Clinical Proteomics

Aims/HypothesisMultiple studies associated enterovirus (EV) infections with type 1 diabetes. The Network for Pancreatic Organ Donors with Diabetes (nPOD) obtained samples from organ donors with/without type 1 diabetes and launched the nPOD-Virus Group to examine viral infections in donor tissues, using complementary approaches. To this end, we aimed to identify virus proteins/peptides in disease-stratified tissues using proteomic and liquid chromatography-mass spectrometry. MethodsnPOD provided specimens from four donor groups: donors without diabetes (ND, n=33), with type 1 diabetes (T1D, n=25), with type 2 diabetes (T2D, n=7), and without diabetes expressing type 1 diabetes-associated autoantibodies (AAb+, n=17; preclinical disease). We studied flash-frozen pancreas tissue chunks, embedded tissue slices, and islets obtained via laser capture microdissection (LCM). We isolated and processed proteins from these specimens for liquid chromatography-mass spectrometry analysis. We utilized different instruments including a Q-Exactive Orbitrap Mass spectrometer and an Orbitrap Fusion Lumos Mass Spectrometer to acquire high resolution, high mass accuracy and high sensitivity MS data using different scanning methods. We used data dependent acquisition (DDA), data independent acquisition (DIA), and parallel reaction monitoring (PRM). Generated mass spectra were processed and used in protein database searches for identification, qualitative and quantitative comparative analyses of viral protein expression in tissue samples. ResultsAdvanced proteomics were applied to pancreata from 82 disease-stratified nPOD donors. These analyses generated >1,000 individual mass spectra data files. We identified enterovirus peptides from different serotypes in 28 donors, including 11 donors with type 1 diabetes. These serotypes included several previously associated with type 1 diabetes. For some donors, identification of virus peptides by discovery proteomics was validated by targeted mass spectrometry and Western blot. Conclusions/InterpretationFor the first time we applied complementary mass spectrometry-based proteomics to detect viral proteins in disease-stratified pancreas samples. Some pancreata, including several from donors with type 1 diabetes, were infected by enteroviruses based on detection of viral proteins; in several instances we identified serotypes, which has been arduous with other methods. We detected both structural and non-structural viral proteins, the latter essential for replication, suggesting that enteroviruses may replicate in pancreas, perhaps at low level, given the absence of acute infection. The complexity of our methodology limited application to large sample sets, and accordingly we did not aim to demonstrate an association with disease; our data complement associative data generated with other approaches by the nPOD-Virus Group, overall supporting a role for enterovirus infections in type 1 diabetes. Research in ContextO_ST_ABSWhat is already known about this subject?C_ST_ABSThere are previous studies that examined an association of enterovirus infections with type 1 diabetes by examining pancreas tissue, but those are largely limited to the assessment of a single viral antigen by immunohistochemistry in pancreas tissues obtained at autopsy from recent onset patients, from either an historical archive, or from a small series of biopsies. What is the key question?To examine viral infections in the pancreas from the largest collection of organ donors and disease duration using unbiased comprehensive proteomic and liquid chromatography-mass spectrometry methods. What are the new findings?We present robust mass spectrometry based proteomics data that are validated by complementary western blot results identifying multiple virus proteins from different virus serotypes in pancreas from disease stratified organ donors. Overall, the findings support the existence of chronic or recurrent infections in the pancreas of some patients with type 1 diabetes. How might this impact on clinical practice in the foreseeable future?These results provide strong rationale for advancing current efforts to prevent or mitigate type 1 diabetes by vaccination and/or anti-viral therapies.

