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Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200 ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5 min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5 min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients., Competing Interests: Conflict of interest K. L. F., A. K., D. M., K. A., D. G., and A. M. are employees of Thermo Fisher Scientific, the manufacturer of Orbitrap instrumentation used in this research. M. M. is an indirect investor in Evosep Biosystems. All other authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Spatially resolved mass spectrometry-based proteomics at single-cell resolution promises to provide insights into biological heterogeneity. We describe a protocol based on multiplexed data-independent acquisition (mDIA) with dimethyl labeling to enhance proteome depth, accuracy, and throughput while minimizing costs. It enables high-quality proteome analysis of single isolated hepatocytes and utilizes liver zonation for single-cell proteomics benchmarking. This adaptable, modular protocol will promote the use of single-cell proteomics in spatial biology., (© 2024. The Author(s).)

Cellular functions are mediated by protein-protein interactions, and mapping the interactome provides fundamental insights into biological systems. Affinity purification coupled to mass spectrometry is an ideal tool for such mapping, but it has been difficult to identify low copy number complexes, membrane complexes and complexes that are disrupted by protein tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive high-throughput method using highly reproducible affinity enrichment coupled to mass spectrometry combined with a quantitative two-dimensional analysis strategy to comprehensively map the interactome of Saccharomyces cerevisiae. Thousand-fold reduced volumes in 96-well format enabled replicate analysis of the endogenous GFP-tagged library covering the entire expressed yeast proteome 1 . The 4,159 pull-downs generated a highly structured network of 3,927 proteins connected by 31,004 interactions, doubling the number of proteins and tripling the number of reliable interactions compared with existing interactome maps 2 . This includes very-low-abundance epigenetic complexes, organellar membrane complexes and non-taggable complexes inferred by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 16 interactors. Similar to social networks between humans, the average shortest distance between proteins is 4.2 interactions. AlphaFold-Multimer provided novel insights into the functional roles of previously uncharacterized proteins in complexes. Our web portal ( www.yeast-interactome.org ) enables extensive exploration of the interactome dataset., (© 2023. The Author(s).)

Mitochondria are essential organelles whose dysfunction causes human pathologies that often manifest in a tissue-specific manner. Accordingly, mitochondrial fitness depends on versatile proteomes specialized to meet diverse tissue-specific requirements. Increasing evidence suggests that phosphorylation may play an important role in regulating tissue-specific mitochondrial functions and pathophysiology. Building on recent advances in mass spectrometry (MS)-based proteomics, we here quantitatively profile mitochondrial tissue proteomes along with their matching phosphoproteomes. We isolated mitochondria from mouse heart, skeletal muscle, brown adipose tissue, kidney, liver, brain, and spleen by differential centrifugation followed by separation on Percoll gradients and performed high-resolution MS analysis of the proteomes and phosphoproteomes. This in-depth map substantially quantifies known and predicted mitochondrial proteins and provides a resource of core and tissue-specific mitochondrial proteins (mitophos.de). Predicting kinase substrate associations for different mitochondrial compartments indicates tissue-specific regulation at the phosphoproteome level. Illustrating the functional value of our resource, we reproduce mitochondrial phosphorylation events on dynamin-related protein 1 responsible for its mitochondrial recruitment and fission initiation and describe phosphorylation clusters on MIGA2 linked to mitochondrial fusion., (© 2023 Hansen et al.)

Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics and spatial omics technologies., (© 2023. The Author(s).)

Hand eczema (HE) is a prevalent skin disease. However, the classification of HE into different subtypes remains challenging. A limited number of previous studies have employed invasive biopsy-based strategies; yet, studies of the HE proteome using noninvasive tape-stripping methodology have not been reported. In this study, we wanted to assess whether global proteomic analysis of skin tape strip samples can be used for subclassification of patients with HE. Tape strips were collected from patients with HE and healthy skin. Liquid chromatography-mass spectrometry proteomics was performed, and the global protein expression was analyzed. We identified 2,919 proteins in stratum corneum-derived skin cells from tape strip samples. Compared with healthy skin, the lesional samples from patients with HE exhibited increased expression of immune-related markers and a decreased expression of structural barrier proteins. The difference between HE subtypes was restricted to the lesional skin areas and included an increased expression of skin barrier-related proteins independently of the concurrent AD. In conclusion, we found that the noninvasive tape strip method used in combination with liquid chromatography-mass spectrometry proteomics can be used for analysis of skin protein expression in patients with HE. Thus, the method shows potential for assessing the proteomic differences between subtypes of HE and biomarker discovery., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Recent advances in mass spectrometry-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks in the bioinformatics pipeline. Although peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with the sample numbers, which may even preclude the analysis of large-scale data. Here we introduce directLFQ, a ratio-based approach for sample normalization and the calculation of protein intensities. It estimates quantities via aligning samples and ion traces by shifting them on top of each other in logarithmic space. Importantly, directLFQ scales linearly with the number of samples, allowing analyses of large studies to finish in minutes instead of days or months. We quantify 10,000 proteomes in 10 min and 100,000 proteomes in less than 2 h, a 1000-fold faster than some implementations of the popular LFQ algorithm MaxLFQ. In-depth characterization of directLFQ reveals excellent normalization properties and benchmark results, comparing favorably to MaxLFQ for both data-dependent acquisition and data-independent acquisition. In addition, directLFQ provides normalized peptide intensity estimates for peptide-level comparisons. It is an important part of an overall quantitative proteomic pipeline that also needs to include high sensitive statistical analysis leading to proteoform resolution. Available as an open-source Python package and a graphical user interface with a one-click installer, it can be used in the AlphaPept ecosystem as well as downstream of most common computational proteomics pipelines., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

