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Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model

David Gomez-Zepeda, Danielle Arnold-Schild, Julian Beyrle, Arthur Declercq (UGent) , Ralf Gabriels (UGent) , Elena Kumm, Annica Preikschat, Mateusz Krzysztof Łącki, Aurélie Hirschler, Jeewan Babu Rijal, et al.
(2024) NATURE COMMUNICATIONS. 15.
Author
Organization
Abstract
Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS<jats:sup>2</jats:sup>PIP model for tryptic and non-tryptic peptides and implement it in MS<jats:sup>2</jats:sup>Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.
Keywords
T-CELL EPITOPES, ANTIGEN PRESENTATION, SAMPLE PREPARATION, MHC

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MLA
Gomez-Zepeda, David, et al. “Thunder-DDA-PASEF Enables High-Coverage Immunopeptidomics and Is Boosted by MS2Rescore with MS2PIP TimsTOF Fragmentation Prediction Model.” NATURE COMMUNICATIONS, vol. 15, 2024, doi:10.1038/s41467-024-46380-y.
APA
Gomez-Zepeda, D., Arnold-Schild, D., Beyrle, J., Declercq, A., Gabriels, R., Kumm, E., … Tenzer, S. (2024). Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model. NATURE COMMUNICATIONS, 15. https://doi.org/10.1038/s41467-024-46380-y
Chicago author-date
Gomez-Zepeda, David, Danielle Arnold-Schild, Julian Beyrle, Arthur Declercq, Ralf Gabriels, Elena Kumm, Annica Preikschat, et al. 2024. “Thunder-DDA-PASEF Enables High-Coverage Immunopeptidomics and Is Boosted by MS2Rescore with MS2PIP TimsTOF Fragmentation Prediction Model.” NATURE COMMUNICATIONS 15. https://doi.org/10.1038/s41467-024-46380-y.
Chicago author-date (all authors)
Gomez-Zepeda, David, Danielle Arnold-Schild, Julian Beyrle, Arthur Declercq, Ralf Gabriels, Elena Kumm, Annica Preikschat, Mateusz Krzysztof Łącki, Aurélie Hirschler, Jeewan Babu Rijal, Christine Carapito, Lennart Martens, Ute Distler, Hansjörg Schild, and Stefan Tenzer. 2024. “Thunder-DDA-PASEF Enables High-Coverage Immunopeptidomics and Is Boosted by MS2Rescore with MS2PIP TimsTOF Fragmentation Prediction Model.” NATURE COMMUNICATIONS 15. doi:10.1038/s41467-024-46380-y.
Vancouver
1.
Gomez-Zepeda D, Arnold-Schild D, Beyrle J, Declercq A, Gabriels R, Kumm E, et al. Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model. NATURE COMMUNICATIONS. 2024;15.
IEEE
[1]
D. Gomez-Zepeda et al., “Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model,” NATURE COMMUNICATIONS, vol. 15, 2024.
@article{01HS8P962E9Y9YFZ3CF8TWW022,
  abstract     = {{Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS<jats:sup>2</jats:sup>PIP model for tryptic and non-tryptic peptides and implement it in MS<jats:sup>2</jats:sup>Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients.}},
  articleno    = {{2288}},
  author       = {{Gomez-Zepeda, David and Arnold-Schild, Danielle and Beyrle, Julian and Declercq, Arthur and Gabriels, Ralf and Kumm, Elena and Preikschat, Annica and Łącki, Mateusz Krzysztof and Hirschler, Aurélie and Rijal, Jeewan Babu and Carapito, Christine and Martens, Lennart and Distler, Ute and Schild, Hansjörg and Tenzer, Stefan}},
  issn         = {{2041-1723}},
  journal      = {{NATURE COMMUNICATIONS}},
  keywords     = {{T-CELL EPITOPES,ANTIGEN PRESENTATION,SAMPLE PREPARATION,MHC}},
  language     = {{eng}},
  pages        = {{18}},
  title        = {{Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS2Rescore with MS2PIP timsTOF fragmentation prediction model}},
  url          = {{http://doi.org/10.1038/s41467-024-46380-y}},
  volume       = {{15}},
  year         = {{2024}},
}
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