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PubMed2026

3D Proteomics: Structural, Functional, Chemical and Biomarker Discovery Proteomics With LiP-MS.

Protein structural dynamics drive changes in protein function, making the capture of such dynamics essential for interrogating biological systems. Here we review limited proteolysis coupled to mass spectrometry (LiP-MS), a structural and chemical proteomics method that uses changes in susceptibility to protease cleavage to profile proteome-wide protein structural changes within complex biological samples. In the decade since its development, LiP-MS has become a broadly used structural proteomics method, with peptide-level resolution. It has identified drug targets, delineated altered cellular pathways in response to complex perturbations, revealed structural information on otherwise challenging protein targets, and demonstrated the new concept of structural biomarkers of disease. Because LiP-MS simultaneously probes numerous types of molecular events, such as molecular binding, changes in enzyme activity, chemical modifications, allosteric conformational changes, aggregation, and unfolding, it supports a new proteomics workflow which we term 3D proteomics. This workflow enables the detection of specific functional sites within proteins that are altered upon perturbation, thereby guiding the generation of molecular hypotheses. Further, by globally profiling structural in addition to protein abundance changes, LiP-MS has proven able to greatly increase the information content of functional proteomics screens. In sum, LiP-MS has supported the development of a novel conceptual framework for generating, visualizing, and interpreting structural proteomics data with peptide level resolution, thereby comprehensively probing biological systems. Here we survey the applications of LiP-MS, discuss methodological variants developed by us and others, and describe the use of this new type of omics readout for structural, functional, chemical, and biomarker discovery proteomics.

Proteomics↗

PubMed2025

Proteomic discovery analysis of quantitatively assessed emphysema in the general population. The MESA Lung Study.

BACKGROUND: Pulmonary emphysema occurs frequently in older adults, often without airflow limitation. Its presence predicts symptoms, respiratory hospitalizations and deaths, and all-cause mortality. Proteomics may provide further insights into emphysema pathogenesis and inform therapeutic targets. OBJECTIVE: We performed a proteomic discovery analysis of percent emphysema on computed tomography (CT) in a population-based, multiethnic sample from the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study. Replication was performed in two chronic obstructive pulmonary disease (COPD)-based studies, the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS) and the Genetic Epidemiology of COPD (COPDGene) Study. METHODS: MESA recruited participants from the general population in 2000-02. The MESA Lung Study performed full-lung CT scans in 2010-12. Percent emphysema was defined as the percentage of lung voxels < -950 Hounsfield units. Over 7,200 plasma aptamers were measured via SomaScan. Cross-sectional linear and least absolute shrinkage and selection operator (LASSO) regression models were adjusted for demographics, anthropometrics, smoking, renal function, and scanner parameters. Statistical significance was defined as a false discovery rate p-value < 0.05. Gene Ontology (GO)/Reactome enrichment analyses were performed. LASSO-selected proteins' predictive performance was evaluated. RESULTS: Among 2,504 participants in the MESA Lung Study, mean age was 69.4 years, 1,291 had ever smoked, and median percent emphysema-like lung was 1.4%. In total, 1,234 aptamers were significantly associated with percent emphysema in the MESA Lung Study, and 35 replicated in the SPIROMICS and COPDGene Studies. Novel associations included protein family with sequence similarity (FAM) 177A1, syntenin-2, ubiquitin carboxyl-terminal hydrolase 25, and uncharacterized protein C20orf173. Previously identified emphysema-associated proteins included soluble advanced glycosylation end product-specific receptor (sRAGE), protein S100-A12, high mobility group protein B1, and roundabout homolog 2. Enrichment analyses identified 40 GO biological processes, including chemokine production and regulation and cell-cell adhesion and regulation, and two Reactome pathways, including RAGE signaling. In tenfold cross-validation, novel proteins were largely retained by LASSO (R2 = 5.4%), improved overall model performance (R2 = 24.8%), and uniquely explained greater variance in percent emphysema. CONCLUSIONS: This analysis in a general population sample identified novel and previously characterized proteins whose functional roles were validated by GO/Reactome enriched pathways, offering new insights into emphysema pathophysiology and therapeutics.

Humans↗

PubMed2026

Advancing proteomic discovery through optimized multi-stage scoring and deep learning-enhanced open search.

