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Advancements in mass spectrometry‐based proteomics have enabled experiments encompassing hundreds of samples. While these large sample sets deliver much‐needed statistical power, handling them introduces technical variability known as batch effects. Here, we present a step‐by‐step protocol for the assessment, normalization, and batch correction of proteomic data. We review established methodologies from related fields and describe solutions specific to proteomic challenges, such as ion intensity drift and missing values in quantitative feature matrices. Finally, we compile a set of techniques that enable control of batch effect adjustment quality. We provide an R package, \"proBatch\", containing functions required for each step of the protocol. We demonstrate the utility of this methodology on five proteomic datasets each encompassing hundreds of samples and consisting of multiple experimental designs. In conclusion, we provide guidelines and tools to make the extraction of true biological signal from large proteomic studies more robust and transparent, ultimately facilitating reliable and reproducible research in clinical proteomics and systems biology. Graphical Abstract In mass spectrometry‐based proteomics, handling large sample sets introduces technical variability known as batch effects. This tutorial provides guidelines and tools for the assessment, normalization, and batch correction of proteomics data.

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. Synopsis A new ultra‐high sensitivity LC‐MS workflow increases sensitivity by up to two orders of magnitude and enables true single‐cell proteome analysis. In‐depth comparison indicates that the single‐cell transcriptome is stochastic while the single‐cell proteome is complete and stable. A highly optimized data independent acquisition powered single‐cell proteomics workflow including sub‐µl sample preparation, very low flow chromatography and trapped ion mobility mass spectrometry (diaPASEF) is presented. Single‐cell proteome analysis is performed by injecting cells one‐by‐one across the cell cycle into the LC‐MS and correctly identifies cell states. Single‐cell proteome information is highly complementary to single‐cell transcriptome information. At the single‐cell level the proteome is quantitatively and qualitatively stable, while the transcriptome is stochastic. Graphical Abstract A new ultra‐high sensitivity LC‐MS workflow increases sensitivity by up to two orders of magnitude and enables true single‐cell proteome analysis. In‐depth comparison indicates that the single‐cell transcriptome is stochastic while the single‐cell proteome is complete and stable.

Recent developments in proteomics have enabled signaling studies where > 10,000 phosphosites can be routinely identified and quantified. Yet, current analyses are limited in throughput, reproducibility, and robustness, hampering experiments that involve multiple perturbations, such as those needed to map kinase–substrate relationships, capture pathway crosstalks, and network inference analysis. To address these challenges, we introduce rapid‐robotic phosphoproteomics (R2‐P2), an end‐to‐end automated method that uses magnetic particles to process protein extracts to deliver mass spectrometry‐ready phosphopeptides. R2‐P2 is rapid, robust, versatile, and high‐throughput. To showcase the method, we applied it, in combination with data‐independent acquisition mass spectrometry, to study signaling dynamics in the mitogen‐activated protein kinase (MAPK) pathway in yeast. Our results reveal broad and specific signaling events along the mating, the high‐osmolarity glycerol, and the invasive growth branches of the MAPK pathway, with robust phosphorylation of downstream regulatory proteins and transcription factors. Our method facilitates large‐scale signaling studies involving hundreds of perturbations opening the door to systems‐level studies aiming to capture signaling complexity. Synopsis The study presents R2‐P2, an automated end‐to‐end phosphoproteomic sample preparation method. Application of R2‐P2 to study phosphorylation temporal dynamics in yeast stimulated to perturb MAPK signaling reveals treatment‐specific responses and pathway crosstalks. R2‐P2 is a novel workflow for proteomic and phosphoproteomic sample preparation that compares favorably to common methods. R2‐P2 is automated, high‐throughput, rapid, robust, reproducible and yields high phosphopeptide enrichment efficiency. R2‐P2 facilitates large‐scale cell signaling studies. R2‐P2 in combination with DIA‐MS reveals broad and specific signaling events along the different branches of the MAPK pathway, with robust phosphorylation of downstream regulatory proteins and transcription factors. Graphical Abstract The study presents R2‐P2, an automated end‐to‐end phosphoproteomic sample preparation method. Application of R2‐P2 to study phosphorylation temporal dynamics in yeast stimulated to perturb MAPK signaling reveals treatment‐specific responses and pathway crosstalks.