The spatiotemporal structure of the human microbiome 1,2 , proteome 3 and metabolome 4,5 reflects and determines regional intestinal physiology and may have implications for disease 6 . Yet, little is known about the distribution of microorganisms, their environment and their biochemical activity in the gut because of reliance on stool samples and limited access to only some regions of the gut using endoscopy in fasting or sedated individuals 7 . To address these deficiencies, we developed an ingestible device that collects samples from multiple regions of the human intestinal tract during normal digestion. Collection of 240 intestinal samples from 15 healthy individuals using the device and subsequent multi-omics analyses identified significant differences between bacteria, phages, host proteins and metabolites in the intestines versus stool. Certain microbial taxa were differentially enriched and prophage induction was more prevalent in the intestines than in stool. The host proteome and bile acid profiles varied along the intestines and were highly distinct from those of stool. Correlations between gradients in bile acid concentrations and microbial abundance predicted species that altered the bile acid pool through deconjugation. Furthermore, microbially conjugated bile acid concentrations exhibited amino acid-dependent trends that were not apparent in stool. Overall, non-invasive, longitudinal profiling of microorganisms, proteins and bile acids along the intestinal tract under physiological conditions can help elucidate the roles of the gut microbiome and metabolome in human physiology and disease., (© 2023. The Author(s).)

Background: Inactivity and unloading induce skeletal muscle atrophy, loss of strength and detrimental metabolic effects. Bed rest is a model to study the impact of inactivity on the musculoskeletal system. It not only provides information for bed-ridden patients care, but it is also a ground-based spaceflight analogue used to mimic the challenges of long space missions for the human body. In both cases, it would be desirable to develop a panel of biomarkers to monitor muscle atrophy in a minimally invasive way at point of care to limit the onset of muscle loss in a personalized fashion., Methods: We applied mass spectrometry-based proteomics to measure plasma protein abundance changes in response to 10 days of bed rest in 10 young males. To validate the correlation between muscle atrophy and the significant hits emerging from our study, we analysed in parallel, with the same pipeline, a cohort of cancer patients with or without cachexia and age-matched controls. Our analysis resulted in the quantification of over 500 proteins., Results: Unloading affected plasma concentration of proteins of the complement cascade, lipid carriers and proteins derived from tissue leakage. Among the latter, teneurin-4 increased 1.6-fold in plasma at bed rest day 10 (BR10) compared with BR0 (6.E9 vs. 4.3E9, P = 0.02) and decreased to 0.6-fold the initial abundance after 2 days of recovery at normal daily activity (R + 2, 2.7E9, P = 3.3E-4); the extracellular matrix protein lumican was decreased to 0.7-fold (1.2E9 vs. 8.5E8, P = 1.5E-4) at BR10 and remained as low at R + 2. We identified six proteins distinguishing subjects developing unloading-mediated muscle atrophy (decrease of >4% of quadriceps cross-sectional area) from those largely maintaining their initial muscle mass. Among them, transthyretin, a thyroid hormone-binding protein, was significantly less abundant at BR10 in the plasma of subjects with muscle atrophy compared with those with no atrophy (1.6E10 vs. 2.6E10, P = 0.001). Haptoglobin-related protein was also significantly reduced in the serum of cancer patients with cachexia compared with that of controls., Conclusions: Our findings highlight a combination or proteomic changes that can be explored as potential biomarkers of muscle atrophy occurring under different conditions. The panel of significant proteomic differences distinguishing atrophy-prone and atrophy-resistant subjects after 10 days of bed rest need to be tested in a larger cohort to validate their potential to predict inactivity-triggered muscle loss in humans., (© 2022 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.)

