Display options
Cite
Kovalchik K, Hamelin D, Caron E. Generation of HLA Allele-Specific Spectral Libraries to Identify and Quantify Immunopeptidomes by SWATH/DIA-MS. Methods Mol Biol. 2022;2420:137-147doi: 10.1007/978-1-0716-1936-0_11.
Kovalchik, K., Hamelin, D., & Caron, E. (2022). Generation of HLA Allele-Specific Spectral Libraries to Identify and Quantify Immunopeptidomes by SWATH/DIA-MS. Methods in molecular biology (Clifton, N.J.), 2420137-147. https://doi.org/10.1007/978-1-0716-1936-0_11
Kovalchik, Kevin, et al. "Generation of HLA Allele-Specific Spectral Libraries to Identify and Quantify Immunopeptidomes by SWATH/DIA-MS." Methods in molecular biology (Clifton, N.J.) vol. 2420 (2022): 137-147. doi: https://doi.org/10.1007/978-1-0716-1936-0_11
Kovalchik K, Hamelin D, Caron E. Generation of HLA Allele-Specific Spectral Libraries to Identify and Quantify Immunopeptidomes by SWATH/DIA-MS. Methods Mol Biol. 2022;2420:137-147. doi: 10.1007/978-1-0716-1936-0_11. PMID: 34905171.
Copy Download .nbib Format: NLM
Share it on

Methods Mol Biol. 2022;2420:137-147. doi: 10.1007/978-1-0716-1936-0_11.

Generation of HLA Allele-Specific Spectral Libraries to Identify and Quantify Immunopeptidomes by SWATH/DIA-MS.

Methods in molecular biology (Clifton, N.J.)

Kevin Kovalchik, David Hamelin, Etienne Caron

Affiliations

  1. CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada. [email protected].
  2. CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada.
  3. CHU Sainte-Justine Research Center, Montreal, QC H3T 1C5, Canada. [email protected].
  4. Department of Pathology and Cellular Biology, Faculty of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada. [email protected].

PMID: 34905171 DOI: 10.1007/978-1-0716-1936-0_11

Abstract

Developing a deep and comprehensive understanding of the collection of peptides presented by class I human leukocyte antigens (HLA ), collectively referred to as the immunopeptidome , is conducive to the success of a wide range of immunotherapies. The development of tools that enable the deconvolution of immunopeptidomes in the context of disease can help improve the specificity and effectiveness of therapeutic strategies targeting these peptides, such as adoptive T-cell therapy and vaccines. Here, we describe a computational workflow that facilitates the processing and interpretation of data-independent acquisition mass spectrometry (DIA-MS). We consider a specific variation of DIA-MS known as SWATH-MS. SWATH-MS is a promising technique that can be utilized to reproducibly characterize and quantify immunopeptidomes isolated from a wide range of biological sources. In this workflow, we use an assortment of database search engines and computational tools to build high-quality HLA allele-specific peptide spectral peptide libraries for the analysis of immunopeptidomic datasets acquired by SWATH-MS. Generating and sharing these spectral libraries are essential for the SWATH-MS technology to meet its full potential and to enable the rapid and reproducible quantification of HLA-specific peptides across multiple samples.

© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: Computational proteomics; Immunopeptidome; Mass spectrometry; Spectral library

References

  1. Koff WC, Burton DR, Johnson PR et al (2013) Accelerating next-generation vaccine development for global. Clin Infect Dis 340. https://doi.org/10.1126/science.1232910 - PubMed
  2. Yi L, WEIFAN Y, HUAN Y (2019) Chimeric antigen receptor–engineered regulatory T lymphocytes: promise for immunotherapy of autoimmune disease. Cytotherapy 21:925–934. https://doi.org/10.1016/j.jcyt.2019.04.060 - PubMed
  3. Bräunlein E, Krackhardt AM (2017) Identification and characterization of Neoantigens as well as respective immune responses in cancer patients. Front Immunol 8:1702. https://doi.org/10.3389/fimmu.2017.01702 - PubMed
  4. Caron E, Vincent K, Fortier M et al (2011) The MHC I immunopeptidome conveys to the cell surface an integrative view of cellular regulation. Mol Syst Biol 7:533. https://doi.org/10.1038/msb.2011.68 - PubMed
  5. Kowalewski DJ, Schuster H, Backert L et al (2015) HLA ligandome analysis identifies the underlying specificities of spontaneous antileukemia immune responses in chronic lymphocytic leukemia (CLL). Proc Natl Acad Sci U S A 112:E166–E175. https://doi.org/10.1073/pnas.1416389112 - PubMed
  6. Falk K, Rötzschke O, Stevanovié S et al (1991) Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 351:290–296. https://doi.org/10.1038/351290a0 - PubMed
  7. Hunt DF, Henderson RA, Shabanowitz J et al (1992) Characterization of peptides bound to the class I MHC molecule HLA-A2.1 by mass spectrometry. Science 255(5049):1261–1263. https://doi.org/10.1126/science.1546328 - PubMed
  8. Admon A, Bassani-Sternberg M (2011) The human Immunopeptidome project, a suggestion for yet another Postgenome next big thing. Mol Cell Proteomics 10:O111.011833. https://doi.org/10.1074/mcp.o111.011833 - PubMed
  9. Granados DP, Laumont CM, Thibault P, Perreault C (2015) The nature of self for T cells—a systems-level perspective. Curr Opin Immunol 34:1–8. https://doi.org/10.1016/j.coi.2014.10.012 - PubMed
  10. Michalski A, Cox J, Mann M (2011) 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 10:1785–1793. https://doi.org/10.1021/pr101060v - PubMed
  11. 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 - PubMed
  12. Caron E, Espona L, Kowalewski DJ et al (2015) An open-source computational and data resource to analyze digital maps of immunopeptidomes. Elife 4:e07661. https://doi.org/10.7554/elife.07661 - PubMed
  13. Deutsch EW, Mendoza L, Shteynberg D et al (2015) Trans-proteomic pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics. Proteomics Clin Appl 9:745–754. https://doi.org/10.1002/prca.201400164 - PubMed
  14. Reynisson B, Alvarez B, Paul S et al (2020) NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res 48:gkaa379. https://doi.org/10.1093/nar/gkaa379 - PubMed
  15. 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 - PubMed
  16. Röst HL, Sachsenberg T, Aiche S et al (2016) OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 13:741–748. https://doi.org/10.1038/nmeth.3959 - PubMed
  17. Chambers MC, Maclean B, Burke R et al (2012) A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol 30:918–920. https://doi.org/10.1038/nbt.2377 - PubMed
  18. Keller A, Nesvizhskii AI, Kolker E, Aebersold R (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem 74:5383–5392. https://doi.org/10.1021/ac025747h - PubMed
  19. Shteynberg D, Deutsch EW, Lam H et al (2011) iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol Cell Proteomics 10:M111.007690. https://doi.org/10.1074/mcp.m111.007690 - PubMed
  20. Lam H, Deutsch EW, Eddes JS et al (2007) Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics 7:655–667. https://doi.org/10.1002/pmic.200600625 - PubMed

Publication Types