BackgroundPre-COVID-19 Postural orthostatic tachycardia syndrome (PC-POTS), Long COVID, and their overlap (LC-POTS) are chronic post-viral conditions marked by debilitating symptoms despite frequently normal routine laboratory results. We previously identified insoluble fibrinaloid microclot complexes (FMCs) in Long COVID. It is not known whether FMCs are also present in PC-POTS, or whether post-translational modifications (PTMs) within FMC-entrapped proteins contribute to disease mechanisms. MethodsPlatelet-poor plasma from healthy controls, PC-POTS patients (collected prior to the COVID-19 pandemic), Long COVID (without POTS) and LC-POTS patients underwent fluorescence imaging flow cytometry to quantify FMCs. Proteomic analyses were performed on insoluble protein fractions using a double trypsin digestion strategy and data-independent liquid chromatography-tandem mass spectrometry (LC-MS/MS). Differential protein abundance, PTMs, and amyloidogenicity were compared across groups. ResultsMeasured with imaging flowcytometry in objects/mL, higher levels of FMCs were present in disease groups compared to controls, although not statistically significant. Statistically significant differences potentially lay within FMC sizes and composition. Furthermore, despite only a few dysregulated proteins, FMC proteomics revealed extensive and disease-specific peptides with PTM dysregulation across coagulation, immune, and metabolic pathways. Long COVID displayed FMCs with PTMs of coagulation proteins including prominent advanced glycation end-products (AGE)- and oxidation-based modifications of fibrinogen subunits, particularly fibrinogen subunit A (FIBA), resembling diabetic glycation profiles. FMCs in PC-POTS showed fewer fibrinogen PTMs but markedly increased modifications in metabolic proteins, including oxidised apoA1 and apoB, and immune patterns with complement-related proteins (C3, C4A/B, IC1), immunoglobulin G1 (IGG1) and alpha 2 macroglobulin (A2MG). LC-POTS shared coagulation pathology with Long COVID and immune pathology with PC-POTS. Many dysregulated peptides were determined by in silco methods to be highly amyloidogenic, consistent with FMCs as {beta}-sheet-rich aggregates. Protein-level differences were minimal compared with PTM changes. ConclusionsThis study provides the first evidence that post-translational modifications (PTMs) within fibrinaloid microclots complexes (FMCs) uniquely distinguish pre-COVID-19 POTS, Long COVID, and Long COVID-POTS. Because PC-POTS samples were collected before SARS-CoV-2, their PTM patterns reflect intrinsic disease biology, allowing a clear separation from Long COVID-related changes. PTM profiling revealed pro-coagulant fibrinogen modifications in Long COVID and LC-POTS, metabolic-oxidative disruptions in Long COVID and PC-POTS, and immune dysregulation in PC-POTS and LC-POTS. None of these is detectable with routine assays, and all are independent of protein abundance. The consistent presence of amyloidogenic peptides suggests a contribution to microvascular dysfunction. These findings define disease-specific PTM landscapes and support new diagnostic and therapeutic avenues across autonomic and post-viral disorders.

ObjectiveA catheter-free, non-radiolabeled method that permits in vivo measurement of tissue-specific glucose uptake does not exist. To address this gap, we sought to develop and validate a new, higher throughput mass spectrometry (MS)-based method that combines an injection of insulin with a non-radiolabeled glucose tracer, 2-fluoro-2-deoxyglucose (2FDG), to determine insulin-stimulated tissue-specific glucose clearance in conscious, unrestrained mice. MethodsInjections of saline or insulin with 2FDG were coupled with LC-Q Exactive Hybrid Quadrupole-Orbitrap (LC) MS-based measures of plasma 2FDG and tissue (2-fluoro-2-deoxyglucose-6-phosphate) 2FDGP to determine glucose clearance in mice under several different conditions. ResultsThe newly developed method was first applied to a dose response experiment in mice. Next, the ability of this method to quantify changes in glucose clearance in response to an insulin stimulus was assessed, and glucose clearance was compared between chow and high fat fed mice. Results from these studies showed that insulin-stimulated skeletal muscle and heart glucose clearance can be estimated following a bolus injection of tracer, and these fluxes are blunted in diet-induced obese mice. The broad applicability of this approach was then demonstrated by assessing glucose clearance in a mouse model with anticipated changes in insulin-stimulated skeletal muscle glucose metabolism. ConclusionsThe results validated a new LC-MS method to quantify insulin-stimulated tissue-specific glucose clearance in vivo without the use of catheters or radiolabeled tracers. The method offers great potential because it is designed for application to pre-clinical studies seeking high throughput tests and/or assays that can be coupled with discovery technologies such as genomics, proteomics and metabolomics. HIGHLIGHTSO_LIIn vivo glucose clearance can be estimated by a new non-radiolabeled method. C_LIO_LIThe plasma tracer to tracee ratio is required to determine tissue tracer phosphorylation. C_LIO_LIMeasures of plasma glucose and tracer kinetics are critical for data interpretation. C_LIO_LIThe new method can be combined with omics technologies such as metabolomics. C_LI