MOTIVATION: Protein search engines are essential for interpreting mass spectrometry data into biological insight. Current tools often face limitations in sensitivity when analyzing complex modern datasets, and lack a unified framework that effectively integrates deep learning features for both restricted and open searches, especially for scenarios aimed at discovering unknown modifications. RESULTS: We present pFind+, a high-performance search engine for data-dependent acquisition (DDA) proteomics, extending pFind. It introduces an enhanced raw scoring that delivers substantially improved pre-filtering ability, while recovering most of the computational overhead through a tailored acceleration strategy. Coupled with an enhanced rescoring framework that effectively integrates deep learning features, pFind+ uniquely supports high-sensitivity, DL-enhanced open search, enabling comprehensive PTM discovery while incorporating hardware-aware inference optimizations for practical deployment. Evaluations across diverse datasets demonstrate its superior sensitivity, with gains of 12.7%-29.3% (average 17.9%) in restricted search and 8.0%-38.4% (average 25.8%) in open search over the best existing tools.

Deep Learning↗

PubMed2026

Proteomics-driven discovery of intervention windows and risk subtypes in osteoporosis: A prospective cohort study.

Given the limited feasibility of population-wide bone mineral density screening and the infrequency of long-term monitoring in healthy individuals, identifying the window for early intervention and the populations to be prioritized for screening is critical. This study aimed to identify intervention windows for osteoporosis and to determine potential high-risk subtypes within the healthy population. Based on proteomic data from 41,408 healthy adults, we conducted the DE-SWAN method to identify change peaks in plasma protein during the pre-diagnostic osteoporosis phase, and employed finite Gaussian mixture model-based clustering to delineate high-risk subtypes of osteoporosis. We identified 122 protein biomarkers significantly associated with osteoporosis risk throughout the follow-up period. Importantly, we identified two critical peaks occurring approximately 10 and 6 years before diagnosis, with the former enriched in immune-related pathways and the latter prominently involving responses to retinoic acid and glucocorticoids. Furthermore, one high-risk subtype for osteoporosis was identified in both males and females, termed the Frailty and Obesity Subtype. This subtype is characterized by a high degree of frailty and obesity, accompanied by a significantly elevated risk of both osteoporosis and fractures. Finally, we developed a predictive model comprising 10 proteins for identifying high-risk subtypes of osteoporosis, which demonstrated better performance than the traditional risk factor model (AUC: 0.743 vs. 0.680). Our findings demonstrate that proteomic profiling can reveal early molecular changes and identify high-risk subtypes years before clinical onset, providing a foundation for screening and precision prevention of osteoporosis.

Proteomics↗

PubMed2025

Pitavastatin, Procollagen Pathways, and Plaque Stabilization in Patients With HIV: A Secondary Analysis of the REPRIEVE Randomized Clinical Trial.

