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PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2006

The utility of ETD mass spectrometry in proteomic analysis.

Mass spectrometry has played an integral role in the identification of proteins and their post-translational modifications (PTM). However, analysis of some PTMs, such as phosphorylation, sulfonation, and glycosylation, is difficult with collision-activated dissociation (CAD) since the modification is labile and preferentially lost over peptide backbone fragmentation, resulting in little to no peptide sequence information. The presence of multiple basic residues also makes peptides exceptionally difficult to sequence by conventional CAD mass spectrometry. Here we review the utility of electron transfer dissociation (ETD) mass spectrometry for sequence analysis of post-translationally modified and/or highly basic peptides. Phosphorylated, sulfonated, glycosylated, nitrosylated, disulfide bonded, methylated, acetylated, and highly basic peptides have been analyzed by CAD and ETD mass spectrometry. CAD fragmentation typically produced spectra showing limited peptide backbone fragmentation. However, when these peptides were fragmented using ETD, peptide backbone fragmentation produced a complete or almost complete series of ions and thus extensive peptide sequence information. In addition, labile PTMs remained intact. These examples illustrate the utility of ETD as an advantageous tool in proteomic research by readily identifying peptides resistant to analysis by CAD. A further benefit is the ability to analyze larger, non-tryptic peptides, allowing for the detection of multiple PTMs within the context of one another.

Amino Acid Sequence↗

PubMed2026

Imaging-Guided Omics Technologies for Resolving Rare Cancer States and Advancing Nanomedicine.

The ability to resolve rare and transient cellular states is critical for understanding metastasis, immune evasion, and therapy resistance in cancer, yet these dynamic processes often escape detection by conventional sequencing and imaging approaches. Recent advances at the interface of nanotechnology, high-resolution live-cell imaging, and single-cell/spatial multiomics methods have enabled functional profiling of cells with unprecedented precision within their native microenvironment. In this Mini-Review, we highlight emerging nanoscale platforms that couple real-time phenotypic imaging with molecular readouts, such as FUNseq and CIN-seq, to directly link functional heterogeneity to transcriptomic, proteomic, and epigenomic information. By integrating nanoscale optical imaging, microengineered perturbation tools, and AI-driven computational analysis, these technologies open up new avenues for dissecting rare metastatic, therapy-resistant, or immune-evasive subpopulations. We further discuss how these next-generation imaging-guided single-cell and spatial omics platforms not only advance fundamental cancer biology but also create opportunities to accelerate the development of nanomedicine applications.

Humans↗

PubMed2026

A streamlined workflow for high throughput metaproteomic analysis of the rumen microbiome.

Metaproteomics can provide direct functional insights into complex microbial communities, yet its application in rumen research remains limited due to labor-intensive and low-throughput sample preparation workflows before the MS analysis. This work aimed to develop and characterize a streamlined, high throughput metaproteomic workflow optimized for rumen samples. Key steps, including microbial cell extraction, cell lysis, protein digestion, and LC-MS/MS acquisition, were systematically assessed and optimized to reduce hands-on time while maintaining deep proteome coverage. The optimized workflow integrates a minimized cell extraction protocol using 0.5 g starting material and in-solution tryptic digestion. Application of the final workflow to 72 samples from in vitro fermentation revealed that biological variability between inocula dominated technical variability, which remained moderate (median CV of 21-24% across batches). Overall, the optimized workflow supports robust taxonomic and functional characterization of the rumen microbiome with improved scalability. These advances provide a foundation for applying metaproteomics to larger experimental designs, including nutritional trials and cohort studies, thereby enabling broader functional interrogation of rumen microbial ecosystems. SIGNIFICANCE: This study addresses current limitations in the application of metaproteomics to rumen microbiome research by developing a streamlined and scalable sample preparation workflow. By optimizing key steps and reducing sample input while maintaining reproducibility and proteome coverage, this work enables more efficient processing of larger sample sets. These advances support the broader use of metaproteomics in rumen studies and facilitate functional investigations relevant to animal nutrition and sustainable livestock production.

Animals↗

PubMed2001

Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae.

Genomic and proteomic approaches can provide hypotheses concerning function for the large number of genes predicted from genome sequences. Because of the artificial nature of the assays, however, the information from these high-throughput approaches should be considered with caution. Although it is possible that more meaningful hypotheses could be formulated by integrating the data from various functional genomic and proteomic projects, it has yet to be seen to what extent the data can be correlated and how such integration can be achieved. We developed a 'transcriptome-interactome correlation mapping' strategy to compare the interactions between proteins encoded by genes that belong to common expression-profiling clusters with those between proteins encoded by genes that belong to different clusters. Using this strategy with currently available data sets for Saccharomyces cerevisiae, we provide the first global evidence that genes with similar expression profiles are more likely to encode interacting proteins. We show how this correlation between transcriptome and interactome data can be used to improve the quality of hypotheses based on the information from both approaches. The strategy described here may help to integrate other functional genomic and proteomic data, both in yeast and in higher organisms.