Protein posttranslational modifications (PTMs) by O-GlcNAc globally rise during pressure-overload hypertrophy (POH). However, only a few specific proteins with altered O-GlcNAc levels during POH are known primarily because this PTM is easily lost during standard mass spectrometry (MS) conditions used for protein identification. Methodologies have recently emerged to stabilize the O-GlcNAc moiety for MS analysis. Accordingly, our goal was to determine the proteins undergoing changes in O-GlcNAc levels during POH. We used C57/Bl6 mice subjected to Sham or transverse aortic constriction (TAC) to create POH. From the hearts, we stabilized and labelled the O-GlcNAc moiety with tetramethylrhodamine azide (TAMRA) before enriching by TAMRA immunoprecipitation (IP). We used LC-MS to identify the captured O-GlcNAcylated proteins. We identified a total of 707 O-GlcNAcylated proteins in Sham and POH. Two hundred thirty-three of these proteins were significantly increased in POH over Sham whereas no proteins were significantly decreased in POH. We examined two MS identified proteins, CPT1B and PDH, to validate the MS data by immunoprecipitation. We corroborated increased O-GlcNAc levels during POH for the metabolic enzymes CPT1B and PDH. Enzyme activity assays showed higher O-GlcNAcylation increased CPT1 activity and decreased PDH activity. In summary, we generated the first comprehensive list of proteins with changes in O-GlcNAc levels during POH and, to our knowledge, the largest list for any cardiac pathology. Our results demonstrate the large number of proteins and cellular processes affected by O-GlcNAc during POH and serve as a guide for testing specific O-GlcNAc-regulated mechanisms.

Analyses of mitochondrial adaptations in human skeletal muscle have mostly used whole-muscle samples, where results may be confounded by the presence of a mixture of type I and II muscle fibres. Using our adapted mass spectrometry-based proteomics workflow, we provide new insights into fibre-specific mitochondrial differences in human skeletal muscle before and after training. Our findings challenge previous conclusions regarding the extent of fibre-type-specific remodelling of the mitochondrial proteome and highlight that most baseline differences in mitochondrial protein abundances between fibre types reported by us, and others, might be due to differences in total mitochondrial content or a consequence of adaptations to habitual physical activity (or inactivity). Most training-induced changes in different mitochondrial functional groups, in both fibre types, were stoichiometrically linked to changes in markers of mitochondrial content.

Understanding cancer metastasis at the proteoform level is crucial for discovering new protein biomarkers for cancer diagnosis and drug development. Proteins are the primary effectors of function in biology and proteoforms from the same gene can have drastically different biological functions. Here, we present the first qualitative and quantitative top-down proteomics (TDP) study of a pair of isogenic human metastatic and non-metastatic colorectal cancer (CRC) cell lines (SW480 and SW620). This study pursues a global view of human CRC proteome before and after metastasis in a proteoform specific manner. We identified 23,319 proteoforms of 2,297 genes from the CRC cell lines using capillary zone electrophoresis-tandem mass spectrometry (CZE-MS/MS), representing nearly one order of magnitude improvement in the number of proteoform identifications from human cell lines compared to literature data. We identified 111 proteoforms containing single amino acid variants (SAAVs) using a proteogenomic approach and revealed drastic differences between the metastatic and non-metastatic cell lines regarding SAAVs profiles. Quantitative TDP analysis unveiled statistically significant differences in proteoform abundance between the SW480 and SW620 cell lines on a proteome scale for the first time. Ingenuity Pathway Analysis (IPA) disclosed that many differentially expressed genes at the proteoform level had diversified functions and were closely related to cancer. Our study represents a milestone in TDP towards the definition of human proteome in a proteoform specific manner, which will transform basic and translational biomedical research. For TOC only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC="FIGDIR/small/466093v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@3ee5faorg.highwire.dtl.DTLVardef@16cae5forg.highwire.dtl.DTLVardef@2c0bd0org.highwire.dtl.DTLVardef@1bb9530_HPS_FORMAT_FIGEXP M_FIG C_FIG

Laser capture microdissection (LCM) - based spatial mass spectrometry proteomics is a rapidly emerging technique with strong potential for use in formalin-fixed, paraffin-embedded (FFPE) tissues. Several sample-preparation methods have been developed to decrosslink FFPE proteins for spatial proteomics; however, residual crosslinks often remain, and depth can remain impaired relative to fresh frozen tissue samples. To increase proteome coverage in spatially resolved LCM-FFPE samples, we investigated a panel of chemical compounds with the potential to catalyze the decrosslinking of nucleophilic functional groups on proteins. Systematic screening and optimization of temperature, incubation time, and reagent concentration led to the identification of 3,4-diaminobenzoic acid as an effective agent for improving proteome coverage in FFPE pancreatic tissue. This compound could boost precursor identifications by more than 10% at both reduced (70 {degrees}C) and high (90 {degrees}C) temperatures. Application of this chemical-decrosslinking strategy to a pancreatic ductal adenocarcinoma tissue section enabled the identification of numerous cell-type-enriched proteins with clinical and therapeutic relevance. Taken together, our findings show that chemical decrosslinking can increase proteome coverage in FFPE tissues, thereby advancing our understanding of tissue microenvironments in physiological and pathological contexts.

