Dimethyl fumarate (DMF) and dimethyl itaconate (DMI) are electrophilic metabolite derivatives with potent immunomodulatory properties. Both have been reported to covalently modify cysteine residues in proteins, thereby altering their structure and function. Thus, we hypothesized that DMI and DMF share common cysteine targets that account for their overlapping immunosuppressive effects. Using tandem mass tag activity-based protein profiling (TMT-ABPP) in mouse embryonic fibroblasts (MEFs), we systematically mapped cysteine sites whose activity toward an iodoacetamide-desthiobiotin probe was altered by DMI and DMF in MEF lysates. A subset of these targets responded to both compounds, and the proteins carrying them included proteins directly involved in immune signaling such as RelA (p65/NF-κB) and the cytosolic RNA sensor Ddx58 (RIG-I). Functional analyses demonstrated that both compounds suppressed RIG-I-dependent antiviral responses, leading to inhibition of type I interferon induction as well as impaired NF-κB nuclear translocation and transcriptional activation. Importantly, these effects were observed in nonimmune stromal cells, highlighting a broader regulatory role beyond professional immune cells. Our findings nominate RIG-I as a shared candidate target of DMI and DMF and associate altered cysteine reactivity with the dampening of antiviral and inflammatory signaling. This work provides new mechanistic insights into the immunosuppressive actions of these electrophiles and highlights their potential as chemical probes and therapeutic leads for modulating innate immunity.

Proteomics has become a data-rich discipline, supported by high-throughput mass spectrometry (MS), public repositories, community standards, and increasingly scalable computational workflows. However, the public availability of proteomics datasets does not guarantee that they can be reanalyzed, compared with other studies, integrated with other omics layers, or used to support clinical interpretation.

Gestational diabetes mellitus (GDM) is associated with adverse maternal and neonatal outcomes, but molecular alterations across the maternal-placental-fetal interface remain incompletely defined. We applied data-independent acquisition mass spectrometry to matched maternal blood (MB), umbilical cord blood (UCB) and placental tissue collected at delivery from 28 women with GDM and 21 controls. Differential protein abundance analysis, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, gene set enrichment analysis, weighted co-expression network analysis and pathway-level corroboration using two independent public placental transcriptomic cohorts were performed. Proteomic changes were compartment specific. Placental tissue showed the greatest number of differentially abundant proteins, whereas maternal blood and umbilical cord blood showed more limited alterations. Exploratory pathway-level analyses in MB suggested metabolic stress- and extracellular matrix-related patterns, whereas UCB suggested immune- and coagulation-related patterns. Placental tissue showed more prominent alterations involving extracellular matrix remodeling and ribosome-related pathways. A GDM-associated placental co-expression module contained both extracellular matrix proteins and ribosomal proteins. Independent transcriptomic datasets provided additional pathway-level support for ribosome-related and inflammatory processes. These findings suggest that GDM is associated with distinct proteomic changes in maternal blood, umbilical cord blood and placental tissue at delivery, with the most prominent alterations involving placental extracellular matrix remodeling and ribosome-related processes. SIGNIFICANCE: This matched multi-compartment proteomic study provides a placenta-centered view of GDM-associated molecular alterations at delivery. By integrating DIA proteomics, co-expression network analysis and pathway-level corroboration using independent transcriptomic datasets, the study identifies placental extracellular matrix remodeling and ribosome-related processes, while exploratory analyses of UCB suggest immune- and coagulation-related pathway patterns. These findings provide a basis for future hypothesis-driven studies of GDM-related placental dysfunction.

