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Abstract Motoneurons critically depend on precise spatial and temporal control of translation for axon growth and the establishment and maintenance of neuromuscular connections. While defects in local translation have been implicated in the pathogenesis of motoneuron disorders, little is known about the mechanisms regulating axonal protein synthesis. Here, we report that motoneurons derived from Hnrnpr knockout mice show reduced axon growth accompanied by lowered synthesis of cytoskeletal and synaptic components in axons. Mutant mice display denervated neuromuscular junctions and impaired motor behavior. In axons, hnRNP R is a component of translation initiation complexes and, through interaction with O-linked β-N-acetylglucosamine (O-GlcNAc) transferase (Ogt), modulates O-GlcNAcylation of eIF4G. Restoring axonal O-GlcNAc levels rescued local protein synthesis and axon growth defects of hnRNP R knockout motoneurons. Together, these findings demonstrate a function of hnRNP R in controlling the local production of key factors required for axon growth and formation of neuromuscular innervations.

Abstract 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.

Abstract Skeletal muscle undergoes atrophy and loss of force during long space missions, when astronauts are persistently exposed to altered gravity and increased ionizing radiation. We previously carried out mass spectrometry-based proteomics from skeletal muscle biopsies of two astronauts, taken before and after a mission on the International Space Station. The experiments were part of an effort to find similarities between spaceflight and bed rest, a ground-based model of unloading, focused on proteins located at the costameres. We here extend the data analysis of the astronaut dataset and show compartment-resolved changes in the mitochondrial proteome, remodeling of the extracellular matrix and of the antioxidant response. The astronauts differed in their level of onboard physical exercise, which correlated with their respective preservation of muscle mass and force at landing in previous analyses. We show that the mitochondrial proteome downregulation during spaceflight, particularly the inner membrane and matrix, was dramatic for both astronauts. The expression of autophagy regulators and reactive oxygen species scavengers, however, showed partially opposite expression trends in the two subjects, possibly correlating with their level of onboard exercise. As mitochondria are primarily affected in many different tissues during spaceflight, we hypothesize that reactive oxygen species (ROS) rather than mechanical unloading per se could be the primary cause of skeletal muscle mitochondrial damage in space. Onboard physical exercise might have a strong direct effect on the prevention of muscle atrophy through mechanotransduction and a subsidiary effect on mitochondrial quality control, possibly through upregulation of autophagy and anti-oxidant responses.

Abstract Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.

Summary: Impaired sleep is a common aspect of aging and often precedes the onset of Alzheimer’s disease. Here, we compare the effects of sleep deprivation in young wild-type mice and their APP/PS1 littermates, a murine model of Alzheimer’s disease. After 7 h of sleep deprivation, both genotypes exhibit an increase in EEG slow-wave activity. However, only the wild-type mice demonstrate an increase in the power of infraslow norepinephrine oscillations, which are characteristic of healthy non-rapid eye movement sleep. Notably, the APP/PS1 mice fail to enhance norepinephrine oscillations 24 h after sleep deprivation, coinciding with an accumulation of cerebral amyloid-β protein. Proteome analysis of cerebrospinal fluid and extracellular fluid further supports these findings by showing altered protein clearance in APP/PS1 mice. We propose that the suppression of infraslow norepinephrine oscillations following sleep deprivation contributes to increased vulnerability to sleep loss and heightens the risk of developing amyloid pathology in early stages of Alzheimer’s disease.

Summary: Colorectal adenomas (CRAs) are potential precursor lesions to adenocarcinomas, currently classified by morphological features. We aimed to establish a molecular feature-based risk allocation framework toward improved patient stratification. Deep visual proteomics (DVP) is an approach that combines image-based artificial intelligence with automated microdissection and ultra-high sensitive mass spectrometry. Here, we used DVP on formalin-fixed, paraffin-embedded (FFPE) CRA tissues from nine male patients, immunohistologically stained for caudal-type homeobox 2 (CDX2), a protein implicated in colorectal cancer, enabling the characterization of cellular heterogeneity within distinct tissue regions and across patients. DVP identified DMBT1, MARCKS, and CD99 as protein markers linked to recurrence, suggesting their potential for risk assessment. It also detected a metabolic shift to anaerobic glycolysis in cells with high CDX2 expression. Our findings underscore the potential of spatial proteomics to refine early stage detection and contribute to personalized patient management strategies and provided novel insights into metabolic reprogramming.

