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Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach’s ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

A major goal in the field of transcriptional regulation is the mapping of changes in the binding of transcription factors to the resultant changes in gene expression. Recently, methods for measuring chromatin accessibility have enabled us to measure changes in accessibility across the genome, which are thought to correspond to transcription factor‐binding events. In concert with RNA‐sequencing, these data in principle enable such mappings; however, few studies have looked at their concordance over short‐duration treatments with specific perturbations. Here, we used tandem, bulk ATAC‐seq, and RNA‐seq measurements from MCF‐7 breast carcinoma cells to systematically evaluate the concordance between changes in accessibility and changes in expression in response to retinoic acid and TGF‐β. We found two classes of genes whose expression showed a significant change: those that showed some changes in the accessibility of nearby chromatin, and those that showed virtually no change despite strong changes in expression. The peaks associated with genes in the former group had lower baseline accessibility prior to exposure to signal. Focusing the analysis specifically on peaks with motifs for transcription factors associated with retinoic acid and TGF‐β signaling did not reduce the lack of correspondence. Analysis of paired chromatin accessibility and gene expression data from distinct paths along the hematopoietic differentiation trajectory showed a much stronger correspondence, suggesting that the multifactorial biological processes associated with differentiation may lead to changes in chromatin accessibility that reflect rather than driving altered transcriptional status. Together, these results show many gene expression changes can happen independently of changes in the accessibility of local chromatin in the context of a single‐factor perturbation. Synopsis Systematic analysis of tandem, bulk ATAC‐seq and RNA‐seq measurements from cells exposed to single‐factor perturbations shows two groups of genes: those with concordance between accessibility and expression changes and those without. MCF‐7 cells exposed to retinoic acid or TGF‐β have genes that show high expression changes without changes in local chromatin. By comparison, data from hematopoietic differentiation show much more concordance between chromatin accessibility changes and gene expression changes. Genes in the single‐factor perturbation data set that demonstrated more concordance had peaks that were less accessible at baseline prior to retinoic acid or TGF‐β exposure. Graphical Abstract Systematic analysis of tandem, bulk ATAC‐seq, and RNA‐seq measurements from cells exposed to single‐factor perturbations shows two groups of genes: those with concordance between accessibility and expression changes and those without.

Tumor relapse from treatment‐resistant cells (minimal residual disease, MRD) underlies most breast cancer‐related deaths. Yet, the molecular characteristics defining their malignancy have largely remained elusive. Here, we integrated multi‐omics data from a tractable organoid system with a metabolic modeling approach to uncover the metabolic and regulatory idiosyncrasies of the MRD. We find that the resistant cells, despite their non‐proliferative phenotype and the absence of oncogenic signaling, feature increased glycolysis and activity of certain urea cycle enzyme reminiscent of the tumor. This metabolic distinctiveness was also evident in a mouse model and in transcriptomic data from patients following neo‐adjuvant therapy. We further identified a marked similarity in DNA methylation profiles between tumor and residual cells. Taken together, our data reveal a metabolic and epigenetic memory of the treatment‐resistant cells. We further demonstrate that the memorized elevated glycolysis in MRD is crucial for their survival and can be targeted using a small‐molecule inhibitor without impacting normal cells. The metabolic aberrances of MRD thus offer new therapeutic opportunities for post‐treatment care to prevent breast tumor recurrence. SYNOPSIS Despite their normal morphology and non‐proliferative phenotype, treatment‐resistant breast cancer cells retain epigenetic imprinting and metabolic characteristics of their prior tumour state. Multi‐omics analysis and flux modelling reveals elevated glycolysis and abnormal urea cycle pathway activity in residual cells (Minimal Residual Disease, MRD). Transcriptome data from human samples also indicate elevated glycolysis of MRD. In contrast to normal epithelial cells and cancer, MRD is dependent on glycolysis for survival and can be selectively targeted using a small‐molecule inhibitor. Epigenetic similarity between MRD‐ and tumour‐cells suggests a mechanistic basis for metabolic memory. Graphical Abstract Despite their normal morphology and non‐proliferative phenotype, treatment‐resistant breast cancer cells retain epigenetic imprinting and metabolic characteristics of their prior tumour state.