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT., Competing Interests: Declaration of interests F.T. reports receiving consulting fees from Roche Diagnostics GmbH and Cellarity, Inc., and ownership interest in Cellarity, Inc., and Dermagnostix. A.E., H.S.B., F.O., S.K., M.I.T., and M.N. have filed a patent related to robotics technologies presented in this work., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Data-independent acquisition (DIA) methods have become increasingly attractive in mass spectrometry-based proteomics because they enable high data completeness and a wide dynamic range. Recently, we combined DIA with parallel accumulation-serial fragmentation (dia-PASEF) on a Bruker trapped ion mobility (IM) separated quadrupole time-of-flight mass spectrometer. This requires alignment of the IM separation with the downstream mass selective quadrupole, leading to a more complex scheme for dia-PASEF window placement compared with DIA. To achieve high data completeness and deep proteome coverage, here we employ variable isolation windows that are placed optimally depending on precursor density in the m/z and IM plane. This is implemented in the freely available py_diAID (Python package for DIA with an automated isolation design) package. In combination with in-depth project-specific proteomics libraries and the Evosep liquid chromatography system, we reproducibly identified over 7700 proteins in a human cancer cell line in 44 min with quadruplicate single-shot injections at high sensitivity. Even at a throughput of 100 samples per day (11 min liquid chromatography gradients), we consistently quantified more than 6000 proteins in mammalian cell lysates by injecting four replicates. We found that optimal dia-PASEF window placement facilitates in-depth phosphoproteomics with very high sensitivity, quantifying more than 35,000 phosphosites in a human cancer cell line stimulated with an epidermal growth factor in triplicate 21 min runs. This covers a substantial part of the regulated phosphoproteome with high sensitivity, opening up for extensive systems-biological studies., Competing Interests: Conflict of interest M. M. is an indirect investor in Evosep Biosystems. All other authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Reactivation of fetal hemoglobin expression by the downregulation of BCL11A is a promising treatment for β-hemoglobinopathies. A detailed understanding of BCL11A-mediated repression of γ-globin gene (HBG1/2) transcription is lacking, as studies to date used perturbations by shRNA or CRISPR-Cas9 gene editing. We leveraged the dTAG PROTAC degradation platform to acutely deplete BCL11A protein in erythroid cells and examined consequences by nascent transcriptomics, proteomics, chromatin accessibility, and histone profiling. Among 31 genes repressed by BCL11A, HBG1/2 and HBZ show the most abundant and progressive changes in transcription and chromatin accessibility upon BCL11A loss. Transcriptional changes at HBG1/2 were detected in <2 h. Robust HBG1/2 reactivation upon acute BCL11A depletion occurred without the loss of promoter 5-methylcytosine (5mC). Using targeted protein degradation, we establish a hierarchy of gene reactivation at BCL11A targets, in which nascent transcription is followed by increased chromatin accessibility, and both are uncoupled from promoter DNA methylation at the HBG1/2 loci., Competing Interests: Declaration of interests E.S.F. is a founder, scientific advisory board (SAB) member, and equity holder in Civetta Therapeutics, Jengu Therapeutics (board member), Neomorph, and Proximity Therapeutics; SAB member and equity holder in Avilar Therapeutics and Photys Therapeutics; and a consultant to Novartis, Sanofi, Deerfield, and EcoR1. The Fischer lab receives or has received research funding from Novartis, Astellas, Ajax, Voronoi, Interline, and Deerfield. B.N. is an inventor on patent applications related to the dTAG system (WO/2017/024318, WO/2017/024319, WO/2018/148440, WO/2018/148443, and WO/2020/146250). K.A.D. is a consultant to Kronos Bio and Neomorph. S.H.O. is a consultant for Syros Pharmaceuticals, serves on the SAB of PeterBio, and is the inventor on issued patents related to BCL11A, which are licensed by Boston Children’s Hospital. The other authors declare no competing interests., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The recent revolution in computational protein structure prediction provides folding models for entire proteomes, which can now be integrated with large-scale experimental data. Mass spectrometry (MS)-based proteomics has identified and quantified tens of thousands of posttranslational modifications (PTMs), most of them of uncertain functional relevance. In this study, we determine the structural context of these PTMs and investigate how this information can be leveraged to pinpoint potential regulatory sites. Our analysis uncovers global patterns of PTM occurrence across folded and intrinsically disordered regions. We found that this information can help to distinguish regulatory PTMs from those marking improperly folded proteins. Interestingly, the human proteome contains thousands of proteins that have large folded domains linked by short, disordered regions that are strongly enriched in regulatory phosphosites. These include well-known kinase activation loops that induce protein conformational changes upon phosphorylation. This regulatory mechanism appears to be widespread in kinases but also occurs in other protein families such as solute carriers. It is not limited to phosphorylation but includes ubiquitination and acetylation sites as well. Furthermore, we performed three-dimensional proximity analysis, which revealed examples of spatial coregulation of different PTM types and potential PTM crosstalk. To enable the community to build upon these first analyses, we provide tools for 3D visualization of proteomics data and PTMs as well as python libraries for data accession and processing., Competing Interests: The authors have declared that no competing interests exist.

Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence-unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time course of primary cell cultures, and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serve as spectral library to characterize a human cohort of non-alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of hepatic stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web-based dashboard application for the interactive exploration of our resource (www.liverproteome.org)., (© 2022 The Authors. Published under the terms of the CC BY 4.0 license.)

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.

Single-cell technologies are revolutionizing biology but are today mainly limited to imaging and deep sequencing. However, proteins are the main drivers of cellular function and in-depth characterization of individual cells by mass spectrometry (MS)-based proteomics would thus be highly valuable and complementary. Here, we develop a robust workflow combining miniaturized sample preparation, very low flow-rate chromatography, and a novel trapped ion mobility mass spectrometer, resulting in a more than 10-fold improved sensitivity. We precisely and robustly quantify proteomes and their changes in single, FACS-isolated cells. Arresting cells at defined stages of the cell cycle by drug treatment retrieves expected key regulators. Furthermore, it highlights potential novel ones and allows cell phase prediction. Comparing the variability in more than 430 single-cell proteomes to transcriptome data revealed a stable-core proteome despite perturbation, while the transcriptome appears stochastic. Our technology can readily be applied to ultra-high sensitivity analyses of tissue material, posttranslational modifications, and small molecule studies from small cell counts to gain unprecedented insights into cellular heterogeneity in health and disease., (© 2022 The Authors Published under the terms of the CC BY 4.0 license.)

N-terminal protein acetylation (NTA) is a prevalent protein modification essential for viability in animals and plants. The dominant executor of NTA is the ribosome tethered N α -acetyltransferase A (NatA) complex. However, the impact of NatA on protein fate is still enigmatic. Here, we demonstrate that depletion of NatA activity leads to a 4-fold increase in global protein turnover via the ubiquitin-proteasome system in Arabidopsis. Surprisingly, a concomitant increase in translation, actioned via enhanced Target-of-Rapamycin activity, is also observed, implying that defective NTA triggers feedback mechanisms to maintain steady-state protein abundance. Quantitative analysis of the proteome, the translatome, and the ubiquitome reveals that NatA substrates account for the bulk of this enhanced turnover. A targeted analysis of NatA substrate stability uncovers that NTA absence triggers protein destabilization via a previously undescribed and widely conserved nonAc/N-degron in plants. Hence, the imprinting of the proteome with acetylation marks is essential for coordinating proteome stability., (© 2022. The Author(s).)

Tumor necrosis factor (TNF) is one of the few cytokines successfully targeted by therapies against inflammatory diseases. However, blocking this well studied and pleiotropic ligand can cause dramatic side-effects. Here, we reason that a systems-level proteomic analysis of TNF signaling could dissect its diverse functions and offer a base for developing more targeted therapies. Therefore, we combine phosphoproteomics time course experiments with subcellular localization and kinase inhibitor analysis to identify functional modules of protein phosphorylation. The majority of regulated phosphorylation events can be assigned to an upstream kinase by inhibiting master kinases. Spatial proteomics reveals phosphorylation-dependent translocations of hundreds of proteins upon TNF stimulation. Phosphoproteome analysis of TNF-induced apoptosis and necroptosis uncovers a key role for transcriptional cyclin-dependent kinase activity to promote cytokine production and prevent excessive cell death downstream of the TNF signaling receptor. This resource of TNF-induced pathways and sites can be explored at http://tnfviewer.biochem.mpg.de/ ., (© 2021. The Author(s).)