Abstract In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Abstract Pharmacologic targeting of chromatin-associated protein complexes has shown significant responses in KMT2A-rearranged (KMT2A-r) acute myeloid leukemia (AML) but resistance frequently develops to single agents. This points to a need for therapeutic combinations that target multiple mechanisms. To enhance our understanding of functional dependencies in KMT2A-r AML, we have used a proteomic approach to identify the catalytic immunoproteasome subunit PSMB8 as a specific vulnerability. Genetic and pharmacologic inactivation of PSMB8 results in impaired proliferation of murine and human leukemic cells while normal hematopoietic cells remain unaffected. Disruption of immunoproteasome function drives an increase in transcription factor BASP1 which in turn represses KMT2A-fusion protein target genes. Pharmacologic targeting of PSMB8 improves efficacy of Menin-inhibitors, synergistically reduces leukemia in human xenografts and shows preserved activity against Menin-inhibitor resistance mutations. This identifies and validates a cell-intrinsic mechanism whereby selective disruption of proteostasis results in altered transcription factor abundance and repression of oncogene-specific transcriptional networks. These data demonstrate that the immunoproteasome is a relevant therapeutic target in AML and that targeting the immunoproteasome in combination with Menin-inhibition could be a novel approach for treatment of KMT2A-r AML.

Objective: Skeletal muscle plasticity and remodeling are critical for adapting tissue function to use, disuse, and regeneration. The aim of this study was to identify genes and molecular pathways that regulate the transition from atrophy to compensatory hypertrophy or recovery from injury. Here, we have used a mouse model of hindlimb unloading and reloading, which causes skeletal muscle atrophy, and compensatory regeneration and hypertrophy, respectively. Methods: We analyzed mouse skeletal muscle at the transition from hindlimb unloading to reloading for changes in transcriptome and extracellular fluid proteome. We then used qRT-PCR, immunohistochemistry, and bulk and single-cell RNA sequencing data to determine Mustn1 gene and protein expression, including changes in gene expression in mouse and human skeletal muscle with different challenges such as exercise and muscle injury. We generated Mustn1-deficient genetic mouse models and characterized them in vivo and ex vivo with regard to muscle function and whole-body metabolism. We isolated smooth muscle cells and functionally characterized them, and performed transcriptomics and proteomics analysis of skeletal muscle and aorta of Mustn1-deficient mice. Results: We show that Mustn1 (Musculoskeletal embryonic nuclear protein 1, also known as Mustang) is highly expressed in skeletal muscle during the early stages of hindlimb reloading. Mustn1 expression is transiently elevated in mouse and human skeletal muscle in response to intense exercise, resistance exercise, or injury. We find that Mustn1 expression is highest in smooth muscle-rich tissues, followed by skeletal muscle fibers. Muscle from heterozygous Mustn1-deficient mice exhibit differences in gene expression related to extracellular matrix and cell adhesion, compared to wild-type littermates. Mustn1-deficient mice have normal muscle and aorta function and whole-body glucose metabolism. We show that Mustn1 is secreted from smooth muscle cells, and that it is present in arterioles of the muscle microvasculature and in muscle extracellular fluid, particularly during the hindlimb reloading phase. Proteomics analysis of muscle from Mustn1-deficient mice confirms differences in extracellular matrix composition, and female mice display higher collagen content after chemically induced muscle injury compared to wild-type littermates. Conclusions: We show that, in addition to its previously reported intracellular localization, Mustn1 is a microprotein secreted from smooth muscle cells into the muscle extracellular space. We explore its role in muscle ECM deposition and remodeling in homeostasis and upon muscle injury. The role of Mustn1 in fibrosis and immune infiltration upon muscle injury and dystrophies remains to be investigated, as does its potential for therapeutic interventions.