IMPORTANCE: In a mechanistic substudy of the Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) randomized clinical trial, pitavastatin reduced noncalcified plaque (NCP) volume, but specific protein and gene pathways contributing to changes in coronary plaque remain unknown. OBJECTIVE: To use targeted discovery proteomics and transcriptomics approaches to interrogate biological pathways beyond low-density lipoprotein cholesterol (LDL-C), relating statin outcomes to reduce NCP volume and promote plaque stabilization among people with HIV (PWH). DESIGN, SETTING, AND PARTICIPANTS: This was a post hoc analysis of the double-blind, placebo-controlled, REPRIEVE randomized clinical trial. Participants underwent coronary computed tomography angiography (CTA), plasma protein analysis, and transcriptomic analysis at baseline and 2-year follow-up. The trial enrolled PWH from April 2015 to February 2018 at 31 US research sites. PWH without known cardiovascular diseases taking antiretroviral therapy and with low to moderate 10-year cardiovascular risk were eligible. Data analyses were conducted from October 2023 to February 2024. INTERVENTION: Oral pitavastatin calcium, 4 mg per day. MAIN OUTCOMES AND MEASURES: Relative change in plasma proteomics, transcriptomics, and noncalcified plaque volume among those receiving treatment vs placebo. RESULTS: Among 558 individuals (mean [SD] age, 51 [6] years; 455 male [82%]) included in the proteomics assessment, 272 (48.7%) received pitavastatin and 286 (51.3%) received placebo. After adjusting for false discovery rates, pitavastatin increased abundance of procollagen C-endopeptidase enhancer 1 (PCOLCE), neuropilin 1 (NRP-1), major histocompatibility complex class I polypeptide-related sequence A (MIC-A) and B (MIC-B), and decreased abundance of tissue factor pathway inhibitor (TFPI), tumor necrosis factor ligand superfamily member 10 (TRAIL), angiopoietin-related protein 3 (ANGPTL3), and mannose-binding protein C (MBL2). Among these proteins, the association of pitavastatin with PCOLCE (a rate-limiting enzyme of collagen deposition) was greatest, with an effect size of 24.3% (95% CI, 18.0%-30.8%; P < .001). In a transcriptomic analysis, individual collagen genes and collagen gene sets showed increased expression. Among the 195 individuals with plaque at baseline (88 [45.1%] taking pitavastatin, 107 [54.9%] taking placebo), changes in NCP volume were most strongly associated with changes in PCOLCE (%change NCP volume/log2-fold change = -31.9%; 95% CI, -42.9% to -18.7%; P < .001), independent of changes in LDL-C level. Increases in PCOLCE related most strongly to change in the fibro-fatty (<130 Hounsfield units) component of NCP (%change fibro-fatty volume/log2-fold change = -38.5%; 95% CI, -58.1% to -9.7%; P = .01) with a directionally opposite, although nonsignificant, increase in calcified plaque (%change calcified volume/log2-fold change = 34.4%; 95% CI, -7.9% to 96.2%; P = .12). CONCLUSIONS AND RELEVANCE: Results of this secondary analysis of the REPRIEVE randomized clinical trial suggest that PCOLCE may be associated with the atherosclerotic plaque stabilization effects of statins by promoting collagen deposition in the extracellular matrix transforming vulnerable plaque phenotypes to more stable coronary lesions. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02344290.

Humans↗

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

Takotsubo Syndrome: The First Non-Acute Proteomic Analysis by Remote Dried Blood Microsampling.

Takotsubo syndrome (TTS) is an under-recognized form of acute-onset heart failure typically precipitated by stress. While recovery of cardiac function is described over the course of weeks, adverse outcomes after apparent recovery are increasingly recognized. However, the pathophysiology of non-acute manifestations remains poorly understood. We used mass-spectrometry-based discovery proteomics from remotely collected non-acute dried blood microsamples to perform a case-control study in 62 participants with a prior TTS episode (median of 2.24 years prior to sample collection) and 47 reference controls. We quantified 398 unique proteins, and found that agnostic clustering techniques showed separation between TTS and reference control samples. This represents the first proteomic characterization of non-acute TTS. Pathway analysis of the 52 differentially regulated proteins demonstrated enrichment of proteins involved in complement activation, nitric oxide signaling, and with antioxidant activity. These enriched pathways may be suggestive of a persistent cardiomyopathy resulting from or predisposing to TTS.

Humans↗

PubMed2026

A validated, modifiable proteomic score from the EXSCEL trial predicts cardiovascular events in diabetes.

BACKGROUNDAdults with type 2 diabetes mellitus (T2DM) are at increased risk for stroke, myocardial infarction, and cardiovascular death, yet individual risk is heterogeneous and incompletely captured by clinical models.METHODSIn the Exenatide Study of Cardiovascular Event Lowering (EXSCEL), adults with T2DM were randomized to a GLP-1 RA (exenatide) or a placebo and followed longitudinally for major adverse cardiovascular events (MACE). High-throughput discovery proteomics was done in plasma collected at baseline and 12 months. Proteins associated with time to MACE were identified using multivariable regression and incorporated into supervised machine learning models. A multi-protein score was developed and externally validated in 2 independent population-based and trial cohorts.RESULTSThe proteomic score showed incremental improvement in cardiovascular risk discrimination beyond clinical factors alone, and several proteins were consistently prioritized across modeling approaches. The protein score and a top-ranked protein, tetranectin, were modified by GLP-1 RA treatment, and a decrease in protein score was associated with improved outcomes, supporting modifiability of MACE risk.CONCLUSIONExternal validation confirmed generalizability across cohorts with and without diabetes. Together, these findings demonstrate that plasma proteomic signatures can enhance cardiovascular risk stratification and identify treatment-responsive biomarkers in T2DM, supporting their potential role in precision prevention strategiesFUNDINGThe EXSCEL study was funded by Amylin Pharmaceuticals. This research was supported by contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006, and grants R01HL146145, U01HL080295, U01HL130114, R01HL172803, and R01HL144483 from the National Heart, Lung, and Blood Institute, with additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided by R01AG023629 from the National Institute on Aging.