A deeper understanding of COVID-19 on human molecular pathophysiology is urgently needed as a foundation for the discovery of new biomarkers and therapeutic targets. Here we applied mass spectrometry (MS)-based proteomics to measure serum proteomes of COVID-19 patients and symptomatic, but PCR-negative controls, in a time-resolved manner. In 262 controls and 458 longitudinal samples of 31 patients, hospitalized for COVID-19, a remarkable 26% of proteins changed significantly. Bioinformatics analyses revealed co-regulated groups and shared biological functions. Proteins of the innate immune system such as CRP, SAA1, CD14, LBP, and LGALS3BP decreased early in the time course. Regulators of coagulation (APOH, FN1, HRG, KNG1, PLG) and lipid homeostasis (APOA1, APOC1, APOC2, APOC3, PON1) increased over the course of the disease. A global correlation map provides a system-wide functional association between proteins, biological processes, and clinical chemistry parameters. Importantly, five SARS-CoV-2 immunoassays against antibodies revealed excellent correlations with an extensive range of immunoglobulin regions, which were quantified by MS-based proteomics. The high-resolution profile of all immunoglobulin regions showed individual-specific differences and commonalities of potential pathophysiological relevance., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

The emergence and global spread of SARS-CoV-2 has resulted in the urgent need for an in-depth understanding of molecular functions of viral proteins and their interactions with the host proteome. Several individual omics studies have extended our knowledge of COVID-19 pathophysiology 1-10 . Integration of such datasets to obtain a holistic view of virus-host interactions and to define the pathogenic properties of SARS-CoV-2 is limited by the heterogeneity of the experimental systems. Here we report a concurrent multi-omics study of SARS-CoV-2 and SARS-CoV. Using state-of-the-art proteomics, we profiled the interactomes of both viruses, as well as their influence on the transcriptome, proteome, ubiquitinome and phosphoproteome of a lung-derived human cell line. Projecting these data onto the global network of cellular interactions revealed crosstalk between the perturbations taking place upon infection with SARS-CoV-2 and SARS-CoV at different levels and enabled identification of distinct and common molecular mechanisms of these closely related coronaviruses. The TGF-β pathway, known for its involvement in tissue fibrosis, was specifically dysregulated by SARS-CoV-2 ORF8 and autophagy was specifically dysregulated by SARS-CoV-2 ORF3. The extensive dataset (available at https://covinet.innatelab.org ) highlights many hotspots that could be targeted by existing drugs and may be used to guide rational design of virus- and host-directed therapies, which we exemplify by identifying inhibitors of kinases and matrix metalloproteases with potent antiviral effects against SARS-CoV-2.

Reduced activity of the insulin/IGF signalling network increases health during ageing in multiple species. Diverse and tissue-specific mechanisms drive the health improvement. Here, we performed tissue-specific transcriptional and proteomic profiling of long-lived Drosophila dilp2-3,5 mutants, and identified tissue-specific regulation of >3600 transcripts and >3700 proteins. Most expression changes were regulated post-transcriptionally in the fat body, and only in mutants infected with the endosymbiotic bacteria, Wolbachia pipientis , which increases their lifespan. Bioinformatic analysis identified reduced co-translational ER targeting of secreted and membrane-associated proteins and increased DNA damage/repair response proteins. Accordingly, age-related DNA damage and genome instability were lower in fat body of the mutant, and overexpression of a minichromosome maintenance protein subunit extended lifespan. Proteins involved in carbohydrate metabolism showed altered expression in the mutant intestine, and gut-specific overexpression of a lysosomal mannosidase increased autophagy, gut homeostasis, and lifespan. These processes are candidates for combatting ageing-related decline in other organisms., Competing Interests: LT, RS, RM, TL, JP, MC, NN, SG, JF, IA, MM, AB, LP No competing interests declared, (© 2021, Tain et al.)

The prevalence of Parkinson's disease (PD) is increasing but the development of novel treatment strategies and therapeutics altering the course of the disease would benefit from specific, sensitive, and non-invasive biomarkers to detect PD early. Here, we describe a scalable and sensitive mass spectrometry (MS)-based proteomic workflow for urinary proteome profiling. Our workflow enabled the reproducible quantification of more than 2,000 proteins in more than 200 urine samples using minimal volumes from two independent patient cohorts. The urinary proteome was significantly different between PD patients and healthy controls, as well as between LRRK2 G2019S carriers and non-carriers in both cohorts. Interestingly, our data revealed lysosomal dysregulation in individuals with the LRRK2 G2019S mutation. When combined with machine learning, the urinary proteome data alone were sufficient to classify mutation status and disease manifestation in mutation carriers remarkably well, identifying VGF, ENPEP, and other PD-associated proteins as the most discriminating features. Taken together, our results validate urinary proteomics as a valuable strategy for biomarker discovery and patient stratification in PD., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV < 1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error (R > 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

Protein ubiquitination is involved in virtually all cellular processes. Enrichment strategies employing antibodies targeting ubiquitin-derived diGly remnants combined with mass spectrometry (MS) have enabled investigations of ubiquitin signaling at a large scale. However, so far the power of data independent acquisition (DIA) with regards to sensitivity in single run analysis and data completeness have not yet been explored. Here, we develop a sensitive workflow combining diGly antibody-based enrichment and optimized Orbitrap-based DIA with comprehensive spectral libraries together containing more than 90,000 diGly peptides. This approach identifies 35,000 diGly peptides in single measurements of proteasome inhibitor-treated cells - double the number and quantitative accuracy of data dependent acquisition. Applied to TNF signaling, the workflow comprehensively captures known sites while adding many novel ones. An in-depth, systems-wide investigation of ubiquitination across the circadian cycle uncovers hundreds of cycling ubiquitination sites and dozens of cycling ubiquitin clusters within individual membrane protein receptors and transporters, highlighting new connections between metabolism and circadian regulation.