Organs in the abdominal cavity are covered by a peritoneal membrane, which is comprised of a monolayer of mesothelial cells (MC). Diseases involving the peritoneal membrane include peritonitis, primary cancer (mesothelioma), and metastatic cancers (ovarian, pancreatic, colorectal). These diseases have gender- and/or age-related pathologies; however, the impact of gender and age on the peritoneal MC is not well evaluated. To address this, we identified and characterized gender- and age-related differences in the proteomes of murine primary peritoneal MC. Primary peritoneal MC were isolated from young female (FY) or male (MY) mice (3-6 months) and aged female (FA) or male (MA) mice (20-23 months), lysed, trypsin digested using S-Traps, then subjected to bottom-up proteomics using an LC-Orbitrap mass spectrometer. In each cohort, we identified >1000 protein groups. Proteins were categorized using Gene Ontology and pairwise comparisons between gender and age cohorts were conducted. This study establishes baseline information for studies on peritoneal MC in health and disease at two physiologic age/gender points. Segregation of the data by gender and age could reveal novel factors to specific disease states involving the peritoneum. [This in vitro primary cell model has utility for future studies on the interaction between the mesothelium and foreign materials.] SUMMARY STATEMENTMany diseases initiate from or involve peritoneal mesothelial cells including peritonitis, primary cancer (mesothelioma) and metastatic cancers. Progression of these diseases is influenced by many host factors including gender and age; however, the influence of these factors on the peritoneal mesothelial cell proteome has not been evaluated. This study provides novel information and identifies proteins exclusive to both male and female young and aged cohorts. Given the importance of the peritoneal mesothelial cell in abdominal homeostasis, and the impact of gender and age on disease progression, these data will be key for future studies examining mesothelium in both health and disease.

Defective renal repair following acute kidney injury (AKI) may lead to acute kidney disease (AKD), defined by persistent functional and/or structural abnormalities lasting up to three months. However, subclinical AKD often goes undetected due to the lack of specific diagnostic tools, as conventional biomarkers such as plasma creatinine (pCr) may remain within normal limits despite underlying structural damage. To identify urinary proteomic signatures associated with subclinical AKD of differing etiologies, we induced AKI in Wistar rats using either toxic (cisplatin-induced) or ischemic (unilateral ischaemia-reperfusion, I/R) models, and monitored renal function, renal histopathology and urine composition over 42 days. Renal function, as assessed by pCr and creatinine clearance, was rapidly and severely impaired following treatment with cisplatin and I/R, and normalized afterwards within 10 and 2 days, respectively. Despite functional recovery, histological analysis revealed persistent tissue injury--characterized by tubular dilation, necrosis, inflammation, and interstitial fibrosis--particularly in cisplatin-treated animals at day 42. Urinary proteomic analysis identified 53 and 23 proteins that were differentially excreted with the urine in the cisplatin and I/R groups, respectively, compared to controls, and 11 proteins differed from one AKI etiology to the other. Additionally, 29 proteins appeared exclusively post-AKI, irrespective of cause. Most of these proteins are predominantly involved in the immune response, complement activation, haemostasis, and gluconeogenesis. These findings suggest that urinary proteomic fingerprints may serve as sensitive indicators of subclinical AKD, reflecting underlying structural damage even in the absence of overt functional impairment. Such profiles could offer etiology-specific insights, while also enabling the early detection of AKD across diverse injury contexts.

BackgroundGlioblastoma is the most common and aggressive primary brain tumour in adults. Hallmarks of glioblastoma include its intra-tumoural heterogeneity and extensive invasion of the surrounding brain. Glioblastoma is known to remodel the extracellular matrix (ECM) of the brain, resulting in altered mechanical properties and the establishment of a tumour-promoting microenvironment. How changes in the expression and spatial distribution of ECM constituents within glioblastoma contribute to invasion and disease progression is still unclear. MethodsHere we report on a protocol for laser-capture microdissection coupled with mass spectrometry (LCM-proteomics) that allowed a spatially resolved and unbiased analysis of the regional ECM proteome (matrisome) in formalin-fixed and paraffin-embedded (FFPE) samples of human glioblastoma. We investigated five molecularly characterised hemispheric adult glioblastomas where the brain/tumour interface and tumour epicentre were represented in the surgical specimens and snap-frozen tissue was available. LCM-proteomic analysis was validated with immunohistochemistry. ResultsLCM-proteomics identified 53 matrisome proteins in FFPE tissue, demonstrating comparable performance with conventional analysis of snap-frozen tissue. The analysis revealed distinct matrisome components in the brain/tumour interface versus the tumour epicentre. Guided by data from LCM-proteomic analysis, immunostaining for tenascin-R confirmed greater staining in the brain/tumour interface, whilst expression of fibronectin was higher in the tumour epicentre. ConclusionThe protocol described in this work allowed for accurate, spatially resolved analysis of ECM in FFPE tissues, with performance comparable to analysis of snap-frozen tissue. While the focus for this work was on the regional ECM composition of glioblastoma, we found that the LCM-proteomics protocol is also applicable to the study of the wider proteome, and represents a valuable tool for investigating tumour/tissue heterogeneity. This protocol opens the possibility to apply LCM-proteomics to retrospective studies with the advantage of accessing clinical history and follow-up information, providing a valuable resource for translational research in glioblastoma.

