Search Results (317)

Purpose: Data stability is a critical factor in Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) single-cell analysis. However, various factors, such as sample processing, instrument condition, and data acquisition, can introduce uncertainties into ToF-SIMS data. Correcting this data is vital, yet current methods mainly focus on total ion intensity normalization or using consistent substrates. No specific correction method exists for ToF-SIMS single-cell metabolomics. Methods: This study utilizes the Normalized Support Vector Regression (Norm-SVR), commonly used methods for correcting large-scale metabolomics data, for the correction of ToF-SIMS single-cell metabolomic analysis and assesses its performance in comparison to traditional total ion intensity normalization. Results and Conclusions: The results suggest that Norm-SVR effectively diminishes batch effects and reduces variability, thereby underscoring the method’s efficacy and practicality. This approach is expected to improve data quality assurance in extensive ToF-SIMS analytical datasets. Full article

Precision medicine, which relies on genomic, multi-omic, phenotypic, and environmental data, has the potential to transform healthcare from population-focused heuristics to individualized prevention, diagnosis, and treatment. Moreover, recent advances in sequencing, molecular profiles, wearable sensors, and machine learning have created opportunities for rapid translational innovation: rapid genomic diagnosis in neonatal and paediatric rare diseases, targeted oncology, pharmacogenomic-based prescribing strategies, and individual sport performance. Nevertheless, the vast majority of innovations remain in centers of specialism or pilot programs, rather than routinely or equitably integrated into clinical or athletic practice. This narrative review synthesizes translational evidence across the life course—in pregnancy, paediatrics, adult medicine, geriatrics, and sportomics—to find reproducible clinical and performance examples which enable precision-based alternative approaches to management, outcome, or preparation; and to reshape those examples into pragmatic, scalable priorities which minimize inequity, and maximize benefit. We undertook a structured narrative synthesis of peer-reviewed literature, trials, clinician translation programs, implementation studies, and sportomics reports, prioritizing examples that demonstrate utility, reproducibility, and impact. Important findings suggest that multi-omics and rapid sequencing improve diagnostic yield and time to diagnosis. Molecular profiling and circulating tumor DNA help realize adaptive treatment selection. Integrated genomics, metabolomics, wearable physiology, and AI analytics facilitate individualized training, injury-risk stratification, and recovery optimization. But systematic value is limited by insufficient representative validation, dataset bias, poor interoperability, regulatory uncertainty, workforce preparedness, and inequities of access. Converting a promise into population- and performance-level value requires coordinated action across four fronts: representative validation; interoperable, privacy-preserving infrastructures; clinician- and coach-centered implementation; and templates for scalable, cost-sensitive deployment. Full article

Background: In high-dimensional, small-sample omics studies such as metabolomics, feature selection not only determines the discriminative performance of classification models but also directly affects the reproducibility and translational value of candidate biomarkers. However, most existing methods primarily optimize classification accuracy and treat stability as a post hoc diagnostic, leading to considerable fluctuations in selected feature sets under different data splits or mild perturbations. Methods: To address this issue, this study proposes FRL-TSFS, a feature selection framework synergistically driven by filter-based Robust Rank Aggregation and L1-sparse modeling. Five complementary filter methods—variance thresholding, chi-square test, mutual information, ANOVA F test, and ReliefF—are first applied in parallel to score features, and Robust Rank Aggregation (RRA) is then used to obtain a consensus feature ranking that is less sensitive to the bias of any single scoring criterion. An L1-regularized logistic regression model is subsequently constructed on the candidate feature subset defined by the RRA ranking to achieve task-coupled sparse selection, thereby linking feature selection stability, feature compression, and classification performance. Results: FRL-TSFS was evaluated on six representative metabolomics and gene expression datasets under a mildly perturbed scenario induced by 10-fold cross-validation, and its performance was compared with multiple baselines using the Extended Kuncheva Index (EKI), Accuracy, and F1-score. The results show that RRA substantially improves ranking stability compared with conventional aggregation strategies without degrading classification performance, while the full FRL-TSFS framework consistently attains higher EKI values than the other feature selection schemes, markedly reduces the number of selected features to several tens of metabolites or genes, and maintains competitive classification performance. Conclusions: These findings indicate that FRL-TSFS can generate compact, reproducible, and interpretable biomarker panels, providing a practical analysis framework for stability-oriented feature selection and biomarker discovery in untargeted metabolomics. Full article

