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PubMed2025

Dynamic Quadrupole Selection to Associate Precursor Masses with MS/MS Products in Data-Independent Acquisition.

Data-independent acquisition (DIA) mass spectrometry facilitates high-throughput, reproducible bottom-up proteomic analyses. Typically, DIA methods coselect multiple precursor ions within a wide selection window. These precursors are simultaneously fragmented, superimposing the product ion signals into a complex chimeric spectrum. A method for varying the quadrupole selection width over the ion accumulation period is described. This method couples the intensity of a product ion to the mass of its precursor ion. By overlapping consecutive selection windows, scan-to-scan product ion intensity profiles can be used to infer precursor mass. We assess the method's sensitivity to quadrupole width, accumulation time, and mass-to-charge range using internal fluoranthene calibrant and FlexMix calibration solution with Q-Orbitrap configured mass analyzers. Additionally, we explore usability of the described technique on a tryptic-digest monoclonal antibody sample, including both direct infusion and liquid chromatography of the sample. With direct infusion, product ions from two precursors separated by 1 thomson (Th) are resolved with this method using 10 Th windows with 5 Th overlap. The product ions are associated within 0.3 Th of their respective precursor ion's m/z. Therefore, product ion spectra have a precursor ion m/z resolving power of ∼33.

Tandem Mass Spectrometry↗

PubMed2024

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Proteomics↗

PubMed2025

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics.

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models. To make Carafe more accessible to the community, we have integrated Carafe into the widely used Skyline tool.

Journal Article↗

PubMed2024

Combining Data Independent Acquisition With Spike-In SILAC (DIA-SiS) Improves Proteome Coverage and Quantification.

Data-independent acquisition (DIA) is increasingly preferred over data-dependent acquisition due to its higher throughput and fewer missing values. Whereas data-dependent acquisition often uses stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mass differential tags for relative and absolute quantification and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC (SiS) methods use an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA-SiS, leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate, and comprehensive proteome profiling.

Isotope Labeling↗

PubMed2026

A Comprehensive Analysis of Differential Protein Expression in the Plasma of Rheumatoid Arthritis Patients Utilizing Data-Independent Acquisition (DIA) Proteomics Technology.

BACKGROUND: Rheumatoid Arthritis (RA) is a Prevalent Autoimmune Disorder Affecting Millions of People Worldwide. A Thorough Understanding of Its Clinical and Pathological Features Is Essential to Improve Patient Outcomes. METHODS: This Study Combined Data-Independent Acquisition Proteomics and Enzyme-Linked Immunosorbent Assay (ELISA) to Identify and Validate Potential Plasma Protein Biomarkers for the Early Diagnosis of RA. RESULTS: Differential Proteomic Analysis Identified Differentially Expressed Proteins Between Patients With RA and Healthy Controls and Characterized Their Functions. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes Enrichment Analyses Were Performed to Explore Protein Functions and Associated Biological Pathways. The STRING Database and the Metascape Platform Were Used to Conduct an in-Depth Analysis of the Protein-Protein Interaction Network, Highlighting the Functional Attributes and Interconnections of Upregulated Proteins and Identifying Key Protein Complexes Involved in RA. ELISA Analysis of Plasma Samples Revealed Significantly Elevated SERPINA3 Levels in Patients With RA, Which Were Positively Correlated With Disease Activity Indicators-Including Erythrocyte Sedimentation Rate, C-Reactive Protein, and Disease Activity Score 28-But Were Not Correlated With Rheumatoid Factor or Its Subtypes. CONCLUSIONS: This Study Provides New Insights and Identifies Potential Biomarkers for the Early Diagnosis of RA.

Humans↗

PubMed2025

Stage-dependent proteomic alterations in aqueous humor of diabetic retinopathy patients based on data-independent acquisition and parallel reaction monitoring.