1.BackgroundHuman infertility affects approximately 17.5% of the global population, with male factors accounting for nearly half of all cases. The identification of reliable molecular biomarkers is crucial for improving the diagnosis and assessment of male fertility. In this study, we developed and optimized an untargeted high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (HPLC-ESI-MS/MS) workflow for comprehensive lipidomic and metabolomic profiling of human spermatozoa using only 1.25 million cells per sample. ResultsCompared to previous reports, our optimized method achieved unprecedented analytical depth, identifying 473 lipid species and 955 structurally annotated metabolites, corresponding to nearly 7.600-fold improvements in detection efficiency per cell over published approaches. Lipidomic analysis revealed cholesterol, fatty acids, phosphatidylcholines, and phosphatidylethanolamine plasmalogens as the most abundant lipid classes, consistent with the structural complexity of the sperm plasma membrane. Metabolomic profiling showed strong enrichment of lipid-related and steroidogenic pathways, including phospholipid biosynthesis, glycerolipid metabolism and androgen and estrogen metabolism. The integration of lipidomic and metabolomic data highlighted functionally interconnected pathways related to membrane dynamics, energy metabolism, and hormone biosynthesis. ConclusionsOverall, this work establishes a robust, sensitive, and scalable analytical framework enabling high-coverage molecular characterization of spermatozoa from limited sample material, laying the groundwork for future biomarker discovery and clinical applications in male infertility research. One Sentence SummaryDevelopment of a highly sensitive untargeted HPLC-ESI-MS/MS lipidomic and metabolomic workflow that achieves unprecedented molecular coverage from only 1.25 million human spermatozoa, revealing interconnected lipid and metabolic pathways and providing a robust foundation for biomarker discovery in male infertility. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/703749v1_ufig1.gif" ALT="Figure 1"> View larger version (74K): org.highwire.dtl.DTLVardef@10b3132org.highwire.dtl.DTLVardef@1caf850org.highwire.dtl.DTLVardef@746adborg.highwire.dtl.DTLVardef@1135539_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundActivation of stress pathways intrinsic to the {beta} cell are thought to both accelerate {beta} cell death and increase {beta} cell immunogenicity in type 1 diabetes (T1D). However, information on the timing and scope of these responses is lacking. MethodsTo identify temporal and disease-related changes in islet {beta} cell protein expression, data independent acquisition-mass spectrometry was performed on islets collected longitudinally from NOD mice and NOD-SCID mice rendered diabetic through T cell adoptive transfer. FindingsIn islets collected from female NOD mice at 10, 12, and 14 weeks of age, we found a time-restricted upregulation of proteins involved in the maintenance of {beta} cell function and stress mitigation, followed by loss of expression of protective proteins that heralded diabetes onset. Pathway analysis identified EIF2 signaling and the unfolded protein response, mTOR signaling, mitochondrial function, and oxidative phosphorylation as commonly modulated pathways in both diabetic NOD mice and NOD-SCID mice rendered acutely diabetic by adoptive transfer, highlighting this core set of pathways in T1D pathogenesis. In immunofluorescence validation studies, {beta} cell expression of protein disulfide isomerase A1 (PDIA1) and 14-3-3b were found to be increased during disease progression in NOD islets, while PDIA1 plasma levels were increased in pre-diabetic NOD mice and in the serum of children with recent-onset T1D compared to age and sex-matched non-diabetic controls. InterpretationWe identified a common and core set of modulated pathways across distinct mouse models of T1D and identified PDIA1 as a potential human biomarker of {beta} cell stress in T1D.

