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PubMed2026

Single-Cell Proteomics Reveals Proteome Remodeling and Cellular Heterogeneity During NGF-Induced PC12 Neuronal Differentiation.

Single-cell proteomics enables direct measurement of cellular heterogeneity during dynamic biological processes, but its application to fragile and highly adherent neuronal models remains challenging. Here, we developed and applied an optimized single-cell proteomics workflow to characterize proteome remodeling during nerve growth factor (NGF)-induced differentiation of PC12 cells. To enable reliable single-cell analysis, we implemented gentle dissociation, antiaggregation strategies, and thermal inkjet-based cell dispensing, achieving high accuracy in single-cell isolation. Inclusion of n-dodecyl-β-d-maltoside (DDM) improved recovery of membrane-associated and low-solubility proteins. Coupled with LC-ion mobility-mass spectrometry, this workflow enabled quantification of 2,000-3,000 proteins per cell across the differentiation time course. Single-cell proteomic analysis revealed progressive and heterogeneous proteome remodeling during differentiation. While undifferentiated cells formed a relatively homogeneous population, later stages (Days 4-6) exhibited increased variability, including multimodal protein abundance distributions and separation into distinct subpopulations. Dimensionality reduction, clustering, and non-negative matrix factorization identified multiple coexisting proteomic states within the same time points, reflecting asynchronous differentiation trajectories. These subpopulations were characterized by coordinated differences in pathways related to intracellular trafficking, protein translation, cytoskeletal organization, and neuronal maturation. Comparison with bulk proteomics demonstrated that proteins associated with differentiated neuronal states, including those involved in neurite formation and structural remodeling, are underrepresented in population-averaged measurements but are enriched within specific single-cell subpopulations. Temporal and cluster-resolved analyses further revealed distinct protein expression trajectories, including early decreases in cell cycle and metabolic pathways and later increases in neuronal structural and regulatory proteins. Together, this study establishes an optimized workflow for single-cell proteomics of neuronal systems and demonstrates that NGF-induced PC12 differentiation proceeds through heterogeneous and divergent proteomic states that are not resolved by bulk analysis.

Animals↗

PubMed2026

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

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

Proteomics↗

PubMed2026

Toward simple, rapid, and deep plant proteome analysis with an in-cell proteomics strategy.

While liquid chromatography-mass spectrometry (LCMS) has revolutionized plant proteomics over the past decade, plant sample preparation remains a major challenge due to rigid cell walls, abundant secondary metabolites, and wide dynamic range of protein abundance. These hurdles demand laborious tissue disruption, complex precipitation, and extensive cleanup prior to LCMS analysis, limiting the widespread adoption of proteomic technologies within the plant biology community. To overcome these barriers, we introduced an "in-cell proteomics" strategy that bypasses cell lysis and protein extraction by performing digestion directly inside methanol-fixed cells. We systematically benchmarked this strategy against conventional lysate-based workflows across 4 model plants (Arabidopsis thaliana, Nicotiana benthamiana, Zea mays, and Sorghum bicolor) and 3 tissue types (leaves, pollen, and seeds). Combined with minimal input material and single-shot LCMS, the in-cell approach consistently identified 9,000 to 12,000 proteins from leaves, 7,000 to 9,000 from pollen grains, and approximately 8,000 from seeds. Our comprehensive dataset demonstrates that this in-cell digestion approach substantially simplifies plant sample preparation while delivering proteomic performance equivalent to established workflows. Finally, to demonstrate the biological utility of this approach, we characterized the proteomes of N. benthamiana leaves infected with 2 fungal strains that exhibit different host specificities. Our in-depth proteomic data revealed distinct host response signatures differentiating the host-adapted Colletotrichum destructivum from the nonhost-adapted Colletotrichum sublineola strain. Overall, this study provides a simple, unbiased alternative for plant proteomic analysis that can be readily applied to tackle complex agricultural and physiological challenges in plant biology.

Proteomics↗

PubMed2026

P1D6 inhibits FnBP-induced extracellular proteome remodeling: proteomic evidence for a novel intervention strategy in atopic dermatitis.