Summary: Endocrine cells employ regulated exocytosis of secretory granules to secrete hormones and neurotransmitters. Secretory granule exocytosis depends on spatiotemporal variables such as proximity to the plasma membrane and age, with newly generated granules being preferentially released. Despite recent advances, we lack a comprehensive view of the molecular composition of insulin granules and associated changes over their lifetime. Here, we report a strategy for the purification of insulin secretory granules of distinct age from insulinoma INS-1 cells. Tagging the granule-resident protein phogrin with a cleavable CLIP tag, we obtain intact fractions of age-distinct granules for proteomic and lipidomic analyses. We find that the lipid composition changes over time, along with the physical properties of the membrane, and that kinesin-1 heavy chain (KIF5b) as well as Ras-related protein 3a (RAB3a) associate preferentially with younger granules. Further, we identify the Rho GTPase-activating protein (ARHGAP1) as a cytosolic factor associated with insulin granules.

Abstract Sporadic Parkinson’s Disease (sPD) is a progressive neurodegenerative disorder caused by multiple genetic and environmental factors. Mitochondrial dysfunction is one contributing factor, but its role at different stages of disease progression is not fully understood. Here, we showed that neural precursor cells and dopaminergic neurons derived from induced pluripotent stem cells (hiPSCs) from sPD patients exhibited a hypometabolism. Further analysis based on transcriptomics, proteomics, and metabolomics identified the citric acid cycle, specifically the α-ketoglutarate dehydrogenase complex (OGDHC), as bottleneck in sPD metabolism. A follow-up study of the patients approximately 10 years after initial biopsy demonstrated a correlation between OGDHC activity in our cellular model and the disease progression. In addition, the alterations in cellular metabolism observed in our cellular model were restored by interfering with the enhanced SHH signal transduction in sPD. Thus, inhibiting overactive SHH signaling may have potential as neuroprotective therapy during early stages of sPD.

Abstract Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

Abstract SARS‐CoV‐2 may directly and indirectly damage lung tissue and other host organs, but there are few system‐wide, untargeted studies of these effects on the human body. Here, we developed a parallelized mass spectrometry (MS) proteomics workflow enabling the rapid, quantitative analysis of hundreds of virus‐infected FFPE tissues. The first layer of response to SARS‐CoV‐2 in all tissues was dominated by circulating inflammatory molecules. Beyond systemic inflammation, we differentiated between systemic and true tissue‐specific effects to reflect distinct COVID‐19‐associated damage patterns. Proteomic changes in the lungs resembled those of diffuse alveolar damage (DAD) in non‐COVID‐19 patients. Extensive organ‐specific changes were also evident in the kidneys, liver, and lymphatic and vascular systems. Secondary inflammatory effects in the brain were related to rearrangements in neurotransmitter receptors and myelin degradation. These MS‐proteomics‐derived results contribute substantially to our understanding of COVID‐19 pathomechanisms and suggest strategies for organ‐specific therapeutic interventions.

Abstract The neuronal RNA-binding protein Ptbp2 regulates neuronal differentiation by modulating alternative splicing programs in the nucleus. Such programs contribute to axonogenesis by adjusting the levels of protein isoforms involved in axon growth and branching. While its functions in alternative splicing have been described in detail, cytosolic roles of Ptbp2 for axon growth have remained elusive. Here, we show that Ptbp2 is located in the cytosol including axons and growth cones of motoneurons, and that depletion of cytosolic Ptbp2 affects axon growth. We identify Ptbp2 as a major interactor of the 3’ UTR of Hnrnpr mRNA encoding the RNA-binding protein hnRNP R. Axonal localization of Hnrnpr mRNA and local synthesis of hnRNP R protein are strongly reduced when Ptbp2 is depleted, leading to defective axon growth. Ptbp2 regulates hnRNP R translation by mediating the association of Hnrnpr with ribosomes in a manner dependent on the translation factor eIF5A2. Our data thus suggest a mechanism whereby cytosolic Ptbp2 modulates axon growth by fine-tuning the mRNA transport and local synthesis of an RNA-binding protein.