BackgroundDysplastic tissue architecture in estrogen receptor-positive (ER+) breast cancer across therapy-naive and therapy-exposed cancer tissues presents unique challenges in the analysis of spatial transcriptomics. Many tools for deconvolution are developed on well-structured tissue architectures such as the 10x Genomics mouse hippocampus dataset. Spatial transcriptomics analysis could offer valuable insights into treatment response, but faces limitations in cellular resolution. MethodsTo address this problem, we developed CITEgeist, a computational tool for spatial transcriptomic deconvolution using integrated proteomics data from the same slide. Visium Antibody Capture technology was applied alongside our novel algorithm to analyze the tumor microenvironment. We demonstrate the reliability of our method using pre- and post-treatment samples from six breast cancer cases. ResultsOur approach revealed previously undetectable cellular interactions within the tumor microenvironment. By taking an interoperable approach to software development and grounding our algorithm in interpretable variables, we demonstrate how CITEgeist deconvolution is not only accurate but robust enough to be directly used as input in external analytical tools developed by other research teams. We then applied this approach to a set of specimens from a prospective trial our group ran and further validated the findings in a series of in vitro experiments as a demonstrated use case of the utility, necessity, and flexibility of CITEgeist; and the potential of our method to rapidly translate novel clinical samples to new biological insights. ConclusionsCITEgeist addresses a critical technical gap in spatial multi-omics analysis through an integrated, multi-disciplinary approach. This work demonstrates the value of combining clinical, translational, and computational expertise to identify novel mechanisms of treatment resistance, potentially transforming therapeutic strategies for resistant disease.

BackgroundThe human urine peptidome reflects physiological and pathological states, making it a valuable resource for biomarker discovery. However, endogenous peptides often exist as cascades of truncated variants, complicating comparative analyses. To address this, we developed a "peptide cluster" approach, grouping overlapping peptides into representative clusters for robust statistical evaluation. MethodsUrine samples from 55 healthy volunteers (23 males, 32 females) were analyzed via LC-MS/MS. Identified peptides were assembled into clusters based on sequence overlap, with the longest peptide designated as the "precursor" and truncated variants as "truncated" ResultsWe identified 30,471 endogenous peptides, assembled into 13,163 peptide clusters--the largest urinary peptidome dataset to date. Gender-specific differences were observed in 26 clusters, while 57 clusters correlated significantly with age. Notably, male-enriched clusters included hepcidin-25 and progranulin-derived peptides, whereas female-enriched clusters were linked to immunoglobulin gamma-1. Age-associated clusters highlighted collagen degradation patterns, consistent with extracellular matrix remodeling. ConclusionOur peptide clustering approach facilitated a comprehensive characterization of the endogenous peptidome, capturing the diversity of naturally occurring truncated peptide forms. The resulting age- and sex-specific peptide clusters serve as a valuable reference framework for future investigations into disease-associated biomarkers.

Type 1 diabetes (T1D) is a heterogeneous disease with a slower evolution in individuals diagnosed at older ages. There are no validated clinical or laboratory biomarkers to predict the rate of insulin secretion decline either before or after the clinical onset of the disease, or the rate of progression to chronic complications of the disease. This pilot study aimed to characterize the proteomic and phosphoproteomic landscape of circulating extracellular vesicles (EVs) across a range of obesity in carefully matched established T1D and control subjects. We used archived serum samples from 17 human subjects (N=10 with T1D and N=7 normal healthy volunteers) from the ACME study (NCT03379792). EVs were isolated using EVtrap(R) technology (Tymora). Mass spectrometry-based methods were used to detect the global circulating EV proteome and phosphoproteome. Differential expression, coexpression network (WGCNA), and pathway enrichment analyses were implemented. The detected proteins and phosphoproteins were highly enriched (75%) in exosomal proteins cataloged in the ExoCarta database. A total of 181 differentially expressed EV proteins and 15 differentially expressed EV phosphoproteins were identified, with 8 upregulated EV proteins (i.e., CD63, RAB14, VCP, BSG, FLNA, GNAI2, LAMP2, and EZR) and 1 downregulated EV phosphoprotein (i.e., TUBA1B) listed among the top 100 ExoCarta proteins. This suggests that T1D could indeed modulate EV biogenesis and secretion. Enrichment analyses of both differentially expressed EV proteins and EV phosphoproteins identified associations of upregulated features with neutrophil, platelet, and immune response functions, as well as prion disease and other neurodegenerative diseases, among others. On the other hand, downregulated EV proteins were involved in MHC class II signaling and the regulation of monocyte differentiation. Potential novel key roles in T1D for C1q, plasminogen, IL6ST, CD40, HLA-DQB1, and phosphorylated S100A9, are highlighted. Remarkably, WGCNA uncovered two protein modules significantly associated with pancreas size, which may be implicated in the pathogenesis of T1D. Similarly, these modules showed significant enrichment for membrane compartments, processes associated with inflammation and the immune response, and regulation of viral processes, among others. This study demonstrates the potential of EV proteomic and phosphoproteomic signatures to provide insight into the pathobiology of type 1 diabetes and its complications.

