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Background Multiplex Substrate Profiling by Mass Spectrometry (MSP-MS) is a powerful method for determining the substrate specificity of proteolytic enzymes, which is essential for developing protease inhibitors, diagnostics, and protease-activated therapeutics. However, the complex datasets generated by MSP-MS pose significant analytical challenges and have limited accessibility for non-specialist users. Results We developed mspms , a Bioconductor R package with an accompanying graphical interface, to streamline the analysis of MSP-MS data. Mspms standardizes workflows for data preparation, processing, statistical analysis, and visualization. The tool is designed for accessibility, serving advanced users through the R package and broader audiences through a web-based interface. We validated mspms using data from four well-characterized cathepsins (A–D), demonstrating that it reliably captures expected substrate specificities. Conclusions mspms is the first publicly available, comprehensive platform for MSP-MS data analysis downstream of peptide identification and quantification. It integrates preprocessing, normalization, statistical testing, and visualization into a single, transparent, and user-friendly framework, making it a valuable resource for the protease research community. The package is distributed via Bioconductor, and a graphical interface is available online for interactive use.

Expression or quantitative proteomics is the comparison of distinct proteomes which enables the identification of protein species which exhibit changes in expression or post‐translational state in response to a given stimulus. Expression proteomics, as with the other – omics, suffers from experiments with many variables but few observations. Many different quantitative techniques are being utilized but many of the data analysis issues are independent of the technique used. Approaches to address the problems that arise with these large lean datasets are discussed to give insight into the types of statistical analyses of data appropriate for the various experimental strategies that can be employed. This review also highlights the importance of employing a robust experimental design and discusses the issues that need consideration. The concepts and examples discussed within will show how robust design and analysis will lead to confident results that will ensure expression proteomics delivers.

A central objective in metabolomics is the identification of metabolite markers that are hidden within a large background of high‐throughput measurements from untargeted metabolomic experiments. Metabolite clustering is an approach for data mining and marker identification, which is based on the grouping of similar intensity profiles of putative metabolites, as obtained from mass spectrometry measurements. For metabolite clustering and visualization, we utilize one‐dimensional self‐organizing maps. This concept was implemented in the MarVis tool, which realizes a user‐friendly framework for clustering, convenient visualization, and marker selection. In two studies on the wound response of Arabidopsis thaliana and in the context of COP9 signalosome complex defects in Aspergillus nidulans, the clustering and visualization capabilities of one‐dimensional self‐organizing maps are demonstrated and used to identify relevant groups of marker candidates. The specialized realization of one‐dimensional self‐organizing maps, as implemented in the MarVis tool, facilitates the analysis of complex pattern variations and the identification of marker metabolites in large data sets.

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.

Background Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology. Results We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode. Conclusions LipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment. Several bottlenecks exist in metabolomics data analysis. Here, the authors present MetaboAnalystR 4.0 as a unified workflow for LC-MS untargeted metabolomics. It highlights significant improvements in LC-MS2 spectral processing and functional analysis, providing an end-to-end computational pipeline.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become an indispensable analytical technique in clinical and forensic toxicology for detection and identification of potentially toxic or harmful compounds. Particularly, non-target LC-MS/MS assays enable extensive and universal screening requested in systematic toxicological analysis. An integral part of the identification process is the generation of information-rich product ion spectra which can be searched against libraries of reference mass spectra. Usually, ‘data-dependent acquisition’ (DDA) strategies are applied for automated data acquisition. In this study, the ‘data-independent acquisition’ (DIA) method ‘Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra’ (SWATH) was combined with LC-MS/MS on a quadrupole-quadrupole-time-of-flight (QqTOF) instrument for acquiring informative high-resolution tandem mass spectra. SWATH performs data-independent fragmentation of all precursor ions entering the mass spectrometer in 21 m/z isolation windows. The whole m/z range of interest is covered by continuous stepping of the isolation window. This allows numerous repeat analyses of each window during the elution of a single chromatographic peak and results in a complete fragment ion map of the sample. Compounds and samples typically encountered in forensic casework were used to assess performance characteristics of LC-MS/MS with SWATH. Our experiments clearly revealed that SWATH is a sensitive and specific identification technique. SWATH is capable of identifying more compounds at lower concentration levels than DDA does. The dynamic range of SWATH was estimated to be three orders of magnitude. Furthermore, the >600,000 SWATH spectra matched led to only 408 incorrect calls (false positive rate = 0.06 %). Deconvolution of generated ion maps was found to be essential for unravelling the full identification power of LC-MS/MS with SWATH. With the available software, however, only semi-automated deconvolution was enabled, which rendered data interpretation a laborious and time-consuming process. Graphical Abstract High-resolution LC-MS/MS with SWATH represents a sensitive and specific compound identification tool that has vast potential to become a leading technique in systematic toxicological analysis. SWATH solves the problem of unused precursor ions often encountered with data-dependent acquisition methods by acquiring complete fragment ion maps of a sample

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography–MS, gas chromatography–MS and MS–imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography–(IMS–)MS, gas chromatography–MS and (IMS–)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis. Key points MZmine is a program designed to process data from untargeted mass spectrometry (MS) experiments acquired in data-dependent acquisition mode; specifically, collision-induced dissociation and higher-energy collisional dissociation. This protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by instrumental setups: liquid chromatography–(ion mobility spectrometry–)MS, gas chromatography–MS and (ion mobility spectrometry–)MS imaging. Untargeted mass spectrometry (MS) produces complex, multidimensional data. The MZmine open-source project enables processing of spectral data from various MS platforms, e.g., liquid chromatography–MS, gas chromatography–MS, MS–imaging and ion mobility spectrometry–MS, and is specialized for metabolomics.

Mass Spectrometry (MS) allows the analysis of proteins and peptides through a variety of methods, such as Electrospray Ionization-Mass Spectrometry (ESI-MS) or Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry (MALDI-MS). These methods allow identification of the mass of a protein or a peptide as intact molecules or the identification of a protein through peptide-mass fingerprinting generated upon enzymatic digestion. Tandem mass spectrometry (MS/MS) allows the fragmentation of proteins and peptides to determine the amino acid sequence of proteins (top-down and middle-down proteomics) and peptides (bottom-up proteomics). Furthermore, tandem mass spectrometry also allows the identification of post-translational modifications (PTMs) of proteins and peptides. Here, we discuss the application of MS/MS in biomedical research, indicating specific examples for the identification of proteins or peptides and their PTMs as relevant biomarkers for diagnostic and therapy.