High‐throughput and streamlined workflows are essential in clinical proteomics for standardized processing of samples from a variety of sources, including fresh‐frozen tissue, FFPE tissue, or blood. To reach this goal, we have implemented single‐pot solid‐phase‐enhanced sample preparation (SP3) on a liquid handling robot for automated processing (autoSP3) of tissue lysates in a 96‐well format. AutoSP3 performs unbiased protein purification and digestion, and delivers peptides that can be directly analyzed by LCMS, thereby significantly reducing hands‐on time, reducing variability in protein quantification, and improving longitudinal reproducibility. We demonstrate the distinguishing ability of autoSP3 to process low‐input samples, reproducibly quantifying 500–1,000 proteins from 100 to 1,000 cells. Furthermore, we applied this approach to a cohort of clinical FFPE pulmonary adenocarcinoma (ADC) samples and recapitulated their separation into known histological growth patterns. Finally, we integrated autoSP3 with AFA ultrasonication for the automated end‐to‐end sample preparation and LCMS analysis of 96 intact tissue samples. Collectively, this constitutes a generic, scalable, and cost‐effective workflow with minimal manual intervention, enabling reproducible tissue proteomics in a broad range of clinical and non‐clinical applications. Synopsis The study presents an automated sample preparation pipeline for low‐input proteomics based on the SP3 method. The seamless integration of tissue lysis with autoSP3 in a 96‐well format features low variability, high sensitivity and longitudinal reproducibility for clinical studies. An automated, scalable, and cost‐effective workflow (autoSP3) allows reproducible tissue proteomics in a broad range of clinical and non‐clinical applications. Automated tissue lysis is integrated with autoSP3 for an end‐to‐end workflow with minimal manual interference. The workflow allows reduced variability in protein quantification and increased longitudinal reproducibility. Minimal sample losses facilitate low‐input applications in a standardized workflow. Graphical Abstract The study presents an automated sample preparation pipeline for low‐input proteomics based on the SP3 method. The seamless integration of tissue lysis with autoSP3 in a 96‐well format features low variability, high sensitivity and longitudinal reproducibility for clinical studies.

A progressive loss of protein homeostasis is characteristic of aging and a driver of neurodegeneration. To investigate this process quantitatively, we characterized proteome dynamics during brain aging in the short‐lived vertebrate Nothobranchius furzeri combining transcriptomics and proteomics. We detected a progressive reduction in the correlation between protein and mRNA, mainly due to post‐transcriptional mechanisms that account for over 40% of the age‐regulated proteins. These changes cause a progressive loss of stoichiometry in several protein complexes, including ribosomes, which show impaired assembly/disassembly and are enriched in protein aggregates in old brains. Mechanistically, we show that reduction of proteasome activity is an early event during brain aging and is sufficient to induce proteomic signatures of aging and loss of stoichiometry in vivo . Using longitudinal transcriptomic data, we show that the magnitude of early life decline in proteasome levels is a major risk factor for mortality. Our work defines causative events in the aging process that can be targeted to prevent loss of protein homeostasis and delay the onset of age‐related neurodegeneration. Synopsis Analyses of proteome dynamics delineate a timeline of molecular events underlying brain aging in the vertebrate Nothobranchius furzeri . Early‐in‐life decline of proteasome activity is associated with loss of stoichiometry of protein complexes and predicts lifespan. Progressive loss of stoichiometry affects multiple protein complexes. Ribosomes aggregate in old brains. Partial reduction of proteasome activity is sufficient to induce loss of stoichiometry. Reduced proteasome levels are a major risk factor for early death in killifish. Graphical Abstract Analyses of proteome dynamics delineate a timeline of molecular events underlying brain aging in the vertebrate Nothobranchius furzeri . Early‐in‐life decline of proteasome activity is associated with loss of stoichiometry of protein complexes and predicts lifespan.

Accurately modeling the structures of proteins and their complexes using artificial intelligence is revolutionizing molecular biology. Experimental data enable a candidate‐based approach to systematically model novel protein assemblies. Here, we use a combination of in‐cell crosslinking mass spectrometry and co‐fractionation mass spectrometry (CoFrac‐MS) to identify protein–protein interactions in the model Gram‐positive bacterium Bacillus subtilis . We show that crosslinking interactions prior to cell lysis reveals protein interactions that are often lost upon cell lysis. We predict the structures of these protein interactions and others in the Subti Wiki database with AlphaFold‐Multimer and, after controlling for the false‐positive rate of the predictions, we propose novel structural models of 153 dimeric and 14 trimeric protein assemblies. Crosslinking MS data independently validates the AlphaFold predictions and scoring. We report and validate novel interactors of central cellular machineries that include the ribosome, RNA polymerase, and pyruvate dehydrogenase, assigning function to several uncharacterized proteins. Our approach uncovers protein–protein interactions inside intact cells, provides structural insight into their interaction interfaces, and is applicable to genetically intractable organisms, including pathogenic bacteria. Synopsis An integrative approach using crosslinking mass spectrometry (MS), co‐fractionation MS and Alphafold‐Multimer discovers novel protein complexes and their topologies in the model gram‐positive bacterium Bacillus subtillis. Crosslinking mass spectrometry and co‐fractionation mass spectrometry identify protein interactions from intact cells. AlphaFold‐Multimer confidently predicts the structure of dimeric complexes which can be validated with crosslinks. The binding site of YneR on the E1 subunit of the pyruvate dehydrogenase complex identifies it as an inhibitor of pyruvate dehydrogenase activity, PdhI. The approach can assign structure, function, and interactors of uncharacterized proteins in whole cells without requiring genetic manipulation. Graphical Abstract An integrative approach using crosslinking mass spectrometry (MS), co‐fractionation MS, and AlphaFold‐Multimer discovers novel protein complexes and their topologies in the model Gram‐positive bacterium Bacillus subtillis .

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein–protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)‐based approaches have allowed unbiased mapping of these disease‐mediated changes in protein–protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein–protein interactions at a system‐level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS‐based protein–protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications. Graphical Abstract This Review discusses mass spectrometry techniques that have been instrumental for identifying protein‐protein interactions. Examples from diverse disease contexts illustrate the potential of these approaches for revealing disease mechanisms and therapeutic targets.

Thermal proteome profiling (TPP) is based on the principle that, when subjected to heat, proteins denature and become insoluble. Proteins can change their thermal stability upon interactions with small molecules (such as drugs or metabolites), nucleic acids or other proteins, or upon post‐translational modifications. TPP uses multiplexed quantitative mass spectrometry‐based proteomics to monitor the melting profile of thousands of expressed proteins. Importantly, this approach can be performed in vitro , in situ , or in vivo . It has been successfully applied to identify targets and off‐targets of drugs, or to study protein–metabolite and protein–protein interactions. Therefore, TPP provides a unique insight into protein state and interactions in their native context and at a proteome‐wide level, allowing to study basic biological processes and their underlying mechanisms. Graphical Abstract This tutorial explains the principles of thermal proteome profiling (TPP) and analyzes the different steps of a TPP experiment. It reviews the recent developments and current applications of this methodology, and provides an outlook of possible new applications.

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. Synopsis A robust proteomic workflow quantifies more than 1,000 proteins in cerebrospinal fluid and reveals an Alzheimer's Disease‐associated signature of more than 20 proteins across three independent cohorts. These include tau, superoxide dismutase 1, PARK7, YKL‐40 and novel biomarker candidates. Proteomics workflow for quantification of more than 1,000 proteins from microliters of cerebrospinal fluid. More than 20 proteins consistently associated with Alzheimer's Disease across three cohorts comprising about 200 individuals in total. Alzheimer's Disease CSF signature of Tau, SOD1, PARK7, YKL‐40, and glycolysis‐related proteins. Graphical Abstract A robust proteomic workflow quantifies more than 1,000 proteins in cerebrospinal fluid and reveals an Alzheimer's Disease‐associated signature of more than 20 proteins across three independent cohorts. These include tau, superoxide dismutase 1, PARK7, YKL‐40 and novel biomarker candidates.

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. Synopsis A robust and automated multiplexed DIA (mDIA) workflow is presented, using complete dimethyl labeling for bulk or single‐cell proteomics. Accurate quantification with a reference channel, combined with the RefQuant algorithm, confirms the hypothesis of a stable single‐cell proteome. Five‐plex quantification at MS1 and MS2 level for multiplexed DIA is enabled by the Lys‐N enzyme. A reference channel in mDIA doubles proteomic depth in single cells at 80 single cells per day. mDIA is combined with Deep Visual Proteomics (DVP) for precision oncology. Graphical Abstract A robust and automated multiplexed DIA (mDIA) workflow is presented, using complete dimethyl labeling for bulk or single‐cell proteomics. Accurate quantification with a reference channel, combined with the RefQuant algorithm, confirms the hypothesis of a stable single‐cell proteome.