Microvascular inflammation and endothelial injury, triggered by interferon-gamma (IFNγ), are hallmarks of antibody-mediated rejection (ABMR), the leading cause of premature kidney allograft loss. Glomerular extracellular matrix (ECM) remodeling and endothelial caveolae formation are important aspects of chronic ABMR. We found galectin-1, an immunomodulatory protein that interacts with the ECM, to be increased in the glomeruli of patients with ABMR, while its gene (LGALS1) expression was decreased by IFNγ stimulation in glomerular endothelial cells. Mechanisms underlying endothelial dysfunction in ABMR, its links to ECM remodeling, and the role of immunomodulatory proteins such as galectin-1 remain incompletely understood. Here we studied the effects of galectin-1 modulation in glomerular microvascular endothelial cells (GMECs) in vitro. We demonstrated that galectin-1 was mainly expressed by glomerular endothelial cells in ABMR kidneys. To model key aspects of endothelial injury in ABMR, we knocked down LGALS1 in GMECs, followed by stimulation with IFNγ, and label-free quantitative proteomic and phosphoproteomic profiling of GMECs. Proteomic analysis identified 5446 proteins (FDR<0.01), of which 236, 827, and 267 were differentially expressed in response to LGALS1 knockdown, IFNγ treatment, and their interaction, respectively (FDR<0.05). Both LGALS1 knockdown and the interaction between treatments significantly altered expression of adhesion proteins (FDR<0.01), particularly integrin subunit β5, which was validated. Phosphoproteomic profiling identified 2727 phosphopeptides (FDR<0.01), with 28 that were differentially expressed across LGALS1 knockdown, IFNγ treatment, and their interaction (P<0.01). Phosphorylation of CAVN1 and co-localization with its partner CAV1, critical for caveolar formation, were decreased in GMECs upon LGALS1 knockdown, IFNγ stimulation, or both. In a microfluidic model of the glomerular microvasculature, addition of recombinant galectin-1 increased both endothelial permeability and secretion of proinflammatory cytokines in LGALS1-silenced GMECs. Thus, endothelial signaling pathways regulated by inflammatory cues and galectin-1 contribute to endothelial injury and may affect caveolae formation, highlighting a potential link between galectin-1 and ABMR.
The ability of Vibrio cholerae to transition between motile and sessile forms in the environment and in the host is critical to its survival and virulence. The molecular cues, sensor proteins, and signaling pathways mediating these transitions are highly complex and often overlapping. Nevertheless, a detailed understanding of them is critical for understanding the persistence and pathogenesis of this deadly pathogen. Nitric oxide (NO) functions as an important signaling molecule in many bacteria, affecting biofilm formation, motility, and virulence, often through interaction with heme protein sensors. The genome of V. cholerae encodes two such sensors called H-NOX and NosP. Here we constructed a Δhnox/nosP mutant and employed a multi-omics methodology that combines tandem-mass-tag (TMT)-based quantitative proteomics, phosphoproteomics, and targeted metabolomics to investigate the function of these sensors. A set of 258 proteins was differentially expressed in the mutant that included many proteins involved in flagellar biosynthesis and motility as well as critical virulence factors, iron acquisition systems, and metabolic enzymes. Many of the identified genes are also part of the ferric uptake regulator (Fur) regulon and iron-dependent transcriptional repression of several Fur targets was disrupted. Phosphoproteomics analysis also revealed proteins involved in motility and virulence as differentially phosphorylated in the mutant strain. In most cases, these phosphoproteins have not been previously observed and provide a wealth of new targets for investigating mechanisms of V. cholerae signaling. Taken together, this work illustrates a role for H-NOX and NosP in promoting factors important for infection while suppressing those important for environmental survival, suggesting a function in priming the organism for infection and/or maintaining the infectious phenotype.
