The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.

T cells recognize a few high-affinity antigens among a vast array of lower affinity antigens. According to the kinetic proofreading model, antigen discrimination properties could be explained by the gradual amplification of small differences in binding affinities as the signal is transduced downstream of the T cell receptor. Which early molecular events are affected by ligand affinity, and how, has not been fully resolved. Here, we used time-resolved high-throughput proteomic analyses to identify and quantify the phosphorylation events and protein–protein interactions encoding T cell ligand discrimination in antigen-experienced T cells. Although low-affinity ligands induced phosphorylation of the Cd3 chains of the T cell receptor and the interaction of Cd3 with the Zap70 kinase as strongly as high-affinity ligands, they failed to activate Zap70 to the same extent. As a result, formation of the signalosome of the Lat adaptor was severely impaired with low- compared with high-affinity ligands, whereas formation of the signalosome of the Cd6 receptor was affected only partially. Overall, this study provides a comprehensive map of molecular events associated with T cell ligand discrimination.

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Hydrogen-Deuterium eXchange coupled to Mass Spectrometry (HDX-MS) is now common practice in structural biology. However, it is most of the time applied to rather small oligomeric complexes. Here, we report on the use of HDX-MS to investigate conformational differences between the human standard 20S (std20S) and immuno 20S (i20s) proteasomes alone or in complex with PA28αβ or PA28γ activators. Their solvent accessibility is analyzed through a dedicated bioinformatic pipeline including stringent statistical analysis and 3D visualization. These data confirm the existence of allosteric differences between the std20S and i20S at the surface of the α-ring triggered from inside the catalytic β-ring. Additionally, binding of the PA28 regulators to the 20S proteasomes modify solvent accessibility due to conformational changes of the β-rings. This work is not only a proof-of-concept that HDX-MS can be used to get structural insights on large multi-protein complexes in solution, it also demonstrates that the binding of the std20S or i20S subtype to any of its PA28 activator triggers allosteric changes that are specific to this 20S/PA28 pair.

The exit of metastasizing tumor cells from the vasculature, extravasation, is regulated by their dynamic interactions with the endothelial cells that line the internal surface of vessels. To elucidate signals controlling tumor cell adhesion to the endothelium and subsequent transendothelial migration, we performed phosphoproteomic analysis to map cell-specific changes in protein phosphorylation that were triggered by contact between metastatic MDA-MB-231 breast cancer cells and endothelial cells. From the 2669 unique phosphorylation sites identified, 77 and 43 were differentially phosphorylated in the tumor cells and endothelial cells, respectively. The receptor tyrosine kinase ephrin type A receptor 2 (EPHA2) exhibited decreased Tyr⁷⁷² phosphorylation in the cancer cells upon endothelial contact. Knockdown of EPHA2 increased adhesion of the breast cancer cells to human umbilical vein endothelial cells (HUVECs) and their transendothelial migration in coculture cell assays, as well as early-stage lung colonization in vivo. EPHA2-mediated inhibition of transendothelial migration of breast cancer cells depended on interaction with the ligand ephrinA1 on HUVECs and phosphorylation of EPHA2-Tyr⁷⁷². When EPHA2 phosphorylation dynamics were compared between cell lines of different metastatic ability, EPHA2-Tyr⁷⁷² was rapidly dephosphorylated after ephrinA1 stimulation specifically in cells targeting the lung. Knockdown of the phosphatase LMW-PTP reduced adhesion and transendothelial migration of the breast cancer cells. Overall, cell-specific phosphoproteomic analysis provides a bidirectional map of contact-initiated signaling between tumor and endothelial cells that can be further investigated to identify mechanisms controlling the transendothelial cell migration of cancer cells.