PMID- 33845167 OWN - NLM STAT- MEDLINE DCOM- 20220321 LR - 20220321 IS - 1535-9484 (Electronic) IS - 1535-9476 (Print) IS - 1535-9476 (Linking) VI - 20 DP - 2021 TI - Sensitive Immunopeptidomics by Leveraging Available Large-Scale Multi-HLA Spectral Libraries, Data-Independent Acquisition, and MS/MS Prediction. PG - 100080 LID - S1535-9476(21)00053-0 [pii] LID - 10.1016/j.mcpro.2021.100080 [doi] LID - 100080 AB - Mass spectrometry (MS) is the state-of-the-art methodology for capturing the breadth and depth of the immunopeptidome across human leukocyte antigen (HLA) allotypes and cell types. The majority of studies in the immunopeptidomics field are discovery driven. Hence, data-dependent tandem MS (MS/MS) acquisition (DDA) is widely used, as it generates high-quality references of peptide fingerprints. However, DDA suffers from the stochastic selection of abundant ions that impairs sensitivity and reproducibility. In contrast, in data-independent acquisition (DIA), the systematic fragmentation and acquisition of all fragment ions within given isolation m/z windows yield a comprehensive map for a given sample. However, many DIA approaches commonly require generating comprehensive DDA-based spectrum libraries, which can become impractical for studying noncanonical and personalized neoantigens. Because the amount of HLA peptides eluted from biological samples such as small tissue biopsies is typically not sufficient for acquiring both meaningful DDA data necessary for generating comprehensive spectral libraries and DIA MS measurements, the implementation of DIA in the immunopeptidomics translational research domain has remained limited. We implemented a DIA immunopeptidomics workflow and assessed its sensitivity and accuracy by matching DIA data against libraries with growing complexity-from sample-specific libraries to libraries combining 2 to 40 different immunopeptidomics samples. Analyzing DIA immunopeptidomics data against a complex multi-HLA spectral library resulted in a two-fold increase in peptide identification compared with sample-specific library and in a three-fold increase compared with DDA measurements, yet with no detrimental effect on the specificity. Furthermore, we demonstrated the implementation of DIA for sensitive personalized neoantigen discovery through the analysis of DIA data with predicted MS/MS spectra of clinically relevant HLA ligands. We conclude that a comprehensive multi-HLA library for DIA approach in combination with MS/MS prediction is highly advantageous for clinical immunopeptidomics, especially when low amounts of biological samples are available. CI - Copyright (c) 2021. Published by Elsevier Inc. FAU - Pak, HuiSong AU - Pak H AD - Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland. FAU - Michaux, Justine AU - Michaux J AD - Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland. FAU - Huber, Florian AU - Huber F AD - Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland. FAU - Chong, Chloe AU - Chong C AD - Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland. FAU - Stevenson, Brian J AU - Stevenson BJ AD - SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. FAU - Muller, Markus AU - Muller M AD - Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland; SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. FAU - Coukos, George AU - Coukos G AD - Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland. FAU - Bassani-Sternberg, Michal AU - Bassani-Sternberg M AD - Department of Oncology, Ludwig Institute for Cancer Research Lausanne, Lausanne University Hospital and the University of Lausanne, Lausanne, Switzerland. Electronic address: Michal.bassani@chuv.ch. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20210409 PL - United States TA - Mol Cell Proteomics JT - Molecular & cellular proteomics : MCP JID - 101125647 RN - 0 (Histocompatibility Antigens) RN - 0 (Peptide Library) RN - 0 (Peptides) SB - IM MH - Computer Simulation MH - *Histocompatibility Antigens MH - Peptide Library MH - *Peptides MH - Proteomics/*methods MH - Tandem Mass Spectrometry PMC - PMC8724634 OTO - NOTNLM OT - DDA OT - DIA OT - HLA OT - HLA binding prediction OT - LC-MS OT - antigen discovery OT - immunopeptidomics OT - in silico MS/MS spectra predictions COIS- Conflict of interest The authors declare no competing interests. EDAT- 2021/04/13 06:00 MHDA- 2022/03/22 06:00 PMCR- 2021/04/09 CRDT- 2021/04/12 20:14 PHST- 2020/12/08 00:00 [received] PHST- 2021/03/18 00:00 [revised] PHST- 2021/04/05 00:00 [accepted] PHST- 2021/04/13 06:00 [pubmed] PHST- 2022/03/22 06:00 [medline] PHST- 2021/04/12 20:14 [entrez] PHST- 2021/04/09 00:00 [pmc-release] AID - S1535-9476(21)00053-0 [pii] AID - 100080 [pii] AID - 10.1016/j.mcpro.2021.100080 [doi] PST - ppublish SO - Mol Cell Proteomics. 2021;20:100080. doi: 10.1016/j.mcpro.2021.100080. Epub 2021 Apr 9.