Systemic immunoglobulin light-chain (AL) amyloidosis is characterized by clonal immunoglobulin-secreting cells that produce a monoclonal light chain prone to misfolding and amyloid fibril formation in tissues. Understanding its molecular basis requires accurate full-length sequencing of amyloidogenic light chains and linkage of circulating light chains to renal deposits. Achieving this is technically challenging because the low abundance and N-glycosylation of amyloidogenic light chains can complicate protein purification and peptide-level sequence analysis. To address these challenges, we implemented a robust analytical pipeline that integrates intact-mass measurement by Q-TOF MS before and after deglycosylation with multi-protease digestion and de novo peptide sequencing to systematically characterize urinary N-glycosylated light chains. Mass shifts observed before and after deglycosylation supported the presence of N-glycosylation, whereas deglycosylated intact masses were used to constrain full-length sequence assembly. The assembled urinary light-chain sequences were subsequently compared with matched renal amyloid proteomes isolated by laser microdissection and analyzed by bottom-up liquid chromatography-tandem mass spectrometry (LC-MS/MS). Enzymatic deglycosylation reduced glycan-induced spectral interference. Eight monoclonal light-chain sequences were assembled from urinary light chains, including five derived from the immunoglobulin kappa variable 1 (IGKV1), two from the immunoglobulin lambda variable 2 (IGLV2), and one from IGKV4. The close agreement between theoretical and experimental intact masses confirmed the accuracy and completeness of sequence assembly. In each renal amyloid proteome, the corresponding urine-derived sequence had the highest score among light-chain identifications of the patient's clinically determined isotype (κ or λ) and showed 87.6-100% variable-region peptide coverage. Together, these findings support urinary N-glycosylated monoclonal light chains as the precursor proteins of the corresponding renal amyloid fibrils. In conclusion, we have demonstrated a robust workflow that applies established de novo peptide sequencing to characterize urinary N-glycosylated light chains and trace the corresponding sequences in renal amyloid deposits, with potential applications across light chain-related diseases.
Extreme high-altitude environment poses severe threats to human health, underscoring the urgent need for effective, safe metabolic interventions. Here, we demonstrate that combined regimen of nicotinamide mononucleotide (NMN) and glucose attenuates tissue injury and prevents body weight loss in mice under hypobaric hypoxia (HH). By leveraging a multi-tissue integrative atlas encompassing metabolome, lipidome, proteome, and phenotypic profiles, we identified HH-induced iron overload and oxidative stress as key pathological drivers. NMN plus glucose supplementation significantly counteracted metabolic disruptions across multiple tissues. Mechanistically, HH induced transferrin-inspired iron delivery, causing iron overload and oxidative stress, along with glutathione depletion and lipid peroxidation across multiple tissues. Notably, we observed no significant changes in the protein levels of ferroptotic markers including ACSL4, GPX4, and FSP1, although cannot rule out the possibility of activity alterations of these proteins. Nevertheless, NMN combined with glucose effectively reversed the ferroptosis-associated metabolic alterations and alleviated mitochondrial dysfunction. Collectively, our study provides a systems-level metabolic atlas and reveals that NMN combined glucose mitigates HH-induced multi-organ injury by suppressing ferroptosis through metabolic reprogramming, offering a therapeutic potential for high-altitude hypoxia.
Reversible oxidation of cysteine residues (redox modifications) plays a crucial role in regulating protein function, signaling, and cellular homeostasis. These dynamic modifications act as molecular switches that transduce redox signals and modulate stress responses, metabolism, and pathogenesis. Redox proteomics enables systematic profiling of these modifications, quantifying the oxidation levels of tens of thousands of cysteine sites across the proteome and providing rich data to understand redox-regulated networks. However, conventional redox proteomic workflows are often limited by low throughput and high sample requirements. Here, we present a high-throughput sample processing workflow for redox proteomics analysis from as little as 2 μg of protein, enabling, for the first time, rapid redox-state profiling of cells cultured in 96-well plates. The workflow integrates 96-well plate-based cell culture, lysis, digestion, and cysteine-peptide enrichment, substantially increasing throughput and reducing hands-on processing time. Incorporating field asymmetric ion mobility spectrometry (FAIMS) further enhances redox proteome coverage by removing singly charged species in low-input samples, thereby increasing the signal of cysteine-containing peptides. Applying the workflow to RAW264.7 cells cultured in 96-well plates (40,000 cells per well), DIA identified >10,000 cysteine sites and revealed a global increase in cysteine oxidation upon diamide treatment. To assess robustness, we repeated the 96-well experiment across three independent batches processed on different days and observed consistent coverage, reproducible quantification, and comparable diamide-induced oxidation of heat shock proteins, transcription factors, and protein kinases. Together, this workflow and new data acquisition scheme enable comprehensive redox proteomics from minimal inputs, paving the way for high-throughput sophisticated studies of redox modifications in cell signaling, disease, or large-scale screening of redox-modulating compounds.