Aims: Unimolecular peptides targeting the receptors for glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) (GLP-1/GIP co-agonist) have been shown to outperform each single peptide in the treatment of obesity and cardiometabolic disease in preclinical and clinical trials. By combining physiological treatment endpoints with plasma proteomic profiling (PPP), we aimed to identify biomarkers to advance non-invasive metabolic monitoring of compound treatment success and exploration of ulterior treatment effects on an individual basis., Materials and Methods: We performed metabolic phenotyping along with PPP in body weight-matched male and female diet-induced obese (DIO) mice treated for 21 days with phosphate-buffered saline, single GIP and GLP-1 mono-agonists, or a GLP-1/GIP co-agonist., Results: GLP-1R/GIPR co-agonism improved obesity, glucose intolerance, non-alcoholic fatty liver disease (NAFLD) and dyslipidaemia with superior efficacy in both male and female mice compared with mono-agonist treatments. PPP revealed broader changes of plasma proteins after GLP-1/GIP co-agonist compared with mono-agonist treatments in both sexes, including established and potential novel biomarkers for systemic inflammation, NAFLD and atherosclerosis. Subtle sex-specific differences have been observed in metabolic phenotyping and PPP., Conclusions: We herein show that a recently developed unimolecular GLP-1/GIP co-agonist is more efficient in improving metabolic disease than either mono-agonist in both sexes. PPP led to the identification of a sex-independent protein panel with the potential to monitor non-invasively the treatment efficacies on metabolic function of this clinically advancing GLP-1/GIP co-agonist., (© 2020 The Authors. Diabetes, Obesity and Metabolism published by John Wiley & Sons Ltd.)

Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass analysis. Here, we make use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by inclusion of the ion mobility dimension for both signal extraction and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quantitative accuracy, even from 10 ng sample amounts.

Human skin provides both physical integrity and immunological protection from the external environment using functionally distinct layers, cell types and extracellular matrix. Despite its central role in human health and disease, the constituent proteins of skin have not been systematically characterized. Here, we combine advanced tissue dissection methods, flow cytometry and state-of-the-art proteomics to describe a spatially-resolved quantitative proteomic atlas of human skin. We quantify 10,701 proteins as a function of their spatial location and cellular origin. The resulting protein atlas and our initial data analyses demonstrate the value of proteomics for understanding cell-type diversity within the skin. We describe the quantitative distribution of structural proteins, known and previously undescribed proteins specific to cellular subsets and those with specialized immunological functions such as cytokines and chemokines. We anticipate that this proteomic atlas of human skin will become an essential community resource for basic and translational research ( https://skin.science/ ).

Pathogenic mutations in the Leucine-rich repeat kinase 2 (LRRK2) are the predominant genetic cause of Parkinson's disease (PD). They increase its activity, resulting in augmented Rab10-Thr73 phosphorylation and conversely, LRRK2 inhibition decreases pRab10 levels. Currently, there is no assay to quantify pRab10 levels for drug target engagement or patient stratification. To meet this challenge, we developed an high accuracy and sensitivity targeted mass spectrometry (MS)-based assay for determining Rab10-Thr73 phosphorylation stoichiometry in human samples. It uses synthetic stable isotope-labeled (SIL) analogues for both phosphorylated and nonphosphorylated tryptic peptides surrounding Rab10-Thr73 to directly derive the percentage of Rab10 phosphorylation from attomole amounts of the endogenous phosphopeptide. The SIL and the endogenous phosphopeptides are separately admitted into an Orbitrap analyzer with the appropriate injection times. We test the reproducibility of our assay by determining Rab10-Thr73 phosphorylation stoichiometry in neutrophils of LRRK2 mutation carriers before and after LRRK2 inhibition. Compared with healthy controls, the PD predisposing mutation carriers LRRK2 G2019S and VPS35 D620N display 1.9-fold and 3.7-fold increased pRab10 levels, respectively. Our generic MS-based assay further establishes the relevance of pRab10 as a prognostic PD marker and is a powerful tool for determining LRRK2 inhibitor efficacy and for stratifying PD patients for LRRK2 inhibitor treatment., Competing Interests: Conflict of interest—The authors declare that they have no conflicts of interest with the contents of this article., (© 2020 Karayel et al.)

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported 1 , advances in mass-spectrometry-based proteomics 2 have enabled increasingly comprehensive identification and quantification of the human proteome 3-6 . However, there have been few comparisons across species 7,8 , in stark contrast with genomics initiatives 9 . Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides from Bacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org.

Neurodegenerative diseases are a growing burden, and there is an urgent need for better biomarkers for diagnosis, prognosis, and treatment efficacy. Structural and functional brain alterations are reflected in the protein composition of cerebrospinal fluid (CSF). Alzheimer's disease (AD) patients have higher CSF levels of tau, but we lack knowledge of systems-wide changes of CSF protein levels that accompany AD. Here, we present a highly reproducible mass spectrometry (MS)-based proteomics workflow for the in-depth analysis of CSF from minimal sample amounts. From three independent studies (197 individuals), we characterize differences in proteins by AD status (> 1,000 proteins, CV < 20%). Proteins with previous links to neurodegeneration such as tau, SOD1, and PARK7 differed most strongly by AD status, providing strong positive controls for our approach. CSF proteome changes in Alzheimer's disease prove to be widespread and often correlated with tau concentrations. Our unbiased screen also reveals a consistent glycolytic signature across our cohorts and a recent study. Machine learning suggests clinical utility of this proteomic signature., (© 2020 The Authors. Published under the terms of the CC BY 4.0 license.)