Aged↗

PubMed2013

The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics.

Early transplant dysfunction and failure because of immunological and nonimmunological factors still presents a significant clinical problem for transplant recipients. A critical unmet need is the noninvasive detection and prediction of immune injury such that acute injury can be reversed by proactive immunosuppression titration. In this study, we used iTRAQ -based proteomic discovery and targeted ELISA validation to discover and validate candidate urine protein biomarkers from 262 renal allograft recipients with biopsy-confirmed allograft injury. Urine samples were randomly split into a training set of 108 patients and an independent validation set of 154 patients, which comprised the clinical biopsy-confirmed phenotypes of acute rejection (AR) (n = 74), stable graft (STA) (n = 74), chronic allograft injury (CAI) (n = 58), BK virus nephritis (BKVN) (n = 38), nephrotic syndrome (NS) (n = 8), and healthy, normal control (HC) (n = 10). A total of 389 proteins were measured that displayed differential abundances across urine specimens of the injury types (p < 0.05) with a significant finding that SUMO2 (small ubiquitin-related modifier 2) was identified as a "hub" protein for graft injury irrespective of causation. Sixty-nine urine proteins had differences in abundance (p < 0.01) in AR compared with stable graft, of which 12 proteins were up-regulated in AR with a mean fold increase of 2.8. Nine urine proteins were highly specific for AR because of their significant differences (p < 0.01; fold increase >1.5) from all other transplant categories (HLA class II protein HLA-DRB1, KRT14, HIST1H4B, FGG, ACTB, FGB, FGA, KRT7, DPP4). Increased levels of three of these proteins, fibrinogen beta (FGB; p = 0.04), fibrinogen gamma (FGG; p = 0.03), and HLA DRB1 (p = 0.003) were validated by ELISA in AR using an independent sample set. The fibrinogen proteins further segregated AR from BK virus nephritis (FGB p = 0.03, FGG p = 0.02), a finding that supports the utility of monitoring these urinary proteins for the specific and sensitive noninvasive diagnosis of acute renal allograft rejection.

Acute Kidney Injury↗

PubMed2025

Association of Lung Quantitative CT Scan Textures With Systemic Inflammation and Mortality in COPD.

BACKGROUND: COPD is characterized by persistent inflammation that is responsible for remodeling the bronchovascular bundles (BVBs), which may lead to poor quality of life. Quantitative CT (QCT) scan textures of the lung can capture local disease patterns of inflammation and related respiratory morbidity. RESEARCH QUESTION: Are BVB textures, obtained from the adaptive multiple feature method, associated with systemic inflammation, morbidity, and mortality in COPD? STUDY DESIGN AND METHODS: We analyzed data from the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS; n = 2,981) and the Genetic Epidemiology of COPD (COPDGene) study (n = 10,305). The predictors included 2 QCT scan biomarkers, the BVB and CT density gradient (CTDG) textures, age, sex, BMI, race, smoking status, pack-years of smoking, CT scan-detected emphysema, and square root of the wall area of a hypothetical airway with a 10-mm lumen perimeter (Pi10). Outcomes included plasma biomarker concentrations from Meso Scale Discovery proteomics assays and CBC counts, both as markers of inflammation, along with FEV1, FEV1 to FVC ratio, St. George's Respiratory Questionnaire score, 6-minute walk distance, and modified Medical Research Council dyspnea scale score. Associations of these QCT scan textures with FEV1 decline and all-cause mortality also were investigated. RESULTS: Increased BVB texture was associated significantly with elevated neutrophil and monocyte counts and the neutrophil to lymphocyte ratio, independent of clinical covariates, CT scan-detected emphysema, and Pi10. Elevated CTDG was associated with increased neutrophil count, NLR, and tumor necrosis factor α. Increased CTDG and BVB textures also were associated with a lower FEV1 and 6-minute walk distance. CTDG at baseline was also associated with decline in FEV1 at the 5-year follow-up in the COPDGene study. We observed a significant association of both BVB texture (SPIROMICS: hazard ratio [HR], 1.084 [95% CI, 1.035-1.135; P < .001]; COPDGene: HR, 1.106 [95% CI, 1.080-1.131; P < .001]) and CTDG texture (SPIROMICS: HR, 1.033 [95% CI, 1.003-1.064; P = .03]; COPDGene: HR, 1.079 [95% CI, 1.061-1.096; P < .001]) with all-cause mortality independent of CT scan-detected emphysema and Pi10. INTERPRETATION: QCT scan textures may provide imaging evidence of the spatial heterogeneity of lung inflammation and overall disease burden in COPD. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; Nos.: NCT01969344 (SPIROMICS) and NCT00608764 (COPDGene); URL: www. CLINICALTRIALS: gov.