INTRODUCTION Alzheimer's disease (AD) is a complex neurodegenerative disorder with heterogeneous genetic and molecular underpinnings. Polygenic scores (PGS) capture little of this complexity. METHODS We conducted genome‐, transcriptome‐, and proteome‐wide association studies (G/T/PWAS) on 15,480 individuals from the Alzheimer's Disease Sequencing Project R4 (ADSP) to identify AD‐associated signals, followed by pathway enrichment analysis. Integrative risk models (IRMs) were developed using genetically regulated components of gene and protein expression and clinical covariates. Elastic‐net logistic regression and random forest classifiers were evaluated using standard metrics and compared against baseline PGS. RESULTS Known and novel signals were identified via G/T/PWAS. Enrichment analyses highlighted cholesterol and immune signaling pathways. The best‐performing IRM, random forest with transcriptomic and covariate features, achieved area under the receiver operating characteristic (AUROC) of 0.703 and area under the precision‐recall curve (AUPRC) of 0.622, significantly outperforming PGS and baseline models. DISCUSSION Integrating univariate discovery approaches with multivariate modeling enhances AD risk prediction and offers novel insights into underlying biological processes. Highlights Identified novel contributions to Alzheimer's disease (AD) from a multi‐omics perspective. Integrated genome‐wide association studies (GWAS), transcriptome‐wide association studies (TWAS), and proteome‐wide association studies (PWAS) in a unified association study framework. Developed a method for predicting heritable risk of late‐onset AD. Demonstrated that ancestry‐aware modeling improves AD risk prediction accuracy.

Natural mating ability is a critical behavioral trait for the reproductive success of captive endangered mammals, and its loss often reflects declining adaptability and potential physiological dysfunctions. However, the underlying molecular regulatory mechanisms remain poorly understood. In this study, we integrated blood transcriptome and whole‐genome DNA methylation (whole‐genome bisulfite sequencing) data to systematically explore the molecular basis of natural mating ability differences in captive male giant pandas (Ailuropoda melanoleuca). A total of 21 male individuals, which were classified into either capable (with successful natural mating experience) or incapable (with repeated mating failure despite physical health) groups, were sampled from three breeding centers. RNA‐seq analysis identified key differentially expressed genes (DEGs) such as ZPBP2, enriched in functional pathways related to GnRH signaling, MAPK cascades, immune modulation, and olfactory perception. Whole‐genome bisulfite sequencing (WGBS) analysis revealed significant differences in CpG (CG) methylation density on the X chromosome, and identified promoter‐ and gene body‐associated differentially methylated regions (DMRs) that were inversely correlated with gene expression. Integrative analysis demonstrated a strong association between gene expression and DNA methylation, with the associated genes enriched in reproduction‐relevant pathways including axon guidance, cysteine and methionine metabolism, and apoptosis/autophagy. These findings suggest that DNA methylation may influence transcriptional activity involved in natural mating behavior. This multi‐omics approach provides valuable insights into the epigenetic regulation of complex reproductive phenotypes in endangered species and offers a theoretical basis for future applications in molecular marker–based individual selection and optimization of captive breeding programs, thereby contributing to wildlife conservation efforts. We investigated the molecular mechanisms underlying natural mating ability in captive male giant pandas using integrated transcriptomic and DNA methylation analyses. Our findings highlight key gene expression and epigenetic differences associated with reproductive behavior, offering novel insights for optimizing captive breeding and conservation strategies.