Background: Heterosis (hybrid vigor) is a fundamental phenomenon in plant breeding, but its molecular basis remains poorly understood in perennial crops such as tea (Camellia sinensis). This study aimed to elucidate the molecular mechanisms underlying heterosis in tea and its hybrids by performing integrated transcriptomic and metabolomic analyses of F₁ hybrids derived from two elite cultivars, Fuding Dabaicha (FD) and Baojing Huangjincha 1 (HJC). Methods: Comprehensive RNA sequencing and widely targeted metabolomic profiling were conducted on the parental lines and F₁ hybrids at the one-bud-one-leaf stage. Primary metabolites (including amino acids, nucleotides, saccharides, and fatty acids) were quantified, and gene expression profiles were obtained. Transcriptomic and metabolomic datasets were integrated using KEGG pathway enrichment and co-expression network analysis to identify coordinated molecular changes underlying heterosis. Results: Metabolomic profiling detected 977 primary metabolites, many of which displayed non-additive accumulation patterns. Notably, linoleic acid derivatives (9(S)-HODE, 13(S)-HODE) and nucleotides (guanosine, uridine) exhibited significant positive mid-parent heterosis. Transcriptomic analysis revealed extensive non-additive gene expression in F₁ hybrids, and upregulated genes were enriched in fatty acid metabolism, nucleotide biosynthesis, and stress signaling pathways. Integrated analysis demonstrated strong coordination between differential gene expression and metabolite accumulation, especially in linoleic acid metabolism, cutin/suberine biosynthesis, and pyrimidine metabolism. Positive correlations between elevated fatty acid levels and transcript abundance of lipid metabolism genes suggest that the transcriptional remodeling of lipid pathways contributes to heterosis. Conclusions: These findings provide novel insights into tea plant heterosis and identify potential molecular targets for breeding high-quality cultivars. Full article

Background/Objectives: Acute lung injury (ALI) is a severe condition driven largely by inflammation and has limited therapeutic options. Although saikosaponin B1 (SSB1), a primary bioactive saponin from Bupleurum Radix, has demonstrated anti-inflammatory properties, its efficacy against ALI and its corresponding molecular mechanisms remain largely unexplored. This study employed an integrated approach combining network pharmacology, transcriptomics, and metabolomics to decipher the protective mechanisms of SSB1 against ALI. Methods: Potential targets were identified via network pharmacology, and core targets were validated through molecular docking, dynamics simulations, and independent GEO transcriptomic datasets. Experimental validation was performed in an LPS-induced murine ALI model, combining histopathology, ELISA, and integrated transcriptomic and metabolomic analyses. Results: Integrated analyses identified IL1B, TNF, and IL6 as core targets through which SSB1 exerts its anti-ALI effects. These targets were validated by high-affinity binding in simulations, confirmed in independent GEO transcriptomic datasets, and shown to be normalized by SSB1 treatment in vivo. Mechanistically, SSB1 appears to modulate the NOD-like receptor and cGAS-STING signaling pathways and rectify the key metabolic pathways orchestrated by these targets, including glycerophospholipid, arachidonic acid, and linoleic acid metabolism. Conclusions: This study systematically investigates the therapeutic effects of SSB1 against ALI by identifying its potential targets and underlying pathways. These results provide crucial mechanistic insights and robust experimental support, thereby paving the way for the clinical translation of SSB1. Full article