Breast cancer (BC) remains the leading cause of cancer-related death among women worldwide, with 2.26 million new cases and ∼685,000 deaths reported in 2020 (Globocan 2020). A major challenge in treating BC, particularly luminal subtypes, is the pronounced molecular heterogeneity both between patients and within individual tumours. This intratumoural diversity underlies many cases of therapy resistance and relapse. Here, we present a theranostic approach that integrates spatial proteomics and patient-derived tumoroids (PDTs) to guide personalized treatment in luminal BC. Formalin-fixed, paraffin-embedded (FFPE) tumour tissues from three patients were analyzed by MALDI mass spectrometry imaging (MSI) and spatially-resolved micro-proteomics, mapping distinct clonal subpopulations and their protein expression profiles. Proteomic pathway analysis revealed subclone-specific vulnerabilities that are not apparent from standard histopathology. We exploited these insights to design alternative combination therapies tailored to each tumour's molecular makeup. The efficacy of proteomics-guided regimens was then evaluated in vitro using PDTs established from the same patients, in direct comparison to conventional chemotherapy. Proteomic-informed treatments demonstrated significantly enhanced anti-tumour activity in the PDT models, yielding lower IC50 values and greater cell death than standard treatments. In several cases, PDTs exhibited resistance to conventional therapy, which was explained by the presence of proteomic resistance markers (such as EDIL3, CA12, PGK1, and CapG) in the corresponding tumour clones. These results underscore the potential of spatial proteomic profiling to expose actionable intratumoural heterogeneity and to inform more effective, clone-specific therapies. Our study highlights an innovative pipeline for luminal BC therapy guidance, combining MALDI MSI and PDT drug testing, and advocates for the integration of spatial proteomics into precision oncology to improve treatment outcomes.
Glycopeptide preparation is a critical step in bottom-up glycoproteomics. In this study, a robust method for N-glycopeptide preparation was developed using low-cost, commercially available magnetic particles. These particles function as a solid phase for hydrophilic interaction liquid chromatography (HILIC) to enrich N-glycopeptides from mixtures of N-glycopeptides and non-glycosylated peptides. Two types of magnetic particles, carboxylated polymer beads and cellulose resin, were compared. The latter demonstrated higher efficiency in N-glycopeptide recovery and non-glycopeptide removal. Biological samples often contain impurities such as salts and detergents that reduce efficiency and reproducibility of trypsin digestion and subsequent HILIC purification. To remove these impurities, we combined an optimized HILIC extraction protocol with a single-pot solid-phase-enhanced sample preparation protocol (SP3) for N-glycopeptide enrichment. Blood-derived samples (serum/plasma) were first tested. Compared with in-solution digestion, the method improved digestion efficiency and achieved N-glycopeptide recoveries comparable to those of conventional HILIC solid-phase extraction in a single tube. This method can be used for N-glycopeptide preparation not only from albumin-immunoglobulin-depleted serum but also from tissue extracts. Furthermore, the method has been successfully applied to glycoproteomic analysis of clinical serum and tissue samples from patients with gastric cancer, enabling detection of cancer-related alterations in glycoproteins.