Humans↗

PubMed2026

Gastrointestinal digestion governs insect protein hydrolysis and predicted bioactive peptide release: Species-dependent implications for functional food applications.

This study investigates the digestion of insect proteins and the release of predicted bioactive peptides during human gastrointestinal digestion. Using the Infogest in vitro model, mealworm, cricket, and black soldier fly larvae (BSFL) proteins were digested and analyzed through discovery proteomics and bioinformatics to identify predicted bioactive peptides. Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) quantified insect proteins including predicted bioactive peptide precursor proteins, the precursors of predicted bioactive peptides. Results indicated that gastrointestinal digestion strongly influences peptide release, with the gastric phase exhibiting a richer predicted bioactive peptide profile than the small intestinal phase. Many predicted bioactive peptides were rapidly hydrolysed under small intestine conditions, which may lead to reduced stability or diminished activity in vivo, potentially explaining why certain peptides show strong bioactivity in vitro but limited effects in vivo. Additionally, predicted bioactive peptide release varied by insect species, influenced by genetic factors and peptide abundance. These findings highlight the importance of species selection and consideration of proteolytic digestion patterns in optimizing insect-derived bioactive peptides for functional foods and nutraceutical applications.

Animals↗

PubMed2026

Isolation of region-specific factors driving antibody class-switch recombination from the immunoglobulin heavy chain locus.

Activation-Induced Cytidine Deaminase (AID) induces DNA double-strand breaks (DSBs) at the switch (S) regions of the Immunoglobulin heavy chain (IgH) locus, which are essential for class switch recombination (CSR) and somatic hypermutation (SHM), key processes for effective antibody production. While AID activity is critical, its off-target effects, such as DSBs at the Myc locus, can cause chromosomal translocations like IgH-Myc fusions, contributing to B-cell lymphomas. The factors assembled on the IgH locus that help restrict AID-induced DSBs and subsequently CSR, remain unknown. To address this, we developed a method to isolate CSR-specific factors by inserting a 5×-GAL4-UAS sequence at the switch-mu (Sμ) region in CH12 cells. This engineered site enables recruitment of a 3-FLAG-GAL4 DNA-binding protein (3F-GAL4-DBD), allowing specific pulldown of proteins enriched at the Sμ region. Successful recovery of the known CSR regulator BRD2 from the Sμ region, along with enrichment of the DNA repair factors 53BP1 and gH2AX, validated this approach. Identification and characterization of IgH-enriched factors establish a validated methodological framework to facilitate future proteomic discovery of CSR regulators and highlight mechanisms that balance antibody diversification with genomic integrity in B cells.

Immunoglobulin Class Switching↗

PubMed2026

From Peaks to Power: Systematic Evaluation of Chromatographic Sampling Reveals Determinants of Quantification and Biological Discovery in DIA Proteomics.

