SUMMARY Identification of human leukocyte antigen (HLA)-bound peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) is poised to provide a deep understanding of rules underlying antigen presentation. However, a key obstacle is the ambiguity that arises from the co-expression of multiple HLA alleles. Here, we have implemented a scalable mono-allelic strategy for profiling the HLA peptidome. By using cell lines expressing a single HLA allele, optimizing immunopurifications, and developing an application-specific spectral search algorithm, we identified thousands of peptides bound to 16 different HLA class I alleles. These data enabled the discovery of subdominant binding motifs and an integrative analysis quantifying the contribution of factors critical to epitope presentation, such as protein cleavage and gene expression. We trained neural-network prediction algorithms with our large dataset (>24,000 peptides) and outperformed algorithms trained on data-sets of peptides with measured affinities. We thus demonstrate a strategy for systematically learning the rules of endogenous antigen presentation.

Deregulation of signaling pathways involving phosphorylation is a hallmark of malignant transformation. Degradation of phosphoproteins generates cancer-specific phosphopeptides that are associated with MHC-I and II molecules and recognized by T-cells. We identified 95 phosphopeptides presented on the surface of primary hematological tumors and normal tissues, including 61 that were tumor-specific. Phosphopeptides were more prevalent on more aggressive and malignant samples. CD8 T-cell lines specific for these phosphopeptides recognized and killed both leukemia cell lines and HLA-matched primary leukemia cells ex vivo. Healthy individuals showed surprisingly high levels of CD8 T-cell responses against many of these phosphopeptides within the circulating memory compartment. This immunity was significantly reduced or absent in some leukemia patients, which correlated with clinical outcome, and was restored following allogeneic stem cell transplantation. These results suggest that phosphopeptides may be targets of cancer immune surveillance in humans, and point to their importance for development of vaccine-based and T-cell adoptive transfer immunotherapies..

Highlights d Affinity-tagging protocol enables proteomic profiling of individual HLA-II alleles d Even in ''hot'' tumors, professional APCs-not cancer cellsdrive HLA-II expression d Cellular localization influences which phagocytosed cancer proteins get presented d Machine-learning models for binding and processing improve HLA-II prediction

Profiling post-translational modifications represents an alternative dimension to gene expression data in characterizing cellular processes. Many cellular responses to drugs are mediated by changes in cellular phosphosignaling. We sought to develop a common platform on which phosphosignaling responses could be profiled across thousands of samples, and created a targeted MS assay that profiles a reduced-representation set of phosphopeptides that we show to be strong indicators of responses to chemical perturbagens.To develop the assay, we investigated the coordinate regulation of phosphosites in samples derived from three cell lines treated with 26 different bioactive small molecules. Phosphopeptide analytes were selected from these discovery studies by clustering and picking 1 to 2 proxy members from each cluster. A quantitative, targeted parallel reaction monitoring assay was developed to directly measure 96 reduced-representation probes. Sample processing for proteolytic digestion, protein quantification, peptide desalting, and phosphopeptide enrichment have been fully automated, making possible the simultaneous processing of 96 samples in only 3 days, with a plate phosphopeptide enrichment variance of 12%. This highly reproducible process allowed ϳ95% of the reduced-representation phosphopeptide probes to be detected in ϳ200 samples.The performance of the assay was evaluated by measuring the probes in new samples generated under treatment conditions from discovery experiments, recapitulating the observations of deeper experiments using a fraction of the analytical effort. We measured these probes in new experiments varying the treatments, cell types, and timepoints to demonstrate generalizability. We demonstrated that the assay is sensitive to disruptions in common signaling pathways (e.g. MAPK, PI3K/mTOR, and CDK). The high-throughput, reduced-representation phosphoproteomics assay provides a platform for the comparison of perturbations across a range of biological conditions, suitable for profiling thousands of samples. We believe the assay will prove highly useful for classification of known and novel drug and genetic mechanisms through comparison of phosphoproteomic signatures.

Mass spectrometry with data-independent acquisition (DIA) has emerged as a promising method to greatly improve the comprehensiveness and reproducibility of targeted and discovery proteomics, in theory systematically measuring all peptide precursors within a biological sample. Despite the technical maturity of DIA, the analytical challenges involved in discriminating between peptides with similar sequences in convoluted spectra have limited its applicability in important cases, such as the detection of single-nucleotide polymorphisms and alternative site localizations in phosphoproteomics data. We have developed Specter, an open-source software tool that uses linear algebra to deconvolute DIA mixture spectra directly in terms of a spectral library, circumventing the problems associated with typical fragment correlation-based approaches. We validate the sensitivity of Specter and its performance relative to other methods by means of several complex datasets, and show that Specter is able to successfully analyze cases involving highly similar peptides that are typically challenging for DIA analysis methods.

SUMMARYAlthough the value of proteomics has been demonstrated, cost and scale are typically prohibitive, and gene expression profiling remains dominant for characterizing cellular responses to perturbations. However, high-throughput sentinel assays provide an opportunity for proteomics to contribute at a meaningful scale. We present a systematic library resource (90 drugs 3 6 cell lines) of proteomic signatures that measure changes in the reduced-representation phosphoproteome (P100) and changes in epigenetic marks on histones (GCP). A majority of these drugs elicited reproducible signatures, but notable cell line- and assay-specific differences were observed. Using the “connectivity” framework, we compared signatures across cell types and integrated data across assays, including a transcriptional assay (L1000). Consistent connectivity among cell types revealed cellular responses that transcended lineage, and consistent connectivity among assays revealed unexpected associations between drugs. We further leveraged the resource against public data to formulate hypotheses for treatment of multiple myeloma and acute lymphocytic leukemia. This resource is publicly available at https://clue.io/proteomics.

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.

Phosphorylation events within cancer cells often become dysregulated, leading to oncogenic signaling and abnormal cell growth. Phosphopeptides derived from aberrantly phosphorylated proteins that are presented on tumors and not on normal tissues by human leukocyte antigen (HLA) class I molecules are promising candidates for future cancer immunotherapies, because they are tumor specific and have been shown to elicit cytotoxic T cell responses. Robust phosphopeptide enrichments that are suitable for low input amounts must be developed to characterize HLA-associated phosphopeptides from clinical samples that are limited by material availability. We present two complementary mass spectrometry–compatible, iron(III)-immobilized metal affinity chromatography (IMAC) methods that use either nitrilotriacetic acid (NTA) or iminodiacetic acid (IDA) in-house-fabricated columns. We developed these protocols to enrich for subfemtomole-level phosphopeptides from cell line and human tissue samples containing picograms of starting material, which is an order of magnitude less material than what is commonly used. In addition, we added a peptide esterification step to increase phosphopeptide specificity from these low-input samples. To date, hundreds of phosphopeptides displayed on melanoma, ovarian cancer, leukemia and colorectal cancer have been identified using these highly selective phosphopeptide enrichment protocols in combination with a program called ‘CAD Neutral Loss Finder’ that identifies all spectra containing the characteristic neutral loss of phosphoric acid from phosphorylated serine and threonine residues. This methodology enables the identification of HLA-associated phosphopeptides presented by human tissue samples containing as little as nanograms of peptide material in 2 d.