Ascites, the pathological accumulation of fluid in the peritoneal cavity, is common at diagnosis and with recurrence in high-grade serous ovarian carcinoma (HGSOC). Higher volumes of ascites fluid are associated with greater likelihood of recurrent disease and poor prognosis. Due to its availability-volume and frequency-and its reflection of tumors and their microenvironments, ascites is a valuable resource for proteomic biomarker discovery. However, ascites has not been widely studied. Similar to plasma, relatively few proteins comprise the majority of the broad dynamic range of protein mass in ascites fluid, necessitating an enrichment or depletion strategy for proteomic sample preparation. ENRICH-iST and ENRICHplus kits enrich low-abundance proteins using paramagnetic beads. These enrichment strategies have not previously been validated for use with ascites fluid. Our goal was to evaluate the ENRICH-iST and ENRICHplus kits using ascites fluid from HGSOC patients with differing extents of tumor resection during debulking surgery. This Dataset Brief describes the technical performance of these enrichment strategies based on DIA-MS proteome depth, reproducibility of identified proteins, Gene Ontology (GO) term enrichment (Biological Processes, Molecular Functions, and Cellular Components), and Human Protein Atlas-based protein abundance ranking. ENRICHplus outperformed ENRICH-iST by 1.4-fold based on depth of proteome coverage. Relative to the total proteome coverage, ENRICH-iST reduced the proportional contribution of the top 12 high-abundance proteins more effectively than ENRICHplus, thereby increasing the representation of lower-abundance proteins despite identifying fewer overall proteins. Analysis of the differential abundance of the proteins enriched from ascites fluid using these enrichment strategies could greatly facilitate HGSOC biomarker development.

Plasma proteomics holds promise for biomarker discovery, yet its characterization remains limited by the complex dynamic range of protein concentrations. We benchmarked seven proteomics workflows including five MS-based strategies (NEAT, PCA-N, TOP14, ENRICH-iST, SEER) on the Orbitrap Astral and Exploris 480, and Olink Target Inflammation and Reveal on 80 lithium heparin plasma samples from two clinical cohorts. Workflows were evaluated for proteome depth, reproducibility, HPPP catalogue coverage and recovery of aging-associated biological signatures. The Astral outperformed the Exploris 480 three-fold in protein identifications at four-fold higher throughput. Proteome coverage ranged from 1159 (NEAT) to 6003 protein groups (SEER), the latter providing over 70% HPPP coverage. TOP14 and ENRICH-iST achieved intermediate depth with high reproducibility. ENRICH-iST further demonstrated applicability to lithium heparin plasma via a simple protocol adaptation. Both Olink assays quantified their complete target panels, capturing low-abundance markers partially inaccessible to MS. Despite limited numerical overlap between MS and Olink datasets, fold-change directionality was broadly concordant. SEER and Olink Reveal uniquely recovered aging-related pathway signatures, including telomere maintenance and immune modulation. No single workflow provides exhaustive plasma proteome coverage. Integration of MS and affinity-based assays should be considered in plasma biomarker research for comprehensive coverage of the plasma proteome. SIGNIFICANCE: Plasma proteomics is central to biomarker discovery and translational research, yet practical adoption of next-generation workflows is hampered by the absence of head-to-head data comparing both mass-spectrometry and affinity platforms on the same clinical samples. Plasma proteomics is central to biomarker discovery and translational research, owing to the accessibility of plasma and its capacity to reflect physiological and pathological processes across tissues. Despite major technological advances in both mass spectrometry-based and affinity-based proteomics, the practical adoption of these innovations in clinical and population-scale studies remains constrained by methodological heterogeneity and the absence of comprehensive comparative frameworks. As a result, study design and platform selection are often driven by technical availability rather than by an informed assessment of analytical trade-offs and biological objectives. To address this gap, we benchmarked seven workflows including five MS-based strategies (NEAT, TOP14 depletion, ENRICH-iST, PCA-N, and Seer Proteograph XT) and two affinity assays (Olink Target Inflammation and the newly released Olink Reveal) on 80 individual lithium-heparin plasma samples from two clinical cohorts, for a total of 1525 injections. To our knowledge, this is the first study to include Olink Reveal in a systematic cross-platform comparison and to directly compare the Orbitrap Astral with the previous-generation Orbitrap Exploris 480 under identical conditions. In our hands, the Astral delivered ~2.5-fold more identifications than the Exploris, and Seer on the Astral reached 6003 proteins (~70% of the HPPP catalogue) at near-complete albumin removal. No single protein was detected by every MS and Olink platform simultaneously, and only the deepest workflows (Seer and Olink Reveal) recovered aging-relevant pathways in a centenarian vs. adult comparison, showing that analytical depth directly determines biological interpretability. We further demonstrate that Olink Reveal can be fully automated on the Firefly System (R[2] = 0.958 vs. manual preparation), enabling the throughput required for large clinical cohorts. Together, these results provide a concrete, evidence-based framework for workflow selection and support the co-deployment of deep MS and high-plex affinity assays as a comprehensive plasma-proteome-profiling strategy for clinical and translational proteomics. In this context, the present work provides a systematic and integrated benchmarking of multiple contemporary plasma proteomics workflows evaluated on a clinically relevant cohort. By comparing advanced mass spectrometry strategies and high-plex affinity-based assays under harmonized analytical conditions, this study goes beyond isolated performance metrics to examine how depth of coverage, reproducibility, throughput, and platform-specific biases influence biological interpretation. Importantly, the results demonstrate that no single workflow achieves exhaustive characterization of the plasma proteome, and that methodological choices directly shape the spectrum of detectable biological signals. The demonstrated complementarity between deep, discovery-oriented mass spectrometry workflows and highly standardized affinity-based platforms has direct implications for clinical implementation. While deep MS approaches maximize proteome coverage and enable hypothesis-generating analyses, affinity-based assays provide robust, scalable, and reproducible quantification of targeted low-abundance proteins that are often of primary clinical interest. In this study, the successful automation of the Olink Reveal workflow further illustrates how affinity-based proteomics can be adapted to high-throughput, standardized pipelines, an essential requirement for large clinical cohorts and longitudinal studies. This automation step reinforces the suitability of such platforms for routine deployment, while maintaining analytical consistency and quantitative reliability. From a clinical and translational perspective, this benchmark offers a reference framework to guide the rational selection and combination of plasma proteomics workflows according to study scale, sample availability, and intended downstream applications. By clarifying the strengths and limitations of each approach, this work contributes to improving experimental design, facilitating cross-study comparability, and supporting the gradual standardization required for clinical adoption. In the longer term, these results lay the groundwork for integrative analytical pipelines and harmonized data generation strategies that may enhance the reliability and interpretability of plasma proteomics in biomarker validation, longitudinal monitoring, and future clinical research settings.

