Finely tuned DDA-PASEF-based immunopeptidomics workflow enhances HLA class I coverage

Abstract

Mass spectrometry-based immunopeptidomics is essential for the direct identification of HLA-presented peptides, but its application to low-input and clinical samples remains challenging. In this study, we present an optimized DDA-PASEF-based workflow to improve HLA class I peptide coverage. First, we optimized a magnetic bead-based sample preparation protocol to support analysis from reduced starting material and increase compatibility with automation. We then evaluated LC gradient length and column dimensions to define complementary configurations for high-throughput and high-coverage analysis. For MS acquisition, we fine-tuned key parameters, including ion accumulation time, number of PASEF scans, ion mobility range, and polygon filtering, to improve sensitivity while maintaining a suitable cycle time and limiting pre-CID truncations. The customized polygon filter improved the detection of singly charged HLA class I peptides, thereby broadening the identified immunopeptide repertoire. Finally, TIMS2Rescore was integrated to improve confidence in peptide identification. Overall, this optimized workflow improves HLA class I immunopeptide coverage across different sample types, even from low injected sample amounts, supporting its future applicability to clinically relevant samples.