Omics sciences are revolutionizing the field of biomedical and dental research by allowing for an integrated understanding of the molecular basis of health and disease. This narrative review analyzes the role of these novel technologies supporting the diagnosis, prognosis, and treatment of the most noteworthy oral diseases, such as dental caries, periodontitis, and oral squamous cell carcinoma. The review discusses the characterization of disease-associated genetic variations and polygenic risk scores as evidenced by genomic studies. It further examines how transcriptomic analyses can identify diagnostic gene expression signatures of immune dysregulation and tumor heterogeneity. The contribution of proteomics and metabolomics studies to the discovery of diagnostic and prognostic protein and metabolites biomarkers is also analyzed. Finally, the integration of different omics datasets within multi-omics frameworks is discussed as a key approach for a holistic interpretation of oral pathogenesis and data-driven precision dentistry. The review also addresses current limitations in the clinical translation of omics sciences into tools for early diagnosis, personalized prevention, and targeted therapy. Full article

Centronuclear and myotubular myopathies (CNMs) are rare, inherited muscle disorders characterized by muscle atrophy, weakness, and altered muscle fiber structure, primarily caused by mutations in MTM1, DNM2, or BIN1. The molecular mechanisms driving CNM are only partially understood, and no curative therapies are available. To elucidate molecular pathways involved in CNMs, we present an integrative multi-omics analysis across several CNM mouse models untreated or treated with pre-clinical strategies, combining transcriptomic, proteomic, and metabolomic datasets with curated interaction, metabolic, tissue, and phenotype knowledge using network-based approaches. Weighted Gene Co-expression Network Analysis (WGCNA) identified gene modules commonly altered in three CNM genetic forms. Modules correlated with improved muscle function were enriched for processes such as muscle contraction, RNA metabolism, and oxidative phosphorylation, whereas modules linked to disease severity were enriched for immune response, innervation, vascularization, and fatty acid oxidation. We further integrated transcriptomic, proteomic, and metabolomic data from the Mtm1^−/y mouse model with public knowledge bases into a multilayer network, and explored it using a random walk with restart approach. These analyses highlighted metabolites closely connected to CNM phenotypes, some of which may represent candidates for nutritional or pharmacological modulation. Our findings illustrate how integrative multi-omics and network analyses reveal both pathogenic and protective pathways in CNM and provide a foundation for identifying novel therapeutic opportunities. Full article

This study aimed to collaboratively investigate the mechanism of variations in intramuscular fat (IMF) content in Wandong cattle using transcriptomics and metabolomics techniques. Longissimus dorsi (LD) muscle samples were collected from thirteen free-range Wandong cattle in Fengyang County, Anhui Province, China. From this initial cohort, eight animals closely matched in age and body weight were selected. Based on IMF content measured by Soxhlet extraction, these eight cattle were divided into two groups: the high-IMF (HF, n = 4) and low-IMF (LF, n = 4) groups. Subsequent analyses were performed on integrated datasets comprising the transcriptome, metabolome, and fatty acid profile. The results revealed a significant increase in IMF in the HF group compared to the LF group (p < 0.05). Specifically, α-linolenic acid (C18:3n3) and γ-linolenic acid (C18:3n6) were significantly more abundant in the LF group compared to the HF group (p < 0.05), whereas oleic acid (C18:1n9c) and cis-9-palmitoleic acid (C16:1) predominated in the HF group. However, saturated fatty acids (SFAs), such as myristic acid (C14:0), palmitic acid (C16:0), stearic acid (C18:0), and Margaric acid (C17:0), did not show significant differences (p > 0.05). A total of 9164 differentially expressed genes (DEGs) were identified via transcriptome analysis, with 2202 genes upregulated and 6962 genes downregulated in the HF group compared to the LF group. The expression profiles exhibited a distinct pattern, characterized by the upregulation of genes such as FABP1, SREBF1, and LIPE, while genes including SCD, PPARGC1A, and LEP were downregulated. GO enrichment analysis demonstrated that the majority of DEGs were predominantly abundant across 25 distinct functional categories distributed across the three primary ontologies. KEGG pathway analysis further identified 341 significantly enriched signaling pathways in the HF group (p < 0.05), predominantly involving metabolic pathways, FoxO, AMPK, and PPAR signaling pathways. Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) metabolomics analysis revealed 404 differential accumulated metabolites (DAMs), with 187 in positive ion mode and 217 in negative ion mode (p < 0.05). These DAMs were notably enriched in pathways such as glycerophospholipid metabolism, terpene and steroid biosynthesis, fatty acid degradation, and fatty acid metabolism. Notably, C16:1, C18:1n9c, arachidonic acid (peroxide free) (C20:4n6), oleoyl-L-carnitine, and linoleoyl-carnitine were identified as key players in lipid metabolism. Integrating transcriptomics with metabolomics data unveiled significant associations between DAMs linked to lipid metabolism and DEGs. Specifically, C18:1n9c exhibited a positive correlation with LPIN3, while C16:1 showed negative associations with PPAP2B, PPAP2A, CDS2, HADHA, LPL, HSD17B12, ELOVL5, ACSL1, and ACOX1, and positive correlations with PLA2G15, CDIPT, AGPSBG1, and GPD1. In summary, the variation in IMF content in Wandong cattle is co-regulated by key genes (SREBF1, ACSL1, SCD) via the AMPK, PPAR, and FoxO signaling pathways, coupled with alterations in specific fatty acid metabolites such as C18:1n9c, C16:1, and C20:4n6. These findings provide critical molecular insights for the genetic selection and breeding of Wandong cattle, which are renowned for their superior meat quality. Full article

