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Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS 2 Rescore with MS 2 PIP timsTOF fragmentation prediction model

A study that was previously a pre-print in Research Square has now been peer-reviewed and published in Nature Communications.

Identifying ligands of the major histocompatibility complex (MHC) or human leukocyte antigen (HLA) is crucial for developing vaccines and immunotherapies. However, LC-MS immunopeptidomics faces challenges due to the diversity, low abundance, and patient individuality.

This study presents Thunder-DDA-PASEF, an optimized LC-IMS-MS method for immunopeptidomics, using a timsTOF Pro-2 MS and an Aurora Ultimate CSI 25 x 75 C18 UHPLC column. The method employs an extended TIMS separation time and a tailored isolation polygon for semi-selectively fragmenting potential HLAIps. Thunder-DDA-PASEF identified 5,738 HLAIps from just one million JY cell equivalents and 14,516 HLAIps from 20 million.

The optimized method enabled in-depth profiling of HLAIps from diverse cell lines and human plasma, identifying 16 SARS-CoV-2 spike HLAIps, 13 of which elicit immune responses in patients. The Aurora Ultimate CSI’s high resolution and sensitivity facilitated the identification of low-abundant HLAIps.


Publication
Nature Communications

Authors

David Gomez-Zepeda, 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 & Stefan Tenzer

Title

Thunder-DDA-PASEF enables high-coverage immunopeptidomics and is boosted by MS 2 Rescore with MS 2 PIP timsTOF fragmentation prediction model

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