Atopic dermatitis (AD) is an inflammatory skin disorder characterized by skin barrier impairment, chronic inflammation, and intense pruritus. Staphylococcus aureus (S. aureus) critically contributes to its pathogenesis; however, the mechanistic role of its virulence factor fibronectin-binding protein (FnBP) in keratinocytes remains poorly understood. This study used bibliometric analysis and quantitative proteomics to examine the relationship. We first performed a bibliometric analysis, revealing a sustained increase in publications on S. aureus and AD, peaking at 99 articles in 2023, with hotspots focused on skin barrier function, immune inflammation, and pediatrics. Quantitative proteomics was employed to investigate how FnBP reshapes the extracellular proteome and whether the anti-α5 integrin antibody P1D6 exerts interventional effects. HaCaT cells were stimulated with recombinant FnBP alone or in combination with P1D6, followed by data-independent acquisition (DIA)-based proteomic analysis of secretome changes. Proteomic analysis identified FnBP-induced differentially expressed proteins enriched in immune- and barrier-related pathways, including cell adhesion, cell junctions, and VEGFA-VEGFR2 signaling. P1D6 intervention significantly inhibited the secretome profile and identified 241 core responsive proteins, of which approximately 52% returned to baseline levels after intervention (P > 0.05). These proteins were primarily enriched in pathways governing protein homeostasis, folding, proteasomal degradation, and interleukin-7 signaling. Notably, P1D6 modulated the downregulation of ATP5F1B and P4HB, key effectors within the interleukin-7 pathway. This study demonstrates that FnBP remodels the keratinocyte secretome by disrupting protein homeostasis, consequently inducing barrier injury and chronic inflammation related to AD, which can be effectively blocked by P1D6. Combined with bibliometric trends and proteomic evidence, this study focuses on FnBP, an underexplored virulence factor, and provides novel insights into AD pathogenesis and therapeutic interventions.

Humans↗

PubMed2026

Quantitative proteomics reveals coordinated changes in the proteome during replicative senescence.

Cellular senescence is a state of irreversible cell cycle arrest triggered by telomere erosion, persistent DNA damage or chronic stress. The accumulation of senescent cells disrupts tissue function and contributes to aging and disease. Here, we employ mass spectrometry-based proteomics to systematically interrogate dynamic proteome changes at multiple levels during the progression of replicative cellular senescence. We demonstrate that proteome changes during senescence occur in a coordinated manner, characterized by widespread protein depletion on chromatin. Moreover, components of the cytoplasmic translation machinery are depleted, while mitochondrial proteins display increased insolubility. Autophagic and proteasome activity is compromised in senescent cells along with remodeling of ubiquitin linkages and depletion of ubiquitin E3 ligases. Comparison of the senescent proteome with different pathophysiological cellular states reveals a distinctive senescent signature shaped by changes in the proteostasis network. Collectively, we provide a resource for the exploration of temporally resolved changes in the senescent proteome.

Cellular Senescence↗

PubMed2026

Spatial Proteomics of the Human Atherosclerotic Microenvironment Reveals Heterogeneity in Intraplaque Proteomes and Extracellular Matrix Remodeling.

Plaque heterogeneity underlies the propensity of atherosclerotic lesions to rupture and trigger cardiovascular events. Most proteomic studies examine bulk changes, obscuring key spatial differences in protein abundance. We report a high-resolution spatial proteomics workflow exploring the molecular landscape of human plaques and a murine myocardium. By combining laser capture microdissection with high-sensitivity ion-mobility mass spectrometry, spatial profiling of cellular and extracellular matrix (ECM) proteomes was achieved. Over 2700 proteins were detected from 50,000 μm2 areas, revealing substantial intraplaque heterogeneity across distinct regions (lipid-rich, media, shoulder, necrotic core, intima) and distance from the artery lumen. Inverse correlations between proteases (cathepsin B) and core structural ECM proteins (perlecan, HSPG2) indicated active ECM remodeling. Analysis of media layers indicated distinct protein signatures associated with smooth muscle contraction and cell-cell communication. Blood coagulation signatures, including platelet degranulation and fibrin formation, were enriched at the intima. Inflammatory (clusters of differentiation 4/68, CD4/CD68; vascular cell adhesion molecule 1, VCAM1) and vascular damage markers (tenascin-C, TNC) were enriched in shoulder regions. The necrotic core was dominated by blood proteins, consistent with intraplaque hemorrhage. This workflow resolves proteomic changes over ∼200 μm distances, providing unprecedented insights into plaque morphology and offers a powerful tool for elucidating plaque biology.