BACKGROUND: Diabetic retinopathy (DR), a microvascular complication of diabetes mellitus (DM), represents the predominant cause of preventable vision loss in working-age populations globally. While the pathophysiological mechanisms underlying DR progression remain incompletely understood, our study employs comprehensive proteomic profiling of aqueous humor (AH) to identify stage-specific biomarkers and therapeutic targets in type 2 diabetes mellitus (T2DM) patients across DR progression. METHODS: Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified AH proteomes in a discovery cohort comprising 24 subjects: 18 T2DM patients stratified by DR severity [6 non-DR, 6 non-proliferative DR (NPDR), 6 proliferative DR (PDR)] and 6 cataract controls without diabetes (non-DM). Validation cohort analysis (including 10 AH samples in each group) was performed using parallel reaction monitoring (PRM) strategy for verification of target proteins. Comprehensive bioinformatics analyses included gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) network construction, receiver operating characteristic (ROC) curve analysis, and ConnectivityMap (Cmap)-based drug prediction. RESULTS: Proteomic profiling identified 739 quantifiable AH proteins (62% extracellular) with clear separation among the four clinical stages in the discovery cohort. GSEA uncovered altered expression of proteins mainly related to complement and coagulation cascades, folate metabolism, and the selenium micronutrient network in patients with DR. WGCNA-derived protein modules yielded 83 PRM-validated targets, including 5 hub proteins differentiating NPDR from non-DR and 33 hub proteins showed significant upregulation in PDR versus NPDR comparison. Clinical correlation analysis identified F2, FGG, FGB, RBP4, AMBP, VTN, C8A, CPB2, and C2 associated with clinical traits. C6, FAM3C, SPP1, and JCHAIN levels were altered post-anti-VEGF treatment. Pharmacological prediction identified potential therapeutic compounds, including perindopril, triciribine, and XAV-939 for NPDR, and topiramate, triciribine, and vecuronium for PDR. CONCLUSION: This study established a comprehensive AH proteomic signature of DR progression, offering insights into the pathogenesis of DR and highlighting potential biomarkers and novel therapeutic targets.

Humans↗

PubMed2025

Benchmark for Quantitative Global and Redox Proteomics Analysis by Combining Protein-Aggregation Capture and Data Independent Acquisition.

Oxidative damage plays a critical role in various diseases including cardiovascular and neurological disorders. Thiol redox reactions, acting as oxidative stress sensors, influence protein structure and function. Redox proteomics, based on the differential alkylation of cysteine sites followed by mass spectrometry, enables the comprehensive analysis of thiol redox status in cells and tissues. However, these approaches require extensive sample manipulation and are not compatible with data-independent acquisition techniques. Here, we introduce PACREDOX, an innovative strategy based on protein aggregation capture (PAC), and demonstrate its compatibility with library-free DIA. Compared with traditional methods such as FASILOX, PACREDOX reduces preparation time and costs while maintaining thiol and proteome coverage. To enable library-free DIA, we corrected in silico spectral libraries in DIA-NN using experimental retention time data from methylthiolated-Cys peptides. PACREDOX with DIA was benchmarked against FASILOX in a myocardial infarction model, yielding the same biological insights, while enhancing peptide and protein coverage. Our results underscore the potential and efficiency of this methodology for studying oxidative damage. Overall, PACREDOX offers an automatable, high-throughput, and cost-effective strategy for redox proteomics.

Proteomics↗

PubMed2025

Four-dimensional data independent acquisition proteomics and metabolomics reveal mechanisms of hydrogen-rich water at Zusanli (ST36) point against triple-negative breast cancer in mice.

OBJECTIVE: To develop a safe and effective green therapy for triple-negative breast cancer, this study combines hydrogen-rich water with acupuncture point injection, and finds that it can prevent tumor growth and minimize cancer metastasis. METHODS: After 21 d of hydrogen rich water injection treatment on 4T1 (mouse breast cancer cells) xenograft mice, in order to systematically identify differentially expressed proteins in tumor samples between the model group and the Zusanli (ST36) group injected with hydrogen rich water at acupoints, with a focus on functional proteins or signaling pathways related to tumor occurrence and development, researchers conducted four-dimensional data independent acquisition (4D-DIA) proteomic analysis on tumor tissues. In order to further investigate the dynamic changes of metabolites after therapeutic intervention, researchers conducted liquid chromatography-tandem mass spectrometry untargeted metabolomics identification and analysis on mouse serum. The results of the joint proteomics-metabolomics analysis were validated using experimental methods such as immunofluorescence, Western blotting, and quantitative reverse transcription polymerase chain reaction detection. RESULTS: Injecting hydrogen-rich water into acupoints significantly inhibited tumor growth (P < 0.05). 4D-DIA proteomics and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses uncovered pathways such as T helper 1 cell (Th1) and T helper 2 cell (Th2) cell differentiation. The KEGG metabolic pathways identified in the metabolomics analysis included galactose metabolism along with fructose and mannose metabolism. Based on the combined proteomics and metabolomics analysis, the key pathways included the C-type lectin receptor signaling pathway. The major cancer-related differential proteins detected in Th1 and Th2 cell differentiation [interleukin 6 signal transducer, nuclear factor of activated T cells 4, recombinant mitogen activated protein kinase 10 (MAPK10), and MAPK11] were upregulated after the injection of hydrogen-rich water into the Zusanli (ST36) acupoint, whereas Linker for activation of T cells (Lat), signal transducer and activator of transcription 1, and protein kinase C, theta were downregulated. CONCLUSION: The injection of hydrogen-rich water into the Zusanli (ST36) acupoint effectively inhibited the hyperplasia of 4T1 BC cells and enhanced their apoptosis, potentially exerting a therapeutic effect through multiple pathways and targeting various sites.

