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Major histocompatibility complex (MHC)-bound peptides that originate from tumor-specific genetic alterations, known as neoantigens, are an important class of anticancer therapeutic targets. Accurately predicting peptide presentation by MHC complexes is a key aspect of discovering therapeutically relevant neoantigens. Technological improvements in mass-spectrometry-based immunopeptidomics and advanced modeling techniques have vastly improved MHC presentation prediction over the past two decades. However, improvement in the sensitivity and specificity of prediction algorithms is needed for clinical applications such as the development of personalized cancer vaccines, the discovery of biomarkers for response to checkpoint blockade, and the quantification of autoimmune risk in gene therapies. Toward this end, we generated allele-specific immunopeptidomics data using 25 monoallelic cell lines and created Systematic HLA Epitope Ranking Pan Algorithm (SHERPA), a pan-allelic MHC-peptide algorithm for predicting MHC-peptide binding and presentation. In contrast to previously published large-scale monoallelic data, we used an HLA-null K562 parental cell line and a stable transfection of HLA alleles to better emulate native presentation. Our dataset includes five previously unprofiled alleles that expand MHC-binding pocket diversity in the training data and extend allelic coverage in under profiled populations. To improve generalizability, SHERPA systematically integrates 128 monoallelic and 384 multiallelic samples with publicly available immunoproteomics data and binding assay data. Using this dataset, we developed two features that empirically estimate the propensities of genes and specific regions within gene bodies to engender immunopeptides to represent antigen processing. Using a composite model constructed with gradient boosting decision trees, multiallelic deconvolution, and 2.15 million peptides encompassing 167 alleles, we achieved a 1.44-fold improvement of positive predictive value compared with existing tools when evaluated on independent monoallelic datasets and a 1.15-fold improvement when evaluating on tumor samples. With a high degree of accuracy, SHERPA has the potential to enable precision neoantigen discovery for future clinical applications., Graphical Abstract, Highlights • Generated 25 stably transfected monoallelic cell lines and applied immunopeptidomics. • Harmonized 512 public immunopeptidomic samples through systematic reprocessing. • Developed pan-allele MHC-binding algorithm (SHERPA) utilizing 167 human HLA alleles. • SHERPA demonstrates up to 1.44-fold increased precision over competing algorithms., In Brief Accurately identifying neoantigens is critical for many clinical applications. We generated immunopeptidomics data from 25 stably transfected monoallelic cell lines. Then, we systematically reprocessed a large corpus of public data to improve major histocompatibility complex (MHC) binding pocket diversity and to empirically learn the rules of antigen presentation. In applying these datasets, we trained SHERPA, an MHC binding and presentation prediction algorithm. SHERPA improves performance compared with existing tools by 1.44-fold in held-out monoallelic data and 1.11-fold for immunogenic epitopes.

Single-cell transcriptomics has revolutionized our understanding of basic biology and disease. Since transcript levels often do not correlate with protein expression, it is crucial to complement transcriptomics approaches with proteome analyses at single-cell resolution. Despite continuous technological improvements in sensitivity, mass-spectrometry-based single-cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultralow input samples. We developed a novel, identification-independent proteomics data-analysis pipeline that allows to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin to identify cell types and single protein knockouts. These proteome signatures overcome the need to impute quantitative data due to accumulating detrimental amounts of missing data in standard multibatch TMT experiments. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. Our data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultralow input samples., Graphical Abstract, Highlights • DIA-TMT provides reproducible, quantitative proteome signatures at high throughput. • Proteome signature inferred cell type characterization is highly accurate. • Proteome signatures accurately highlight underrepresented cell types. • ID-independent DIA-TMT is more reproducible than standard DDA acquisition strategies., In Brief Proteomics faces the challenge of reproducibly comparing the protein expression profiles across large sample cohorts. Here, we combined two hitherto opposing analytical strategies, DIA and isobaric labeling to generate highly reproducible, quantitative “proteome signatures”. These signatures decouple peptide identification from quantification to quantitatively compare hundreds of samples. DIA-TMT data provides complete quantitative signatures independent of peptide identification that distinguish cell types down to single protein knockouts in high-throughput even at ultralow input.