BackgroundBlood-based proteomics offers a complementary path to multi-cancer early detection (MCED) by capturing the tumor secretome and host response. We analyze recent (2020-2025) evidence and add pathway/hallmark context, cross-platform validation, and proteome-scale protein-protein interaction (PPI) inference to guide translational panel design. MethodsWe reviewed extensive prospective and multi-cancer studies using Olink, SomaScan, and mass spectrometry, contrasting case-control versus prospective performance. Candidates were organized into Known and Novel sets and mapped with GeneCodis (GO/KEGG/Reactome) and Cancer Hallmarks. Clinical relevance was assessed using GEPIA 3.0 Cox Forest plots and TCGA-survival Kaplan-Meier curves (median split; log-rank). Protein-level corroboration used TPCPA/RPPA Z-score distributions across tumor types. To contextualize molecular crosstalk, we incorporated in silico PPI prediction to evaluate whether candidates cluster into interaction sub-modules relevant to secretory/TGF-{beta}, matrix remodeling, and immune-follicular biology. ResultsBeyond classic antigens/inflammatory markers (e.g., CEACAM5, WFDC2/HE4, GDF15), the Novel set converged on a matrix-immune-secretory axis comprising ECM proteases (MMP12, ADAM8), antigen presentation/B-cell programs (CD74, CXCL13), secretory/TGF-{beta} signaling (TGFB1), and epithelial invasion (CDCP1). Forest plots show adverse hazards for secretory/matrix genes across multiple epithelial cancers, while immune-follicular genes exhibited context-dependent effects; TCGA-survival curves reproduced these directions. TPCPA demonstrated tumor-type-specific protein elevation, supporting detectability. PPI inference organized candidates into coherent interaction modules (e.g., TGFB1, CDCP1 protease, and CXCL13; CD74 hubs), reinforcing multiplex, not single-marker, readouts. ConclusionsCross-platform agreement, augmented by PPI-defined interaction modules, supports a two-bucket MCED strategy that pairs high-risk secretory/matrix markers with immune-context sensors to enhance sensitivity, tissue-of-origin interpretability, and clinical triage. Prospective validation and down-selection to a cost-scalable targeted assay are warranted for population screening.

Prostate cancer is the most common cancer in men worldwide. Despite its prevalence, there is a critical knowledge gap regarding the underlining molecular events that result in higher incidence and mortality rate in Black men. Identifying molecular features that separate racial disparities is a critical step in prostate cancer research that could lead to predictive biomarkers and personalized therapy. N-linked glycosylation is a co-translational event during protein folding that modulates a myriad of cellular processes. Recently, aberrant N-linked glycosylation has been reported in prostate cancers. However, the full clinical implications of dysregulated glycosylation in prostate cancer has yet to be explored. Herein, we performed high-throughput matrix-assisted laser desorption ionization mass spectrometry analysis to characterize the N-glycan profile from tissue microarrays of over 100 patient tumors with over 10 years of follow up data. We identified several species of N-glycans that were profoundly different between low grade prostate tumors resected from White and Black patients. Further, these glycans predict opposing overall survival between White and Black patients with prostate cancer. These data suggest differential N-linked glycosylation underline the racial disparity of prostate cancer prognosis. Our study highlights the potential applications of MALDI-MSI for digital pathology and biomarker to study racial disparity of prostate cancer patients. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/260026v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@15f9b35org.highwire.dtl.DTLVardef@1c6d200org.highwire.dtl.DTLVardef@5188eorg.highwire.dtl.DTLVardef@8f347e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Skeletal muscle is a complex syncytial arrangement of an array of cell types and, in the case of muscle specific cells (myofibers), sub-types. There exists extensive heterogeneity in skeletal muscle functional behaviour and molecular landscape, at the cell composition, myofiber sub-type and intra-myofiber sub-type level. This heterogeneity highlights limitations in currently applied methodological approaches, which has stagnated our understanding of fundamental skeletal muscle biology in both healthy and myopathic contexts. Here, we developed a novel approach that combines a fluorescence based assay for the biophysical examination of the sarcomeric protein, myosin, coupled with same-myofiber high sensitivity proteome profiling, termed Single Myofiber Protein Function-Omics (SMPFO). Successfully applying this approach to healthy human skeletal muscle tissue, we identify the integrate relationship between myofiber functionality and the underlying proteomic landscape that guides divergent, but physiologically important, behaviour in myofiber sub-types. By applying SMPFO to two forms of human nemaline myopathy (ACTA1 and TNNT1 mutations), we reveal significant reduction in the divergence of myofiber sub-types, across both biophysical and proteomic behaviour. Collectively, we develop SMPFO as a novel approach to study skeletal muscle with greater specificity, accuracy and resolution then currently applied methods, facilitating that advancement in understanding of SkM tissue in both healthy and diseased states.