Abstract Background Acute leukemias represent deadly malignancies that require better treatment. As a challenge, treatment is counteracted by a microenvironment protecting dormant leukemia stem cells. Methods To identify responsible surface proteins, we performed deep proteome profiling on minute numbers of dormant patient-derived xenograft (PDX) leukemia stem cells isolated from mice. Candidates were functionally screened by establishing a comprehensive CRISPR‒Cas9 pipeline in PDX models in vivo. Results A disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) was identified as an essential vulnerability required for the survival and growth of different types of acute leukemias in vivo, and reconstitution assays in PDX models confirmed the relevance of its sheddase activity. Of translational importance, molecular or pharmacological targeting of ADAM10 reduced PDX leukemia burden, cell homing to the murine bone marrow and stem cell frequency, and increased leukemia response to conventional chemotherapy in vivo. Conclusions These findings identify ADAM10 as an attractive therapeutic target for the future treatment of acute leukemias.

Graphical Abstract In this Editorial, our Chief Editor and members of our Advisory Editorial Board discuss recent breakthroughs, current challenges, and emerging opportunities in single‐cell biology and share their vision of “where the field is headed.”

Introduction Human albumin is used in the treatment of complications of cirrhosis. However, the use of long-term human albumin administration is costly and resource demanding for both patients and healthcare systems. A precision medicine approach with biomarkers to predict human albumin treatment response, so-called predictive biomarkers, could make this a viable treatment option in patients with cirrhosis and ascites.Methods and analysis ALB-TRIAL is a multinational, double-blind, placebo-controlled randomised controlled trial. We aim to validate a predictive biomarker, consisting of a panel of circulating metabolites, to predict the treatment response to human albumin in patients with cirrhosis and ascites. All enrolled patients are stratified into a high-expected or low-expected effect stratum of human albumin based on the biomarker outcome. After stratification, patients in each group are randomised into either active treatment (20% human albumin) or corresponding placebo (0.9% NaCl) every 10th day for 6 months. The primary outcome is the cumulative number of liver-related events (composite of decompensation episodes, transjugular intrahepatic shunt insertion, liver transplantation and death). Key secondary outcomes include time-to-event analysis of primary outcome components, an analysis of the total healthcare burden and a health economic analysis.Ethics and dissemination The trial obtained ethical and regulatory approval in Denmark, Germany, the Netherlands, Belgium, Hungary and Spain through the Clinical Trials Information System (CTIS) from 13 February 2023, while UK approvals from the Health Regulatory Authority, Medicines and Healthcare products Regulatory Agency and Research Ethics Committee are pending. Findings will be published in peer-reviewed journals, presented at conferences, communicated to relevant stakeholders and in the public registry of CTIS, following trial completion.Trial registration number NCT05056220 EU CT: 2022-501006-34-01

With the advent of robust and high-throughput mass spectrometric technologies and bioinformatics tools to analyze large data sets, proteomics has penetrated broadly into basic and translational life sciences research. More than 95% of FDA-approved drugs currently target proteins, and most diagnostic tests are protein-based. The introduction of proteomics to the clinic, for instance to guide patient stratification and treatment, is already ongoing. Importantly, ethical challenges come with this success, which must also be adequately addressed by the proteomics and medical communities. Consortium members of the H2020 European Union-funded proteomics initiative: European Proteomics Infrastructure Consortium-providing access (EPIC-XS) met at the Core Technologies for Life Sciences (CTLS) conference to discuss the emerging role and implementation of proteomics in the clinic. The discussion, involving leaders in the field, focused on the current status, related challenges, and future efforts required to make proteomics a more mainstream technology for translational and clinical research. Here we report on that discussion and provide an expert update concerning the feasibility of clinical proteomics, the ethical implications of generating and analyzing large-scale proteomics clinical data, and recommendations to ensure both ethical and effective implementation in real-world applications.

