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
that can guide vaccine design and immunotherapies. While data-dependent
acquisition (DDA) has been the standard for navigating through the
complexity associated with nonenzymatic 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 workflows that search pseudo-MS/MS spectra complement
DDA analysis by uncovering additional human and bacterial immunopeptides.
Furthermore, we leveraged DIA-NN for generating and searching proteome-wide
predicted HLA class I peptide spectral libraries, scoring approximately
150 million immunopeptide peptide precursors. This approach outperformed
other spectrum-based methods in the 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 identify low-abundant immunopeptides.