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Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein–protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice. This Review summarizes advances in mass-spectrometry-based proteomics and explores the potential applications of these technologies in the clinic.

An ultrafast, fragment-ion indexing–based database search tool, MSFragger, makes open searching practical and enables comprehensive identification of modified peptides in mass spectrometry–based proteomics data sets. There is a need to better understand and handle the 'dark matter' of proteomics—the vast diversity of post-translational and chemical modifications that are unaccounted in a typical mass spectrometry–based analysis and thus remain unidentified. We present a fragment-ion indexing method, and its implementation in peptide identification tool MSFragger, that enables a more than 100-fold improvement in speed over most existing proteome database search tools. Using several large proteomic data sets, we demonstrate how MSFragger empowers the open database search concept for comprehensive identification of peptides and all their modified forms, uncovering dramatic differences in modification rates across experimental samples and conditions. We further illustrate its utility using protein–RNA cross-linked peptide data and using affinity purification experiments where we observe, on average, a 300% increase in the number of identified spectra for enriched proteins. We also discuss the benefits of open searching for improved false discovery rate estimation in proteomics.

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.

Global quantification of protein abundances in single cells could provide direct information on cellular phenotypes and complement transcriptomics measurements. However, single-cell proteomics is still immature and confronts many technical challenges. Herein we describe a nested nanoPOTS (N2) chip to improve protein recovery, operation robustness, and processing throughput for isobaric-labeling-based scProteomics workflow. The N2 chip reduces reaction volume to <30 nL and increases capacity to >240 single cells on a single microchip. The tandem mass tag (TMT) pooling step is simplified by adding a microliter droplet on the nested nanowells to combine labeled single-cell samples. In the analysis of ~100 individual cells from three different cell lines, we demonstrate that the N2 chip-based scProteomics platform can robustly quantify ~1500 proteins and reveal membrane protein markers. Our analyses also reveal low protein abundance variations, suggesting the single-cell proteome profiles are highly stable for the cells cultured under identical conditions. Single-cell proteomics is an emerging technology but protein coverage, throughput and quantitation accuracy are often still insufficient. Here, the authors develop a nested nanowell chip that improves protein recovery, throughput and robustness of isobaric labeling-based quantitative single-cell proteomics.

Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N - and O -linked glycopeptides and open glycan searches. Reanalysis of recent N -glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O -glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation. MSFragger-Glyco allows identification of N - and O -linked glycopeptides using the localization-aware open search strategy of the MSFragger search engine.

Single-cell proteomics (SCP) promises to revolutionize biomedicine by providing an unparalleled view of the proteome in individual cells. Here, we present a high-sensitivity SCP workflow named Chip-Tip, identifying >5,000 proteins in individual HeLa cells. It also facilitated direct detection of post-translational modifications in single cells, making the need for specific post-translational modification-enrichment unnecessary. Our study demonstrates the feasibility of processing up to 120 label-free SCP samples per day. An optimized tissue dissociation buffer enabled effective single-cell disaggregation of drug-treated cancer cell spheroids, refining overall SCP analysis. Analyzing nondirected human-induced pluripotent stem cell differentiation, we consistently quantified stem cell markers OCT4 and SOX2 in human-induced pluripotent stem cells and lineage markers such as GATA4 (endoderm), HAND1 (mesoderm) and MAP2 (ectoderm) in different embryoid body cells. Our workflow sets a benchmark in SCP for sensitivity and throughput, with broad applications in basic biology and biomedicine for identification of cell type-specific markers and therapeutic targets. Chip-Tip is a label-free quantification-based single-cell proteomics workflow for deep single-cell proteomics, which identifies over 5,000 proteins and 40,000 peptides in single HeLa cells.

