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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. A protein interaction network constructed with data from high-throughput affinity enrichment coupled to mass spectrometry provides a highly saturated yeast interactome with 31,004 interactions, including low-abundance complexes, membrane protein complexes and non-taggable protein complexes.

Toxic epidermal necrolysis (TEN) is a fatal drug-induced skin reaction triggered by common medications and is an emerging public health issue 1 – 3 . Patients with TEN undergo severe and sudden epidermal detachment caused by keratinocyte cell death. Although molecular mechanisms that drive keratinocyte cell death have been proposed, the main drivers remain unknown, and there is no effective therapy for TEN 4 – 6 . Here, to systematically map molecular changes that are associated with TEN and identify potential druggable targets, we utilized deep visual proteomics, which provides single-cell-based, cell-type-resolution proteomics 7 , 8 . We analysed formalin-fixed, paraffin-embedded archived skin tissue biopsies of three types of cutaneous drug reactions with varying severity and quantified more than 5,000 proteins in keratinocytes and skin-infiltrating immune cells. This revealed a marked enrichment of type I and type II interferon signatures in the immune cell and keratinocyte compartment of patients with TEN, as well as phosphorylated STAT1 activation. Targeted inhibition with the pan-JAK inhibitor tofacitinib in vitro reduced keratinocyte-directed cytotoxicity. In vivo oral administration of tofacitinib, baricitinib or the JAK1-specific inhibitors abrocitinib or upadacitinib ameliorated clinical and histological disease severity in two distinct mouse models of TEN. Crucially, treatment with JAK inhibitors (JAKi) was safe and associated with rapid cutaneous re-epithelialization and recovery in seven patients with TEN. This study uncovers the JAK/STAT and interferon signalling pathways as key pathogenic drivers of TEN and demonstrates the potential of targeted JAKi as a curative therapy. Cell-type-resolved spatial proteomics of the skin from patients with toxic epidermal necrolysis reveals that it is driven by JAK/STAT signaling, leading to successful treatment of this potentially fatal condition in patients using JAK inhibitors.

Mouse models of human cancer have transformed our ability to link genetics, molecular mechanisms and phenotypes. Both reverse and forward genetics in mice are currently gaining momentum through advances in next-generation sequencing (NGS). Methodologies to analyze sequencing data were, however, developed for humans and hence do not account for species-specific differences in genome structures and experimental setups. Here, we describe standardized computational pipelines specifically tailored to the analysis of mouse genomic data. We present novel tools and workflows for the detection of different alteration types, including single-nucleotide variants (SNVs), small insertions and deletions (indels), copy-number variations (CNVs), loss of heterozygosity (LOH) and complex rearrangements, such as in chromothripsis. Workflows have been extensively validated and cross-compared using multiple methodologies. We also give step-by-step guidance on the execution of individual analysis types, provide advice on data interpretation and make the complete code available online. The protocol takes 2–7 d, depending on the desired analyses. Here, the authors present standardized computational pipelines tailored specifically to the analysis of cancer genome sequencing data from mice. The protocol enables detection of single-nucleotide variants, indels, copy-number variations, loss of heterozygosity and complex rearrangements such as those of chromothripsis.

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. Synopsis This study reports a proteomic investigation of fatal COVID‐19 across organs using mass spectrometry, highlighting the central role of circulating inflammatory molecules and uncovering tissue‐specific alterations beyond inflammation. Parallelized sonification and streamlined MS‐based proteomics for FFPE tissue. Circulating inflammatory effectors dominate tissue responses to SARS‐CoV‐2. Deconvolution of the inflammatory response unmasks organ‐specific effects. Distinct remodeling of lung tissue compared to other destructive lung diseases. Graphical Abstract This study reports a proteomic investigation of fatal COVID‐19 across organs using mass spectrometry, highlighting the central role of circulating inflammatory molecules and uncovering tissue‐specific alterations beyond inflammation.

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.

Protein misfolding diseases, including α1-antitrypsin deficiency (AATD), pose substantial health challenges, with their cellular progression still poorly understood 1 , 2 – 3 . We use spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis 4 , 5 , we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudotime across fibrosis stages. We achieve proteome depth of up to 4,300 proteins from one-third of a single cell in formalin-fixed, paraffin-embedded tissue. This dataset reveals a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show α1-antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with artificial intelligence-guided image-based phenotyping across several disease stages, revealing a late-stage hepatocyte phenotype characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10 (also known as TRAIL) amounts. This phenotype may represent a critical disease progression stage. Our study offers new insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue. High-resolution spatial proteomics were used to map molecular events during hepatocyte stress in pseudotime across all fibrosis stages, recapitulating known disease progression markers and revealing early peroxisomal activation and late-stage proteotoxic phenotypes.