Abstract Single‐cell proteomics aims to characterize biological function and heterogeneity at the level of proteins in an unbiased manner. It is currently limited in proteomic depth, throughput, and robustness, which we address here by a streamlined multiplexed workflow using data‐independent acquisition (mDIA). We demonstrate automated and complete dimethyl labeling of bulk or single‐cell samples, without losing proteomic depth. Lys‐N digestion enables five‐plex quantification at MS1 and MS2 level. Because the multiplexed channels are quantitatively isolated from each other, mDIA accommodates a reference channel that does not interfere with the target channels. Our algorithm RefQuant takes advantage of this and confidently quantifies twice as many proteins per single cell compared to our previous work (Brunner et al, PMID 35226415), while our workflow currently allows routine analysis of 80 single cells per day. Finally, we combined mDIA with spatial proteomics to increase the throughput of Deep Visual Proteomics seven‐fold for microdissection and four‐fold for MS analysis. Applying this to primary cutaneous melanoma, we discovered proteomic signatures of cells within distinct tumor microenvironments, showcasing its potential for precision oncology.

Abstract Multi‐omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non‐antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi‐omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

Abstract 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.

How the histone variant H2A.Z controls cell fate remains unclear. Here, the authors reveal that the H2A.Z interacting partner HMG20A plays a key role in regulating transcription during early head and heart development.

Nagao et al. show that the insulin receptor can mediate receptor-dependent, but ligand- and tyrosine kinase-independent, events. These are associated with regulation of extracellular matrix, cell cycle, ATM signaling, senescence and apoptosis.

Deep learning (DL) has been frequently used in mass spectrometry-based proteomics but there is still a lot of potential. Here, the authors develop a framework that enables building DL models to predict arbitrary peptide properties with only a few lines of code.

Reusswig et al. use engineered systems to force DNA replication in the G1 phase of the cell cycle. This unscheduled G1 replication shows hallmarks of S phase replication, but leads to over-replication and DNA breaks from replication collisions.

The GID E3 ligase regulates glucose-induced degradation in yeast, and key physiology. This study unveils E3 ligase regulation by reshaping the substrate binding site, blocking substrate access to ubiquitination active sites, and a Cage-like assembly.

With the advent of robust and high-throughput mass spectrometric technologies and bioinformatics tools to analyze large data sets, proteomics has penetrated broadly into basic and translational life sciences research. More than 95% of FDA-approved drugs currently target proteins, and most diagnostic tests are protein-based. The introduction of proteomics to the clinic, for instance to guide patient stratification and treatment, is already ongoing. Importantly, ethical challenges come with this success, which must also be adequately addressed by the proteomics and medical communities. Consortium members of the H2020 European Union-funded proteomics initiative: European Proteomics Infrastructure Consortium-providing access (EPIC-XS) met at the Core Technologies for Life Sciences (CTLS) conference to discuss the emerging role and implementation of proteomics in the clinic. The discussion, involving leaders in the field, focused on the current status, related challenges, and future efforts required to make proteomics a more mainstream technology for translational and clinical research. Here we report on that discussion and provide an expert update concerning the feasibility of clinical proteomics, the ethical implications of generating and analyzing large-scale proteomics clinical data, and recommendations to ensure both ethical and effective implementation in real-world applications.

Summary: A major evolution from purely clinical diagnoses to biomarker supported clinical diagnosing has been occurring over the past years in neurology. High-throughput methods, such as next-generation sequencing and mass spectrometry-based proteomics along with improved neuroimaging methods, are accelerating this development. This calls for a consensus framework that is broadly applicable and provides a spot-on overview of the clinical validity of novel biomarkers. We propose a harmonized terminology and a uniform concept that stratifies biomarkers according to clinical context of use and evidence levels, adapted from existing frameworks in oncology with a strong focus on (epi)genetic markers and treatment context. We demonstrate that this framework allows for a consistent assessment of clinical validity across disease entities and that sufficient evidence for many clinical applications of protein biomarkers is lacking. Our framework may help to identify promising biomarker candidates and classify their applications by clinical context, aiming for routine clinical use of (protein) biomarkers in neurology.

Abstract 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 ).