Microalgae of the genus Nannochloropsis are known for their ability to accumulate large amounts of lipids, particularly triacylglycerides (TAGs), when exposed to nitrogen-limiting conditions. This trait makes them promising candidates for biofuel production. While previous studies have used transcriptomics and metabolomics to explore how these organisms respond to nutrient stress, the role of post-translational modifications—especially protein phosphorylation—remains poorly understood. To address this gap, we conducted a comprehensive analysis of protein phosphorylation events in Nannochloropsis oceanica under both nitrogen-replete and nitrogen-depleted conditions over a time-course experiment. Using mass spectrometry-based phosphoproteomics, we identified 1371 phosphorylation sites across 884 proteins. Temporal clustering of these phosphorylation events revealed two distinct regulatory phases: an early response aimed at conserving nitrogen resources, and a later phase that promotes lipid accumulation. Notably, we identified 11 phosphorylated proteins associated with the Target of Rapamycin (TOR) signaling pathway, suggesting that this conserved regulatory network plays a key role in coordinating the cellular response to nitrogen deficiency. By integrating our phosphoproteomic result with previously published transcriptomic and metabolomic datasets, we provide a more complete view of how N. oceanica adapts to nitrogen stress at the molecular level. This systems-level approach highlights the importance of protein phosphorylation in regulating metabolic shifts and offers new insights into engineering strategies for enhancing lipid production in microalgae. Full article

Spatial metabolomics is a rapidly advancing field offering powerful insights into metabolic heterogeneity in biological tissues. However, its widespread adoption is hindered by fragmented tools and the lack of comprehensive, open-source GUI software covering the full analytical workflow (quality control, preprocessing, identification, pattern, and differential analysis). To address this, we developed SMAnalyst, an open-source, integrated web-based platform. SMAnalyst consolidates core functionalities, including multi-dimensional data quality assessment (background consistency, intensity, missing values), a comprehensive metabolite annotation scoring system (mass accuracy, isotopic similarity, adduct evidence), and dual-dimension spatial pattern discovery (metabolite co-expression and pixel clustering). It also offers flexible differential analysis (cluster- or user-defined regions). With its intuitive GUI and modular workflow, SMAnalyst significantly lowers the analysis barrier, by providing a unified solution that eliminates the need for tool switching and advanced computational skills. Tested with a mouse brain dataset, SMAnalyst efficiently handles large-scale data (e.g., >14,000 pixels, >3000 ion peaks), effectively filling a critical gap in integrated analytical solutions for spatial metabolomics. Full article

