T cell-mediated immunity plays an important role in controlling SARS-CoV-2 infection, but the repertoire of naturally processed and presented viral epitopes on class I human leukocyte antigen (HLA-I) remains uncharacterized. Here, we report the first HLA-I immunopeptidome of SARS-CoV-2 in two cell lines at different times post infection using mass spectrometry. We found HLA-I peptides derived not only from canonical open reading frames (ORFs) but also from internal out-of-frame ORFs in spike and nucleocapsid not captured by current vaccines. Some peptides from out-of-frame ORFs elicited T cell responses in a humanized mouse model and individuals with COVID-19 that exceeded responses to canonical peptides, including some of the strongest epitopes reported to date. Whole-proteome analysis of infected cells revealed that early expressed viral proteins contribute more to HLA-I presentation and immunogenicity. These biological insights, as well as the discovery of out-of-frame ORF epitopes, will facilitate selection of peptides for immune monitoring and vaccine development.

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Multiomic characterization of patient tissues provides insights into the function of different biological pathways in the context of disease. Much work has been done to serialize proteome and post-translational modification (PTM) analyses to conserve precious patient samples. However, characterizing clinically relevant tissues with multi-ome workflows that have distinct sample processing requirements remains challenging. To overcome the obstacles of combining enrichment workflows that have unique input amounts and utilize both label free and chemical labeling strategies, we developed a highly-sensitive multi-omic networked tissue enrichment (MONTE) workflow for the full analysis of HLA-I and HLA-II immunopeptidome, ubiquitylome, proteome, phosphoproteome and acetylome all from the same tissue sample. The MONTE workflow enables identification of a median of 9,000 HLA-I peptides, 6,000 HLA-II peptides, 10,000 Ub sites, 12,000 proteins, 20,000 phosphorylation sites and 15,000 acetylation sites from patient LUAD tumors. Because all omes are generated from the exact same tissue sample, there is less biological variability in the data enabling more robust integration. The information available in MONTE datasets facilitates the identification of putative immunotherapeutic targets, such as CT antigens and neoantigens presented by HLA complexes, as well as reveal insights into how disease-specific changes in protein expression, protein degradation, cell signaling, metabolic, and epigenetic pathways are involved in disease pathology and treatment.

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