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis, and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Herein we describe a MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from histopathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including low-input cancer tissue isolated by laser microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent acquisition (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total workup took less than a day. We observed a moderate trend towards lower protein identification in long-term stored samples (>15 years), but clustering into distinct proteomic subtypes was independent of archival time. Our results underscore the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and they prove the feasibility of analyzing large tissue cohorts in a robust, timely, and streamlined manner. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland., (© 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.)

The inflammatory functions of the cytokine tumor necrosis factor (TNF) rely on its ability to induce cytokine production and to induce cell death. Caspase-dependent and caspase-independent pathways-apoptosis and necroptosis, respectively-regulate immunogenicity by the release of distinct sets of cellular proteins. To obtain an unbiased, systems-level understanding of this important process, we here applied mass spectrometry-based proteomics to dissect protein release during apoptosis and necroptosis. We report hundreds of proteins released from human myeloid cells in time course experiments. Both cell death types induce receptor shedding, but only apoptotic cells released nucleosome components. Conversely, necroptotic cells release lysosomal components by activating lysosomal exocytosis at early stages of necroptosis-induced membrane permeabilization and show reduced release of conventionally secreted cytokines., Competing Interests: Declaration of Interests The authors declare no competing interests., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

The mosaic distribution of cytochrome c oxidase + (COX + ) and COX - muscle fibers in mitochondrial disorders allows the sampling of fibers with compensated and decompensated mitochondrial function from the same individual. We apply laser capture microdissection to excise individual COX + and COX - fibers from the biopsies of mitochondrial myopathy patients. Using mass spectrometry-based proteomics, we quantify >4,000 proteins per patient. While COX + fibers show a higher expression of respiratory chain components, COX - fibers display protean adaptive responses, including upregulation of mitochondrial ribosomes, translation proteins, and chaperones. Upregulated proteins include C1QBP, required for mitoribosome formation and protein synthesis, and STOML2, which organizes cardiolipin-enriched microdomains and the assembly of respiratory supercomplexes. Factoring in fast/slow fiber type, COX - slow fibers show a compensatory upregulation of beta-oxidation, the AAA + protease AFG3L1, and the OPA1-dependent cristae remodeling program. These findings reveal compensatory mechanisms in muscle fibers struggling with energy shortage and metabolic stress., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Plasma and serum are rich sources of information regarding an individual's health state, and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)-based proteomics now allows highly specific and quantitative readout of the plasma proteome. Here, we employ Plasma Proteome Profiling to define quality marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma, and whole blood of 20 individuals (> 6,000 proteins), and compare serum and plasma proteomes. Based on spike-in experiments, we determine sample quality-associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource ( www.plasmaproteomeprofiling.org) to assess overall sample-related bias in clinical studies and to prevent costly miss-assignment of biomarker candidates., (© 2019 The Authors. Published under the terms of the CC BY 4.0 license.)

Neurons have adapted mechanisms to traffic RNA and protein into distant dendritic and axonal arbors. Taking a biochemical approach, we reveal that forebrain synaptic transcript accumulation shows overwhelmingly daily rhythms, with two-thirds of synaptic transcripts showing time-of-day-dependent abundance independent of oscillations in the soma. These transcripts formed two sharp temporal and functional clusters, with transcripts preceding dawn related to metabolism and translation and those anticipating dusk related to synaptic transmission. Characterization of the synaptic proteome around the clock demonstrates the functional relevance of temporal gating for synaptic processes and energy homeostasis. Unexpectedly, sleep deprivation completely abolished proteome but not transcript oscillations. Altogether, the emerging picture is one of a circadian anticipation of messenger RNA needs in the synapse followed by translation as demanded by sleep-wake cycles., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Non-alcoholic fatty liver disease (NAFLD) affects 25% of the population and can progress to cirrhosis with limited treatment options. As the liver secretes most of the blood plasma proteins, liver disease may affect the plasma proteome. Plasma proteome profiling of 48 patients with and without cirrhosis or NAFLD revealed six statistically significantly changing proteins (ALDOB, APOM, LGALS3BP, PIGR, VTN, and AFM), two of which are already linked to liver disease. Polymeric immunoglobulin receptor (PIGR) was significantly elevated in both cohorts by 170% in NAFLD and 298% in cirrhosis and was further validated in mouse models. Furthermore, a global correlation map of clinical and proteomic data strongly associated DPP4, ANPEP, TGFBI, PIGR, and APOE with NAFLD and cirrhosis. The prominent diabetic drug target DPP4 is an aminopeptidase like ANPEP, ENPEP, and LAP3, all of which are up-regulated in the human or mouse data. Furthermore, ANPEP and TGFBI have potential roles in extracellular matrix remodeling in fibrosis. Thus, plasma proteome profiling can identify potential biomarkers and drug targets in liver disease., (© 2019 The Authors. Published under the terms of the CC BY 4.0 license.)

In bottom-up proteomics, peptides are separated by liquid chromatography with elution peak widths in the range of seconds, whereas mass spectra are acquired in about 100 microseconds with time-of-flight (TOF) instruments. This allows adding ion mobility as a third dimension of separation. Among several formats, trapped ion mobility spectrometry (TIMS) is attractive because of its small size, low voltage requirements and high efficiency of ion utilization. We have recently demonstrated a scan mode termed parallel accumulation - serial fragmentation (PASEF), which multiplies the sequencing speed without any loss in sensitivity (Meier et al. , PMID: 26538118). Here we introduce the timsTOF Pro instrument, which optimally implements online PASEF. It features an orthogonal ion path into the ion mobility device, limiting the amount of debris entering the instrument and making it very robust in daily operation. We investigate different precursor selection schemes for shotgun proteomics to optimally allocate in excess of 100 fragmentation events per second. More than 600,000 fragmentation spectra in standard 120 min LC runs are achievable, which can be used for near exhaustive precursor selection in complex mixtures or accumulating the signal of weak precursors. In 120 min single runs of HeLa digest, MaxQuant identified more than 6,000 proteins without matching to a library and with high quantitative reproducibility (R > 0.97). Online PASEF achieves a remarkable sensitivity with more than 2,500 proteins identified in 30 min runs of only 10 ng HeLa digest. We also show that highly reproducible collisional cross sections can be acquired on a large scale (R > 0.99). PASEF on the timsTOF Pro is a valuable addition to the technological toolbox in proteomics, with a number of unique operating modes that are only beginning to be explored., (© 2018 Meier et al.)