Fungal Proteins↗

PubMed2026

Research progress and application prospects of multi-omics integration strategies in precision risk stratification of type 1 diabetes mellitus.

Type 1 diabetes (T1D) is a chronic metabolic disease mediated by autoimmunity. Its pathogenesis involves complex interactions between genetic susceptibility and environmental factors. Conventional T1D risk stratification primarily relies on genetic markers, islet autoantibodies, and glycemic indicators. Although these biomarkers remain indispensable in current clinical practice, they are often insufficient when used alone to accurately identify ultra-early high-risk individuals, predict disease progression rates, or support individualized preventive strategies. Consequently, more comprehensive molecular approaches are needed to improve precision risk stratification. In recent years, the rapid development of multi-omics technologies has provided new strategies for precise risk stratification of T1D. This narrative review critically evaluates how multi-omics integration strategies can improve precision risk stratification throughout the T1D disease continuum by integrating complementary molecular information from genomics, transcriptomics, proteomics, metabolomics, epigenomics, and the microbiome. Particular emphasis is placed on stage-specific biomarker discovery, multi-omics data integration frameworks, artificial intelligence-assisted prediction models, biomarker validation, and the opportunities and challenges associated with clinical translation. Current evidence suggests that integrated multi-omics approaches have the potential to improve risk prediction accuracy, distinguish heterogeneous disease trajectories, identify individuals at imminent risk of progression, and provide biologically informed targets for precision intervention. However, important challenges remain, including data harmonization, external validation, model interpretability, cost-effectiveness, and integration into routine clinical screening programs. Future research should prioritize prospective multicenter cohorts, standardized analytical pipelines, externally validated prediction models, and clinically interpretable multi-omics frameworks to facilitate the translation of precision risk stratification into routine T1D prevention and management.

Humans↗

PubMed2006

Mass spectrometric identification of proteins in complex post-genomic projects. Soluble proteins of the metabolically versatile, denitrifying 'Aromatoleum' sp. strain EbN1.

The rapidly developing proteomics technologies help to advance the global understanding of physiological and cellular processes. The lifestyle of a study organism determines the type and complexity of a given proteomic project. The complexity of this study is characterized by a broad collection of pathway-specific subproteomes, reflecting the metabolic versatility as well as the regulatory potential of the aromatic-degrading, denitrifying bacterium 'Aromatoleum' sp. strain EbN1. Differences in protein profiles were determined using a gel-based approach. Protein identification was based on a progressive application of MALDI-TOF-MS, MALDI-TOF-MS/MS and LC-ESI-MS/MS. This progression was result-driven and automated by software control. The identification rate was increased by the assembly of a project-specific list of background signals that was used for internal calibration of the MS spectra, and by the combination of two search engines using a dedicated MetaScoring algorithm. In total, intelligent bioinformatics could increase the identification yield from 53 to 70% of the analyzed 5,050 gel spots; a total of 556 different proteins were identified. MS identification was highly reproducible: most proteins were identified more than twice from parallel 2DE gels with an average sequence coverage of >50% and rather restrictive score thresholds (Mascot >or=95, ProFound >or=2.2, MetaScore >or=97). The MS technologies and bioinformatics tools that were implemented and integrated to handle this complex proteomic project are presented. In addition, we describe the basic principles and current developments of the applied technologies and provide an overview over the current state of microbial proteome research.

Amino Acid Sequence↗

PubMed2001

Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.

Algorithms↗

PubMed2005

The membrane proteome of Halobacterium salinarum.

The identification of 114 integral membrane proteins from Halobacterium salinarum was achieved using liquid chromatography/tandem mass spectrometric (LC/MS/MS) techniques, representing 20% of the predicted alpha-helical transmembrane proteins of the genome. For this experiment, a membrane preparation with only minor contamination by soluble proteins was prepared. From this membrane preparation a number of peripheral membrane proteins were identified by the classical two dimensional gel electrophoresis (2-DE) approach, but identification of integral membrane proteins largely failed with only a very few being identified. By use of a fluorescently labeled membrane preparation, we document that this is caused by an irreversible precipitation of the membrane proteins upon isoelectric focusing (IEF). Attempts to overcome this problem by using alternative IEF methods and IEF strip solubilisation techniques were not successful, and we conclude that the classical 2-DE approach is not suited for the identification of integral membrane proteins. Computational analysis showed that the identification of integral membrane proteins is further complicated by the generation of tryptic peptides, which are unfavorable for matrix assisted laser desorption/ionization time of flight mass spectrometric peptide mass fingerprint analysis. Together with the result from the analysis of the cytosolic proteome (see preceding paper), we could identify 34% (943) of all gene products in H. salinarum which can be theoretically expressed. This is a cautious estimate as very stringent criteria were applied for identification. These results are available under www.halolex.mpg.de.