Aim: Nephrotic syndrome (NS) represents a complex glomerular disorder with significant clinical heterogeneity across pediatric and adult populations. Although glucocorticosteroids have constituted the mainstay of therapeutic intervention for more than six decades, primary treatment resistance manifests in approximately 20% of pediatric patients and 50% of adult cohorts. Steroid-resistant nephrotic syndrome (SRNS) is associated with substantially greater morbidity compared to steroid-sensitive nephrotic syndrome (SSNS), characterized by both iatrogenic glucocorticoid toxicity and progressive nephron loss with attendant decline in renal function. Based on this, the current study aims to develop a robust machine learning (ML) model integrated with explainable artificial intelligence (XAI) to distinguish SRNS and identify important biomarker candidate metabolites. Methods: In the study, biomarker candidate compounds obtained from proton nuclear magnetic resonance (1 H NMR) metabolomics analyses on plasma samples taken from 41 patients with NS (27 SSNS and 14 SRNS) were used. We developed ML models to predict steroid resistance in pediatric NS using metabolomic data. After preprocessing with MICE-LightGBM imputation for missing values (<30%) and standardization, the dataset was randomly split into training (80%) and testing (20%) sets, repeated 100 times for robust evaluation. Four supervised algorithms (XGBoost, LightGBM, AdaBoost, and Random Forest) were trained and evaluated using AUC, sensitivity, specificity, F1-score, accuracy, and Brier score. XAI methods including SHAP (for global feature importance and model interpretability) and LIME (for individual patient-level explanations) were applied to identify key metabolomic biomarkers and ensure clinical transparency of predictions. Results: Among four ML algorithms evaluated, Random Forest demonstrated superior performance with the highest accuracy (0.87 ± 0.12), sensitivity (0.90 ± 0.18), AUC (0.92 ± 0.09), and lowest Brier score (0.20 ± 0.03), followed by LightGBM, AdaBoost, and XGBoost. The superiority of the Random Forest model was confirmed by paired t-tests, which revealed significantly higher AUC and lower Brier scores compared to all other algorithms (p < 0.05). SHAP analysis identified key metabolomic biomarkers consistently across all models, including glucose, creatine, 1-methylhistidine, homocysteine, and acetone. Low glucose and creatine levels were positively associated with steroid resistance risk, while higher propylene glycol and carnitine concentrations increased SRNS probability. LIME analysis provided patient-specific interpretability, confirming these metabolomic patterns at individual level. The XAI approach successfully identified clinically relevant metabolomic signatures for predicting steroid resistance with high accuracy and interpretability. Conclusions: The present study successfully identified candidate metabolomic biomarkers capable of predicting SRNS prior to treatment initiation and elucidating critical molecular mechanisms underlying steroid resistance regulation. Full article

Background/Objectives: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating complex disease with an elusive etiology, lacking objective diagnostic biomarkers. This study leverages advanced Automated Machine Learning (AutoML) to analyze plasma metabolomic and lipidomic profiles for the purpose of ME/CFS detection. Methods: We utilized a publicly available dataset comprising 888 metabolic features from 106 ME/CFS patients and 91 matched controls. Three AutoML frameworks—TPOT, Auto-Sklearn, and H2O AutoML—were benchmarked under identical time constraints. Univariate ROC and PLS-DA analyses with cross-validation, permutation testing, and VIP-based feature selection were applied to standardized, log-transformed omics data to identify significant discriminatory metabolites/lipids and assess their intercorrelations. Results: TPOT significantly outperformed its counterparts, achieving an area under the curve (AUC) of 92.1%, accuracy of 87.3%, sensitivity of 85.8%, and specificity of 89.0%. The PLS-DA model revealed a moderate but statistically significant discrimination between ME/CFS and controls. Explainable artificial intelligence (XAI) via SHAP analysis of the optimal TPOT model identified key metabolites implicating dysregulated pathways in mitochondrial energy metabolism (succinic acid, pyruvic acid, leucine), chronic inflammation (prostaglandin D₂, 11,12-EET), gut–brain axis communication (glycocholic acid), and cell membrane integrity (pc(35:2)a). Conclusions: Our results demonstrate that TPOT-derived models not only provide a highly accurate and robust diagnostic tool but also yield biologically interpretable insights into the pathophysiology of ME/CFS, highlighting its potential for clinical decision support and elucidating novel therapeutic targets. Full article

