Assessing systemic effects of Bothrops jararaca venom in the lungs in a mouse model by label-free proteomics using DDA and DIA.

Cross-tissue deep profiling of redox proteome in obese mice using enhanced resin-assisted capture and data-independent acquisition.

Plasma protein signatures associated with functional outcome heterogeneity in rtPA-treated acute ischemic stroke.

Proteomic comparison of human brain tissue preservation methods.

Highly Repeatable Tissue Proteomics for Kidney Transplant Pathology: Technical and Biological Validation of Protein Analysis Using Liquid Chromatography-Tandem Mass Spectrometry/Mass Spectrometry (LC-MS/MS).

A comprehensive characterization strategy for natural products using dual-mode hybrid acquisition on high-resolution MS with intelligent subdivision polygonal mass defect filtering: a case study on Typhae Pollen.

Saliva and salivary pellicle composition and proteomic profile in smokers vs. non-smokers and its effect on dental erosion.

Proteomic profiling of early-stage non-small cell lung cancer identifies a high-performance protein signature associated with postoperative recurrence.

Integrated transcriptome and data-independent acquisition proteome analysis of the biosynthesis of Monascus azaphilone pigments and citrinin

Small extracellular vesicles (sEVs) are membrane-bound particles whose protein, lipid, and metabolite cargo reflects the molecular state of their cells of origin, making them attractive targets for biomarker discovery and therapeutic development. However, comprehensive characterization of sEVs remains challenging due to the extremely limited material available. Here, we present an integrated mass spectrometry-based multi-omics platform for simultaneous characterization of lipids, metabolites, and proteins from a single sEV sample enabled by sequential extraction, maximizing sample utilization. To enhance molecular coverage and analytical depth, the platform combines iterative tandem mass spectrometry for improved small-molecule fragmentation and nano-flow proteomics with data-independent acquisition. We achieved deep and reproducible multi-omic characterization of proteins, lipids, and metabolites using 10 million sEVs. We further demonstrated the compatibility of our multi-omics platform with sEVs isolated from plasma by ultracentrifugation, size-exclusion chromatography with ultrafiltration, and polymer precipitation, revealing purification-dependent differences in molecular profiles associated with tradeoffs in yield and purity of sEVs. By enabling integrated multi-omics from the same sample, this strategy addresses a key challenge in low-input sEV analysis and establishes a robust analytical foundation for synergistic biomarker discovery and therapeutic applications.

Kawasaki disease (KD) is classified as a "WenBing" syndrome in traditional Chinese medicine (TCM), characterized by systemic inflammation and vasculitis. Jiawei Baihu Tang (JWBHT) is an optimized herbal formulation derived from the classical Baihu Tang (BHT), a renowned ancient TCM prescription traditionally used to treat WenBing syndromes by clearing heat, reducing fever, and promoting body fluid production. However, the precise pharmacological mechanisms underlying JWBHT's intervention effects remain largely unexplored. We aimed to assess the protective effects of JWBHT on coronary artery inflammation in mice with KD and explore the underlying mechanisms, with a focus on the lipocalin-2/matrix metalloproteinase 9 (LCN2/MMP9) axis in vascular remodeling. Histopathological analysis, multiplex immunofluorescence, data-independent acquisition (DIA) proteomics, and non-targeted metabolomics were employed to comprehensively measure the effects of JWBHT on coronary artery injury. The experiments were conducted using a Candida albicans water-soluble fraction (CAWS)-induced murine model of KD vasculitis. Multi-omics integration also revealed the role of JWBHT in regulating extracellular matrix (ECM)-receptor interaction and enhancing α-linolenic acid metabolism. JWBHT significantly alleviated coronary arteritis by reducing inflammatory cell infiltration, preserving ECM integrity, and alleviating fibrosis. Multi-omics analysis indicated that JWBHT regulated ECM-receptor interaction and restored lipid metabolism, particularly α-linolenic acid metabolism, which was closely linked to ECM stabilization. Mechanistically, JWBHT suppressed the LCN2/MMP9 axis, a critical mediator of vascular remodeling. Validation based on the clinical dataset revealed elevated levels of LCN2/MMP9 in patients with acute KD, which were normalized after treatment. For the first time, this study unveiled the multi-target mode of action of JWBHT in KD through the crosstalk between ECM and lipid metabolism, and LCN2 was identified as a potential novel intervention target. Our findings suggest that JWBHT is a promising traditional Chinese medicine for preventing the cardiovascular complications of KD.