Distinction of non-self from self is the major task of the immune system. Immunopeptidomics studies the peptide repertoire presented by the human leukocyte antigen (HLA) protein, usually on tissues. However, HLA peptides are also bound to plasma soluble HLA (sHLA), but little is known about their origin and potential for biomarker discovery in this readily available biofluid. Currently, immunopeptidomics is hampered by complex workflows and limited sensitivity, generally requiring several mL of plasma for the detection of hundreds of HLA peptides. Here, we take advantage of recent improvements in the throughput and sensitivity of mass spectrometry (MS)-based proteomics to develop a highly-sensitive, automated and economical workflow for HLA peptide analysis, termed Immunopeptidomics by Biotinylated Antibodies and Streptavidin (IMBAS). IMBAS-MS quantifies more than 5,000 HLA class I peptides from only 200 μL of plasma, in just 30 minutes. Our technology revealed that the plasma immunopeptidome of healthy donors is remarkably stable throughout a year and strongly correlated between individuals with overlapping HLA types. Immunopeptides originating from diverse tissues, including the brain, are proportionately represented. We conclude that sHLAs are a promising avenue for immunology and precision oncology.

Mass spectrometry (MS)-based phosphoproteomics has transformed our understanding of cell signaling, yet current workflows face limitations in sensitivity and spatial resolution at sub-microgram inputs. Here, we present nanoPhos, a robust method that extends phosphoproteomics to nanogram scale, making it compatible with cell-type-resolved spatial analysis. It employs loss-less solid phase extraction capture (SPEC) for sample preparation, followed by automated phosphopeptide enrichment using Fe(III)-NTA cartridges. nanoPhos identifies over 57,000 unique phosphorylation sites from 1 µg cell lysate and over 4,000 from only 10 ng, a hundred-fold improvement from recent protocols. Combined with Deep Visual Proteomics (DVP), it enables region- and cell-type resolved phosphoproteomics of mouse brain tissue with spatial fidelity and a depth of 13,000 phosphosites from only 1000 cell shapes. This establishes nanoPhos as a versatile and ultra-sensitive platform that extends DVP to post-translational modifications and opens up for cell-type-specific signaling analysis in intact tissue.

Nearly all cellular functions are mediated by protein-protein interactions and mapping the interactome provides fundamental insights into the regulation and structure of biological systems. In principle, affinity purification coupled to mass spectrometry (AP-MS) is an ideal and scalable tool, however, it has been difficult to identify low copy number complexes, membrane complexes and those disturbed 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, and highly reproducible AP-MS technology combined with a quantitative two-dimensional analysis strategy for comprehensive interactome mapping of Saccharomyces cerevisiae. We reduced required cell culture volumes thousand-fold and employed 96-well formats throughout, allowing replicate analysis of the endogenous green fluorescent protein (GFP) tagged library covering the entire expressed yeast proteome. The 4159 pull-downs generated a highly structured network of 3,909 proteins connected by 29,710 interactions. Compared to previous large-scale studies, we double the number of proteins (nodes in the network) and triple the number of reliable interactions (edges), including very low abundant epigenetic complexes, organellar membrane complexes and non-taggable complexes interfered by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 15 interactors, the majority of them unreported so far. Similar to social networks between humans, the average shortest distance is 4.2 interactions. A web portal (www.yeast-interactome.org) enables exploration of our dataset by the network and biological communities and variations of our AP-MS technology can be employed in any organism or dynamic conditions.

Protein misfolding diseases, including alpha-1 antitrypsin deficiency (AATD), pose significant health challenges, with their cellular progression still poorly understood1–3. We utilize spatial proteomics by mass spectrometry and machine learning to map AATD in human liver tissue. Combining Deep Visual Proteomics (DVP) with single-cell analysis4,5, we probe intact patient biopsies to resolve molecular events during hepatocyte stress in pseudo-time across fibrosis stages. We achieve unprecedented proteome depth of up to 3,800 proteins from a third of a single cell in formalin-fixed, paraffin-embedded (FFPE) tissue. This dataset revealed a potentially clinically actionable peroxisomal upregulation that precedes the canonical unfolded protein response. Our single-cell proteomics data show alpha-1 antitrypsin accumulation is largely cell-intrinsic, with minimal stress propagation between hepatocytes. We integrated proteomic data with AI-guided image-based phenotyping across multiple disease stages, revealing a terminal hepatocyte state characterized by globular protein aggregates and distinct proteomic signatures, notably including elevated TNFSF10/TRAIL expression. This phenotype may represent a critical disease progression stage. Our study offers novel insights into AATD pathogenesis and introduces a powerful methodology for high-resolution, in situ proteomic analysis of complex tissues. This approach holds potential to unravel molecular mechanisms in various protein misfolding disorders, setting a new standard for understanding disease progression at the single-cell level in human tissue.