Chemoproteomics is a popular approach for the identification of small molecule-protein interactions in biological systems. Several chemoproteomics workflows leverage functionalized chemical probes and mass spectrometry to measure protein engagement through direct protein enrichment or competition using a range of small molecule concentrations. Statistical methods for analysis of such dose-response chemoproteomics datasets are limited. For example, existing methods rely on fixed curve shapes and are sensitive to experimental variation, particularly when the number of doses or replicates is limited. Here, we present MSstatsResponse, a semi-parametric statistical framework for analyzing chemoproteomic dose-response experiments that uses isotonic regression that does not require a fixed curve shape. This approach improves the accuracy and robustness of curve fitting, target identification, and half-response estimation across diverse experimental designs. We evaluate MSstatsResponse by generating a benchmark chemoproteomic dataset that profiled the competition between the kinase-binding probe XO44 and the drug Dasatinib using three mass spectrometry acquisition strategies: data-independent acquisition, tandem mass tag-based data-dependent acquisition, and selected reaction monitoring. We further evaluate the method on simulated datasets that vary the number of doses, number of replicates, and levels of noise, and demonstrate that MSstatsResponse consistently improves sensitivity, specificity, and reproducibility compared to existing methods, particularly in low-replicate and low-dose settings. MSstatsResponse is implemented as an open-source R/Bioconductor package that integrates with the MSstats ecosystem for quantitative proteomics. It provides a unified workflow for preprocessing, curve fitting, target identification, and experimental design, enabling researchers to select the number of doses and replicates appropriate to their experimental goals. The software and documentation are freely available at https://bioconductor.org/packages/MSstatsResponse.
To develop a noninvasive, urine-based approach for dynamic monitoring of tumor burden and early detection of recurrence in hepatocellular carcinoma (HCC), addressing the limited sensitivity of conventional serum biomarkers such as AFP and DCP, particularly for minimal residual disease (MRD) assessment. We established a prospective, multi-cohort urinary proteomics framework encompassing four longitudinal clinical cohorts (378 patients, 972 urine samples). In the discovery cohort, 26 patients contributed 130 longitudinal urine samples from those undergoing primary and secondary resections, which were analyzed by mass spectrometry at five standardized follow-up time points to identify proteins associated with tumor burden dynamics. The validation cohort (n = 46) used parallel reaction monitoring (PRM) to confirm candidate biomarkers and construct a composite urine-based tumor burden monitoring model integrating HPGD, AFP, DCP, and GGT. The model was then applied to an early recurrence cohort (306 patients, 612 urine samples) to detect MRD and predict recurrence prior to radiological confirmation. Among 8563 quantified urinary proteins, 217 significantly correlated with tumor burden, with HPGD closely mirroring dynamic changes. The integrated model achieved a pre-recurrence AUC of 0.86, sensitivity of 73%, and specificity of 87%, outperforming AFP (0.73, 39%, 96%) and DCP (0.64, 59%, 88%). It predicted recurrence a median 4.1 months earlier than imaging and served as an independent prognostic factor for recurrence-free (RFS) and overall survival (OS, p < 0.001). This urine-based model enables dynamic assessment of tumor burden and early recurrence detection, surpassing conventional serum biomarkers and providing a clinically actionable tool for personalized surveillance and therapeutic decision-making in HCC.
Cascaded database searches boost identification sensitivity in vast proteomic search spaces but challenge false discovery rate (FDR) control. The standard target-decoy approach to FDR control relies on decoy matches providing an exchangeable and properly scaled representation of incorrect target matches. This assumption can be disrupted when protein-level filtering is used to define a reduced search space, because target and decoy entries may no longer undergo symmetric retention during database reduction. Although entrapment provides an external benchmark for assessing FDR control, conventional separate-entrapment implementations can become invalid in cascaded searches because entrapment sequences may be disproportionately discarded during protein-level filtering. Here we introduce Fusion Entrapment, a strategy that computationally fuses entrapment sequences with target proteins to preserve identical selection pressure during database reduction. Simulations show that this strategy provides accurate entrapment-based false discovery proportion (FDP) estimation in cascaded searches involving protein-level filtering. Applying Fusion Entrapment to human gut metaproteomic datasets, we further observed that conventional separate target-decoy database reduction led to substantial inflation of the entrapment-estimated FDP relative to the reported FDR threshold. In contrast, fusion target-decoy reduction maintained empirical FDR control under Fusion Entrapment assessment while retaining substantial sensitivity gains over single-step analysis.