Animals↗

PubMed2026

Proteome-wide Ubiquitinome Profiling Reveals Substrate-specific Dynamics Within the USP7 Network.

USP7 is a pleiotropic deubiquitylating enzyme that is involved in tumor suppression, (neuro) development, chromatin regulation and the DNA damage response. How USP7 regulates these diverse pathways is still unclear. Here, we report data-independent acquisition and label free quantitation mass spectrometry to profile the proteome-wide impact of USP7 on substrate de-ubiquitylation and overall protein abundance. First, we identified proteins associated with endogenous USP7 by immunopurification followed by data-independent acquisition and label free quantitation mass spectrometry. Integration of our new results with earlier interactomes of epitope-tagged USP7 yielded a consensus set of high-confidence protein targets. Domain mapping analysis revealed that, in addition to the TRAF domain, the ubiquitin-like domains of USP7 play a key role in substrate selection. Using specific enrichment of tryptic K-ε-GG peptides, we mapped proteome-wide changes in ubiquitinome dynamics following inhibition of USP7. Combining unbiased proteome-wide and targeted quantitative mass spectrometry revealed that deubiquitylation by USP7 can have different effects on the stability of distinct substrates, and suggests that USP7's activity profile is substrate-dependent rather than an intrinsic enzymatic property. Thus, in addition to providing a proteome-wide map of USP7 target sites, our multi-angle proteomics approach reveals that the effects of USP7-mediated deubiquitylation on its targets are remarkably variable and substrate-specific. Finally, based on these detailed molecular insights we show how USP7 connects various neurodevelopmental syndromes and tumor suppression pathways.

Ubiquitin-Specific Peptidase 7↗

PubMed2026

Limited Impact of Column Chemistry and Length on Proteome Coverage Under High-Speed DIA.

The evolution of mass spectrometry (MS)-based proteomics has been driven by continuous technological advances in sample preparation, liquid-phase separations, instrumentation, and data acquisition. Chromatographic performance has been recognized as a contributing factor to identification depth, particularly on earlier-generation MS platforms. Recent advances in MS sampling speed and sensitivity now raise the question of how strongly chromatographic quality continues to determine overall proteome coverage. We investigate how column chemistry and length influence proteome coverage and chromatographic selectivity under modern data-independent acquisition conditions, and whether traditional optimization priorities still apply. Spanning a matrix of experiments with five distinct stationary phases, including C18 chemistries, C8, and Phenyl-Hexyl, across eight column lengths (40-140 mm), we evaluate protein identification performance using data-independent acquisition on the Orbitrap Astral mass spectrometer. Despite differences in stationary-phase chemistry and column length, we observed remarkably convergent proteome coverage metrics. All C18 and C8 phases consistently achieved over 150,000 precursor- and approximately 9000 protein group identifications, regardless of column length variations. While retention fingerprints persisted across chemistries, these chromatographic differences did not translate into meaningful variations in proteome coverage under high-speed acquisition conditions at 200 Hz. Within the range of modern sub-2 μm reversed-phase materials tested, identification depth showed limited dependence on column chemistry and length, suggesting that for state-of-the-art stationary phases, method development priorities may increasingly favor operational robustness, throughput, and reproducibility over traditional separation optimization.

Proteome↗

PubMed2026

DiaReport: reproducible workflow for differential expression analysis and interactive reporting in DIA-based proteomics.