Humans↗

PubMed2025

Hepatocyte proteome destabilization and novel targets for PFASs unveiled through combined thermal proteome profiling and deep transfer learning.

Identifying protein targets for per- and polyfluoroalkyl substances (PFASs) is essential to understand their toxicity and health risks. However, knowledge about their interacting proteins is limited since reliable identification methods are lacking. We developed an integrated approach combining thermal proteome profiling (TPP) and deep transfer learning (DTL) modeling to efficiently identify cellular targets of PFAS. TPP measured PFAS binding proteins and the affinities by nanospray liquid chromatography tandem mass spectrometry, while DTL models were constructed to predict PFAS-protein affinities using neural network algorithms. TPP results revealed that PFASs uniquely destabilized the proteome of HepG2 cells, unlike the stabilizing effects by other xenobiotics. Key protein targets for three representative PFASs (PFOA, GenX and Novec 649) were identified, which exhibited weak binding affinities (median EC50 ≈ 30 μM). The number of protein targets increased with molecular weights among the three PFASs. The DTL model achieved a higher Pearson correlation coefficient of 0.89, and reduced mean squared errors by 54 % over previous models for drug-protein interactions. Notably, TPP and DTL jointly pinpointed ribosomal proteins as novel targets of GenX, potentially linking it to cell apoptosis through disrupted protein synthesis. Biolayer interferometry validated GenX binding to RPL4 protein, driven by electrostatic interactions and halogen bonds. This integrated approach effectively uncovers novel PFASs targets, advancing insights into their adverse health effects.

Humans↗

PubMed2026

Identification of Immune Response-Related Proteomic Biomarkers in Moyamoya Disease Using Serum Olink Proteomics.

Moyamoya disease, a rare chronic cerebrovascular disorder, requires invasive digital subtraction angiography (DSA) for diagnosis. This study employed high-throughput proteomics to identify plasma biomarkers for Moyamoya disease diagnosis. We conducted immunopanel analysis using the Olink platform to evaluate 92 immune-related proteins in plasma samples from 88 Moyamoya disease patients and 88 healthy controls. Key proteins were identified through differential expression analysis, GO, and KEGG enrichment analysis. A diagnostic model was constructed using LASSO regression, Boruta algorithm, and machine learning models including random forest and XGBoost. Validation of these proteins was performed using GEO external data sets, followed by prediction of potential therapeutic drugs and molecular docking validation through pharmacogenomic databases. A total of 44 differentially expressed proteins were identified through the Olink immunopanel, with 12 downregulated and 32 upregulated. GO and KEGG analyses revealed significant enrichment of these proteins in innate immune responses and signaling pathways such as NF-kB and MAPK. Through LASSO, random forest, and protein under-area analysis, four potential biomarkers for Moyamoya disease (MGMT, SIT1, PRDX1, TRAF2) were identified. A diagnostic model using these proteins showed the highest AUC value with the XGBoost model. Additionally, TRAF2 and PRDX1 exhibited significant expression differences in Moyamoya disease patients within the GEO data set. Our study revealed the immune landscape of Moyamoya disease, identified four biomarkers, and established a variety of diagnostic models.

Humans↗

PubMed2026

Proteomics at scale: Bottlenecks and opportunities for early-career researchers in a fast developing field.

