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Researchers in many biological areas now routinely characterize proteins by mass spectrometry. Among the many formats for quantitative proteomics, stable-isotope labelling by amino acids in cell culture (SILAC) has emerged as a simple and powerful one. SILAC removes false positives in protein-interaction studies, reveals large-scale kinetics of proteomes and - as a quantitative phosphoproteomics technology - directly uncovers important points in the signalling pathways that control cellular decisions.

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

John Fenn’s electrospray mass spectrometry (ESMS) was awarded the chemistry Nobel Prize in 2002 and is now the basis of the entire field of MS-based proteomics. Technological progress continues unabated, enabling single cell sensitivity and clinical applications.

Quantitative proteomics now provides abundance ratios for thousands of proteins upon perturbations. These need to be functionally interpreted and correlated to other types of quantitative genome-wide data such as the corresponding transcriptome changes. We describe a new method, 2D annotation enrichment, which compares quantitative data from any two 'omics' types in the context of categorical annotation of the proteins or genes. Suitable genome-wide categories are membership of proteins in biochemical pathways, their annotation with gene ontology terms, sub-cellular localization, presence of protein domains or membership in protein complexes. 2D annotation enrichment detects annotation terms whose members show consistent behavior in one or both of the data dimensions. This consistent behavior can be a correlation between the two data types, such as simultaneous up- or down-regulation in both data dimensions, or a lack thereof, such as regulation in one dimension but no change in the other. For the statistical formulation of the test we introduce a two-dimensional generalization of the nonparametric two-sample test. The false discovery rate is stringently controlled by correcting for multiple hypothesis testing. We also describe one-dimensional annotation enrichment, which can be applied to single omics data. The 1D and 2D annotation enrichment algorithms are freely available as part of the Perseus software.

Aging promotes lung function decline and susceptibility to chronic lung diseases, which are the third leading cause of death worldwide. Here, we use single cell transcriptomics and mass spectrometry-based proteomics to quantify changes in cellular activity states across 30 cell types and chart the lung proteome of young and old mice. We show that aging leads to increased transcriptional noise, indicating deregulated epigenetic control. We observe cell type-specific effects of aging, uncovering increased cholesterol biosynthesis in type-2 pneumocytes and lipofibroblasts and altered relative frequency of airway epithelial cells as hallmarks of lung aging. Proteomic profiling reveals extracellular matrix remodeling in old mice, including increased collagen IV and XVI and decreased Fraser syndrome complex proteins and collagen XIV. Computational integration of the aging proteome with the single cell transcriptomes predicts the cellular source of regulated proteins and creates an unbiased reference map of the aging lung. Aging impacts lung functionality and makes it more susceptible to chronic diseases. Combining proteomics and single cell transcriptomics, the authors chart molecular and cellular changes in the aging mouse lung, discover aging hallmarks, and predict the cellular sources of regulated proteins.

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.

Cox and Mann describe MaxQuant, a suite of algorithms for the analysis of high-resolution mass spectrometry data. The approach achieves substantial improvements in the accuracy of mass measurements and the peptide identification rate. Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope–labeled (SILAC) peptide pairs as three-dimensional objects in m / z , elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

Perseus is a comprehensive, user-friendly software platform for the biological analysis of quantitative proteomics data. It is intended to help biologists with little bioinformatics training to interpret protein expression, post-translational modification and interaction data. Also in this issue, see the Perspective by Röst et al . A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform ( http://www.perseus-framework.org ) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

A streamlined, robust sample-preparation method for mass spectrometry–based proteome analysis is reported. All sample preparation steps are carried out in a single enclosed reactor, reducing the potential for contamination and losses, and enabling comprehensive proteome coverage. Mass spectrometry (MS)-based proteomics typically employs multistep sample-preparation workflows that are subject to sample contamination and loss. We report an in-StageTip method for performing sample processing, from cell lysis through elution of purified peptides, in a single, enclosed volume. This robust and scalable method largely eliminates contamination or loss. Peptides can be eluted in several fractions or in one step for single-run proteome analysis. In one day, we obtained the largest proteome coverage to date for budding and fission yeast, and found that protein copy numbers in these cells were highly correlated ( R 2 = 0.78). Applying the in-StageTip method to quadruplicate measurements of a human cell line, we obtained copy-number estimates for 9,667 human proteins and observed excellent quantitative reproducibility between replicates ( R 2 = 0.97). The in-StageTip method is straightforward and generally applicable in biological or clinical applications.

Phosphoproteomes are rapidly measured in parallel to track the dynamics of insulin signaling in the liver. Mass spectrometry has enabled the study of cellular signaling on a systems-wide scale, through the quantification of post-translational modifications, such as protein phosphorylation 1 . Here we describe EasyPhos, a scalable phosphoproteomics platform that now allows rapid quantification of hundreds of phosphoproteomes in diverse cells and tissues at a depth of >10,000 sites. We apply this technology to generate time-resolved maps of insulin signaling in the mouse liver. Our results reveal that insulin affects ∼10% of the liver phosphoproteome and that many known functional phosphorylation sites, and an even larger number of unknown sites, are modified at very early time points (<15 s after insulin delivery). Our kinetic data suggest that the flow of signaling information from the cell surface to the nucleus can occur on very rapid timescales of less than 1 min in vivo . EasyPhos facilitates high-throughput phosphoproteomics studies, which should improve our understanding of dynamic cell signaling networks and how they are regulated and dysregulated in disease.