Glycosylation plays an important role in regulating innate and adaptive immunity. With promising advances in structural and site‐specific glycoproteomics, how to thoroughly extract important information from these multi‐dimensional data has become another unresolved issue. The present study reports a comprehensive data mining strategy to systematically extract overall and altered glycan features from quantitative glycoproteome data. By applying the strategy to investigation of thymic involution, the study not only presents a high‐resolution glycoproteome map of the mouse thymus, displaying distinct glycan structure patterns among immune‐relevant cellular components, but also uncovers four major altered glycan features associated with thymic involution, including elevated LacdiNAc mainly on the MHC class I complex, increased sialoglycans that perform multiple immune functions, down‐regulated bisecting glycans mostly linked to a sole GlcNAc branch, as well as possible shifts of glycan structures at the same glycosites. Regulatory network analyses further reveal the coordinated interactions of altered glycans with upstream regulators, including glycosyltransferases, glycosidases, and glycan‐binding proteins, as well as downstream signaling pathways. These data offer valuable resources for future functional studies on glycosylation and the mechanistic investigation of thymic involution, supporting the strategy as a powerful tool for in‐depth mining of structural and site‐specific glycoproteome data from various biomedical samples. This study presents a comprehensive data mining strategy for in‐depth extraction of overall and altered glycan features from structural and site‐specific glycoproteome data across various biomedical samples. The strategy enables the uncovery of four major altered glycan features associated with thymic involution and reveals their coordinated interactions with upstream regulators and downstream signaling pathways.

Background Chronic thromboembolic pulmonary hypertension (CTEPH) is driven by unresolved pulmonary arterial thrombi and involves complex processes such as vascular remodeling and immune dysregulation. Early identification of molecular markers may support more accurate diagnosis and individualized therapy. Methods We integrated anthropometric and biochemical data with single‐cell RNA sequencing (scRNA‐seq), DNA methylation, and Mendelian randomization (MR) analyses. scRNA‐seq data were analyzed to determine altered cell populations and pathways. MR and colocalization analyses were conducted to identify genetically supported candidates related to CTEPH. Results scRNA‐seq analysis revealed altered immune composition, with a modest increase in NK cells and an angiogenesis‐associated enrichment of monocytes and HSC‐G‐CSF cells in CTEPH patients, accompanied by activation of toll‐like receptor and MAPK signaling pathways. MR and colocalization identified several genetically associated genes—CLEC7A, TNFSF13B, LRP1, ETS1, and FGR—of which ETS1 and FGR demonstrated good diagnostic performance. DNA methylation analysis indicated marked alterations in chromatin assembly and epigenetic regulation. Conclusions This multi‐omics study highlights critical genes, epigenetic features, and immune‐related cell populations associated with CTEPH. These findings improve understanding of disease mechanisms and offer potential biomarkers for early diagnosis and personalized management. This study presents an integrative multi‐omics framework to uncover the molecular mechanisms and potential biomarkers of chronic thromboembolic pulmonary hypertension (CTEPH). Anthropometric and biochemical data were correlated using canonical correlation analysis, revealing key cardiometabolic associations. Single‐cell RNA sequencing identified immune and stem cell subpopulations—particularly monocytes and HSC–G‐CSF cells—enriched in angiogenesis‐ and inflammation‐related pathways. Bulk RNA‐seq deconvolution with the BayesPrism algorithm quantified cell‐type composition, whereas Mendelian randomization and colocalization analyses identified causally linked hub genes (CLEC7A, TNFSF13B, FGR, LRP1, ETS1) with diagnostic potential. DNA methylation profiling further highlighted epigenetic dysregulation in vascular remodeling. Together, these integrated multi‐omics analyses delineate the molecular landscape of CTEPH and propose novel biomarkers and therapeutic targets for precision medicine.