Tape-stripping of the stratum corneum is a promising non-invasive approach for proteomic analysis of skin lesions, but the effects of sampling design and disc pressure on proteome readout remain poorly defined. Here, we assessed how inclusion of surrounding normal-looking skin and variation in adhesive disc pressure influence protein detection from small melanocytic lesions. D-squame tape strips were collected from six participants, including three moles and one matched normal skin site per participant. For lesion 1 (L1), discs were applied using light finger pressure. For lesion 2 (L2), the mole area was marked on the disc and separated from the surrounding skin, which was analysed separately (L2S). For lesion 3 (L3), increased pressure was applied using the upper barrel tip of a marker before excising the lesion-matched area. Samples were analysed by DIA-MS with DIA-NN processing. In total, 2882 protein groups were identified, including 1719 shared across all groups. L3 yielded the highest number of unique proteins (77) and significantly altered proteins versus normal skin (142), compared with L2 (54 unique; 52 altered), L1 (30 unique; 4 altered), L2S (16 unique; 3 altered), and normal skin (7 unique). These findings show that precise lesion-matched disc trimming and increased application pressure substantially improve tape-strip proteomic depth and lesion-specific signal detection. Multivariate analysis and supervised classification further separated lesional samples-most clearly the pressure-assisted L3-from other collection methods, supporting these observations, although the small cohort warrants cautious interpretation.

Chemoproteomics aims to achieve precise and comprehensive quantification of protein-small molecule interactions, yet methodological comparisons across quantitative proteomics workflows remain scarce. Here, we systematically benchmark tandem mass tag data-dependent acquisition (TMT-DDA) on Orbitrap Exploris/Eclipse instruments against label-free data-independent acquisition parallel accumulation-serial fragmentation (LFQ-diaPASEF) on a timsTOF Pro 2 mass spectrometer when applied for 2-dimensional thermal proteome profiling (2D-TPP) and Kinobeads-based chemoproteomics. Our findings demonstrate that TMT-DDA provided more confident detection of compound-induced thermal shifts despite having lower proteome coverage in high-complexity 2D-TPP datasets. In contrast, LFQ-diaPASEF excelled in low-complexity affinity enrichment experiments, achieving up to 63% broader total proteome coverage and comparable quantitative accuracy, even with ultra-short gradients. Additionally, we show that normalization strategies in DIA require careful selection to avoid artifacts when transforming IC50s into apparent dissociation constants (Kd [app]s). These findings emphasize that the trade-offs inherent to each platform critically impact chemoproteomics experiments and that instrument-method pairings should be selected based on experimental complexity, quantitative precision, data completeness needs, and throughput demands.

