Human leukocyte antigen class I (HLA-I) molecules present short peptide sequences from endogenous or foreign proteins to cytotoxic T cells. The low abundance of HLA-I peptides poses significant technical challenges for their identification and accurate quantification. While mass spectrometry (MS) is currently a method of choice for direct system-wide identification of cellular immunopeptidome, there is still a need for enhanced sensitivity in detecting and quantifying tumor specific epitopes. As gas phase separation in data-dependent MS data acquisition (DDA) increased HLA-I peptide detection by up to 50%, here, we aimed to evaluate the performance of data-independent acquisition (DIA) in combination with ion mobility (diaPASEF) for high-sensitivity identification of HLA presented peptides. Our streamlined diaPASEF workflow enabled identification of 11,412 unique peptides from 12.5 million A375 cells and 3,426 8-11mers from as low as 500,000 cells with high reproducibility. By taking advantage of HLA binder-specific in-silico predicted spectral libraries, we were able to further increase the number of identified HLA-I peptides. We applied SILAC-DIA to a mixture of labeled HLA-I peptides, calculated heavy-to-light ratios for 7,742 peptides across 5 conditions and demonstrated that diaPASEF achieves high quantitative accuracy up to 4-fold dilution. Finally, we identified and quantified shared neoantigens in a monoallelic C1R cell line model. By spiking in heavy synthetic peptides, we verified the identification of the peptide sequences and calculated relative abundances for 13 neoantigens. Taken together, diaPASEF analysis workflows for HLA-I peptides can increase the peptidome coverage for lower sample amounts. The sensitivity and quantitative precision provided by DIA can enable the detection and quantification of less abundant peptide species such as neoantigens across samples from the same background.
