.2021.100080

  9. Wilhelm M, Zolg DP, Graber M et al (2021) Deep learning boosts sensitivity of mass spectrometry-based immunopeptidomics. Nat Commun 12:3346. https://doi.org/10.1038/s41467-021-23713-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Saidi M, Kamali S, Beaudry F (2019) Neuropeptidomics: comparison of parallel reaction monitoring and data-independent acquisition for the analysis of neuropeptides using high-resolution mass spectrometry. Biomed Chromatogr 33:e4523. https://doi.org/10.1002/bmc.4523

    Article  CAS  PubMed  Google Scholar 

  11. Lin L, Zheng J, Zheng F et al (2020) Advancing serum peptidomic profiling by data-independent acquisition for clear-cell renal cell carcinoma detection and biomarker discovery. J Proteome 215:103671. https://doi.org/10.1016/j.jprot.2020.103671

    Article  CAS  Google Scholar 

  12. Arju G, Taivosalo A, Pismennoi D et al (2020) Application of the UHPLC-DIA-HRMS method for determination of cheese peptides. Foods 9:979. https://doi.org/10.3390/foods9080979

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. López-Pedrouso M, Borrajo P, Amarowicz R et al (2021) Peptidomic analysis of antioxidant peptides from porcine liver hydrolysates using SWATH-MS. J Proteome 232:104037. https://doi.org/10.1016/j.jprot.2020.104037

    Article  CAS  Google Scholar 

  14. Xu LL, Gao HY, Yang F et al (2022) Major shrimp allergen peptidomics signatures and potential biomarkers of heat processing. Food Chem 382:132567. https://doi.org/10.1016/j.foodchem.2022.132567

    Article  CAS  PubMed  Google Scholar 

  15. 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. https://doi.org/10.1038/nprot.2015.015

    Article  CAS  PubMed  Google Scholar 

  16. Tsou C-C, Avtonomov D, Larsen B et al (2015) DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics. Nat Methods 12:258–264. https://doi.org/10.1038/nmeth.3255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Rosenberger G, Koh CC, Guo T et al (2014) A repository of assays to quantify 10,000 human proteins by SWATH-MS. Scientific Data 1:140031. https://doi.org/10.1038/sdata.2014.31

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Vizcaíno JA, Csordas A, del-Toro N et al (2016) 2016 update of the PRIDE database and its related tools. Nucleic Acids Res 44:D447–D456. https://doi.org/10.1093/nar/gkv1145

    Article  CAS  PubMed  Google Scholar 

  19. Desiere F, Deutsch EW, King NL et al (2006) The PeptideAtlas project. Nucleic Acids Res 34:D655–D658. https://doi.org/10.1093/nar/gkj040

    Article  CAS  PubMed  Google Scholar 

  20. Shao W, Pedrioli PGA, Wolski W et al (2018) The SysteMHC atlas project. Nucleic Acids Res 46:D1237–D1247. https://doi.org/10.1093/nar/gkx664

    Article  CAS  PubMed  Google Scholar 

  21. Demichev V, Messner CB, Vernardis SI et al (2020) DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 17:41–44. https://doi.org/10.1038/s41592-019-0638-x

    Article  CAS  PubMed  Google Scholar 

  22. Gessulat S, Schmidt T, Zolg DP et al (2019) Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning. Nat Methods 16:509–518. https://doi.org/10.1038/s41592-019-0426-7

    Article  CAS  PubMed  Google Scholar 

  23. Gabriels R, Martens L, Degroeve S (2019) Updated MS2PIP web server delivers fast and accurate MS2 peak intensity prediction for multiple fragmentation methods, instruments and labeling techniques. Nucleic Acids Res 47:W295–W299. https://doi.org/10.1093/nar/gkz299

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Tiwary S, Levy R, Gutenbrunner P et al (2019) High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. Nat Methods 16:519–525. https://doi.org/10.1038/s41592-019-0427-6