Cell Membrane↗

PubMed2025

Tonic signaling of the B-cell antigen-specific receptor is a common functional hallmark in chronic lymphocytic leukemia cell phosphoproteomes at early disease stages.

B-cell chronic lymphocytic leukemia (B-CLL) is characterized by highly heterogeneous genomic alterations and altered signaling pathways, with limited studies on its proteome. Our study presents a comprehensive analysis of the proteome and phosphoproteome in B-CLL and CLL-like monoclonal B-cell lymphocytosis (MBL) primary cells. Using high-resolution mass spectrometry, we identified 2970 proteins and 316 phosphoproteins across five tumor samples, including 55 newly identified phosphopeptides (ProteomeXchange-PXD005997). Our multifaceted approach also integrated protein microarrays and western blotting for further data validation in a new patient cohort of 14 patients. Despite sharing 73% of their proteomes, the phosphoproteomes varied significantly among samples, independent of cytogenetic alterations and immunoglobulin heavy variable cluster (IGHV) mutational status. We identified common functional hallmarks in B-CLL and MBL phosphoproteomes, notably tonic signaling (low-level, constitutive signaling) of the B-cell antigen-specific receptor (BCR) and nuclear factor NF-kappa-B (NF-kβ)/signal transducer and activator of transcription 3 (STAT3) pathways. Nine phosphoproteins involved in BCR signaling were further validated, showing a high correlation with early disease stages. Our study advances the field by providing a detailed perspective on the proteome and phosphoproteome of B-CLL cells, revealing signaling pathways crucial for disease development and progression. Integrating diverse proteomics techniques and identifying novel phosphopeptides offers new insights into CLL biology, potentially informing future therapeutic strategies and biomarker development for early diagnosis and personalized treatment.

Humans↗

PubMed2003

Proteomics analysis of rat brain postsynaptic density. Implications of the diverse protein functional groups for the integration of synaptic physiology.

The postsynaptic density contains multiple protein complexes that together relay the presynaptic neurotransmitter input to the activation of the postsynaptic neuron. In the present study we took two independent proteome approaches for the characterization of the protein complement of the postsynaptic density, namely 1) two-dimensional gel electrophoresis separation of proteins in conjunction with mass spectrometry to identify the tryptic peptides of the protein spots and 2) isolation of the trypsin-digested sample that was labeled with isotope-coded affinity tag, followed by liquid chromatography-tandem mass spectrometry for the partial separation and identification of the peptides, respectively. Functional grouping of the identified proteins indicates that the postsynaptic density is a structurally and functionally complex organelle that may be involved in a broad range of synaptic activities. These proteins include the receptors and ion channels for glutamate neurotransmission, proteins for maintenance and modulation of synaptic architecture, sorting and trafficking of membrane proteins, generation of anaerobic energy, scaffolding and signaling, local protein synthesis, and correct protein folding and breakdown of synaptic proteins. Together, these results imply that the postsynaptic density may have the ability to function (semi-) autonomously and may direct various cellular functions in order to integrate synaptic physiology.

Animals↗

PubMed2005

Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database.

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anti-coagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay-based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs. We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches. This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.

Algorithms↗

PubMed2006

[The progress in proteomics researches of Alzheimer's disease].

Proteomics, an important discipline in genomic era, is the integrated study of protein properties on a large scale. Application of proteomics researches in Alzheimer's disease (AD), which is one of the most common and complex neurodegeneration disorders, we can not only elucidate the essential issues of disease in protein level, but give a whole view in the pathological mechanisms identification, diagnosis and therapeutic targets discovery as well. The three aspects about the progresses in proteomics researches of AD were reviewed in this article.

Alzheimer Disease↗

PubMed2005

Stress-induced changes in the Schizosaccharomyces pombe proteome using two-dimensional difference gel electrophoresis, mass spectrometry and a novel integrated robotics platform.