Abstract Acute myeloid leukemia (AML) patients suffer dismal prognosis upon treatment resistance. To study functional heterogeneity of resistance, we generated serially transplantable patient-derived xenograft (PDX) models from one patient with AML and twelve clones thereof, each derived from a single stem cell, as proven by genetic barcoding. Transcriptome and exome sequencing segregated clones according to their origin from relapse one or two. Undetectable for sequencing, multiplex fluorochrome-guided competitive in vivo treatment trials identified a subset of relapse two clones as uniquely resistant to cytarabine treatment. Transcriptional and proteomic profiles obtained from resistant PDX clones and refractory AML patients defined a 16-gene score that was predictive of clinical outcome in a large independent patient cohort. Thus, we identified novel genes related to cytarabine resistance and provide proof of concept that intra-tumor heterogeneity reflects inter-tumor heterogeneity in AML.

N-terminal protein acetylation is required for plant viability. Here the authors show that reducing N-terminal acetylation by NatA leads to an increase in global protein turnover that is facilitated by absent masking of a novel N-degron

Abstract 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.

Insulin and IGF-1 receptors (IR and IGF1R) are highly homologous and share similar signaling systems, but each has a unique physiological role, with IR primarily regulating metabolic homeostasis and IGF1R regulating mitogenic control and growth. Here, we show that replacement of a single amino acid at position 973, just distal to the NPEY motif in the intracellular juxtamembrane region, from leucine, which is highly conserved in IRs, to phenylalanine, the highly conserved homologous residue in IGF1Rs, resulted in decreased IRS-1/PI3K/Akt/mTORC1 signaling and increased Shc/Gab1/MAPK cell cycle signaling. As a result, cells expressing L973F-IR exhibited decreased insulin-induced glucose uptake, increased cell growth, and impaired receptor internalization. Mice with knockin of the L973F-IR showed similar alterations in signaling in vivo, and this led to decreased insulin sensitivity, a modest increase in growth, and decreased weight gain when mice were challenged with a high-fat diet. Thus, leucine-973 in the juxtamembrane region of the IR acts as a crucial residue differentiating IR signaling from IGF1R signaling.

The development of haematopoietic stem cells into mature erythrocytes – erythropoiesis – is a controlled process characterized by cellular reorganization and drastic reshaping of the proteome landscape. Failure of ordered erythropoiesis is associated with anaemias and haematological malignancies. Although the ubiquitin system is a known crucial post-translational regulator in erythropoiesis, how the erythrocyte is reshaped by the ubiquitin system is poorly understood. By measuring the proteomic landscape of in vitro human erythropoiesis models, we found dynamic differential expression of subunits of the CTLH E3 ubiquitin ligase complex that formed maturation stage-dependent assemblies of topologically homologous RANBP9- and RANBP10-CTLH complexes. Moreover, protein abundance of CTLH’s cognate E2 ubiquitin conjugating enzyme UBE2H increased during terminal differentiation, and UBE2H expression depended on catalytically active CTLH E3 complexes. CRISPR-Cas9-mediated inactivation of CTLH E3 assemblies or UBE2H in erythroid progenitors revealed defects, including spontaneous and accelerated erythroid maturation as well as inefficient enucleation. Thus, we propose that dynamic maturation stage-specific changes of UBE2H-CTLH E2-E3 modules control the orderly progression of human erythropoiesis.

Abstract Background Human skeletal muscle is composed of three major fiber types, referred to as type 1, 2A, and 2X fibers. This heterogeneous cellular composition complicates the interpretation of studies based on whole skeletal muscle lysate. A single-fiber proteomics approach is required to obtain a fiber-type resolved quantitative information on skeletal muscle pathophysiology. Methods Single fibers were dissected from vastus lateralis muscle biopsies of young adult males and processed for mass spectrometry-based single-fiber proteomics. We provide and analyze a resource dataset based on relatively pure fibers, containing at least 80% of either MYH7 (marker of slow type 1 fibers), MYH2 (marker of fast 2A fibers), or MYH1 (marker of fast 2X fibers). Results In a dataset of more than 3800 proteins detected by single-fiber proteomics, we selected 404 proteins showing a statistically significant difference among fiber types. We identified numerous type 1 or 2X fiber type–specific protein markers, defined as proteins present at 3-fold or higher levels in these compared to other fiber types. In contrast, we could detect only two 2A-specific protein markers in addition to MYH2. We observed three other major patterns: proteins showing a differential distribution according to the sequence 1 > 2A > 2X or 2X > 2A > 1 and type 2–specific proteins expressed in 2A and 2X fibers at levels 3 times greater than in type 1 fibers. In addition to precisely quantifying known fiber type–specific protein patterns, our study revealed several novel features of fiber type specificity, including the selective enrichment of components of the dystrophin and integrin complexes, as well as microtubular proteins, in type 2X fibers. The fiber type–specific distribution of some selected proteins revealed by proteomics was validated by immunofluorescence analyses with specific antibodies. Conclusion We here show that numerous muscle proteins, including proteins whose function is unknown, are selectively enriched in specific fiber types, pointing to potential implications in muscle pathophysiology. This reinforces the notion that single-fiber proteomics, together with recently developed approaches to single-cell proteomics, will be instrumental to explore and quantify muscle cell heterogeneity.