MOTIVATION: Data-independent acquisition (DIA) has become the preferred data acquisition method for mass spectrometry-based proteomics, yet, reproducible workflows for differential expression (DE) analysis and results reporting remain limited. We present DiaReport, an R package that performs precursor- and protein-level DE analysis from DIA-NN output using MSqRob and QFeatures, while generating high-quality, interactive HTML reports through Quarto. DiaReport integrates precursor data, filtering of missing values, normalization, protein summarization and statistical modeling within a single function, supporting both simple pairwise as well as complex experimental designs. The package provides structured outputs and configuration files to ensure computational reproducibility across different studies. To accommodate diverse research needs, DiaReport includes multiple reporting templates tailored to different proteomic applications. Applying DiaReport to an extracellular vesicle (EV) proteomics dataset demonstrates its ability to efficiently analyze DIA data and provide rapid insights into sample quality and protein level differences. AVAILABILITY: DiaReport is an open-source R package available at https://github.com/Gevaert-Lab/diareport (DOI: 10.5281/zenodo.20120604). The package is platform-independent and distributed under the MIT license. Reports are generated using Quarto and require only standard R dependencies. Detailed documentation, installation guides and usage vignettes are provided within the repository. The interactive HTML reports discussed in this study, including the UPS2 benchmark and EV case study, are archived on Zenodo (10.5281/zenodo.20122506 and 10.5281/zenodo.20123378).

Proteomics↗

PubMed2026

Quantitative Proteomic Profiling of Pinctada fucata Shell Nacre Defines a Solubility-Based Type Classification of Shell Matrix Proteins.

Shell matrix proteins (SMPs) are key organic components of molluscan biominerals, yet previous nacre proteomics have remained largely qualitative, limiting evaluation of the abundance and fraction association of individual SMPs. Here, we established a quantitative proteomic approach for the nacreous layer of the pearl oyster Pinctada fucata by integrating optimized shell preservation, stepwise fractionation, and data-independent acquisition (DIA) proteomics. SMPs were separated into an ethylenediaminetetraacetic acid (EDTA)-soluble matrix (ESM), an EDTA-insoluble but sodium dodecyl sulfate/dithiothreitol (SDS/DTT)-soluble matrix (SSM), and an SDS/DTT-insoluble matrix (ISM). DIA outperformed data-dependent acquisition in proteome coverage and enabled quantification of 327 SMPs across a broad dynamic range. Fraction-resolved abundance profiling showed that each fraction was characterized by distinct SMP compositions. To summarize these distributions, we introduced a solubility-based type classification that grouped SMPs into four types according to their quantitative partitioning among fractions. Well-known SMPs, including nacrein, Pif 80, and MSI60, were assigned to intuitively consistent types, whereas proteases, protease inhibitors, and tyrosinases also showed biased type distributions. These results support a three-compartment model of the nacreous layer consisting of (i) an insoluble interlamellar membrane core, (ii) a relatively extractable interfacial layer, and (iii) a soluble matrix fraction enriched in proteins potentially involved in ionic regulation and protein maturation. This study provides a quantitative framework for understanding coordinated SMP functions during nacre formation and for comparative analyses of molluscan shell proteomes.

Animals↗

PubMed2026

Cross-Platform Proteomics and Machine Learning Algorithms Nominate Plasma Biomarkers of Stroke Diagnosis.

BACKGROUND: Blood-based biomarkers for stroke subtyping could improve triage in emergency settings. We used cross-platform proteomics to identify plasma biomarkers differentiating major stroke diagnostic groups. METHODS: We conducted a case-control study using 2 biorepositories. Plasma was collected in the emergency department from adults with suspected stroke before therapeutic intervention. Differentially enriched proteins were identified across acute ischemic stroke, intracerebral hemorrhage, transient ischemic attack, and stroke mimics using SomaScan discovery proteomics (Grady). Differentially enriched proteins were nominated using pairwise and multigroup comparisons and adjusted for clinical covariates. Protein panels were created using least absolute shrinkage and selection operator logistic regression. Internal validation used repeated nested cross-validation (rCV) and targeted mass spectrometry (MS), while external validation used data-independent acquisition  mass spectrometry in an independent cohort (Yale). RESULTS: We included 100 subjects (40 with acute ischemic stroke, 20 with intracerebral hemorrhage, 20 with transient ischemic attack, 20 with stroke mimics) in discovery and 80 subjects (20 per group) in external validation cohorts. SomaScan quantified 7307 proteins, of which 61 differentiated stroke subtypes. We identified 7 protein classifiers for acute ischemic stroke (rCV-area under the curve, 0.82 [95% CI, 0.78-0.86]), 6 for intracerebral hemorrhage (rCV-area under the curve, 0.70 [95% CI, 0.64-0.76]), 8 for transient ischemic attack (rCV-area under the curve, 0.78 [95% CI, 0.73-0.84]), and 7 for stroke mimics (rCV-area under the curve, 0.81 [95% CI, 0.77-0.86]). Targeted proteomics internally validated 11 proteins, and data-independent acquisition-mass spectrometry externally validated 32 proteins, including VTN (vitronectin), PLG (plasminogen), and S100A9 as top stroke mimics, transient ischemic attack, and intracerebral hemorrhage classifiers. CONCLUSIONS: This study highlights plasma proteomics as a valuable tool for discovering protein biomarkers of stroke diagnosis. These findings support further validation in larger, multicenter cohorts to facilitate biomarker-guided stroke diagnosis in acute care.

