Regulation of gene expression is primarily controlled by changes in the proteins that occupy their regulatory elements. Chromatin immunoprecipitation can confirm a protein’s occupancy at a genomic locus but requires specific, high-quality, IP-competent antibodies against nominated proteins, limiting its utility. Here, we combine, genome targeting, proximity labeling, and quantitative proteomics to develop genomic locus proteomics, a method able to identify proteins associated a specific genomic locus in native cellular contexts.

A streamlined and readily accessible sample preparation protocol has been developed to enable TMT-based proteomic profiling of relatively low numbers of cells directly from a flow cytometer. These methods were applied to 12 freshly isolated immune cell types from mice to a depth of over 7000 quantified proteins. These data recapitulate many aspects of known immunology, nominate new cell type specific protein markers, and provide evidence for post-transcriptional regulation of gene expression across the immune system. Graphical Abstract+ Int. Ref IM M U -N O L O -G Y Collection microreactor On-column TMT labeling bRP fractionation Long gradient, long column, high resolution LC-MS/MS % ACN −20 −10 0 10 20 −10 0 1 0 20 PC 1 PC 2 B1a GN DC CD8T CD4T B cell MC Treg Tgd NKT NK MF Streamlined protocol for reduced cell numbers GN GN T4 T8 NKT MF MC NK DC B B1a Treg Tgd mRNA co-regulation Protein co-regulation Proteomic profiling of murine immune cells Highlights• A low input proteomic profiling sample preparation workflow has been developed.• The protocol components are accessible and are widely applicable.• Over 7000 proteins across 12 immune cell types were quantified from 3e5 cells.• The data provide evidence for global post-transcriptional regulation.

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.

Highlights d CausalPath builds mechanistic models from proteomic profiles d It integrates biological pathway models with molecular measurements d It supports logical reasoning with post-translational modifications d A web server, free software, and a source code are available