Abstract Data processing tools for the data-independent acquisition method, rapidly gaining popularity in proteomics in recent years, are crucially important for this method’s efficiency. Quite a number of them, serving both the identification and quantitation of proteins, have been developed over the past decade, yet the issue is still in its infancy, and the research on their comparison and integration is scarce. In this study, we collected four DIA search workflows─DIA-NN, DIA-Umpire, diaTracer, and MSFragger-DIA─as well as four protein quantitation utilities─directLFQ, AlphaPeptStats, Triqler, and simple t test─to evaluate the efficiencies of quantitation workflows integrating these tools in different combinations. The combinations were evaluated in terms of the number of protein identifications, differential expression sensitivity, fold change accuracy, and quantitative false discovery rate control, using two spike-in benchmark data sets. The study provides insights into the compatibility of the evaluated software pairs and practical recommendations for improving the data analysis pipeline for DIA proteomics.
Abstract Matrix vesicles (MVs) are extracellular vesicles released by mineralizing-competent cells, including chondrocytes and osteoblasts. Although MVs are classically associated with biomineralization, whether distinct MV populations are produced throughout osteoblast differentiation remains poorly understood. Here, we investigated the biophysical, biochemical, and proteomic profiles of MVs isolated throughout osteogenic differentiation to evaluate the hypothesis of functional transitions during biomineralization progression. MVs were isolated from osteoblast cultures at three time points, representing the onset, establishment, and propagation stages of biomineralization. Biophysical and biochemical characterization revealed temporal differences in MV properties. Stage-specific proteomic signatures were identified among MV groups. Early stage MVs (7–14 days) exhibited enrichment of proteins associated with the extracellular matrix (ECM) organization, intracellular trafficking, cytoskeletal dynamics, and adhesion, suggesting a role in preparing the mineralizing microenvironment. In contrast, later-stage MVs (14–21 days) showed enrichment of proteins related to ECM interactions, calcium response, proteoglycan binding, metabolism, and regulatory signaling pathways, consistent with enhanced mineral propagation. Collectively, our findings demonstrate that distinct MVs are produced throughout osteoblast differentiation and support a functional transition model during biomineralization, in which MV cargo and properties are dynamically coordinated with the temporal demands of ECM mineralization.
Abstract Detecting multiple viruses in biofluids such as saliva remains challenging because viral proteins are of low abundance, embedded in a complex matrix, and subject to rapid sequence variation. Mass spectrometry-based proteomics complements nucleic acid–based assays by directly resolving protein-level sequence variation, but standard database cross-correlation searches lose sensitivity as viral variant sequences increase the database size. Here, we report a sequence-filtering strategy integrated with single amino acid variant (SAAV) detection to enable sensitive mutation variant detection while minimizing the loss of search sensitivity. In silico analysis of SARS-CoV-2 and influenza sequences reveals bimodal prevalence distributions of the mutated peptides. By using a three-month time window and removing low-prevalence peptides, the peptide database size is reduced by 82.1% for SARS-CoV-2 and 94.6% for influenza at a 10% prevalence threshold with only a <0.8% loss in search sensitivity while retaining 97.8% and 98.4% of viral peptides in the subsequent month. A mutation discovery module further mitigates loss of database coverage and improves variant detection in peptide-spiked saliva experiments compared with de novo sequencing, achieving SAAV peptide-spectrum match recovery rates of 73.6% for the SARS-CoV-2 spike and 88.4% for H1N1 hemagglutinin. This study establishes a scalable proteomics framework for the sensitive detection of known and emerging viral variants in complex biofluid matrices.
Abstract Heatstroke is a critical medical condition marked by hyperthermia-induced immune dysregulation, endothelial damage, and organ dysfunction, yet its molecular mechanisms are not fully elucidated. In this study, serum samples from heatstroke (HS) patients and normal controls (NCs) were subjected to untargeted ultrahigh-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) metabolomic and quantitative proteomic analyses. Differential proteins and metabolites were identified and further analyzed through pathway enrichment, protein–protein interaction, correlation, and integrated multivariate analyses. Compared to NC, HS patients demonstrated 161 differential proteins (136 up-regulated, 25 down-regulated) and 427 differential metabolites (330 up-regulated, 97 down-regulated). The proteins B2M, MMP9, and APOB were identified as key hub proteins linked to immune activation, endothelial injury, and lipid metabolism. Metabolic changes were observed in kynurenine, purine, and mitochondrial energy metabolism pathways, while lipid metabolites showed an inverse correlation with inflammatory proteins. Integrated principal component analysis (PCA) and canonical correlation analysis (CCA) effectively distinguished HS patients from the NC. Through joint pathway analysis, 30 enriched KEGG pathways were identified, notably including cAMP signaling and arachidonic acid metabolism. These findings elucidate coordinated alterations in immune, vascular, and metabolic processes associated with heatstroke, and underscore B2M, MMP9, and ANGPTL3 as potential molecular features warranting further investigation.
Abstract Comprehensive proteome profiling requires improved depth and throughput, which have traditionally relied on advances in mass spectrometry (MS) and chromatographic separation. Here, we present fracSLIM, a high-throughput, deep-coverage proteomics workflow that integrates structures for lossless ion manipulation (SLIM)-based high-resolution ion mobility spectrometry–mass spectrometry (IMS-MS) with offline peptide fractionation. This approach leverages ion mobility-derived information, including enhanced separation of isomeric and isobaric species and accurate collision cross-section (CCS) measurements, to enable confident peptide identification. We evaluated the utility of this approach for complex proteomics samples by analyzing three fractions of peptide digests from human HeLa cells, enabling an over 10-fold increase in peptide identifications compared to conventional single-shot LC-MS workflows while maintaining high reproducibility. Furthermore, the workflow supports quantitative analysis by comparing endogenous peptides to heavy-isotope-labeled standards. These results highlight the potential of fracSLIM for greatly improved high-throughput and deep proteome profiling of complex samples.