Humans↗

PubMed2026

Enzyme-Metabolite Network Analysis of Endometrial Cancer-Derived Extracellular Vesicles Through Integrated Proteomics and Metabolomics.

Endometrial cancer (EC) is the most common gynecological malignancy in high-income countries. Extracellular vesicles (EVs) are key mediators of intercellular communication and metabolic reprogramming, but their molecular cargo in EC remains poorly characterized. EVs were isolated from four EC cell lines representing Type I and Type II subtypes (AN3CA, ISHIKAWA, HEC1A, and KLE). Untargeted metabolomics was performed by HILIC-LC-MS/MS, proteomics by data-independent acquisition (DIA) mass spectrometry, and multi-omics integration using MetaboAnalyst and OmicsNet. Metabolomic profiling identified 1463 annotated features and revealed significant differences among EC cell lines (PERMANOVA, p = 0.002). Twenty-eight differentially abundant metabolites, including lactic acid, succinic acid, and uric acid, were identified. Proteomic analysis quantified 8513 proteins with subtype-specific expression patterns. Integrated analysis revealed seven significantly enriched pathways, including glycolysis/gluconeogenesis, central carbon metabolism in cancer, and the pentose phosphate pathway. Increased LDHA abundance in metastatic AN3CA-derived EVs was confirmed by Western blot (p = 0.047). EC-derived EVs display subtype- and metastatic-status-specific metabolo-proteomic signatures, with glycolysis, TCA cycle remodeling, and central carbon metabolism as convergent pathway signatures of molecular reprogramming. These findings establish a multi-omics framework for characterizing EV cargo in EC and identify candidate enzyme-metabolite nodes for future biomarker validation in patient-derived specimens.

Female↗

PubMed2026

Reframing Proteomics Measurement: Super Mass Spectrometry Framework and the Role of Delayed Electrospray Ionization Technique.

Dynamic range, repeatability, and reproducibility remain the central limitations of data-independent acquisition (DIA) proteomics. Current workflows emphasize protein group identification counts and throughput, but these metrics mask the fundamental measurement challenge: generating a repeatable, reproducible, high-fidelity, and relatively complete digital representation of complex proteomes. In particular, plasma proteomics spans more than 10 orders of magnitude in protein abundance, far exceeding the capacity and dynamic range of any single mass spectrometer. Incremental advances have not closed this gap. In this Perspectives article, I introduce the Super Mass Spectrometry framework and then highlight the Delayed Electrospray Ionization (Delayed-ESI) technique, as a practical approach to address these limitations. By producing compositionally identical but temporally staggered ion beams, the Delayed-ESI technique enables deterministic remeasurement of the same analyte profile, supporting various novel strategies to improve analytical figures of merit. While recent implementations of the Delayed-ESI technique have emphasized throughput, I argue that the broader value of the Delayed-ESI technique lies in extending dynamic range and improving repeatability and reproducibility─objectives that should take precedence if proteomics is to evolve into a robust measurement science capable of supporting population-scale proteomics studies.

Proteomics↗

PubMed2026

Plasma proteomic profiling characterizes candidate biomarkers of perimesencephalic non-aneurysmal subarachnoid hemorrhage.