Purpose: Bone is the most predominant site of distant metastasis in prostate cancer, and patients have limited therapeutic options at this stage. Experimental Design: We performed a system-wide quantitative proteomic analysis of bone metastatic prostate tumors from 22 patients operated to relieve spinal cord compression. At the time of surgery, most patients had relapsed after androgen-deprivation therapy, while 5 were previously untreated. An extended cohort of prostate cancer bone metastases ( n = 65) was used for immunohistochemical validation. Results: On average, 5,067 proteins were identified and quantified per tumor. Compared with primary tumors ( n = 26), bone metastases were more heterogeneous and showed increased levels of proteins involved in cell-cycle progression, DNA damage response, RNA processing, and fatty acid β-oxidation; and reduced levels of proteins were related to cell adhesion and carbohydrate metabolism. Within bone metastases, we identified two phenotypic subgroups: BM1, expressing higher levels of AR canonical targets, and mitochondrial and Golgi apparatus resident proteins; and BM2, with increased expression of proliferation and DNA repair-related proteins. The two subgroups, validated by the inverse correlation between MCM3 and prostate specific antigen immunoreactivity, were related to disease prognosis, suggesting that this molecular heterogeneity should be considered when developing personalized therapies. Conclusions: This work is the first system-wide quantitative characterization of the proteome of prostate cancer bone metastases and a valuable resource for understanding the etiology of prostate cancer progression. Clin Cancer Res; 24(21); 5433-44. ©2018 AACR ., (©2018 American Association for Cancer Research.)

Mass spectrometry has transformed the field of cell signaling by enabling global studies of dynamic protein phosphorylation ('phosphoproteomics'). Recent developments are enabling increasingly sophisticated phosphoproteomics studies, but practical challenges remain. The EasyPhos workflow addresses these and is sufficiently streamlined to enable the analysis of hundreds of phosphoproteomes at a depth of >10,000 quantified phosphorylation sites. Here we present a detailed and updated workflow that further ensures high performance in sample-limited conditions while also reducing sample preparation time. By eliminating protein precipitation steps and performing the entire protocol, including digestion, in a single 96-well plate, we now greatly minimize opportunities for sample loss and variability. This results in very high reproducibility and a small sample size requirement (≤200 μg of protein starting material). After cell culture or tissue collection, the protocol takes 1 d, whereas mass spectrometry measurements require ~1 h per sample. Applied to glioblastoma cells acutely treated with epidermal growth factor (EGF), EasyPhos quantified 20,132 distinct phosphopeptides from 200 μg of protein in less than 1 d of measurement time, revealing thousands of EGF-regulated phosphorylation events.

Rationale: Analyzing the molecular heterogeneity of different forms of organ fibrosis may reveal common and specific factors and thus identify potential future therapeutic targets., Objectives: We sought to use proteome-wide profiling of human tissue fibrosis to (1) identify common and specific signatures across end-stage interstitial lung disease (ILD) cases, (2) characterize ILD subgroups in an unbiased fashion, and (3) identify common and specific features of lung and skin fibrosis., Methods: We collected samples of ILD tissue (n = 45) and healthy donor control samples (n = 10), as well as fibrotic skin lesions from localized scleroderma and uninvolved skin (n = 6). Samples were profiled by quantitative label-free mass spectrometry, Western blotting, or confocal imaging., Measurements and Main Results: We determined the abundance of more than 7,900 proteins and stratified these proteins according to their detergent solubility profiles. Common protein regulations across all ILD cases, as well as distinct ILD subsets, were observed. Proteomic comparison of lung and skin fibrosis identified a common upregulation of marginal zone B- and B1-cell-specific protein (MZB1), the expression of which identified MZB1 + /CD38 + /CD138 + /CD27 + /CD45 - /CD20 - plasma B cells in fibrotic lung and skin tissue. MZB1 levels correlated positively with tissue IgG and negatively with diffusing capacity of the lung for carbon monoxide., Conclusions: Despite the presumably high molecular and cellular heterogeneity of ILD, common protein regulations are observed, even across organ boundaries. The surprisingly high prevalence of MZB1-positive plasma B cells in tissue fibrosis warrants future investigations regarding the causative role of antibody-mediated autoimmunity in idiopathic cases of organ fibrosis, such as idiopathic pulmonary fibrosis.

The heart is a central human organ and its diseases are the leading cause of death worldwide, but an in-depth knowledge of the identity and quantity of its constituent proteins is still lacking. Here, we determine the healthy human heart proteome by measuring 16 anatomical regions and three major cardiac cell types by high-resolution mass spectrometry-based proteomics. From low microgram sample amounts, we quantify over 10,700 proteins in this high dynamic range tissue. We combine copy numbers per cell with protein organellar assignments to build a model of the heart proteome at the subcellular level. Analysis of cardiac fibroblasts identifies cellular receptors as potential cell surface markers. Application of our heart map to atrial fibrillation reveals individually distinct mitochondrial dysfunctions. The heart map is available at maxqb.biochem.mpg.de as a resource for future analyses of normal heart function and disease.

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)-based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous "triangular strategies" aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a "rectangular" plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision-making, and we discuss some first steps in this direction., (© 2017 The Authors. Published under the terms of the CC BY 4.0 license.)