Modern DIA proteomics increasingly emphasizes throughput and depth for large-cohort studies, but methods are often optimized using proxy metrics that can mask losses in quantifiable signal and statistical power. Here, we evaluate how data points per peak and other chromatographic features jointly contribute to quantification and downstream biological discovery. Using a matrix-matched calibration curve dataset, we checked how the number of data points per peak (DPPP) affects the limits of detection and quantification (LOD/LOQ). Reduced DPPP minimally affected LOD but substantially degraded LOQ. Feature modeling and nonparametric association analyses identified precursor peak area as the strongest feature-level predictor of LOQ, whereas DPPP showed weaker and context-dependent effects. Simulations of chromatographic peak integration recapitulated these trends, showing that increased sampling primarily improves integration precision, while quantitative accuracy is strongly governed by peak height and peak shape. Finally, when comparing 20 cancer vs 20 control plasma samples processed with Seer Proteograph, the decrease in DPPP led to a loss of statistical significance for proteins with low-abundance precursors. These findings argue that DIA optimization should prioritize LOQ and statistical power metrics─not identifications alone─by balancing sampling density with chromatographic peak height and quality to maximize useful biological signal.

Proteomics↗

PubMed2026

Bifunctional covalent organic framework for rapid isolation of extracellular vesicles and proteomics-based biomarker discovery.

Extracellular vesicles (EVs), serving as crucial carriers of biomarkers for tumor diagnosis and prognostic evaluation, as well as drug delivery vehicles and therapeutic targets, making it a research hotspot. The isolation methods represent a key aspect of EV-associated research. In this work, an alkynyl-functionalized covalent organic framework (COF) was synthesized under acidic conditions at room temperature and further modified by photo-initiated thiol-yne click reaction, yielding a bifunctionalized COF material decorated with distearoyl phosphatidylethanolamine (DSPE) and Ti4+. This bifunctional COF material (COF-DSPE-Ti) can leverage the bifunctional synergistic effect between DSPE and Ti4+ sites, thereby facilitating the efficient isolation of EVs. This synergistic effect enables the efficient isolation of EVs within 3 min. Proteomic analysis reveals that this isolation method significantly outperforms ultracentrifugation, and an effective EV isolation and analysis can be completed using only 10 μL of plasma sample. For clinical liquid biopsy, the integration of the COF-DSPE-Ti method with proteomics lead to the identification of 64 upregulated proteins in plasma samples from colorectal cancer (CRC) patients, among which S100A9 emerged as a potential EV biomarker. In addition, KLK2, KLK3, and FOLH1, which have been established as diagnostic markers for prostate cancer (PCa), are successfully identified in EVs isolated from the urine of PCa patients. These findings demonstrate the reliability of this approach for screening EV-associated biomarkers and provide a novel strategy for the early diagnosis and prognostic assessment of CRC and PCa.

Proteomics↗

PubMed2025

Proteomic-based biomarker discovery reveals panels of diagnostic biomarkers for early identification of heart failure subtypes.

BACKGROUND: Limited access to echocardiography can delay the diagnosis of suspected heart failure (HF), which in turn postpones the initiation of optimal guideline-directed medical therapy. Although natriuretic peptides like B-type natriuretic peptide (BNP) are valuable biomarkers for diagnosing and managing HF, the utility of combining BNP with other blood-based biomarkers to predict subtypes of new-onset HF remains underexplored. OBJECTIVES: This study sought to investigate and evaluate the diagnostic significance of adding blood-based biomarkers to BNP for identifying heart failure with preserved ejection fraction (HFpEF) or reduced ejection fraction (HFrEF), with the goal of enhancing diagnostic assays beyond BNP measurements. METHODS: We identified candidate blood protein biomarkers using untargeted proteomics workflows from a cohort of individuals recruited to the STOP-HF trial who were at risk of HF and subsequently developed either HFpEF or HFrEF over time ("HF progressors"; n = 40). Candidate biomarkers were verified in an independent cohort (n = 52) from a community-based rapid access HF diagnostic clinic. The biological processes associated with these proteins were assessed, and the diagnostic values of biomarker panels were evaluated using a machine learning approach. RESULTS: Within HF progressors, we identified 3 proteins associated with HFpEF development: vascular cell adhesion protein 1 (VCAM1), insulin-like growth factor 2 (IGF2), and inter-alpha-trypsin inhibitor heavy chain 3 (ITIH3). Additionally, 4 proteins were linked to HFrEF development: C-reactive protein (CRP), interleukin-6 receptor subunit beta (IL6RB), phosphatidylinositol-glycan-specific phospholipase D (PHLD), and noelin (NOE1). These findings were verified in an independent cohort to distinguish HF subtypes from controls. Moreover, a random forest algorithm demonstrated that combining these candidate biomarkers with BNP measurement significantly improved the prediction of HF subtypes. CONCLUSIONS: We identified candidate proteins linked to HFpEF and HFrEF in a longitudinal HF progressor cohort and validated them in a community-based cohort. Adding these proteins to BNP led to a significant improvement in HF subtype prediction. Study results have clinical implications for blood-based screening of HF subtypes using panels of biomarkers, particularly in resource-limited settings.