Epitope detection sensitivity remains a primary bottleneck in mass spectrometry (MS)-based immunopeptidomics, as conventional discovery-based workflows such as data-dependent (DDA) and data-independent acquisition (DIA) frequently lack the sensitivity required to detect ultra-low abundant targets. While these untargeted methods are powerful for mapping the general immunopeptidome, the stochastic nature of precursor selection and the presence of complex, chimeric spectra mean that rare species, such as viral or mutation-derived neoepitopes, often remain undetected. In this study, we present optiPRM+, an ultra-sensitive targeted-first workflow for the Orbitrap Exploris 480 platform that integrates systematically optimized targeted acquisition with untargeted DIA contextualization to bridge this sensitivity gap. Our approach centers on the empirical characterization of target peptides using direct infusion-MS to determine optimal fragmentation conditions and inclusion list-driven data-dependent acquisition (iDDA). To maximize signal-to-noise ratios for these trace-level targets, we employed ultra-high MS2 resolutions (up to 480,000), ion injection times up to 1000 ms, and narrow precursor isolation windows. Additionally, we discovered that precursors with a charge state exceeding their basic amino acid count require unusually low energies for optimal fragmentation, which is especially relevant for the non-tryptic peptides characteristic of the immunopeptidome. We applied the optiPRM+ workflow to the challenging biological case of Human Papillomavirus type 16 (HPV16), a virus known to suppress antigen presentation pathways. This optimized strategy enabled the confident identification and validation of the human leukocyte antigen (HLA)-A∗02:01-restricted epitope TIHDIILECV and, to our knowledge, the first MS-based detection of two novel viral targets: ISEYRHYCY (HLA-A∗01:01) and CVYCKQQLLR (HLA-A∗11:01). Subsequent global immunopeptidome analysis via DIA confirmed that these ultra-low abundance peptides were not detectable through untargeted methods despite being clearly validated by our targeted approach. By successfully detecting these viral peptides, we demonstrate that a systematically optimized targeted-first approach can uncover biologically relevant epitopes that remain invisible to conventional discovery-based workflows.
This review paper summarizes the pathophysiology of rheumatoid arthritis (RA), its incidence and etiology, and conventional RA protein-based biomarkers. A brief overview of novel protein-based biomarkers and biomarkers with potential to be used in the diagnostics of seronegative RA is also provided. The core of the review resides in the comprehensive characterization of the glycosylation of immunoglobulins and their utility as novel biomarkers for the diagnostics of RA and seropositive RA. Two main approaches to the analysis of glycans present on immunoglobulins and other proteins are provided, including instrument-based and lectin-based approaches showing their clinical performance as RA biomarkers.
Deep characterization of intact proteoforms remains an analytical challenge in functional proteomics, particularly for heterogenous multi-site post-translational modifications at distinct amino acid residues. Histones are among the most dynamically and diversely post-translationally modified proteins in eukaryote cells, carrying multiple, co-occurring and reversible modifications that can give rise to isomeric proteoform species. Tandem mass spectrometry with multimodal fragmentation capabilities is a promising approach for deep characterization of intact proteoforms, such as modified histones. We applied the novel timsOmni mass spectrometer, which incorporates the Omnitrap platform enabling multimodal MSn workflows using controlled in vitro acetylation of recombinant histones H3.1 and H4 by GCN5, PCAF and p300, followed by analysis of endogenous H4 proteoforms. Complementary MS2 electron- and collision-based dissociation (ECD, EID, RCID and ECciD), together with MS3 strategies, produced complete or near-complete backbone fragmentation of intact protein ions (>92% amino acid sequence coverage). For monoacetylated species generated by the more site-selective lysine acetyltransferases, the dominant proteoform matched the known catalytic preferences of the enzymes (H3.1K14ac for GCN5 and PCAF, and H4K8ac for PCAF), while minor positional isomers were also identified and their relative abundance estimated. In contrast, the broader substrate specificity of p300 produced a wide distribution of H4 proteoforms bearing up to seven acetylated lysine residues. Species carrying six and seven acetylations were characterized by multimodal MS2/MS3 experiments, enabling localization of individual acetylation sites and discrimination of positional isomers. Finally, top-down sequencing of endogenous intact H4 proteoforms from human liver extracts yielded amino acid sequence coverages of 92-93% for the most abundant species and confident localization of multiple, distant PTMs (acetylation and methylation). These results demonstrate that multimodal MSn fragmentation of intact proteins supports residue-level assignment of combinatorial histone marks and coexisting positional isomers in controlled and endogenous proteoform mixtures.