    Article  CAS  PubMed  Google Scholar 

  25. Bichmann L, Gupta S, Rosenberger G et al (2021) DIAproteomics: a multifunctional data analysis pipeline for data-independent acquisition proteomics and Peptidomics. J Proteome Res 20:3758–3766. https://doi.org/10.1021/acs.jproteome.1c00123

    Article  CAS  PubMed  Google Scholar 

  26. Deutsch EW, Mendoza L, Shteynberg D et al (2010) A guided tour of the trans-proteomic pipeline. Proteomics 10:1150–1159. https://doi.org/10.1002/pmic.200900375

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. 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 

  28. Pino LK, Searle BC, Bollinger JG et al (2020) The skyline ecosystem: informatics for quantitative mass spectrometry proteomics. Mass Spectrom Rev 39:229–244. https://doi.org/10.1002/mas.21540

    Article  CAS  PubMed  Google Scholar 

  29. Li C, Gao M, Yang W et al (2021) Diamond: a multi-modal DIA mass spectrometry data processing pipeline. Bioinformatics 37:265. https://doi.org/10.1093/bioinformatics/btaa1093

    Article  CAS  PubMed  Google Scholar 

  30. Sinitcyn P, Hamzeiy H, Salinas Soto F et al (2021) MaxDIA enables library-based and library-free data-independent acquisition proteomics. Nat Biotechnol 39:1563–1573. https://doi.org/10.1038/s41587-021-00968-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Di Tommaso P, Chatzou M, Floden EW et al (2017) Nextflow enables reproducible computational workflows. Nat Biotechnol 35:316–319. https://doi.org/10.1038/nbt.3820

    Article  CAS  PubMed  Google Scholar 

  32. Ewels PA, Peltzer A, Fillinger S et al (2020) The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol 38:276–278. https://doi.org/10.1038/s41587-020-0439-x

    Article  CAS  PubMed  Google Scholar 

  33. Röst HL, Sachsenberg T, Aiche S et al (2016) OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 13:741. https://doi.org/10.1038/nmeth.3959

    Article  CAS  PubMed  Google Scholar 

  34. Rosenberger G, Bludau I, Schmitt U et al (2017) Statistical control of peptide and protein error rates in large-scale targeted DIA analyses. Nat Methods 14:921–927. https://doi.org/10.1038/nmeth.4398

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Gupta S, Ahadi S, Zhou W, Röst H (2019) DIAlignR provides precise retention time alignment across distant runs in DIA and targeted proteomics. Mol Cell Proteomics 18:806–817. https://doi.org/10.1074/mcp.TIR118.001132

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Choi M, Chang C-Y, Clough T et al (2014) MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30:2524–2526. https://doi.org/10.1093/bioinformatics/btu305

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the German Ministry for Research and Education (BMBF) as part of the German Network for Bioinformatics infrastructure (FKZ: 31A535A) and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2180-390900677. In addition, the work was initiated through a travel stipend by the Boehringer Ingelheim Fonds for basic research in medicine and supported by the Chan Zuckerberg Initiative program “Essential Open-Source Software for Science (EOSS).” We thank all nf-core and OpenMS team members for supporting the development and debugging of the pipeline as well as for the provision of the template.

Author information

Authors and Affiliations

  1. Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany

    Leon Bichmann

  2. Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada

    Shubham Gupta & Hannes Röst

Authors
  1. Leon Bichmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shubham Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hannes Röst
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Bioengineering Sciences, Weihenstephan-Tr. University of Applied Science, Freising, Germany

    Michael Schrader

  2. Department of Molecular Pharmacology, Albert Einstein College of Medicine, Bronx, NY, USA

    Lloyd D. Fricker

Rights and permissions

Reprints and permissions

Copyright information

© 2024 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

Bichmann, L., Gupta, S., Röst, H. (2024). Data-Independent Acquisition Peptidomics. In: Schrader, M., Fricker, L.D. (eds) Peptidomics. Methods in Molecular Biology, vol 2758. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3646-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3646-6_4

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3645-9

  • Online ISBN: 978-1-0716-3646-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation