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Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH‐MS is a specific variant of data‐independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH‐MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH‐MS data, a strategy based on peptide‐centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH‐MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH‐MS data using peptide‐centric scoring. Furthermore, concepts on how to improve SWATH‐MS data acquisition, potential trade‐offs of parameter settings and alternative data analysis strategies are discussed. Graphical Abstract SWATH‐MS combines deep proteome coverage with quantitative consistency and accuracy and is often the method of choice for personalized medicine, drug screens or systems biology. This tutorial provides guidelines on how to set up SWATH‐MS experiments, perform the mass spectrometric measurements and analyse the data.

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. Graphical Abstract The performance of mass spectrometry (MS)‐based proteomics has reached a sensitivity and dynamic range that makes it suitable for biomarker studies. This Review discusses plasma proteome profiling strategies and how they can be translated into clinical practice.

Agonist‐induced ubiquitination of the β 2 adrenergic receptor (β 2 AR) functions as an important post‐translational modification to sort internalized receptors to the lysosomes for degradation. We now show that this ubiquitination is reversed by two deubiquitinating enzymes, ubiquitin‐specific proteases (USPs) 20 and 33, thus, inhibiting lysosomal trafficking when concomitantly promoting receptor recycling from the late‐endosomal compartments as well as resensitization of recycled receptors at the cell surface. Dissociation of constitutively bound endogenously expressed USPs 20 and 33 from the β 2 AR immediately after agonist stimulation and reassociation on prolonged agonist treatment allows receptors to first become ubiquitinated and then deubiquitinated, thus, providing a ‘trip switch’ between degradative and recycling pathways at the late‐endosomal compartments. Thus, USPs 20 and 33 serve as novel regulators that dictate both post‐endocytic sorting as well as the intensity and extent of β 2 AR signalling from the cell surface.

Glycosylation is the most abundant and diverse posttranslational modification of proteins. While several types of glycosylation can be predicted by the protein sequence context, and substantial knowledge of these glycoproteomes is available, our knowledge of the GalNAc‐type O ‐glycosylation is highly limited. This type of glycosylation is unique in being regulated by 20 polypeptide GalNAc‐transferases attaching the initiating GalNAc monosaccharides to Ser and Thr (and likely some Tyr) residues. We have developed a genetic engineering approach using human cell lines to simplify O ‐glycosylation (SimpleCells) that enables proteome‐wide discovery of O ‐glycan sites using ‘bottom‐up’ ETD‐based mass spectrometric analysis. We implemented this on 12 human cell lines from different organs, and present a first map of the human O ‐glycoproteome with almost 3000 glycosites in over 600 O ‐glycoproteins as well as an improved NetOGlyc4.0 model for prediction of O ‐glycosylation. The finding of unique subsets of O ‐glycoproteins in each cell line provides evidence that the O ‐glycoproteome is differentially regulated and dynamic. The greatly expanded view of the O ‐glycoproteome should facilitate the exploration of how site‐specific O ‐glycosylation regulates protein function. Comprehensive proteomics survey in 12 human cell lines and development of an improved NetOGlyc4.0 prediction tool greatly expand the view on mucin‐type protein O ‐glycosylation.

An important issue for molecular biology is to establish whether transcript levels of a given gene can be used as proxies for the corresponding protein levels. Here, we have developed a targeted proteomics approach for a set of human non‐secreted proteins based on parallel reaction monitoring to measure, at steady‐state conditions, absolute protein copy numbers across human tissues and cell lines and compared these levels with the corresponding mRNA levels using transcriptomics. The study shows that the transcript and protein levels do not correlate well unless a gene‐specific RNA‐to‐protein (RTP) conversion factor independent of the tissue type is introduced, thus significantly enhancing the predictability of protein copy numbers from RNA levels. The results show that the RTP ratio varies significantly with a few hundred copies per mRNA molecule for some genes to several hundred thousands of protein copies per mRNA molecule for others. In conclusion, our data suggest that transcriptome analysis can be used as a tool to predict the protein copy numbers per cell, thus forming an attractive link between the field of genomics and proteomics. Synopsis A comparison of absolute protein copy numbers with mRNA levels across human tissues and cell lines shows that protein levels correlate well with transcript levels, if a gene‐specific and cell/tissue‐independent RNA‐to‐protein (RTP) conversion factor is introduced. A targeted proteomics approach based on spike‐in of stable isotope‐labeled protein fragments is developed to measure absolute protein copy numbers across human tissues and cell lines. Transcript and protein levels within a sample do not correlate well, unless a gene‐specific RNA‐to‐protein (RTP) factor is introduced. The RTP‐ratio varies significantly between genes, ranging from thousands to millions of protein copies per mRNA molecule, but does not vary across tissues. Transcriptome analysis can be used as a tool to predict protein copy numbers per cell, thus forming an attractive link between genomics and proteomics. Graphical Abstract A comparison of absolute protein copy numbers with mRNA levels across human tissues and cell lines shows that protein levels correlate well with transcript levels, if a gene‐specific and cell/tissue‐independent RNA‐to‐protein (RTP) conversion factor is introduced.

A general principle of biology is the self‐assembly of proteins into functional complexes. Characterizing their composition is, therefore, required for our understanding of cellular functions. Unfortunately, we lack knowledge of the comprehensive set of identities of protein complexes in human cells. To address this gap, we developed a machine learning framework to identify protein complexes in over 15,000 mass spectrometry experiments which resulted in the identification of nearly 7,000 physical assemblies. We show our resource, hu.MAP 2.0, is more accurate and comprehensive than previous state of the art high‐throughput protein complex resources and gives rise to many new hypotheses, including for 274 completely uncharacterized proteins. Further, we identify 253 promiscuous proteins that participate in multiple complexes pointing to possible moonlighting roles. We have made hu.MAP 2.0 easily searchable in a web interface ( http://humap2.proteincomplexes.org/ ), which will be a valuable resource for researchers across a broad range of interests including systems biology, structural biology, and molecular explanations of disease. SYNOPSIS Integration of orthogonal high‐throughput mass spectrometry datasets using a machine learning framework reveals nearly 7,000 human protein assemblies and offers insights into novel protein function. Hu.MAP 2.0 represents the most accurate and comprehensive human protein complex map available to date. It indicates potential functions for 274 completely uncharacterized proteins. It identifies 253 proteins that participate in multiple complexes suggesting multi‐functionality. A searchable web resource is available at humap2.proteincomplexes.org . Graphical Abstract Integration of orthogonal high‐throughput mass spectrometry datasets using a machine learning framework reveals nearly 7,000 human protein assemblies and offers insights into novel protein function.

In order to obtain a systems‐level understanding of a complex biological system, detailed proteome information is essential. Despite great progress in proteomics technologies, thorough interrogation of the proteome from quantity‐limited biological samples is hampered by inefficiencies during processing. To address these challenges, here we introduce a novel protocol using paramagnetic beads, termed Single‐Pot Solid‐Phase‐enhanced Sample Preparation (SP3). SP3 provides a rapid and unbiased means of proteomic sample preparation in a single tube that facilitates ultrasensitive analysis by outperforming existing protocols in terms of efficiency, scalability, speed, throughput, and flexibility. To illustrate these benefits, characterization of 1,000 HeLa cells and single Drosophila embryos is used to establish that SP3 provides an enhanced platform for profiling proteomes derived from sub‐microgram amounts of material. These data present a first view of developmental stage‐specific proteome dynamics in Drosophila at a single‐embryo resolution, permitting characterization of inter‐individual expression variation. Together, the findings of this work position SP3 as a superior protocol that facilitates exciting new directions in multiple areas of proteomics ranging from developmental biology to clinical applications. Synopsis A new proteomic sample preparation protocol allows fast, efficient and ultra‐sensitive analyses. The method is illustrated by profiling proteomes from sub‐microgram amounts of material, including the first proteome screen of Drosophila development at a single‐embryo resolution. A novel protocol using paramagnetic beads, termed Single‐Pot Solid‐Phase‐enhanced Sample Preparation (SP3) is presented. SP3 enables protein and peptide enrichment, cleanup, digestion, chemical isotope labeling and fractionation in a single tube, without limitations arising from reagent compatibility. SP3 allows unmatched ultra‐sensitive proteome profiling from sub‐microgram amounts of material, as low as 1,000 HeLa cells or a single fly embryo. The first quantitative analysis of early Drosophila development at a single‐embryo resolution reveals dynamic trends in the developmental proteome. Graphical Abstract A new proteomic sample preparation protocol allows fast, efficient and ultra‐sensitive analyses. The method is illustrated by profiling proteomes from sub‐microgram amounts of material, including the first proteome screen of Drosophila development at a single‐embryo resolution.

Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research. Graphical Abstract A practical guide to the fundamentals of protein interaction network mapping, providing information to help researchers make effective use of proteomics approaches. A range of new and well‐established experimental and computational methods and resources are covered.

Macromolecular protein complexes carry out many of the essential functions of cells, and many genetic diseases arise from disrupting the functions of such complexes. Currently, there is great interest in defining the complete set of human protein complexes, but recent published maps lack comprehensive coverage. Here, through the synthesis of over 9,000 published mass spectrometry experiments, we present hu.MAP, the most comprehensive and accurate human protein complex map to date, containing > 4,600 total complexes, > 7,700 proteins, and > 56,000 unique interactions, including thousands of confident protein interactions not identified by the original publications. hu.MAP accurately recapitulates known complexes withheld from the learning procedure, which was optimized with the aid of a new quantitative metric ( k ‐cliques) for comparing sets of sets. The vast majority of complexes in our map are significantly enriched with literature annotations, and the map overall shows improved coverage of many disease‐associated proteins, as we describe in detail for ciliopathies. Using hu.MAP, we predicted and experimentally validated candidate ciliopathy disease genes in vivo in a model vertebrate, discovering CCDC138, WDR90, and KIAA1328 to be new cilia basal body/centriolar satellite proteins, and identifying ANKRD55 as a novel member of the intraflagellar transport machinery. By offering significant improvements to the accuracy and coverage of human protein complexes, hu.MAP ( http://proteincomplexes.org ) serves as a valuable resource for better understanding the core cellular functions of human proteins and helping to determine mechanistic foundations of human disease. Synopsis Integrating the largest‐scale mass spectrometry protein interaction datasets from a variety of human and animal cells and tissues in a machine‐learning framework generates the most comprehensive and accurate human protein complex map to date. Thousands of new interactions are identified from affinity purification/mass spectrometry datasets by applying a weighted matrix model of interactions. The resulting protein complex map strongly improves coverage of disease related genes and is examined in depth for ciliopathies. Novel centriolar satellite members are predicted and experimentally validated, and the map reveals ANKRD55 to be a new member of the intraflagellar transport machinery. Graphical Abstract Integrating the largest‐scale mass spectrometry protein interaction datasets from a variety of human and animal cells and tissues in a machine‐learning framework generates the most comprehensive and accurate human protein complex map to date.

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. Synopsis Applying Plasma Proteome Profiling to liver disease in different human cohorts associated PIGR and ALDOB and other proteins to non‐alcoholic fatty liver disease. Potential biomarkers were validated in a mouse model. Plasma proteome profiling augmented by Boxcar acquisition identified potential biomarkers of human liver diseases. PIGR and ALDOB are associated with NAFLD, among other novel proteins. DPP4, ANPEP, PIGR, APOE, and TGFBI highly correlate with AST, ALT, GGT and ALP. A mouse NAFLD model recapitulated many of the changes seen in humans. Graphical Abstract Applying Plasma Proteome Profiling to liver disease in different human cohorts associated PIGR and ALDOB and other proteins to non‐alcoholic fatty liver disease. Potential biomarkers were validated in a mouse model.