Saliva remains underexplored in metabolomics compared with widely used biofluids such as blood and urine. However, its noninvasive, rapid, and cost-effective collection, together with its suitability for self-sampling, makes it attractive for clinical and personalized medicine. Moreover, saliva is expected to provide complementary metabolic information on other biofluids. In this context, this study aims to identify an optimal sample collection and preparation protocol for maximizing informative metabolomic data from salivary nuclear magnetic resonance (NMR) profiles. Four preparation protocols, selected and adapted from the literature, were systematically compared using the number of identified metabolites, their concentrations, and intraday repeatability as evaluation criteria. Among them, an in-house-adapted method combining centrifugation, freeze-drying, and ultrafiltration proved most effective. This approach provided the broadest metabolome coverage, with 42 metabolites quantified. This method was further assessed using analysis of variance to determine intra- and interday precision. Most metabolites demonstrated excellent repeatability (coefficients of variation below 10%), confirming the protocol reliability for quantitative metabolomics. Overall, the optimized approach yields high-quality spectra, wide metabolic coverage, and strong analytical precision, supporting reproducible salivary NMR metabolomics. Beyond its methodological contribution, this work highlights saliva as a promising biofluid for diagnostics, disease monitoring, and personalized medicine, provided that collection and preparation procedures are appropriately standardized.

Proteomics provides a systematic and high-throughput approach to comprehensively characterize protein networks, enabling insights into cellular functions and disease mechanisms. Carbamidomethylation using iodoacetamide (IAA), a common method for cysteine alkylation, is known to cause nonspecific modifications that increase spectral complexity in mass spectrometry and reduce quantitative accuracy. Here, we established a reproducibility-focused 2-mercaptoethanol (2-ME)/dimethyl sulfoxide (DMSO) workflow and systematically evaluated its quantitative performance at the proteome-wide level. Mouse liver proteomes were processed using either 2-ME/DMSO or conventional IAA treatment, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The optimized 2-ME treatment increased the number of cysteine-modified peptides by 1.6- to 1.9-fold. Although total protein identifications were comparable, 77% of proteins exhibited improved sequence coverage with the optimized 2-ME treatment. Quantitative reproducibility was also enhanced, with the peptide quantified CV ≤ 20% increasing from 61.4% with IAA treatment to 86.1% with 2-ME treatment, and protein quantified CV ≤ 20% increasing from 80.6% with IAA treatment to 93.5% with 2-ME treatment. Application of this new workflow to ovarian clear cell carcinoma reliably detected cisplatin-induced alterations. The 2-ME/DMSO workflow offers a simple and highly reproducible proteomics strategy for accurate quantitative proteomics.

Human plasma proteomics is critical for biomedical research but challenged by an extraordinary dynamic range. We evaluated six mass spectrometry workflows, including bead-based enrichment (Proteograph XT, P2, Mag-Net), depletion of the 14 most abundant proteins, and neat plasma analysis. Enrichment methods achieved superior proteome coverage, identifying up to 4600 proteins, but exhibited acute susceptibility to preanalytical bias from cellular contaminants and vesicles in plasma. Increased centrifugation stringency before proteomics analysis drastically decreased protein identifications. Crucially, the abundance of the vast majority of secreted proteins was not significantly affected by centrifugation for all tested workflows, suggesting that their quantification is little influenced by contaminating cells or vesicles, whereas depleted proteins are enriched for cellular components like cytoskeleton organization and platelet activation. Our results underscore that nanoparticle-based methods allow robust and deep measurement of the secreted plasma proteome, providing additional insights by reporting the contributions of proteins of cellular origin when applying the common practice of 2000 × g centrifugation prior to analysis.

Hair fibers are a common form of biological trace evidence. Taxonomic classification can provide valuable forensic intelligence and aid in evidence prioritization. Traditional methods of classification, based on hair morphology, require expert interpretation, offer limited taxonomic resolution, and are not easily scalable. Mass-spectrometry-based proteomics provides a biomolecular alternative through taxon-specific amino acid polymorphisms to resolve species identification. In this study, a proteomics workflow was developed for the discovery and characterization of taxonomically diagnostic hair peptides in humans and 14 additional species, selected based on relevance to crime scene investigation. A panel of 226 taxonomically informative peptides were identified, with 207 novel to this study. This included 59 peptides with single-species resolution, while the remainder were diagnostic due to restricted taxonomic distribution or convergent evolution. The panel of taxa-discriminating markers was subsequently integrated into a forensic workflow. The developed workflow was used to analyze single fur-hair (20 mm), from four individuals from each species of interest. The data were processed using a curated hair protein database, providing an efficient bioinformatics workflow that did not require prior source information for identification. The method was able to classify all hairs analyzed with genus- or species-level resolution.

We, the PTMeXchange Consortium, present a meta-analysis of eight high-quality data sets to map 56,694 phosphosites in brewer's yeast (Saccharomyces cerevisiae) using strict control for false identifications. Each site has been classified into the Gold-Silver-Bronze confidence categories. First, we identified 55 significant motifs and grouped these into kinase classes to perform pathway enrichment analysis. Next, we leveraged disorder region predictions and AlphaFold 3's ability to consider post-translational modifications (PTMs) when modeling proteins to understand the structural context of phosphosites. Here, we determined that phosphorylation tends to occur on disordered serine and threonine residues. AlphaFold predictions suggest that phosphosites induce alpha helices to form in proteins, although many "induced helices" appear to be unusually short and require further validation. As artificial intelligence (AI) is being applied in proteomics, we must ensure that publicly available data are accurate and of high-quality to be used for downstream analyses and training models. With this motivation, our results are available in PRIDE (PXD071918), PeptideAtlas and UniProtKB, ensuring that this PTM data is FAIR and "AI-ready".