Robotic and manual methods have been used to obtain identification of significantly changing proteins regulated when Schizosaccharomyces pombe is exposed to oxidative stress. Differently treated S. pombe cells were lysed, labelled with CyDye and analysed by two-dimensional difference gel electrophoresis. Gel images analysed off-line, using the DeCyder image analysis software [GE Healthcare, Amersham, UK] allowed selection of significantly regulated proteins. Proteins displaying differential expression were excised robotically for manual digestion and identified by matrix-assisted laser desorption/ionisation - mass spectrometry (MALDI-MS). Additionally the same set of proteins displaying differential expression were automatically cut and digested using a prototype robotic platform. Automated MALDI-MS, peak label assignment and database searching were utilised to identify as many proteins as possible. The results achieved by the robotic system were compared to manual methods. The identification of all significantly altered proteins provides an annotated peroxide stress-related proteome that can be used as a base resource against which other stress-induced proteomic changes can be compared.

Electrophoresis, Gel, Two-Dimensional↗

PubMed2006

From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding.

This article reviews methods of integration of transcriptomics (and equally proteomics and metabolomics), genetics, and genomics in the form of systems genetics into existing genome analyses and their potential use in animal breeding and quantitative genomic modeling of complex traits. Genetical genomics or the expression quantitative trait loci (eQTL) mapping method and key findings in this research are reviewed. Various procedures and potential uses of eQTL mapping, global linkage clustering, and systems genetics are illustrated using actual analysis on recombinant inbred lines of mice with data on gene expression (for diabetes- and obesity-related genes), pathway, and single nucleotide polymorphism (SNP) linkage maps. Experimental and bioinformatics difficulties and possible solutions are discussed. The main uses of this systems genetics approach in quantitative genomics were shown to be in refinement of the identified QTL, candidate gene and SNP discovery, understanding gene-environment and gene-gene interactions, detection of candidate regulator genes/eQTL, discriminating multiple QTL/eQTL, and detection of pleiotropic QTL/eQTL, in addition to its use in reconstructing regulatory networks. The potential uses in animal breeding are direct selection on heritable gene expression measures, termed "expression assisted selection," and genetical genomic selection of both QTL and eQTL based on breeding values of the respective genes, termed "expression-assisted evaluation."

Animals↗

PubMed2026

The molecular landscape of chordoma: Current frontiers from multi-omics to artificial intelligence.

Chordoma is a rare and aggressive malignant bone tumor of the axial skeleton that has historically challenged clinicians due to its complex anatomical locations and a high recurrence rate of up to 85%. This review synthesizes the most recent advances in chordoma research and offers an overview of how multi-omics, advanced immunology, and artificial intelligence are reshaping the treatment paradigm. Central to its pathogenesis is the T-box transcription factor Brachyury, which this review highlights as both the pathognomonic diagnostic marker and the primary therapeutic vulnerability. Cutting-edge innovations targeting this driver include covalent small-molecule binders, targeted protein degradation, and peptide-centric CAR-T cells designed to attack the intracellular oncoprotein. The tumor immune microenvironment is functionally dynamic, and new dimensions in cellular therapy, such as dual-specific CAR constructs and NK-cell platforms, are being engineered to neutralize immunosuppressive factors. Beyond biological insights, the review emphasizes the role of computational biology, specifically how deep-learning and machine-learning models achieve expert-level precision in tumor segmentation and personalized survival forecasting. By integrating genomic, transcriptomic, epigenomic, and proteomic data, multiomics approaches can fully elucidate chordoma subtypes and underlying resistance mechanisms, ultimately paving the way for more precise and personalized therapeutic strategies.

Humans↗

PubMed2004

Psoriasis: future research needs and goals for the twenty-first century.

Psoriasis research is entering a new era. Progress in delineating immunogenetics and pathomechanisms of disease brings with it a need to understand fully the clinical spectrum of disease and integrate phenotype with genomics and proteomics. Involvement of patient groups and an understanding of the psychosocial aspects of psoriasis, particularly its significant impairment of quality of life, are keys to progress. The biologic revolution in therapy of inflammatory disease has embraced psoriasis bringing with it an urgent need for evidence-base, consensus on outcome measures, and long-term real-life studies.A concerted effort between academia, industry, and patients will take forward the understanding of, and therapy for, psoriasis.

Combined Modality Therapy↗

PubMed2005

Synthetic modular systems--reverse engineering of signal transduction.

During the last decades, biology has decomposed cellular systems into genetic, functional and molecular networks. It has become evident that these networks consist of components with specific functions (e.g., proteins and genes). This has generated a considerable amount of knowledge and hypotheses concerning cellular organization. The idea discussed here is to test the extent of this knowledge by reconstructing, or reverse engineering, new synthetic biological systems from known components. We will discuss how integration of computational methods with proteomics and engineering concepts might lead us to a deeper and more abstract understanding of signal transduction systems. Designing and successfully introducing synthetic proteins into cellular pathways would provide us with a powerful research tool with many applications, such as development of biosensors, protein drugs and rewiring of biological pathways.

Animals↗