Many interactions between viral and host proteins are mediated by short peptide motifs. Here, using a phage-based viral peptide library, the authors identify 269 peptide-based interactions for 18 coronaviruses, including an interaction between SARS-CoV-2 N and G3BP1/2 that affects stress granules.

Abstract Alzheimer’s disease (AD) is an unremitting neurodegenerative disorder characterized by cerebral amyloid-β (Aβ) accumulation and gradual decline in cognitive function. Changes in brain energy metabolism arise in the preclinical phase of AD, suggesting an important metabolic component of early AD pathology. Neurons and astrocytes function in close metabolic collaboration, which is essential for the recycling of neurotransmitters in the synapse. However, this crucial metabolic interplay during the early stages of AD development has not been sufficiently investigated. Here, we provide an integrative analysis of cellular metabolism during the early stages of Aβ accumulation in the cerebral cortex and hippocampus of the 5xFAD mouse model of AD. Our electrophysiological examination revealed an increase in spontaneous excitatory signaling in the 5xFAD hippocampus. This hyperactive neuronal phenotype coincided with decreased hippocampal tricarboxylic acid (TCA) cycle metabolism mapped by stable 13C isotope tracing. Particularly, reduced astrocyte TCA cycle activity and decreased glutamine synthesis led to hampered neuronal GABA synthesis in the 5xFAD hippocampus. In contrast, the cerebral cortex of 5xFAD mice displayed an elevated capacity for oxidative glucose metabolism, which may suggest a metabolic compensation in this brain region. We found limited changes when we explored the brain proteome and metabolome of the 5xFAD mice, supporting that the functional metabolic disturbances between neurons and astrocytes are early primary events in AD pathology. In addition, synaptic mitochondrial and glycolytic function was selectively impaired in the 5xFAD hippocampus, whereas non-synaptic mitochondrial function was maintained. These findings were supported by ultrastructural analyses demonstrating disruptions in mitochondrial morphology, particularly in the 5xFAD hippocampus. Collectively, our study reveals complex regional and cell-specific metabolic adaptations in the early stages of amyloid pathology, which may be fundamental for the progressing synaptic dysfunctions in AD.

Abstract Background Poly (ADP)-ribose polymerase (PARP) inhibitors have entered routine clinical practice for the treatment of high-grade serous ovarian cancer (HGSOC), yet the molecular mechanisms underlying treatment response to PARP1 inhibition (PARP1i) are not fully understood. Methods Here, we used unbiased mass spectrometry based proteomics with data-driven protein network analysis to systematically characterize how HGSOC cells respond to PARP1i treatment. Results We found that PARP1i leads to pronounced proteomic changes in a diverse set of cellular processes in HGSOC cancer cells, consistent with transcript changes in an independent perturbation dataset. We interpret decreases in the levels of the pro-proliferative transcription factors SP1 and β-catenin and in growth factor signaling as reflecting the anti-proliferative effect of PARP1i; and the strong activation of pro-survival processes NF-κB signaling and lipid metabolism as PARPi-induced adaptive resistance mechanisms. Based on these observations, we nominate several protein targets for therapeutic inhibition in combination with PARP1i. When tested experimentally, the combination of PARPi with an inhibitor of fatty acid synthase (TVB-2640) has a 3-fold synergistic effect and is therefore of particular pre-clinical interest. Conclusion Our study improves the current understanding of PARP1 function, highlights the potential that the anti-tumor efficacy of PARP1i may not only rely on DNA damage repair mechanisms and informs on the rational design of PARP1i combination therapies in ovarian cancer.