PeptideVisualizer is an open-source software for PROTOMAP analysis, offering an intuitive graphical user interface and command-line compatibility. The software processes mass spectrometry data output from MaxQuant to visualize protein migration in polyacrylamide gel electrophoresis and sequence coverage. In addition, it integrates information regarding protein secondary structure and features from the UniProt database to visualize them within comprehensive peptographs. These features allow us to assess the occurrence of a proteolytic event based on sequence context and peptides abundance. The key advantages of PeptideVisualizer include the integration of quantitative information, handling of multiple experiments with biological replicates, and the introduction of a novel mismatch factor─a metric designed to rapidly identify proteolytic events. Herein, the software was validated in an apoptosis-related data set, demonstrating its effectiveness and usefulness in large-scale proteomic data analysis.

Protein evidence derived from mass spectrometry (MS) across cancer cohorts and model systems is extensive but remains fragmented across individual studies and repositories, limiting rapid retrieval and evidence-based benchmarking of cancer-context protein detection. Here we present the Mass Spectrometric Detected Cancer Proteins (MSCP) resource, an integrated database assembled from 27 large-scale cancer proteomics sources spanning human tumor cohorts, cancer cell lines, and patient-derived xenograft (PDX) models. Protein identifications were harmonized to UniProtKB-Swiss-Prot (release 2025_01) and integrated under FDR-controlled identification outputs to generate a unified catalog of 15,964 MS-supported human proteins. Benchmarking against neXtProt PE1 identified 525 proteins newly supported by MS evidence in the integrated cancer context, including proteins previously associated with chromosome-level evidence inconsistencies. Functional interpretation of the newly identified set using GO and Reactome enrichment highlighted immune- and barrier-associated processes and chromatin- and genome-regulatory pathways, including DNA methylation and histone deacetylation. Orthogonal verification using synthetic unique peptides confirmed representative newly identified proteins by concordant precursor m/z and fragment-ion patterns. MSCP provides a provenance-aware, UniProtKB-aligned resource for cancer proteomics that supports both cohort- and model-specific querying and coverage-oriented evidence aggregation, enabling standardized comparisons to reference proteomes and facilitating downstream assay planning and translational studies.

Untargeted 1H NMR metabolomics offers a noninvasive means to identify biomarkers in breast cancer (BC) patients; however, metabolic signatures specific to Nigerian women remain poorly understood. This study aimed to identify plasma metabolomic and lipidomic biomarkers associated with BC in Nigerian patients, evaluate their diagnostic performance using machine learning (ML), and identify dysregulated metabolic pathways. This case-control study recruited 100 BC patients and 100 healthy controls from 4 Nigerian teaching hospitals. Plasma metabolites and lipids were profiled using 1H NMR spectroscopy and the Liposcale test. Multivariate and ML analyses revealed a clear distinction between BC and controls (PLS-DA accuracy: 92.4-94.4%). Twenty-four metabolites were significantly altered (FDR < 0.05), with decreased glycine and glutamine, and increased GlycA, GlycB, glucose, and ketone bodies. Lipoprotein profiling showed reduced small HDL, LDL, and large VLDL particles, alongside with increased HDL diameter. The random forest model achieved the best classification performance (AUC = 0.985) and identified 23 key biomarkers. Pathway analysis revealed 29 enriched metabolic pathways, including glyoxylate and dicarboxylate metabolism. Overall, these findings highlight distinct metabolic alterations in Nigerian BC patients and demonstrate the potential of combining NMR-based metabolomics with ML for population-specific, noninvasive BC diagnostics.

The reusability of proteomics data sets depends on the ability to obtain accurate metadata to guide reprocessing pipelines. However, many data sets deposited in public data repositories lack sufficient and reliable annotation, limiting large-scale reanalyses. To address this challenge, we developed RunAssessor, a tool that systematically extracts and summarizes information directly from mass spectrometry data files prior to peptide identification analysis. RunAssessor extracts and summarizes sample preparation and instrument acquisition parameters directly from the data where possible. Using one complete data set and test files from 18 other data sets as examples, we demonstrate RunAssessor's ability to extract instrument models, isobaric labels, phosphoenrichment, precursor and fragment ion tolerances, along with the dynamic exclusion time used by the instrument. These extracted metadata are stored in a comprehensive output file, and summarized in a standard Sample and Data Relationship Format (SDRF) file, thereby reducing the burden of manual curation and improving the reliability of proteomics data set metadata, facilitating the reuse of public data.

Colorectal cancer (CRC) remains a major global health burden, necessitating innovative therapeutic approaches with improved selectivity and reduced toxicity. DNA minor groove binders (MGBs) represent a promising class of agents that modulate DNA-associated processes without inducing permanent DNA damage. In this study, two previously reported distamycin-like DNA minor groove binders, MGB30 and MGB32, were investigated to elucidate their molecular mechanisms of action in HCT-116 human colorectal cancer cells. An integrated multiomics approach combining metabolomics and proteomics was employed using TIMS-QTOF-UHPLC-MS. Four biological replicates were used for each treatment condition. Following MGB30 treatment, 12 metabolites and 187 proteins were significantly dysregulated, whereas MGB32 treatment resulted in alterations of 41 metabolites and 409 proteins using a Student's t-test with q-value <0.05. Pathway enrichment analysis revealed that both compounds significantly disrupted purine metabolism, while MGB32 additionally affected beta-alanine metabolism, glutathione metabolism, and spermidine and spermine biosynthesis. Proteomic analysis further demonstrated deactivation of RNA processing, translation, and ribosome biogenesis, leading to impaired protein synthesis and reduced cancer cell proliferation. This study provides mechanistic insights into the downstream molecular effects of MGB30 and MGB32 that disrupt key mechanisms underlying tumor growth, offering new avenues for CRC treatment.