The field of proteomics has rapidly evolved over the last five years enabled by rapid advances in instrumentation and computation. At the same time, the proteomics community is also growing. This is reflected by the increasing participation in international conferences such as those organized by the European Proteomics Association and the Human Proteome Organization. These events provide early-career researchers with unique opportunities to exchange ideas, develop collaborations, and build networks that support professional development. One such network is the Young Proteomics Investigators Club, a European initiative supported by European Proteomics Association and led by early-career researchers. In this Community-Driven project, we investigate recent trends in proteomics by screening conference abstracts and evaluating the session attendance at Human Proteome Organization Congresses and European Proteomics Association conferences. Based on these analyses, we identified five areas that, from our perspective, are shaping the current trends in proteomics: clinical proteomics, proteomics of post-translational modifications, single-cell proteomics, systems biology and multi-omics, and computational proteomics. For each area, we highlight both unique challenges and identify a common theme: a shift from exploratory studies with manageable sample numbers towards large screenings and cohorts and the generation of big data, which often comes with the lack of computational support, organizational networks, and infrastructure. In this light, we describe the unique challenges and opportunities faced by early-career researchers. We point to actionable directions for enabling reproducible and transparent proteomics as well as community-driven projects and initiatives, which are often providing training and support. SIGNIFICANCE: In this perspective, the Young Proteomics Investigators Club (YPIC) discusses advances in analytical developments and computational approaches in proteomics research. Based on empirical analysis of recent European Proteomics Association conference and Human Proteome Organization congresses contributions, we identify clinical, single-cell, post-translational and systems-level proteomics as the research areas that have gained most momentum in the last three to five years. What makes this work distinctive is that it is written by and for early-career researchers, thereby uniquely identifying where momentum, challenges, and unmet needs converge for the newest generation of proteomics researchers. Rather than cataloguing advances, we examine the widening gap between what modern proteomics can generate and what individual researchers can realistically process, validate, and interpret. We describe specific structural barriers including access to high performance computing, limited formal training in scalable data analysis, the need for unified benchmarking standards and navigating clinical collaboration frameworks. We then highlight opportunities for the field, such as community-curated benchmarks, interdisciplinary mentorship models, and shared computational infrastructure. By making these challenges explicit from an early-career researchers standpoint, we aim to inform how training, funding, and community initiatives can be shaped to support the next generation of proteomics researchers.

Proteomics↗

PubMed2026

Proteomics in environmental pollution research: Advances, challenges, and future directions.

Environmental proteomics has emerged as a powerful approach for elucidating the molecular mechanisms underlying pollutant-induced biological effects. Although this field has developed rapidly, the systematic review of recent proteomics applications in environmental pollution research remains limited. This review explored the emerging roles of toxicoproteomics in biomarker discovery and mechanistic elucidation, as well as ecotoxicoproteomics in ecological risk assessment and bioremediation strategies. Here, we review the field, highlighting recent trends such as the integration of proteomics with genomics, transcriptomics, and metabolomics to provide a comprehensive view of biological responses to environmental stressors. We further discuss the growing application of artificial intelligence in improving proteomics data interpretation and accelerating biomarker discovery. In addition, recent technological advances in environmental proteomics are highlighted, including next-generation tissue microarray proteomics, nanoscale proteomics, single-cell proteomics, and spatial proteomics. Despite its potential, proteomics faces challenges, such as high operational costs, computational complexity in analysis, and technical limitations in low-abundance protein detection. We propose that the convergence of proteomics with artificial intelligence and multi-omics approaches offers promising solutions to these challenges, enhancing the practical application of proteomics in environmental monitoring and risk assessment.

Proteomics↗

PubMed2026

Cardiac mitochondrial proteome of lean, healthy Ossabaw minipigs with predisposition to metabolic syndrome versus that of Göttingen minipigs.