As global proteomics continues to advance, the number of identifiable proteins has increased substantially. However, this does not inherently ensure optimal quantitative performance. While targeted assays using isotope-labeled peptides can be developed, label-free strategies remain an attractive and cost-efficient option for methodological validation. Yet, systematic evaluations of data analysis workflows for label-free targeted proteomics, particularly those incorporating artificial intelligence (AI)-based tools, are still limited. Therefore, this study aimed to benchmark multiple data analysis approaches for label-free targeted proteomics as a validation framework for results obtained from global analyses. Missing-data imputation (MDI) strategies, including no MDI, k-nearest neighbors, data-driven, MSstats and AI-based methods, were evaluated, alongside consolidation and testing frameworks such as mathematical summation, best-peak selection, AI-based scaling with univariate statistics, p-value integration, MSstats Tukey's median polish or linear models and multivariate testing. Data-driven MDI combined with p-value integration consistently showed the strongest performance across accuracy, precision, specificity, and false-discovery rate, outperforming all other strategies. These findings demonstrate that careful selection of data analysis workflows can yield substantially improved quantitative outcomes compared with commonly used approaches such as simple mathematical summation. Although our conclusions are based on a controlled benchmarking dataset comprising three yeast proteins spiked into a constant human background using only one targeted approach, they can be generally applied as a default workflow for biomarker validation using the label-free approach.
Glycosphingolipids are membrane lipids characterized by highly diverse glycosylation patterns. They are involved in numerous cellular processes and can be linked to diseases. With the aim to investigate their role in early mammalian developmental processes in vitro, we knocked out the UDP-glucose ceramide glucosyltransferase gene (UGCG) encoding the enzyme glucosylceramide synthase (UGCG) in human induced pluripotent stem cells (hiPSCs) by CRISPR/Cas9. The functional impairment of UGCG was confirmed by glycomic profiling. UGCG KO hiPSCs displayed normal morphology, growth behavior, and expression of stem cell markers compared to WT hiPSCs. Furthermore, the KO cells maintained pluripotency, as evidenced by their capacity for in vitro differentiation into all three embryonic germ layers and their ability to form teratomas in vivo. Quantitative proteomic analysis of explanted teratoma derived from WT and UGCG KO hiPSCs revealed differential expression of numerous proteins, with notable enrichment of pathways associated with signal transduction or protein localization and transport. Phosphorylation profiling of key kinases and their targets uncovered reduced activation of STAT family transcription factors in UGCG KO hiPSCs. Complementary transcriptomic profiling combined with Ingenuity Pathway Analysis of differentially expressed genes between WT and UGCG KO hiPSCs, as well as their ectodermal derivatives, revealed a predicted positive enrichment of proteins associated with signaling pathways in UGCG KO hiPSCs. Global lipidomic profiling showed a significant increase in sphingomyelin levels in UGCG KO hiPSCs and their ectodermally differentiated derivatives, whereas total ceramide content remained comparable between UGCG KO and WT cells. In summary, despite the absence of overt phenotypic changes, our findings demonstrate that glycosphingolipid deficiency induces multiple molecular perturbations in hiPSCs.