The humoral immune response plays a critical role in cancer immune surveillance, producing autoantibodies against tumor-associated antigens (TAAs). These autoantibodies show potential as non-invasive biomarkers for early cancer detection in colorectal cancer (CRC), a leading cause of cancer-related mortality worldwide. Despite their potential, the existence of non-seroreactive CRC patients to common TAAs poses a significant challenge, limiting the effectiveness of autoantibody-based diagnostics. Focusing on previously identified non-seroreactive CRC patients, we have employed high-resolution proteomics to profile their humoral immune response, enabling the discovery of novel CRC-specific TAAs to address this diagnostic gap. These TAAs were expressed in mammalian cells with 6xHisTag and HaloTag to enhance their detection and seroreactivity assessment through ELISA-like tests. Our integrative approach revealed previously undetected TAAs, expanding the panel of CRC-specific biomarkers. By overcoming the limitations of traditional diagnostics, this study provides a deeper understanding of the humoral immune response in CRC and underscores the potential of proteomic-driven strategies to improve early detection. These findings expand the repertoire of CRC-associated serological targets and support the use of immunoproteomic strategies to identify complementary antigens in CRC patients, paving the way for the development of liquid biopsy-based diagnostic approaches for individuals previously classified as non-seroreactive.

Disease-related activation of glial cells leads to changes in the cerebrospinal fluid (CSF) proteome. However, assigning such protein changes to their cellular origins is often difficult. Here, we used microglia (and macrophage) depletion in mice to identify CSF proteins of microglial origin. Young mice were treated with an inhibitor of the colony-stimulating factor 1 receptor, resulting in an almost complete ablation of microglial cells (and brain macrophages). Depleting microglia revealed profound changes in the CSF proteome. Nineteen proteins showed an overall reduction of at least 90%, suggesting a direct microglial or macrophage origin. Strikingly, we also observed an increase in many neuronal and synaptic proteins, likely reflecting compensatory neural changes including the accumulation of certain neuronal proteins that normally act as ligands for receptors on microglia cells, such as fractalkine or interleukin-34. In contrast to the robust changes in microglial and neuronal proteins, there was no evidence of astrogliosis after microglia ablation as determined by proteome analysis and brain histology. These observations are crucial for interpreting changes to the CSF proteome in relation to neuroinflammatory changes in neurological diseases.

The absence of robust biomarkers for early detection of severe liver disease (SLD) highlights the critical need for high-throughput proteomics-driven discovery. In this prospective cohort study, we aimed to identify plasma protein signatures associated with incident SLD and assess their clinical utility. Using the large-scale Olink Explore 1536 platform, we quantified 1461 plasma proteins in 46951 participants from the UK Biobank community-based cohort without baseline liver disease. Over a median follow-up of 14.1 years, we identified 490 proteins significantly associated with incident SLD risk. Growth differentiation factor 15 (GDF15) emerged as the strongest predictor, achieving a C-index of 0.80 and outperforming conventional clinical indices (LiverRisk score: 0.75; FIB-4: 0.68; APRI: 0.68). Temporal trajectories revealed that GDF15 levels began increasing up to 10 years before diagnosis, with progressive elevation as the diagnosis timepoint approached. Mendelian randomization analysis supported genetic associations linking higher protein levels of GDF15, FABP1, SPON2, CHI3L1, and PIGR with SLD risk. Our large-scale proteome-wide study not only reveals significant proteomic changes preceding SLD diagnosis but also establishes GDF15 as both a promising preclinical biomarker, opening new avenues for early intervention in at-risk individuals.