Ossabaw minipigs differ from other (mini)pig strains by their genetic predisposition to develop full metabolic syndrome and their nonresponsiveness to cardioprotective interventions, even before developing the diseased phenotype. Previous DNA sequencing data revealed differences in a cluster of mitochondrial protein-coding genes between Ossabaw and Göttingen minipigs-a large animal model without such a genetic predisposition and a responsiveness to cardioprotection. Alterations in mitochondrial protein composition affect mitochondrial function, and mitochondria play a crucial role in the development of metabolic syndrome and for cardioprotection. Therefore, we aimed to compare the cardiac mitochondrial proteome between lean Ossabaw minipigs with a healthy phenotype and Göttingen minipigs to gain initial insights into potential differences in mitochondrial protein composition and function. Cardiac mitochondria (left ventricular tissue) of both minipig strains (male/female pigs) were isolated, and the proteome was analyzed by liquid chromatography-tandem mass spectrometry. An unbiased, nonhypothesis-driven proteome analysis identified 97% overlap in the proteome. Among the 3% of differentially expressed proteins, 19 were related to mitochondrial metabolism, 8 to transcription and translation, 3 to small molecule transport, 2 to oxidative phosphorylation, and 1 to dynamics and surveillance. These small differences in protein composition were associated with an altered mitochondrial energy turnover-ATP production was reduced by 49% in Ossabaw compared with Göttingen minipig mitochondria. This proteome analysis provides a broader basis to understand how genetic alterations result in changes of the mitochondrial proteome and function, which might be relevant for the development and progression of metabolic syndrome and/or the primordial nonresponsiveness to cardioprotection in Ossabaw minipigs.NEW & NOTEWORTHY Our comprehensive cardiac mitochondrial proteome of Ossabaw and Göttingen minipigs is a valuable resource for cardiac biomedical research. Moreover, our proteome analysis provides a broader basis for understanding how genetic alterations result in changes of the mitochondrial proteome and support a mechanistic link between subtle, strain-specific mitochondrial proteomic signatures and altered mitochondrial energy turnover. These changes may be relevant for the development and progression of metabolic syndrome and/or primordial nonresponsiveness to cardioprotection in Ossabaw minipigs.

Animals↗

PubMed2026

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases.

KEY POINTS: Plasma proteome profiling identified distinct signatures across biopsy-proven primary glomerular disease subtypes. An elastic net model using 93 proteins classified primary glomerular disease subtypes and controls, with external validation. Integrating proteomics with machine learning yields biologically interpretable insights in primary glomerular diseases. BACKGROUND: Primary GN is a heterogeneous group of kidney disorders where understanding of their pathophysiology remains incomplete. Despite the diagnostic potential of high-throughput proteomics, constrained proteomic depth and a reliance on binary comparisons have left the feasibility of using systemic signatures to differentiate multiple GN subtypes largely unexplored. METHODS: To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5416 plasma proteins via Olink Explore HT in a discovery cohort ( n =147) and an external validation cohort ( n =85) of Korean participants (mean age, 41±13 years; 46% female). The study population included patients with four GN subtypes-focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy-alongside healthy controls. We developed a machine learning (ML) model using logistic regression with elastic net regularization to classify disease groups based on proteomic profiles and evaluated its performance in the independent validation cohort. RESULTS: Plasma proteome profiles were distinct among disease subtypes, emerging as a significant source of data variation independent of conventional markers such as eGFR or proteinuria levels. The ML model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve >0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy. The model, even without clinical information, correctly identified 93% of minimal change disease cases (14 of 15) and 63% of IgA nephropathy cases (20 of 32), but its performance was limited for focal segmental glomerulosclerosis, with only 21% of cases (three of 14) correctly classified. Functional analysis of key proteins highlighted distinct biologic pathways, such as hemostasis in minimal change disease. CONCLUSIONS: We identified distinct systemic proteome signatures for primary glomerular diseases, where disease subtype served as a major determinant of proteomic variance alongside conventional clinical markers. ML models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.

Humans↗

PubMed2026

Single-organ proteomics in Drosophila melanogaster larva.

The combination of genetic accessibility, organ complexity, evolutionary conservation, and cost-efficiency makes Drosophila melanogaster (Dm) a well-known model system for biomedical and fundamental biological research. Proteomic analysis of single organs enables the identification and quantification of proteins expressed in specific organs. This will help to uncover specific biological functions and unique protein profiles that are not detectable in whole-organism analyses. In this study we have isolated single organs form Dm larvae, and we have performed a deep proteomics mapping by following a minimal manipulation preparation procedure. The combined dataset across all organs comprised 9132 identified proteins. As anticipated, principal component analysis (PCA) revealed clear separation between the proteomes of most organs, confirming distinct protein profiles. These findings demonstrate the applicability of the sample preparation strategy for high-resolution proteomic characterization of individual organs in Drosophila. Given the extensive genetic tools available for this model organism, our approach has the potential to open new avenues for proteomic studies in Drosophila melanogaster and any other biological systems where the sample amount is limiting. SIGNIFICANCE STATEMENT: Drosophila melanogaster is a well-known model system for biomedical and fundamental biological research that serves as a valuable in vivo model organism due to its high degree of evolutionary conservation with higher vertebrates, tractable genetics, and logistical efficiency. However, the proteome of Drosophila at single organ level has been elusive to date, due to several factors like low sensitivity of previous generation mass spectrometers and sample preparation procedures, difficult isolation of some organs. In this study we have applied a compilation of advanced methods including minimal sample manipulation together with simple, straightforward and efficient protein extraction and digestion methods. Obtained peptides were minimally handled to be analyzed by applying specific and sensitive nLC methods coupled on-line to state-of-the-art MS/MS system. Altogether, the applied strategy allowed us to get the first single organ study to date for this animal. These datasets represent a significative resource for future genomic, transcriptomic and proteomic studies in Drosophila, as multi-omic integration requires deep proteomics to translate data into functional biochemistry, and serves as a critical bridge and an indispensable standalone resource across the genomic, transcriptomic, and proteomic landscapes.