Humans↗

PubMed2024

Bridging the Gap From Proteomics Technology to Clinical Application: Highlights From the 68th Benzon Foundation Symposium.

The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry-based proteomics and artificial intelligence to revolutionize personalized medicine. This report highlights key discussions on recent technological advances in mass spectrometry-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations, and the need for robust "business cases" to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multimodal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialog between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.

Proteomics↗

PubMed2026

Targeted Modulation of Abundant Proteins Enhances Proteomic Profiling of Ovarian Cancer Ascites: A Pilot Technical Workflow Comparison.

Ascites from ovarian cancer patients are increasingly recognized as a valuable biofluid for cancer research, as its protein composition reflects the disease state and may reveal biomarkers of treatment sensitivity and response. However, the detection of low-abundance proteins is hindered by the presence of highly abundant proteins such as albumin. In this study, we evaluated five protein preparation methods for their effectiveness in depleting high-abundance or enriching low-abundance proteins in ovarian cancer ascites. The Norgen (Nor), Minutes (Min), and Perchloric acid (PerCA) methods were based on abundant protein depletion, while the Urine (Uri) and Nanomics (Nano) kits focused on low-abundance protein enrichment. Processed samples were analyzed using label-free quantitative bottom-up proteomics by LC-MS/MS, followed by a bioinformatics assessment. Compared with undepleted ascites (UnD), Min, Nor, Nano, and PerCA increased protein identifications, whereas Uri produced profiles similar to those of UnD. Notably, PerCA and Nano enabled the identification of distinct protein subsets associated with cancer-related pathways, including immune responses and autophagy. PerCA enriched transmembrane and secreted immunomodulatory glycoproteins, whereas Nano enrichment primarily captured secreted, nuclear, and cytoplasmic soluble proteins. Overall, our results show that both high-abundance protein depletion and low-abundance enrichment improve ascites proteome coverage, each offering distinct advantages in identifying biologically relevant low-abundance proteins.

Female↗

PubMed2026

Enrichment Performance Assessment of Extracellular Vesicles Using Different Functionalized Magnetic Materials and Application in Urinary Proteomics of Prostate Cancer.

Extracellular vesicles (EVs) are lipid bilayer nanovesicles that mediate intercellular communication and hold significant potential for clinical applications. Although material-based isolation strategies offer promising alternatives to conventional methods, their relative performances have not been systematically evaluated. In this study, we conducted a comparative assessment of magnetic nanomaterials with distinct surface functionalities, including metal oxides (TiO2), metal-organic frameworks (UiO-66), biopolymeric materials (chitosan), and lipid probes (DSPE-PEG, DOPE-PEG, and CLS-PEG). A comprehensive evaluation across multiple dimensions including capture capacity, capture rate, sample volume, and product purity reveals that the bifunctional magnetic nanomaterial Fe3O4@UiO-66@DSPE material exhibits superior EV capture performance. This material enables the efficient and stable enrichment of high-purity EVs by synergizing Zr4+-phosphate coordination with lipid bilayer anchoring, and preserves EV biological integrity and activity. Meanwhile, this method could be highly compatible with proteomics, and over 1000 proteins are identified by proteomic analysis of urinary EVs, while 34 proteins are upregulated and 25 proteins are downregulated in prostate cancer patients relative to healthy donors. Notably, the differentially expressed proteins, such as AGT, ITIH4, and PGLYRP2, are associated with disease progression. Overall, this work highlights the superior performance of the Fe3O4@UiO-66@DSPE material for efficient and selective EV isolation. It provides a powerful tool for clinical liquid biopsy and proteomic biomarker discovery, enabling early diagnosis, prognostic evaluation, and precision therapy.

Humans↗