Tumor necrosis factor (TNF) has various effects on phosphorylation-mediated cellular signaling. Combining phosphoproteomics, subcellular localization analyses and kinase inhibitor assays, the authors provide systems level insights into TNF signaling and identify modulators of TNF-induced cell death.

An extensive RNA binding protein atlas (RBPome) for primary T cells would be a useful resource. Here the authors use two different methods to characterise the mouse and human T cell RBPome and show regulation of Roquin-1/2 dependent and independent pathways.

Breast cancer is a highly heterogeneous disease both at the histological and molecular levels. We have previously shown that RANK-c is a regulator of NF-κB signaling and exerts a suppressive effect on aggressive properties of ER negative breast cancer cells, while there is an opposite effect on ER positive cell lines. In order to identify molecular determinants that govern the opposing function of RANK-c in breast cancer cells we employed the two cell lines with the highest degree of phenotypic divergence upon RANK-c-expression (SKBR3 and BT474) and identified proteins that interact with RANK-c by affinity-enrichment mass spectrometry (AE-MS) analysis. Annotating enriched proteins with NF-κB signaling pathway revealed TRAF3 as an interacting partner of RANK-c in SKBR3 cell protein lysates, but not in BT474 breast cancer cells in which RANK-c induces cell aggressiveness. To determine the role of TRAF3 in the phenotype of BT474-RANK-c cells, we reconstructed the TRAF3/RANK-c interaction both in parental BT474 and RANK-c expressing cells and tested for aggressive properties through colony formation, migration and invasion assays. TRAF3 forced expression was able to reverse BT474 phenotypic changes imposed by RANK-c, rendering cells less aggressive. Finally, TRAF3 gene expression data and TRAF3 immunohistochemical (IHC) analysis on breast cancer samples indicated that TRAF3 expression correlates with Overall Survival (OS), Recurrence Free Survival (RFS) and several clinicopathological parameters (histological grade, proliferation index) of breast cancer disease.

Abstract Cells signal through rearrangements of protein communities governed by covalent modifications and reversible interactions of distinct sets of proteins. A method that identifies those post‐transcriptional modifications regulating signaling complex composition and functional phenotypes in one experimental setup would facilitate an efficient identification of novel molecular signaling checkpoints. Here, we devised modifications, interactions and phenotypes by affinity purification mass spectrometry (MIP‐APMS), comprising the streamlined cloning and transduction of tagged proteins into functionalized reporter cells as well as affinity chromatography, followed by MS‐based quantification. We report the time‐resolved interplay of more than 50 previously undescribed modification and hundreds of protein–protein interactions of 19 immune protein complexes in monocytes. Validation of interdependencies between covalent, reversible, and functional protein complex regulations by knockout or site‐specific mutation revealed ISGylation and phosphorylation of TRAF2 as well as ARHGEF18 interaction in Toll‐like receptor 2 signaling. Moreover, we identify distinct mechanisms of action for small molecule inhibitors of p38 (MAPK14). Our method provides a fast and cost‐effective pipeline for the molecular interrogation of protein communities in diverse biological systems and primary cells.

Abstract 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.

Abstract The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single‐cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single‐cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type‐2 epithelial cell health status in lavage fluid and plasma. Using cross‐modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes.

Proteomics has been advanced by algorithms that can predict different peptide features, but predicting peptide collisional cross sections (CCS) has remained challenging. Here, the authors measure over one million CCS values of tryptic peptides and develop a deep learning model for peptide CCS prediction.

The noncoding RNA 7SK controls the transcription of mRNAs. Here, the authors show that the 7SK complex interacts with the Smn complex, suggesting crosstalk between transcription and snRNP assembly.

Abstract 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.

Protein ubiquitylation is often studied by proteomics but how data independent acquisition (DIA) may advance these studies remains to be explored. Here, the authors show that DIA improves ubiquitylation site identification and quantification, enabling them to characterize the circadian ubiquitinome in human cells.