Animals↗

PubMed2026

Quantitative Proteomic Analysis of APP/PS1 Transgenic Mice.

BACKGROUND: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the central nervous system (CNS), with its etiology still shrouded in uncertainty. The interplay of extracellular amyloid-β (Aβ) deposition, intracellular neurofibrillary tangles (NFTs) composed of tau protein, cholinergic neuronal impairment, and other pathogenic factors is implicated in the progression of AD. OBJECTIVE: The current study endeavors to delineate the proteomic landscape alterations in the hippocampus of an AD murine model, utilizing proteomic analysis to identify key physiological and pathological shifts induced by the disease. This endeavor aims to shed light on the underlying pathogenic mechanisms, which could facilitate early diagnosis and pave the way for novel therapeutic interventions for AD. METHODS: To dissect the proteomic perturbations induced by Aβ and Presenilin-1 (PS1) in the AD pathogenesis, we undertook a label-free quantitative (LFQ) proteomic analysis focusing on the hippocampal proteome of the APP/PS1 transgenic mouse model. Employing a multi-faceted approach that included differential protein functional enrichment, cluster analysis, and protein-protein interaction (PPI) network analysis, we conducted a comprehensive comparative proteomic study between APP/PS1 transgenic mice and their wild-type C57BL/6 counterparts. RESULTS: Mass spectrometry identified a total of 4817 proteins in the samples, with 2762 proteins being quantifiable. Comparative analysis revealed 396 proteins with differential expression between the APP/PS1 and control groups. Notably, 35 proteins exhibited consistent temporal regulation trends in the hippocampus, with concomitant alterations in biological pathways and PPI networks. CONCLUSIONS: This study presents a comparative proteomic profile of transgenic (APP/PS1) and wild-type mice, highlighting the proteomic divergences. Furthermore, it charts the trajectory of proteomic changes in the AD mouse model across the developmental stages from 2 to 12 months, providing insights into the physiological and pathological implications of the disease-associated genetic mutations.

Animals↗

PubMed2025

Analytical Considerations for the Development of Plate-Based Proteomics Platforms Using Isobaric Labeling.

Mass spectrometer-based proteomics platforms have great potential to rapidly advance our systematic understanding of complex biological problems, enable drug discovery, decipher drug mechanisms of action, and discover novel biomarkers. As the demand for processing large sets of samples in an automatic manner is constantly increasing, the integration of automation platforms (nanoliter dispensers, liquid handlers, etc.) has become a routinary configuration paired with liquid chromatography-mass spectrometers. The functional integration of all of those instruments into a single unit is what we call a plate-based high-throughput proteomics platform (HT proteomics). The readout of the platform is the quantitative proteome data at the protein or peptide level. In this work, we developed a plate-based HT proteomics standard that we called the HT-sKO. The HT-sKO allows the evaluation of accuracy and the estimation of the relative limit of quantification when the target proteins vary up to 60-fold in abundance. The HT-sKO utilizes nonhuman recombinant proteins that can be spiked into the samples, allowing for sample acquisition and HT proteomics platform evaluation at the same time. We also showed the foundational role of a robust acquisition strategy for developing a stable HT proteomics platform and the value of using a tube-based method as an informant assay on data quality expectations for the platform. Using this new standard, we demonstrated that the intra- and inter-plate variance is around 4-6% for the protein level or around 10% for the peptide-level readout. We also showed that the HT-sKO standard is compatible with whole-proteome, phospho-proteome, and reactive cysteine profiling platforms.

