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Aggregation of polyglutamine-expanded huntingtin exon 1 (HttEx1) in Huntington's disease (HD) proceeds from soluble oligomers to late-stage inclusions. The nature of the aggregates and how they lead to neuronal dysfunction is not well understood. We employed mass spectrometry (MS)-based quantitative proteomics to dissect spatiotemporal mechanisms of neurodegeneration using the R6/2 mouse model of HD. Extensive remodeling of the soluble brain proteome correlated with insoluble aggregate formation during disease progression. In-depth and quantitative characterization of the aggregates uncovered an unprecedented complexity of several hundred proteins. Sequestration to aggregates depended on protein expression levels and sequence features such as low-complexity regions or coiledcoil domains. In a cell-based HD model, overexpression of a subset of the sequestered proteins in most cases rescued viability and reduced aggregate size. Our spatiotemporally resolved proteome resource of HD progression indicates that widespread loss of cellular protein function contributes to aggregate- mediated toxicity. [ABSTRACT FROM AUTHOR]

Summary We previously developed a mass spectrometry-based method, dynamic organellar maps, for the determination of protein subcellular localization and identification of translocation events in comparative experiments. The use of metabolic labeling for quantification (stable isotope labeling by amino acids in cell culture [SILAC]) renders the method best suited to cells grown in culture. Here, we have adapted the workflow to both label-free quantification (LFQ) and chemical labeling/multiplexing strategies (tandem mass tagging [TMT]). Both methods are highly effective for the generation of organellar maps and capture of protein translocations. Furthermore, application of label-free organellar mapping to acutely isolated mouse primary neurons provided subcellular localization and copy-number information for over 8,000 proteins, allowing a detailed analysis of organellar organization. Our study extends the scope of dynamic organellar maps to any cell type or tissue and also to high-throughput screening. [ABSTRACT FROM AUTHOR]

Computational workflows for mass spectrometry-based shotgun proteomics and untargeted metabolomics share many steps. Despite the similarities, untargeted metabolomics is lagging behind in terms of reliable fully automated quantitative data analysis. We argue that metabolomics will strongly benefit from the adaptation of successful automated proteomics workflows to metabolomics. MaxQuant is a popular platform for proteomics data analysis and is widely considered to be superior in achieving high precursor mass accuracies through advanced nonlinear recalibration, usually leading to five to ten-fold better accuracy in complex LC–MS/MS runs. This translates to a sharp decrease in the number of peptide candidates per measured feature, thereby strongly improving the coverage of identified peptides. We argue that similar strategies can be applied to untargeted metabolomics, leading to equivalent improvements in metabolite identification. [ABSTRACT FROM AUTHOR]

Summary Regulatory protein phosphorylation controls normal and pathophysiological signaling in eukaryotic cells. Despite great advances in mass-spectrometry-based proteomics, the extent, localization, and site-specific stoichiometry of this posttranslational modification (PTM) are unknown. Here, we develop a stringent experimental and computational workflow, capable of mapping more than 50,000 distinct phosphorylated peptides in a single human cancer cell line. We detected more than three-quarters of cellular proteins as phosphoproteins and determined very high stoichiometries in mitosis or growth factor signaling by label-free quantitation. The proportion of phospho-Tyr drastically decreases as coverage of the phosphoproteome increases, whereas Ser/Thr sites saturate only for technical reasons. Tyrosine phosphorylation is maintained at especially low stoichiometric levels in the absence of specific signaling events. Unexpectedly, it is enriched on higher-abundance proteins, and this correlates with the substrate K M values of tyrosine kinases. Our data suggest that P-Tyr should be considered a functionally separate PTM of eukaryotic proteomes. [ABSTRACT FROM AUTHOR]