PurposeThis study is aimed at developing a clinic-friendly proteomics protocol and a machine learning (ML)-based molecular diagnostic test for T-cell-mediated rejection (TCMR) using formalin-fixed, paraffin-embedded (FFPE) biopsies. Experimental designBased on the procedures we reported for proteomic profiling of FFPE biopsies using Tandem Mass Tag (TMT)-based technology, a label-free-based quantitative proteomics protocol was developed as a more clinical-practical and cost-efficient molecular diagnostic test for renal transplant injection. This new protocol was applied to a set of FFPE biopsies from renal allograft injury patients and normal controls, including 5 TCMR, 5 polyomavirus BK nephropathy (BKPyVN) and 5 stable graft function (STA). Three different machine learning algorithms, linear discriminant analysis (LDA), support vector machine (SVM) and random forests (RF), were tested to build a prediction model for TCMR. ResultsAbout 750-1250 proteins were identified and quantified in each sample with high confidence using the label-free-based proteomics protocol. 178, 450 and 281 proteins were defined as differential expression (DE) proteins for TCMR vs STA, BKPyVN vs STA and TCMR vs BKPyVN, respectively. By comparing the quantitative data from the TMT- and label-free-based proteomics profiling, a classifier panel comprised of 234 DE proteins commonly quantified by two methods was generated to test different ML algorithms. Leave-one-out cross-validation result suggested that the RF-based model achieved the best prediction power for TCMR at both proteome and transcriptome level. Conclusions and clinical relevanceProteomics profiling of FFPE biopsies using a platform integrated of label-free quantitative proteomics with ML-based predictive model can help to discover biomarker panels and provide clinical molecular diagnostic tests to enhance biopsy interpretation for renal allograft rejection. Clinical RelevanceThis study is to develop a molecular diagnostic test for kidney rejection. An easy-to-use and cost-efficient protocol using label-free quantitative strategy was developed to profile proteome of FFPE biopsies from kidney allografts. A list of 234 DE identified from TCMR, BKPyVN and STA was generated as a classifier panel for these different phenotypes. This classifier panel was subjected to the optimized ML model, achieving high accuracy among both positive and negative control. This proof-of-principle study demonstrated the clinical feasibility of implementation of molecular diagnostic tests integrated of label-free-based quantitative proteomics and ML-derived disease predictive models to enhance biopsy interpretation for kidney transplantation patients. More accurate and specific molecular tests can lead to more effective treatment, prolong graft life, and improve the quality of life for patients with chronic kidney failure.

Mass spectrometry (MS)-based proteomics offers powerful opportunities for biomarker discovery; nevertheless, it is associated with technical challenges, some of them being missing values and batch effects. Both can obscure biological signal and bias results. Although imputation and batch-correction methods are well established in transcriptomics, their impact, particularly on large-scale, real-world clinical proteomics datasets, remains unclear. In this study, we systematically compared the impact of two popular imputation methods ([1/2] LOD replacement and KNN) in combination with three batch-effect correction approaches (ComBat, ComBat with disease covariate, and MNN) on differential expression analysis in a CE-MS urine peptidomics dataset of 1,050 samples across 13 batches collected for early detection of chronic kidney disease (CKD), separated into discovery (n = 525) and validation (n = 525) sets. Our results show that the choice of imputation method (between [1/2] LOD and KNN) had minimal impact on the final list of differentially expressed peptides (DEPs). In contrast, batch-effect correction had a much stronger influence on the results. ComBat without covariate adjustment removed most DEPs, suggesting loss of true biological signal. Along these lines, incorporating disease status into the model preserved most of this information. MNN yielded a moderate to low number of validated DEPs overall, especially when paired with KNN imputation. These findings show that imputation and batch correction are not entirely independent processes and that they can influence downstream results. Overall, preprocessing choices should be chosen based on the characteristics of each dataset and especially considering batch severity and biological covariates. Statement of significance of the studyFinding reliable biomarkers in clinical proteomics first requires addressing the technical noise that can hide true biological signals. In this work, we investigate how different imputation and batch correction methods influence the list of peptides that emerge as differentially expressed. Instead of relying on simulations or small datasets, we examine a large, real-world urine-peptidomics cohort of more than 1,000 samples screened for early-stage chronic kidney disease. The results show that no preprocessing pipeline is universally optimal and that the best choice depends on the characteristics of the dataset. This study offers practical guidance for improving reproducibility in urine-based peptide studies and supports more confident identification of disease-associated molecular signatures.