The push for new clinical biomarkers has seen rapid innovation in biofluid analysis, particularly for plasma. For mass-spectrometry (MS)-based analysis, achieving depth and quantitative accuracy whilst ensuring throughput continues to shape plasma methods development. Numerous workflows have emerged that mitigate high-abundance suppression and expand dynamic range, especially when paired with next-generation MS instrumentation. Yet systematic evaluations that also consider biological variables (e.g., biofluid type, species) and technical parameters (e.g., MS methods) are limited. Here, we benchmarked eight sample-preparation workflows spanning neat approaches (SP3, STrap), depletion (perchloric acid, PerCA), and corona-enrichment strategies (MagNet HILIC/SAX, Enrich-iST, ProteoNano). We compared their performance across human plasma, human serum, and rat plasma, analyzing all samples on an Orbitrap Astral (Thermo) using two plasma-optimized data-independent acquisition (DIA) methods: one discovery-maximized and one throughput-maximized. We identified 2726 human and 3767 rat proteins across workflows and methods, including ∼1000 from neat plasma. Increasing throughput incurred a ∼20 to 30% reduction in depth, depending on workflow and species. EV-enrichment produced the deepest proteomes but with distinct compositions relative to neat, depleted, and secreted-protein-enriched samples, revealing a unique sub-proteome niche. Several workflows also performed markedly better in rat plasma, supporting improved sensitivity for preclinical analyses. Enrichment or depletion dramatically reshaped the balance of tissue- and cell-specific proteins detectable in plasma, suggesting that workflow choice should be guided by the organs, immune targets, or inflammatory signals most relevant to the study. In this vein, statistical analysis of differentially abundant proteins showed that >90% of detected proteins were significantly altered between workflows, with the largest numbers arising from the corona-enrichment strategies, underscoring how strongly workflow choice shapes the downstream proteome. Taken together, these findings emphasize a rapidly expanding plasma methodological landscape, where the most effective workflow is the one most precisely tailored to a cohort's biology.
Rupture of vulnerable carotid atherosclerotic plaques (CAPs) is a major precipitating cause of ischemic stroke. Current imaging-based risk stratification is useful for anatomical and structural assessment, but it does not readily provide noninvasive access to plaque-associated molecular information. Here, we aimed to identify and validate a noninvasive urinary biomarker of CAP instability and to explore its biological relevance. We employed a multi-stage study design integrating untargeted urinary proteomics data-independent acquisition mass spectrometry in a discovery cohort of 179 patients with systematic interrogation of plaque tissue proteomic and transcriptomic datasets. Candidate proteins were further evaluated by ELISA in an independent cohort of 225 individuals, including patients with stable or unstable CAPs and healthy controls. Cellular and tissue-level analyses included single-cell RNA sequencing re-analysis, human plaque validation by immunohistochemistry, western blotting, and immunofluorescence, as well as in vitro macrophage phenotypic assays. Urinary proteomic profiling identified 227 shared differential proteins associated with plaque instability. In the independent validation cohort, urinary ADAM-Like Decysin 1 (ADAMDEC1) was confirmed as a candidate urinary marker, showing a stepwise increase from healthy controls to stable and unstable CAPs. Urinary ADAMDEC1 showed robust discriminatory performance for unstable plaques (AUC = 0.882, 95% CI 0.822-0.941). Higher urinary ADAMDEC1 levels were associated with symptomatic status and plaque vulnerability features, while higher intraplaque ADAMDEC1 expression was associated with adverse cerebrovascular outcomes. Single-cell and human plaque analyses showed that ADAMDEC1 was enriched in inflammatory macrophage-associated plaque regions, and in vitro assays supported an association between ADAMDEC1 and pro-inflammatory macrophage polarization. Urinary ADAMDEC1 is a noninvasive biomarker associated with carotid plaque instability. Integrated tissue, single-cell, and macrophage assays support a link between ADAMDEC1 and macrophage-associated inflammatory features in vulnerable plaques.
Primary open-angle glaucoma (POAG) is a leading cause of blindness, yet the biochemical mechanisms underlying disease progression remain poorly understood. This study characterized aqueous humor (AH) across early and advanced stages of POAG using large-scale proteomics. AH samples were collected from patients with mild POAG, advanced POAG, and non-glaucomatous controls in this prospective cross-sectional study. Profiling of 8000 proteins was performed using an antibody array. Hierarchical clustering, principal component analysis (PCA), Gene Ontology (GO), and pathway enrichment analyses were performed to identify differentially expressed proteins, biological processes, and molecular functions associated with disease stages. Top differential proteins were also correlated with key clinical parameters. Proteomic signatures demonstrated distinct clustering between control and glaucoma patients, with progressive shifts from mild to advanced stages. Comparisons between control and each glaucoma group revealed significant enrichment in pathways related to wound healing, extracellular matrix (ECM) remodeling, and immune responses. Notably, distinct proinflammatory and immune proteins and pathways were enriched when comparing mild to advanced POAG stages. Finally, the top differential AH proteins correlated with multiple clinical endpoints, including intraocular pressure and a variety of retinal outcomes. This signature revealed a tight network associated with disease stage, oriented on immune cell migration and function, and identifying soluble TRPC6 as a novel biomarker. To our knowledge this is the first study to compare large scale proteomic analysis of AH from patients with POAG across disease stages, suggesting new mechanisms underlying glaucoma progression.