Proteomics↗

PubMed2026

Machine Learning-Driven Prediction of Coronary Artery Disease Risk Based on UK Biobank Plasma Proteomics.

BACKGROUND: Coronary artery disease (CAD) is a leading global cause of mortality, yet the predictive accuracy of conventional risk models is limited. Here, we integrate conventional risk factors, polygenic risk scores, and large-scale proteomics to develop a unified model for enhanced CAD risk prediction. METHODS: Using data from UK Biobank, participants with plasma proteomics and genetic risk data were included after excluding prevalent CAD. Participants from England were split into training (n=32 330) and internal validation (n=13 857) sets, and Scotland/Wales participants formed an external validation set (n=5775). Incident CAD was ascertained from linked health records. A 202-protein proteomic risk score was derived by least absolute shrinkage and selection operator Cox regression, and CatBoost models were trained using conventional risk factors alone and with incremental addition of polygenic risk scores and protein proteomic risk scores; Shapley Additive Explanations-guided forward selection identified a compact protein panel. RESULTS: Across cohorts, the median age was 58 years and ∼45% were men. Protein proteomic risk score was dose-dependently associated with CAD risk. Compared with conventional risk factors alone, integrating polygenic risk scores and protein proteomic risk scores improved discrimination, with the area under the curve increasing from 0.750 (95% CI, 0.732-0.767) to 0.789 (95% CI, 0.772-0.805) in internal validation and from 0.717 (95% CI, 0.683-0.750) to 0.762 (95% CI, 0.732-0.791) in external validation. A 9-protein panel (GDF15 [growth differentiation factor 15], MMP12 [matrix metalloproteinase 12], NPPB [natriuretic peptide B], PGF [placental growth factor], REN [renin], ADGRG2 [adhesion G-protein coupled receptor], ACE2 [angiotensin-converting enzyme 2], CDCP1 [CUB domain-containing protein 1], CXCL17 [C-X-C motif chemokine ligand 17)]) captured most proteomic predictive information. CONCLUSIONS: Our findings demonstrate that integrating conventional risk factors, polygenic risk scores, and proteomic data improves CAD risk prediction. This study highlights the utility of proteomics in precision cardiovascular medicine and simplified risk stratification tools.

Humans↗

PubMed2024

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

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

Proteomics↗

PubMed2026

Nuclear Proteome Map of Mouse Heart Chambers.

Heart specialization involves nuclear programs; however, chamber-specific regulation of the nuclear proteome landscape remains unknown. In this study, we isolated the nucleus from four major anatomical regions of healthy mouse heart (fresh) and employed quantitative mass spectrometry-based proteomics to construct a comprehensive nuclear proteome landscape of left ventricle (LV, 2403 proteins), right ventricle (RV, 2242 proteins), left atrium (LA, 2368 proteins), and right atrium (RA, 1816 proteins). This led to the discovery of nuclear regional proteome signatures (ventricular signature, 297 proteins; atrial signature, 183 proteins) associated with oxidative metabolism and redox regulation, ferroptosis, extracellular-matrix remodeling, SUMO- and stress-responsive control and transcriptional regulation. Chamber-level analyses further identify distinct nuclear features in LV (120 proteins), LA (188 proteins), and RA (72 proteins). In addition, we defined conserved core nuclear proteome (230 proteins) shared across all anatomical regions, enriched for transcription-regulator complexes, nucleolar/ribosome-associated, RNA-processing, and chromatin-organization components. Within this core network, we report 78 transcription factors/co-factors and select nuclear, chromatin and RNA export-associated proteins, including 29 specific factors (e.g., Alpk3, Rbm14, Arglu1, Hmgb1, Myef2, Sf1) associated with the heart. Regionally, we verified spatial localization in heart of H2ac21 and Sun2 in LA and Ptbp2 in LV by immunofluorescence. This study provides insights into the chamber-resolved view of the nuclear proteome in the heart, establishes a framework for linking nuclear proteomic signatures to atrial and ventricular biology, unique features of the heart nuclear proteome landscape relative to other organs, and a baseline for studying nuclear remodeling in cardiac pathophysiology.

Animals↗