Bone destruction associated with active rheumatoid arthritis (RA) remains a major therapeutic challenge, with a lack of reliable molecular markers reflecting bone injury. This study aims to identify novel biomarkers linked to bone destruction in active RA through proteomic analysis, providing new strategies for precise monitoring and targeted therapy. Data-independent acquisition mass spectrometry was used for proteomic quantification and bioinformatic analysis on plasma samples from 160 patients with RA and 40 healthy controls. Key proteins associated with bone destruction were screened by integrating Sharp scores with synovial single-cell RNA sequencing data and subsequently validated in two independent cohorts (N1 = 50 and N2 = 10) using enzyme-linked immunosorbent assay and multiplex immunohistochemistry. Functional studies were conducted using fibroblast-like synoviocytes (FLSs) in vitro and a collagen-induced arthritis (CIA) mouse model in vivo. A total of 4,998 plasma proteins were identified, with 506 showing significant differential expression between active and remitted RA. Thousand-and-one-amino acid kinase 3 (TAOK3) levels were positively associated with Sharp scores and markedly elevated in patients with active RA. Combining TAOK3 with C-reactive protein improved diagnostic accuracy for active RA (area under the curve = 0.915). High TAOK3 expression was also associated with increased relapse frequency. Functional studies showed that TAOK3 knockdown suppressed the tumor-like phenotype of FLSs and down-regulated matrix metalloproteinase 1/2/3 and cathepsin K, whereas TAOK3 overexpression promoted pannus cell-mediated bone erosion, mitigated by TAOK3-targeted inhibitor. In vivo, its inhibition showed therapeutic effects in CIA mice. TAOK3 serves as a potential biomarker for bone destruction in active RA and as a therapeutic target for precision monitoring and intervention.

Kinase inhibitors represent a vital class of therapeutic agents widely used in cancer research, immunology, and other disease areas. Mass spectrometry (MS) employing specially designed small-molecule kinase-binding probes has become an essential strategy for identifying novel kinase drug targets. While traditional MS approaches often rely on targeted proteomics (e.g., multiple reaction monitoring [MRM]) or data-dependent acquisition (DDA), data-independent acquisition (DIA) offers broader and more reproducible quantification, especially for low-abundance peptides. In this study, we systematically developed an activity-based protein profiling (ABPP) platform leveraging DIA, through integrated in-house informatics tools for data filtering and motif analysis, to provide an effective kinase profiling workflow. Compared to DDA, the DIA approach yielded more than a 100% increase in identified biotinylated peptides and over 40% improvement in kinase peptide coverage, while reducing the analysis time by half (90 min vs. 180 min per sample). Additionally, there was a modest improvement to the coefficient of variation (CV) in kinase peptide quantification (decrease from 11.41% to 10.70%; mean CV). Shorter liquid chromatography (LC) gradient times (60, 45, and 30 min) were evaluated as a means for increasing sample analysis throughput. Notably, no significant loss in kinase peptide coverage was observed due to shorter gradients, highlighting the capability of DIA to significantly enhance the efficiency and scalability of kinase profiling workflows.

Recurrent implantation failure (RIF) is a significant clinical challenge in assisted reproduction, yet its underlying molecular mechanisms remain poorly understood. This study aimed to characterize and integrate the metabolomic, proteomic, and transcriptomic profiles of the RIF endometrium to identify novel pathophysiological networks and potential diagnostic biomarkers. We performed an integrated multi-omics analysis on endometrial tissues from fertile controls and RIF patients during the window of implantation. Metabolomic profiles were assessed using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Proteomic changes were quantified using data-independent acquisition (DIA)-based quantitative proteomics. Transcriptomic alterations were analyzed using bulk and single-cell RNA sequencing (scRNA-seq), complemented by spatial metabolomics to localize metabolic shifts. Bioinformatics analyses, including pathway enrichment and gene-metabolite network construction, were used to integrate the datasets. Distinct endometrial metabolomic profiles were observed in RIF patients compared to controls, with dysregulation in amino acid pathways (e.g., tyrosine, phenylalanine, tryptophan, histidine metabolism) and glycerophospholipid disruptions. Key differential metabolites, including 4-hydroxyphenylpyruvic acid, α-ketoglutaric acid, and indole-3-carboxaldehyde, showed high diagnostic potential. Proteomics identified 282 differentially expressed proteins, highlighting impaired extracellular matrix-receptor interactions, cell adhesion, and amino acid transport, with hubs such as fibronectin and integrins. Single-cell analysis localized these deficits primarily to the luminal and glandular epithelial cells. Spatial metabolomics further displayed disrupted amino acid distribution. Integrated multi-omics analysis revealed significant disruptions in amino acid and energy metabolism pathways in RIF. L-Glutamic acid served as a central metabolic hub connecting critical proteins (SLC1A1, ARG2, TGFB2) and genes, with pyruvate carboxylase (PC) linking glycolysis to the citrate cycle. This integrated multi-omics approach elucidates the complex molecular crosstalk in the non-receptive endometrium. The findings suggest that epithelial metabolic insufficiency and adhesive defects contribute to RIF, offering potential biomarkers and therapeutic targets to improve implantation success.

