Data-independent immunopeptidomics discovery of low-abundant bacterial epitopes

Mass spectrometry-based immunopeptidomics is a powerful approach to uncover peptides presented by human leukocyte antigen (HLA) molecules and serves as an important discovery tool guiding vaccine design and immunotherapies. While data-dependent acquisition (DDA) has been the standard for navigating through the complexity associated with non-enzymatic immunopeptide database searches, data-independent acquisition (DIA) is increasingly adopted in immunopeptidomics research. In this work, we compare diaPASEF to conventional ddaPASEF in terms of global immunopeptidome profiling and bacterial epitope discovery of the model intracellular pathogen Listeria monocytogenes. We show that DIA spectrum-centric searches of pseudo-MS/MS spectra complements DDA analysis by uncovering additional human and bacterial immunopeptides. Furthermore, we leveraged the latest DIA-NN release for proteome-wide predicted spectral library searches of all HLA class I peptides, scoring approximately 150 million immunopeptide peptide precursors. This approached outperformed other methods in identification of MHC class I peptides and recovered low-abundant peptide precursors missed by other methods. Taken together, our results demonstrate how both DIA spectrum- and peptide-centric immunopeptidomics analysis are promising strategies to recover low-abundant immunopeptides.