Paracoccidioidomycosis (PCM) is a major systemic mycosis in Latin America caused by Paracoccidioides brasiliensis, yet the contribution of aspartic proteases to fungal physiology and pathogenicity remains poorly understood. Here, we employed data-independent acquisition (DIA)-based quantitative proteomics to investigate the impact of pepstatin A-mediated inhibition of aspartic proteases on the P. brasiliensis proteome. High-coverage analysis identified up to 21,156 peptides and approximately 3000 proteins per condition, revealing progressive and time-dependent proteomic remodeling after inhibitor exposure. Differential abundance analysis demonstrated changes in proteins associated with membrane organization, protein turnover, RNA metabolism, stress adaptation, and cellular homeostasis. Functional enrichment analyses indicated modulation of metabolic processes, oxidative phosphorylation-associated proteins, and other biological functions related to fungal adaptation. Proteins associated with autophagy-related processes were differentially regulated, while MDC staining revealed temporal changes in acidic vesicle formation, suggesting alterations in vesicle-associated responses. Additionally, pepstatin A treatment reduced reactive oxygen species (ROS) production, consistent with changes in proteins related to oxidative metabolism. The inhibitor also impaired fungal adhesion to A549 pulmonary epithelial cells, indicating that aspartic proteases may influence host-pathogen interactions through mechanisms beyond adhesin abundance alone. Collectively, these findings provide a systems-level view of the cellular responses induced by aspartic protease inhibition and highlight the importance of these enzymes in regulating P. brasiliensis adaptation, homeostasis, and pathogenic potential.

Microbial infectious diseases pose a significant threat to global health. Early and accurate pathogen identification directly influences antimicrobial strategies and patient outcomes. Extracellular vesicles (EVs) serve as key mediators of intercellular signaling and can transport nucleic acids and proteins from both host and pathogen sources. Proteomic analysis of EVs captures pathogen activity, host response, and tissue injury, offering a review-focused perspective on biomarker discovery for microbial infectious diseases.

Protein profiles of amniotic fluid extracellular vesicle-enriched preparations may provide information on the fetal and gestational intrauterine environment, but their normal variation remains incompletely characterized. We used data-independent acquisition-based quantitative proteomics to compare AF-EV-enriched preparations sampled at approximately 19 and 24 weeks from pregnancies with normal fetal cytogenetic findings and uncomplicated outcomes. Of 3321 quantified proteins, 2611 detected in at least three of four samples in each group were included in the primary analysis without imputation. Primary analysis identified 904 candidates. Of these, 698 were also supported by the KNN sensitivity analysis with concordant direction, including 385 higher at 19 W and 313 higher at 24 W. A separate complete-case analysis identified 596 candidates, all within the 698-protein set. Nominal over-representation analysis using the 2611 eligible protein groups as the background highlighted metabolic/digestion and apical/brush-border annotations among 19 W-higher candidates and proteasome-centered annotations among 24 W-higher candidates. These enrichment findings are exploratory because the displayed P values were not adjusted for multiple comparisons. Given the small cohort and absence of independent validation and comprehensive EV characterization, the results are hypothesis-generating and cannot establish EV-specific gestational remodeling. SIGNIFICANCE: Gestational age is a recognized source of variation in total amniotic fluid, but its influence on proteins recovered from AF-EV-enriched preparations during mid-pregnancy is less well defined. Using DIA proteomics, we compared preparations sampled at approximately 19 and 24 weeks from pregnancies with normal fetal cytogenetic findings and uncomplicated outcomes. Of 3321 quantified proteins, 698 were supported by both the primary analysis and KNN sensitivity analysis and were retained as hypothesis-generating candidates. Nominal over-representation analysis using the 2611 protein groups eligible for the primary analysis as the background highlighted metabolic/digestion and apical/brush-border annotations among 19 W-higher candidates and proteasome-centered annotations among 24 W-higher candidates. These unadjusted enrichment results are descriptive and do not establish EV-specific remodeling. The findings nevertheless indicate that gestational age should be considered when designing and interpreting AF-EV proteomic studies and provide candidates for validation in larger independent cohorts.

Abnormalities in the physiological processes of platelet activation can lead to the formation of unwanted blood clots and the onset of cardiovascular diseases. Due to their anucleated nature, platelet activity is regulated primarily by post-translational modifications in receptors and their associated signaling pathways; consequently, proteomic techniques are the most suitable tool for the study and characterization of the molecular mechanisms involved in platelet pathophysiology.