MS-based immunopeptidomics is maturing into an automatized and high-throughput technology, producing small- to large-scale datasets of clinically relevant major histocompatibility complex (MHC) class I-associated and class II-associated peptides. Consequently, the development of quality control (QC) and quality assurance systems capable of detecting sample and/or measurement issues is important for instrument operators and scientists in charge of downstream data interpretation. Here, we created MhcVizPipe (MVP), a semiautomated QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS, including datasets generated from large sample cohorts. In essence, MVP provides a rapid and consolidated view of sample quality, composition, and MHC specificity to greatly accelerate the “pass–fail” QC decision-making process toward data interpretation. MVP parallelizes the use of well-established immunopeptidomic algorithms (NetMHCpan, NetMHCIIpan, and GibbsCluster) and rapidly generates organized and easy-to-understand reports in HTML format. The reports are fully portable and can be viewed on any computer with a modern web browser. MVP is intuitive to use and will find utility in any specialized immunopeptidomic laboratory and proteomics core facility that provides immunopeptidomic services to the community., Graphical Abstract, Highlights • MhcVizPipe is a software tool for QC in immunopeptidomics. • It provides numerical scores for QC analysis of tumor biopsies. • It performs rapid QC analysis of large clinical immunopeptidomic sample cohorts. • It generates organized and easy-to-understand reports in HTML format., In Brief Automated QC software tools capable of detecting sample and/or measurement issues are important for downstream data interpretation. We created MhcVizPipe, the first semiautomated GUI-based QC software tool that enables rapid and simultaneous assessment of multiple MHC class I and II immunopeptidomic datasets generated by MS. MVP is intuitive to use, generate QC reports in HTML formats, and will find utility in any immunopeptidomic laboratory.

Antibodies against posttranslational modifications (PTMs) such as lysine acetylation, ubiquitin remnants, or phosphotyrosine have resulted in significant advances in our understanding of the fundamental roles of these PTMs in biology. However, the roles of a number of PTMs remain largely unexplored due to the lack of robust enrichment reagents. The addition of N-acetylglucosamine to serine and threonine residues (O-GlcNAc) by the O-GlcNAc transferase (OGT) is a PTM implicated in numerous biological processes and disease states but with limited techniques for its study. Here, we evaluate a new mixture of anti-O-GlcNAc monoclonal antibodies for the immunoprecipitation of native O-GlcNAcylated peptides from cells and tissues. The anti-O-GlcNAc antibodies display good sensitivity and high specificity toward O-GlcNAc-modified peptides and do not recognize O-GalNAc or GlcNAc in extended glycans. Applying this antibody-based enrichment strategy to synaptosomes from mouse brain tissue samples, we identified over 1300 unique O-GlcNAc-modified peptides and over 1000 sites using just a fraction of sample preparation and instrument time required in other landmark investigations of O-GlcNAcylation. Our rapid and robust method greatly simplifies the analysis of O-GlcNAc signaling and will help to elucidate the role of this challenging PTM in health and disease., Graphical Abstract, Highlights • Anti-O-GlcNAc antibodies are fast and simple enrichment reagents. • Anti-O-GlcNAc antibodies are sensitive and achieve significant depth of coverage. • Anti-O-GlcNAc antibodies are specific for singular O-GlcNAc modifications. • Anti-O-GlcNAc antibody enrichment techniques can be applied to cells and tissues. • HCD product-triggered EThcD data acquisition improves depth of coverage., In Brief O-GlcNAc, the single N-acetylglucosamine coupled to serine and threonines of nucleocytoplasmic proteins, is an enigmatic posttranslational modification implicated in most cellular functions. Methods to study the O-GlcNAc modified proteome have been challenging, in part, due to limitations in its enrichment from native tissue. Here, we characterize novel antibodies for the enrichment of native O-GlcNAc modified peptides. Our enrichment strategy shows strong sensitivity and excellent specificity for O-GlcNAc. We expect these reagents to be a significant advancement for the field.