OBJECT: This study aims to explore the plasma proteomic profiles of angiographically confirmed pmSAH and aSAH, and to identify candidate protein biomarkers for discriminating these subtypes on a biological level. METHODS: The differentially abundant proteins of plasma samples from patients with pmSAH (n = 30) and aSAH (n = 30) were analyzed by data-independent acquisition proteomics, and candidate biomarkers were screened. RESULTS: 291 candidate biomarkers were obtained that could be used to distinguish pmSAH patients from aSAH patients, among which 76 were upregulated and 215 were downregulated in pmSAH. Subsequently, the 10 candidate biomarkers were validated by enzyme-linked immunosorbent assay in a validation cohort of 72 subjects. ORM1, ORM2, HP and NMNAT1 were specifically down-regulated in the pmSAH group, while ANP32A was specifically up-regulated in the pmSAH group. FGL2 was specifically up-regulated in the aSAH group. The combined model of ORM2, HP and ANP32A had the best discriminative power (AUC = 0.880). CONCLUSIONS: This study identified ORM2, HP, and ANP32A as candidate biomarkers reflecting biological differences between pmSAH and aSAH. SIGNIFICANCE: Although some proteomic studies have analyzed aneurysmal subarachnoid hemorrhage, to date, there have been no reports on the circulating proteomic analysis of pmSAH. Comparative analysis of the circulating proteomic differences between pmSAH and aSAH may not only help understand the causes of pmSAH, but also contribute to a deeper understanding of mechanisms showing how pmSAH differs from the formation and rupture mechanisms of intracranial aneurysms.

Humans↗

PubMed2026

Characteristics of Protein Profiling and Biomarkers in Aortic Regurgitation With Heart Failure.

BACKGROUND: Valvular heart disease, particularly aortic valve disease including stenosis and regurgitation, is a common heart disease. This study aimed to explore the protein profiling and the biomarkers in severe aortic valve disease and to provide new insights into the therapeutic strategy. METHODS: Blood samples from 80 subjects were collected and analyzed by data independent acquisition technique in 3 comparisons (mild/moderate-control, severe-control, and severe-mild/moderate) and validated by ELISA. The diagnostic value of differentially expressed proteins associated with severe valvular heart disease was also evaluated by the receiver operating characteristic curve. RESULTS: A total of 9976 peptides and 451 proteins were identified through liquid chromatography-tandem mass spectrometry analysis. From these, 64 in mild/moderate-control, 50 in severe-control, and 50 in severe-mild/moderate comparisons were identified as differentially expressed proteins. IGFBP7 (insulin-like growth factor-binding protein 7; 5581.0±697.0 ng/mL), DSG1 (desmoglein-1; 21.0±2.0 pg/mL), ADIPOQ (adiponectin; 26 686.0±3730 ng/mL), and JUP (junction plakoglobin; 10.2±0.6 ng/mL) levels in the severe group were significantly higher than that in the mild/moderate (P<0.05) group. Additionally, ADIPOQ and JUP levels in the severe group were also higher than that in control (P<0.001). Receiver operating characteristic curve analysis showed that IGFBP7, DSG1, JUP, and ADIPOQ had strong potential value to be associated with severe aortic valve disease. CONCLUSIONS: By constructing proteomics profile to identify the protein characteristics this study found that increased IGFBP7, DSG1, JUP, and ADIPOQ are the characteristics of proteins in patients with severe valvular heart disease. These findings provide new insight into the diagnosis and pathogenesis of valvular heart disease, particularly aortic valve disease.

Humans↗

PubMed2026

Hyperlactate-Associated Lysine Lactylome Remodeling in Laryngeal Squamous Cell Carcinoma.

Laryngeal squamous cell carcinoma (LSCC) lacks reliable biomarkers, and the roles of lactate metabolism and lysine lactylation (Kla) remain largely unknown. We profiled the lysine lactylome of LSCC, paired it with adjacent normal tissues, and integrated the data with quantitative proteomic and transcriptomic analyses. LSCC exhibited a hyperlactate-associated phenotype characterized by dysregulated lactate-related genes (LRGs), altered protein abundance, increased tissue lactate, and globally increased Kla levels. Data-independent acquisition mass spectrometry (DIA-MS) identified 1616 Kla sites on 1468 peptides from 688 proteins, with most differential sites being upregulated in tumors. Differentially lactylated proteins were enriched in cell-matrix adhesion, cell migration, chromatin remodeling, and gene-regulatory processes and were clustered into cytoskeletal and nuclear regulatory modules. Multiple Kla sites were also detected on the core histones. Immunoblotting and tissue microarray analyses confirmed increased pan-Kla expression in the LSCC. Pan-Kla levels were independent of sex and age but positively correlated with the tumor stage and lymph-node metastasis. These findings provide a systematic resource for hyperlactate-associated lactylome remodeling in LSCCs and identify candidate Kla-related molecular features associated with clinicopathological progression for future functional and clinical evaluation.

Humans↗