A crosslinking-mass spectrometry strategy, including a new proteome database search engine called XlinkX, enables the identification of inter- and intra-protein cross-links in cell lysates on a proteome-wide scale. We describe an integrated workflow that robustly identifies cross-links from endogenous protein complexes in human cellular lysates. Our approach is based on the application of mass spectrometry (MS)-cleavable cross-linkers, sequential collision-induced dissociation (CID)–tandem MS (MS/MS) and electron-transfer dissociation (ETD)-MS/MS acquisitions, and a dedicated search engine, XlinkX, which allows rapid cross-link identification against a complete human proteome database. This approach allowed us to detect 2,426 unique cross-links at a 5% FDR (2,013 intraprotein and 413 interprotein cross-links) or 1,822 cross-links at a 1% FDR (1,622 intraprotein and 200 interprotein cross-links), indicating the detection of 326 or 134 protein-protein interactions at 5% FDR or 1% FDR, respectively, in HeLa cell lysates. We validated the confidence of our cross-linking results by using a target-decoy strategy and mapping the observed cross-link distances onto existing high-resolution structures. Our data provided new structural information about many protein assemblies and captured dynamic interactions of the ribosome in contact with different elongation factors.

TRIC, a cross-run alignment algorithm and software tool, enables reproducible quantification of thousands of peptides across multiple targeted liquid chromatography–tandem mass spectrometry runs. Next-generation mass spectrometric (MS) techniques such as SWATH-MS have substantially increased the throughput and reproducibility of proteomic analysis, but ensuring consistent quantification of thousands of peptide analytes across multiple liquid chromatography–tandem MS (LC-MS/MS) runs remains a challenging and laborious manual process. To produce highly consistent and quantitatively accurate proteomics data matrices in an automated fashion, we developed TRIC ( http://proteomics.ethz.ch/tric/ ), a software tool that utilizes fragment-ion data to perform cross-run alignment, consistent peak-picking and quantification for high-throughput targeted proteomics. TRIC reduced the identification error compared to a state-of-the-art SWATH-MS analysis without alignment by more than threefold at constant recall while correcting for highly nonlinear chromatographic effects. On a pulsed-SILAC experiment performed on human induced pluripotent stem cells, TRIC was able to automatically align and quantify thousands of light and heavy isotopic peak groups. Thus, TRIC fills a gap in the pipeline for automated analysis of massively parallel targeted proteomics data sets.

We report O-Pair Search, an approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides via an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus . O-Pair search identifies O-glycopeptides and localizes O-glycosites using a fragment-ion-indexed open modification search combined with a graph-based approach. It also introduces a classification scheme to unify data reporting for glycoproteomics.

To map the occurrence of amyloid types in a large clinical cohort using mass spectrometry-based shotgun proteomics, an unbiased method that unambiguously identifies all amyloid types in a single assay. A mass spectrometry-based shotgun proteomics assay was implemented in a central reference laboratory. We documented our experience of typing 16,175 amyloidosis specimens over an 11-year period from January 1, 2008, to December 31, 2018. We identified 21 established amyloid types, including AL (n=9542; 59.0%), ATTR (n=4600; 28.4%), ALECT2 (n=511; 3.2%), AA (n=463; 2.9%), AH (n=367; 2.3%), AIns (n=182; 1.2%), KRT5-14 (n=94; <1%), AFib (n=71; <1%), AApoAIV (n=57; <1%), AApoA1 (n=56; <1%), AANF (n=47; <1%), Aβ2M (n=38; <1%), ASem1 (n=34; <1%), AGel (n=29; <1%), TGFB1 (n=29; <1%), ALys (n=15; <1%), AIAPP (n=13; <1%), AApoCII (n=11; <1%), APro (n=8; <1%), AEnf (n=6; <1%), and ACal (n=2; <1%). We developed the first comprehensive organ-by-type map showing the relative frequency of 21 amyloid types in 31 different organs, and the first type-by-organ map showing organ tropism of 18 rare types. Using a modified bioinformatics pipeline, we detected amino acid substitutions in cases of hereditary amyloidosis with 100% specificity. Amyloid typing by proteomics, which effectively recognizes all amyloid types in a single assay, optimally supports the diagnosis and treatment of amyloidosis patients in routine clinical practice.