The ongoing challenge of doping in sports has triggered the adoption of advanced scientific strategies for the detection and prevention of doping abuse. This review examines the potential of integrating metabolomics aided by artificial intelligence (AI) and machine learning (ML) for profiling small-molecule metabolites across biological systems to advance anti-doping efforts. While traditional targeted detection methods serve a primarily forensic role—providing legally defensible evidence by directly identifying prohibited substances—metabolomics offers complementary insights by revealing both exogenous compounds and endogenous physiological alterations that may persist beyond direct drug detection windows, rather than serving as an alternative to routine forensic testing. High-throughput platforms such as UHPLC-HRMS and NMR, coupled with targeted and untargeted metabolomic workflows, can provide comprehensive datasets that help discriminate between doped and clean athlete profiles. However, the complexity and dimensionality of these datasets necessitate sophisticated computational tools. ML algorithms, including supervised models like XGBoost and multi-layer perceptrons, and unsupervised methods such as clustering and dimensionality reduction, enable robust pattern recognition, classification, and anomaly detection. These approaches enhance both the sensitivity and specificity of diagnostic screening and optimize resource allocation. Case studies illustrate the value of integrating metabolomics and ML—for example, detecting recombinant human erythropoietin (r-HuEPO) use via indirect blood markers and uncovering testosterone and corticosteroid abuse with extended detection windows. Future progress will rely on interdisciplinary collaboration, open-access data infrastructure, and continuous methodological innovation to fully realize the complementary role of these technologies in supporting fair play and athlete well-being. Full article

Flavonoids, a ubiquitous class of plant secondary metabolites, are increasingly pivotal as chemical markers for ensuring the quality, safety, and efficacy of herbal medicines (HMs). Their broad distribution, biological activities, and detectability make them ideal for this role. This comprehensive review critically examines current trends and analytical perspectives regarding flavonoids in HM quality control. We first explore advanced quality control strategies that move beyond single-compound quantification, including chemical fingerprinting, metabolomics, network pharmacology, and the innovative concept of Q-markers. The review then provides an in-depth analysis of the analytical techniques central to flavonoid analysis, from the routine use of HPTLC and HPLC-UV to advanced hyphenated systems like UHPLC-QTOF-MS, highlighting their applications in authentication, standardization, and adulteration detection. Furthermore, we emphasize the growing importance of modern data analysis workflows, particularly the integration of chemometrics and molecular networking, for interpreting complex datasets and identifying robust, bioactivity-relevant markers. By synthesizing recent research (2017–2024), this work underscores a paradigm shift towards holistic, multi-marker approaches and data-driven methodologies. It concludes that the synergistic application of advanced analytical techniques with sophisticated data modeling is essential for the future of HM quality control, ensuring reliable and standardized herbal products for global consumers. Full article

Neurodevelopmental disorders (NDDs), including autism spectrum disorder, intellectual disability, and attention-deficit/hyperactivity disorder, are genetically and phenotypically heterogeneous conditions affecting millions worldwide. High-throughput omics technologies—transcriptomics, proteomics, metabolomics, and epigenomics—offer a unique opportunity to link genetic variation to molecular and cellular mechanisms underlying these disorders. However, the high dimensionality, sparsity, batch effects, and complex covariance structures of omics data present significant statistical challenges, requiring robust normalization, batch correction, imputation, dimensionality reduction, and multivariate modeling approaches. This review provides a comprehensive overview of statistical frameworks for analyzing high-dimensional omics datasets in NDDs, including univariate and multivariate models, penalized regression, sparse canonical correlation analysis, partial least squares, and integrative multi-omics methods such as DIABLO, similarity network fusion, and MOFA. We illustrate how these approaches have revealed convergent molecular signatures—synaptic, mitochondrial, and immune dysregulation—across transcriptomic, proteomic, and metabolomic layers in human cohorts and experimental models. Finally, we discuss emerging strategies, including single-cell and spatially resolved omics, machine learning-driven integration, and longitudinal multi-modal analyses, highlighting their potential to translate complex molecular patterns into mechanistic insights, biomarkers, and therapeutic targets. Integrative multi-omics analyses, grounded in rigorous statistical methodology, are poised to advance mechanistic understanding and precision medicine in NDDs. Full article