Limited proteolysis coupled to mass spectrometry (LiP-MS) probes protein conformational dynamics, but interpretation of LiP-MS data is complicated by heterogeneous proteolytic cleavage patterns and missing data. Recent advances in data-independent acquisition (DIA) and machine learning-based search engines promise improved sensitivity and reproducibility, yet their performance in LiP-MS workflows remains underexplored. We systematically evaluated selected library-free DIA workflows using a rapamycin-treated human cell lysate and a yeast heat shock data set, benchmarking DIA-NN and Spectronaut for identification depth, reproducibility, and false discovery rate control. Our results show that library-free approaches achieve high sensitivity, eliminating the experimental overhead and sample requirements associated with empirical libraries. Building on these advances, we introduce a DIA-based Limited Proteolysis data Analysis pipeline (DIA-LiPA), a data analysis workflow tailored for LiP-MS data that integrates semitryptic- and tryptic-level precursor data and accounts for missingness to enable structural interpretation. Validation across multiple data sets confirmed that DIA-LiPA reproduces known structural signatures and uncovers additional regulatory patterns, providing a robust framework for mechanistic insights into protein dynamics.

Fragilariopsis cylindrus is a key diatom in the Southern Ocean, where low iron and manganese availability constrain primary production and biogeochemical activity. The molecular mechanisms used by polar diatoms, including F. cylindrus, to cope with trace metal limitations remain largely unexplored. Here we present phenotypic characterizations and proteomic profiles of F. cylindrus grown under controlled iron (low, medium, high) and manganese (low, high) conditions that reflect those observed in the Southern Ocean. Using data-independent acquisition mass spectrometry, we measured over 8000 unique proteins capturing diverse metabolic responses, including those related to photosynthesis, elemental transport, and intracellular trafficking. We confirm consistent expression of canonical iron stress proteins (e.g., phytotransferrin) under low iron, and identify additional candidate biomarkers for iron and manganese stress that could be explored in future laboratory and field experiments. Our data also support the notion that one flavodoxin isoform in F. cylindrus is iron responsive and one is not, and show that PsaE, a protein associated with the iron-rich photosystem-I, is upregulated under low iron. Altogether, this dataset is among the most comprehensive proteomic characterizations of trace metal physiology in polar diatoms to date, providing a foundation for connecting molecular responses to trace metal availability and ocean biogeochemistry.

Cotton (Gossypium hirsutum L.) is a globally important cash crop. Cytoplasmic male sterility (CMS) is a key biological tool for hybrid seed production. However, a systematic, stage-specific proteomic profile of CMS anther development in cotton remains lacking. To address this, we employed, for the first time in cotton CMS research, a high-resolution quantitative proteomic approach using data-independent acquisition mass spectrometry (DIA-MS) to compare the CMS line C2P5A and its maintainer line C2P5B across three critical anther developmental stages: pollen mother cell (Pms), tetrad stage (Tds), and mononuclear stage (Ms). A total of 498 significantly differentially expressed proteins (DEPs) were identified, with 194 upregulated and 304 downregulated. Bioinformatic analysis revealed that these DEPs are significantly enriched in key metabolic pathways essential for anther development, including glycolysis/gluconeogenesis, pyruvate metabolism, the tricarboxylic acid (TCA) cycle, starch and sucrose metabolism, and fatty acid degradation. Notably, we identified 12 aldehyde dehydrogenases (ALDHs) and several cytochrome P450 proteins associated with reactive oxygen species (ROS) homeostasis and pollen wall formation. This study presents a systematic, stage-resolved proteomic atlas of CMS in cotton using DIA-MS, which reveals that widespread dysregulation of central energy and secondary metabolite pathways underpins the sterility phenotype. Our integrated multi-omics and functional validation approach provides novel molecular insights into CMS mechanisms and pinpoints potential targets for hybrid breeding.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains the leading cause of death from a single infectious agent worldwide. Exposure to Mtb results in diverse outcomes: bacterial clearance, latent infection, asymptomatic, or symptomatic TB. Current diagnostic tools cannot reliably distinguish these outcomes. Our previous studies identified Mtb proteins in extracellular vesicles (EVs) from TB patients' serum, suggesting their potential as biomarkers. Here, we aimed to discover Mtb proteins and peptides in serum EVs across TB stages, focusing on asymptomatic individuals. Serum was obtained from healthy, HIV-negative South African adult volunteers enrolled in a TB risk study. Based on patients' outcomes, samples were classified as prevalent, incident (controls that progressed to TB), activated TB, or community controls. EVs were isolated using ExoQuick™, followed by protein digestion and mass spectrometry (MS) analysis. Data-independent acquisition (DIA) with five different data analysis strategies, and two data-dependent (DDA) methods were used to identify Mtb proteins. Our DIA analysis using ion-mobility and spectral libraries enriched with Mtb-MS data revealed 19 Mtb proteins. Rv2997 was significantly higher at baseline in individuals who were initially TB-negative (incident) but later became bacteriologically positive, asymptomatic-TB (activated). HspX, GroEL2, and GroES, together with a MtrB peptide were significantly different between controls and asymptomatic-TB cases. DDA approaches did not resolve additional Mtb proteins. Quantitative DIA analysis discovered Mtb proteins/peptides in serum-derived EVs that were differentially abundant in individuals with early, asymptomatic TB. These findings highlight their potential as biomarkers and provide insight into host-pathogen interactions during subclinical infection.