Glucocorticoid‐induced myopathy is characterized by progressive muscle atrophy and impaired regeneration, yet effective microbiota‐oriented interventions for preserving muscle homeostasis remain largely unexplored. Here, we demonstrate that dietary chondroitin sulfate (DCS) restores muscle mass and function through a microbiota‐dependent gut–muscle metabolic axis. DCS failed to confer protection in germ‐free or antibiotic‐treated mice, establishing gut microbiota as a prerequisite for its efficacy. Microbiota transplantation and mono‐colonization experiments identified Lactobacillus johnsonii Z‐RW as a functionally relevant mediator capable of recapitulating muscle protection under controlled microbial conditions. Integrated metagenomic, metabolomic, and proteomic analyses revealed coordinated reprogramming of intestinal sugar utilization and bile acid metabolism following DCS administration. Notably, DCS promoted bile acid deconjugation and enrichment of secondary bile acids, coinciding with restoration of muscle regenerative and energetic programs, including upregulation of NMRK2, PAX7, and SIRT1. Metabolite supplementation further implicated bile acids as candidate mediators linking microbial metabolism to muscle phenotypes. To quantitatively integrate these shifts, we introduce the sugar‐bile acid ratio as a systems‐level descriptor of microbiota‐driven metabolic remodeling. Our findings delineate a microbiota‐dependent metabolic framework through which a functional polysaccharide reshapes intestinal biochemistry to influence distal muscle physiology. This work highlights bile acid‐associated signaling as a central relay within the gut‐muscle axis and provides a conceptual foundation for microbiota‐targeted strategies to mitigate muscle wasting. The graphical illustrates the molecular mechanism by which chondroitin sulfate (DCS) alleviates glucocorticoid‐induced myopathy through the gut–muscle axis. DCS selectively enriches L. johnsonii Z‐RW, enhancing bshA‐encoded BSH activity to promote bile acid deconjugation and reduce the sugar–bile acid ratio, thereby re‐establishing intestinal metabolic homeostasis. The resulting secondary bile acids cross the intestinal barrier and reach muscle tissue, where they activate NMRK2‐mediated NAD⁺ biosynthesis and PAX7 signaling, upregulating myogenic proteins such as KRT18, MYHC, and RGN. These coordinated molecular events restore energy metabolism and muscle function. Highlights Chondroitin sulfate (DCS) reverses muscle atrophy by remodeling gut–axis. Enriched L. johnsonii Z‐RW bile salt hydrolase reprograms gut sugar‐bile acid metabolism. Z‐RW activates PAX7/NMRK2‐mediated muscle regeneration.

Single‐cell and spatial omics have revolutionized biomedical research by enabling high‐resolution molecular profiling across cells and tissues, thereby overcoming key limitations of bulk sequencing and revealing unprecedented cellular heterogeneity and spatial organization central to development, homeostasis, and disease. Specifically, advances in high‐throughput, subcellular, and multiomics profiling are promoting the field toward deeper insights. In parallel, computational progress, including generative artificial intelligence (AI) and foundation models, is developing rapidly for manipulating multimodal multiomics data. These advancements have been applied to diverse diseases and biological systems, facilitating innovative biomedical findings. However, a significant gap persists between rapid methodological advances and their systematic application for deciphering human biology and pathology. This review synthesizes recent breakthroughs in single‐cell and spatial technologies and surveys computational methods, including AI‐driven approaches, foundation models, and multi‐omics integration algorithms for both single‐cell and spatial analyses. We then summarize representative applications across major human organ systems in health and disease, highlighting opportunities for biomarker discovery, therapeutic target identification, and precision medicine. Finally, we discuss current challenges and future directions for bridging technological innovation with robust biomedical discovery and translational impact. This review provides a vital guide for researchers in the field, offering critical insights for accelerating the translation of single‐cell and spatial omics. Systematically summarized the breakthrough sequencing technologies and computational methods for single‐cell and spatial omics across multiple omics layers, including genome, epigenome, transcriptome, proteome, and metabolome. State‐of‐the‐art methods for multi‐omics integration, cross‐modal integration, and cross‐scale integration were reviewed, with AI‐based algorithms and foundation models were highlighted. Comprehensively summarized the application of single‐cell and spatial omics in human biology and diseases of all organs, identifying how single‐cell and spatial omics drive biomedical discovery. Critically discussed current challenges and future directions for the field, especially how to fill the gap between methodological developments and applications.