Advances in single-nucleus RNA sequencing have demonstrated advantages over single-cell transcriptomics by enabling capture of dynamic cellular states and transcriptional activity in rare or difficult-to-isolate cell types. To achieve analogous enhancements in intact protein profiling, we applied single-cell proteoform imaging mass spectrometry (scPiMS) to nuclei for label-free, proteoform-resolved analysis of >104 human skin cells. Using scPiMS, we surveyed ∼400 proteoforms across over 17,000 single nuclei isolated from human epidermal keratinocytes spanning undifferentiated (UD) proliferative to differentiated (DF) states, generating single-nucleus proteoform maps of epidermal differentiation. Proteoforms were assigned using intact mass tag matching to a reference proteoform library generated from bulk top-down LC-MS/MS analyses of UD and DF nuclei and subsequently applied to single-nucleus proteoform assignment scores. Unsupervised clustering of single-nucleus proteoform imaging mass spectrometry (snPiMS) data resolved 12 distinct proteoform-defined clusters spanning a continuum from UD proliferative states to terminal differentiation. A progenitor-enriched cluster (cluster 4) exhibited elevated high-mobility group-17, acetylated H2A/H2B variants, and H2A.Z, consistent with open and developmentally poised chromatin. An early differentiating population (cluster 3) was marked by histone H3 bearing H3K4 acetylation together with H3K9 monomethylation, indicative of transcriptional activation. Progressive histone H4 methylation (H4K20me1, H4K20me2, and H4K20me3) reflected cell cycle-coupled modification of newly synthesized H4 in progenitor-like nuclei prior to differentiation, corresponding to clusters 5, 2, and 8, respectively. Cluster 1 was enriched in H3 proteoforms bearing H3K4me3 together with H3K9me1, consistent with an active yet transcriptionally poised chromatin state. Differentiation-committed states (cluster 0) exhibited H3 proteoforms containing H3K4 and H3K9 acetylation together with H3K36me2 reflecting transcriptionally active chromatin. Bulk histone post-translational modification profiling corroborated these trends, with UD nuclei enriched in H3K9me2 and H4K20me1 and DF nuclei enriched in H4K20me2, H3K79me2, H3K27me2/3, and H3K36me3. Together, snPiMS uniquely resolves combinatorial histone proteoforms within individual nuclei, revealing a continuous chromatin trajectory across epidermal differentiation.
Previously, we demonstrated that high levels of mitochondrial DNA (mtDNA) were functionally associated with "stemness" and aggressive phenotypic behaviors in human breast cancer cells, including spontaneous metastasis. More specifically, we showed that treatment with Alovudine induced mtDNA-depletion in MDA-MB-231 cells and prevented their ability to form colonies in vitro and metastasize in vivo. To better understand the underlying mechanism(s) and identify candidate mtDNA-dependent mitochondrial protein biomarkers relevant to metastatic breast cancer, Alovudine-treated MDA-MB-231 cells were subjected to proteomics analysis. For comparison purposes, mtDNA-depleted cells (MDA-MB-231 and MCF-7) were generated and also subjected to proteomics analysis. Intersection of these three distinct data sets revealed that a small number of proteins were commonly downregulated in mtDNA-depleted cells and Alovudine-treated cells. Remarkably, many of these nuclear-encoded mitochondrial genes (>20) exhibited recurrent genomic amplification across metastatic breast cancer cohorts. Therefore, the action of a single drug, namely Alovudine, was sufficient to effectively suppress the expression of a large number of mitochondrial proteins associated with i) gene amplification and ii) cancer cell metastasis, in human breast cancer patients. These findings may have important clinical implications for the development of new therapeutics targeting advanced breast cancer.