The immune system is unique in its dynamic interplay between numerous cell types. However, a system-wide view of how immune cells communicate to protect against disease has not yet been established. We applied high-resolution mass-spectrometry-based proteomics to characterize 28 primary human hematopoietic cell populations in steady and activated states at a depth of >10,000 proteins in total. Protein copy numbers revealed a specialization of immune cells for ligand and receptor expression, thereby connecting distinct immune functions. By integrating total and secreted proteomes, we discovered fundamental intercellular communication structures and previously unknown connections between cell types. Our publicly accessible (http://www.immprot.org/) proteomic resource provides a framework for the orchestration of cellular interplay and a reference for altered communication associated with pathology.

Insulin-secreting beta cells play an essential role in maintaining physiological blood glucose levels, and their dysfunction leads to the development of diabetes. To elucidate the signalling events regulating insulin secretion, we applied a recently developed phosphoproteomics workflow. We quantified the time-resolved phosphoproteome of murine pancreatic cells following their exposure to glucose and in combination with small molecule compounds that promote insulin secretion. The quantitative phosphoproteome of 30,000 sites clustered into three main groups in concordance with the modulation of the three key kinases: PKA, PKC and CK2A. A high-resolution time course revealed key novel regulatory sites, revealing the importance of methyltransferase DNMT3A phosphorylation in the glucose response. Remarkably a significant proportion of these novel regulatory sites is significantly downregulated in diabetic islets. Control of insulin secretion is embedded in an unexpectedly broad and complex range of cellular functions, which are perturbed by drugs in multiple ways., Competing Interests: This study was done as a collaboration between the Max-Planck Institute of Biochemistry and Sanofi-Aventis Deutschland (SAD), who provided a grant for this purpose to MPIB. M.M, A.S, T.K & M.S. are employed at Sanofi.

Numerous biological processes are concurrently and coordinately active in every living cell. Each of them encompasses synthetic, catalytic and regulatory functions that are, almost always, carried out by proteins organized further into higher-order structures and networks. For decades, the structures and functions of selected proteins have been studied using biochemical and biophysical methods. However, the properties and behaviour of the proteome as an integrated system have largely remained elusive. Powerful mass-spectrometry-based technologies now provide unprecedented insights into the composition, structure, function and control of the proteome, shedding light on complex biological processes and phenotypes.

Protein glycation is a concentration-dependent nonenzymatic reaction of reducing sugars with amine groups of proteins to form early as well as advanced glycation (end-) products (AGEs). Glycation is a highly disease-relevant modification but is typically only studied on a few blood proteins. To complement our blood proteomics studies in diabetics, we here investigate protein glycation by higher energy collisional dissociation (HCD) fragmentation on Orbitrap mass spectrometers. We established parameters to most efficiently fragment and identify early glycation products on in vitro glycated model proteins. Retaining standard collision energies does not degrade performance if the most dominant neutral loss of H6O3 is included into the database search strategy. Glycation analysis of the entire HeLa proteome revealed an unexpected intracellular preponderance for arginine over lysine modification in early and advanced glycation (end-) products. Single-run analysis from 1 μL of undepleted and unenriched blood plasma identified 101 early glycation sites as well as numerous AGE sites on diverse plasma proteins. We conclude that HCD fragmentation is well-suited for analyzing glycated peptides and that the diabetic status of patients can be directly diagnosed from single-run plasma proteomics measurements.

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., (Copyright © 2015 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Background: The oral cavity is home to one of the most diverse microbial communities of the human body and a major entry portal for pathogens. Its homeostasis is maintained by saliva, which fulfills key functions including lubrication of food, pre-digestion, and bacterial defense. Consequently, disruptions in saliva secretion and changes in the oral microbiome contribute to conditions such as tooth decay and respiratory tract infections. Here we set out to quantitatively map the saliva proteome in great depth with a rapid and in-depth mass spectrometry-based proteomics workflow., Methods: We used recent improvements in mass spectrometry (MS)-based proteomics to develop a rapid workflow for mapping the saliva proteome quantitatively and at great depth. Standard clinical cotton swabs were used to collect saliva form eight healthy individuals at two different time points, allowing us to study inter-individual differences and interday changes of the saliva proteome. To accurately identify microbial proteins, we developed a method called "split by taxonomy id" that prevents peptides shared by humans and bacteria or between different bacterial phyla to contribute to protein identification., Results: Microgram protein amounts retrieved from cotton swabs resulted in more than 3700 quantified human proteins in 100-min gradients or 5500 proteins after simple fractionation. Remarkably, our measurements also quantified more than 2000 microbial proteins from 50 bacterial genera. Co-analysis of the proteomics results with next-generation sequencing data from the Human Microbiome Project as well as a comparison to MALDI-TOF mass spectrometry on microbial cultures revealed strong agreement. The oral microbiome differs between individuals and changes drastically upon eating and tooth brushing., Conclusion: Rapid shotgun and robust technology can now simultaneously characterize the human and microbiome contributions to the proteome of a body fluid and is therefore a valuable complement to genomic studies. This opens new frontiers for the study of host-pathogen interactions and clinical saliva diagnostics.

Proteins in the circulatory system mirror an individual's physiology. In daily clinical practice, protein levels are generally determined using single-protein immunoassays. High-throughput, quantitative analysis using mass-spectrometry-based proteomics of blood, plasma, and serum would be advantageous but is challenging because of the high dynamic range of protein abundances. Here, we introduce a rapid and robust "plasma proteome profiling" pipeline. This single-run shotgun proteomic workflow does not require protein depletion and enables quantitative analysis of hundreds of plasma proteomes from 1 μl single finger pricks with 20 min gradients. The apolipoprotein family, inflammatory markers such as C-reactive protein, gender-related proteins, and >40 FDA-approved biomarkers are reproducibly quantified (CV <20% with label-free quantification). Furthermore, we functionally interpret a 1,000-protein, quantitative plasma proteome obtained by simple peptide pre-fractionation. Plasma proteome profiling delivers an informative portrait of a person's health state, and we envision its large-scale use in biomedicine., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)