Summary: Cancer progresses through distinct stages, and mouse models recapitulating traits of this progression are frequently used to explore genetic, morphological, and pharmacological aspects of tumor development. To complement genomic investigations of this process, we here quantify phosphoproteomic changes in skin cancer development using the SILAC mouse technology coupled to high-resolution mass spectrometry. We distill protein expression signatures from our data that distinguish between skin cancer stages. A distinct phosphoproteome of the two stages of cancer progression is identified that correlates with perturbed cell growth and implicates cell adhesion as a major driver of malignancy. Importantly, integrated analysis of phosphoproteomic data and prediction of kinase activity revealed PAK4-PKC/SRC network to be highly deregulated in SCC but not in papilloma. This detailed molecular picture, both at the proteome and phosphoproteome level, will prove useful for the study of mechanisms of tumor progression. [ABSTRACT FROM AUTHOR]

Cross-linking mass spectrometry (MS) has substantially matured as a method over the past 2 decades through parallel development in multiple labs, demonstrating its applicability to protein structure determination, conformation analysis, and mapping protein interactions in complex mixtures. Cross-linking MS has become a much-appreciated and routinely applied tool, especially in structural biology. Therefore, it is timely that the community commits to the development of methodological and reporting standards. This white paper builds on an open process comprising a number of events at community conferences since 2015 and identifies aspects of Cross-linking MS for which guidelines should be developed as part of a Cross-linking MS standards initiative. Leitner et al. propose steps toward the development of best practices in Cross-linking mass spectrometry in this white paper. This community-driven effort represents a consensus of approximately 30 groups from academia and industry and, if successfully implemented, will increase transparency and facilitate data reuse. [ABSTRACT FROM AUTHOR]

Background Clinical management of the prostate needs improved prognostic tests and treatment strategies. Because proteins are the ultimate effectors of most cellular reactions, are targets for drug actions and constitute potential biomarkers; a quantitative systemic overview of the proteome changes occurring during prostate cancer (PCa) initiation and progression can result in clinically relevant discoveries. Objectives To study cellular processes altered in PCa using system-wide quantitative analysis of changes in protein expression in clinical samples and to identify prognostic biomarkers for disease aggressiveness. Design, setting, and participants Mass spectrometry was used for genome-scale quantitative proteomic profiling of 28 prostate tumors (Gleason score 6–9) and neighboring nonmalignant tissue in eight cases, obtained from formalin-fixed paraffin-embedded prostatectomy samples. Two independent cohorts of PCa patients (summing 752 cases) managed by expectancy were used for immunohistochemical evaluation of proneuropeptide-Y (pro-NPY) as a prognostic biomarker. Results and limitations Over 9000 proteins were identified as expressed in the human prostate. Tumor tissue exhibited elevated expression of proteins involved in multiple anabolic processes including fatty acid and protein synthesis, ribosomal biogenesis and protein secretion but no overt evidence of increased proliferation was observed. Tumors also showed increased levels of mitochondrial proteins, which was associated with elevated oxidative phosphorylation capacity measured in situ. Molecular analysis indicated that some of the proteins overexpressed in tumors, such as carnitine palmitoyltransferase 2 (CPT2, fatty acid transporter), coatomer protein complex, subunit alpha (COPA, vesicle secretion), and mitogen- and stress-activated protein kinase 1 and 2 (MSK1/2, protein kinase) regulate the proliferation of PCa cells. Additionally, pro-NPY was found overexpressed in PCa (5-fold, p < 0.05), but largely absent in other solid tumor types. Pro-NPY expression, alone or in combination with the ERG status of the tumor, was associated with an increased risk of PCa specific mortality, especially in patients with Gleason score ≤ 7 tumors. Conclusions This study represents the first system-wide quantitative analysis of proteome changes associated to localized prostate cancer and as such constitutes a valuable resource for understanding the complex metabolic changes occurring in this disease. We also demonstrated that pro-NPY, a protein that showed differential expression between high and low risk tumors in our proteomic analysis, is also a PCa specific prognostic biomarker associated with increased risk for disease specific death in patients carrying low risk tumors. Patient summary The identification of proteins whose expression change in prostate cancer provides novel mechanistic information related to the disease etiology. We hope that future studies will prove the value of this proteome dataset for development of novel therapies and biomarkers. [ABSTRACT FROM AUTHOR]