Novel duck reovirus encodes a new nucleus-localized protein, p18. We aimed to investigate whether the nuclear entry of p18 is dependent on viral replication, identify the cellular proteins that interact with p18, and determine the transporters involved in the nuclear import. The subcellular localization of p18 was observed by confocal microscopy. Cellular proteins interacting with p18 were identified through data-independent acquisition qualitative proteomics. The interaction between p18 and importin α was determined by confocal microscopy, co-immunoprecipitation (Co-IP) and molecular docking. We observed that p18 was localized to the nucleus in transfected cells. Importin α1, α3, α4, α5, and α7 were colocalized and co-immunoprecipitated with p18. The importin α/β1 pathway inhibitor reduced the nuclear distribution of p18. The truncated form of p18, lacking the C-terminal region, was predominantly distributed in the cytoplasm. Collectively, our research suggests that the nuclear entry of p18 is independent of viral infection, importin α is implicated in the nuclear import of p18, and the C-terminal region of p18 is crucial for nuclear localization.

Inactive ovaries (IO) are a common reproductive disorder in early lactating dairy cows. This condition significantly reduces reproductive efficiency and economic returns. Although many studies have explored the metabolic characteristics of postpartum IO, systematic proteomic analyses using multiple biological samples are still lacking. This study used data-independent acquisition (DIA) proteomics to systematically analyze protein expression profiles in serum, follicular fluid (FF), and ovarian tissues of dairy cows. The goal was to identify potential molecular regulatory mechanisms and key biological pathways involved in IO. Serum, FF, and ovarian tissue samples were collected from six healthy and six IO multiparous Holstein cows at 63 (± 3) days postpartum. These samples were analyzed using DIA-based quantitative proteomics. In total, 155, 171, and 157 differentially expressed proteins (DEPs, P < 0.05 and FC ≥ 1.2 or ≤ 0.83) were identified in serum, FF, and ovarian tissues, respectively. Among these, 67 DEPs were upregulated and 88 were downregulated in serum, 38 were upregulated and 133 downregulated in FF, and 71 were upregulated and 86 downregulated in ovarian tissue. Integrated analysis revealed seven consistently altered proteins (APCS, KLKB1, HRG, TTR, CTSS, RBP4, and CUTA), of which CTSS was upregulated, while the others were downregulated across all sample types. Functional enrichment analysis indicated that most metabolic pathways, particularly glycolysis/gluconeogenesis, the tricarboxylic acid cycle, carbon metabolism, starch and sucrose metabolism, and amino acid biosynthesis and degradation (including branched-chain amino acid pathways), were markedly downregulated in IO cows, suggesting metabolic suppression. In contrast, cholesterol metabolism and complement and coagulation cascades showed a general upregulation trend. This study, for the first time, systematically characterizes coordinated alterations in key metabolic and reproductive pathways of IO cows through integrated multi-tissue proteomics. The findings provide insight into the molecular mechanisms of IO and suggest potential biomarkers and targets for improving ovarian function.