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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.

Recent breakthroughs in spatially resolved multi‐omics have unlocked the ability to simultaneously profile multiple molecular layers within tissues, offering unprecedented insights into their coordinated roles in development and disease. Despite these advancements, integrative analysis of multi‐omics data remains a formidable challenge due to inherent biological and technical discrepancies across assays, often leading to gradient conflicts during joint learning. These conflicts arise as optimization trajectories from different omics compete or contradict, thereby constraining integration performance. To overcome this challenge, SpaBalance, a unified computational framework designed to harmonize cross‐omics learning via gradient coordination and adaptive feature decomposition, is proposed. SpaBalance introduces a novel gradient equilibrium mechanism that dynamically balances inter‐omics contributions during backpropagation, resolving conflicts through task‐specific prioritization without requiring manual weighting. Concurrently, SpaBalance leverages a dual‐stream architecture to simultaneously learn shared representations and preserve omics‐specific features. Extensive evaluations across a variety of spatial omics datasets, including paired epigenome‐transcriptome and proteome‐transcriptome data from human tumors and brain tissues, demonstrate SpaBalance's superior ability to delineate complex spatial domains and uncover previously hidden multi‐omics regulatory hubs, significantly improving clustering accuracy and biological interpretability. Moreover, SpaBalance flexibly scales to integrate multiple omics, bridging data integration with biological discovery and advancing spatially resolved systems biology. SpaBalance is a computational framework that harmonizes multi‐omics learning via gradient equilibrium and dual‐stream feature decomposition, achieving superior clustering accuracy, biological interpretability, and scalable integration of three or more spatial omics modalities.

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.

Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that 54% of the identified proteins were annotated and enriched in carbohydrate metabolism (13%), amino acid metabolism (16%) and genetic information processing (18%) (Figure 1a). The results reveal that the transcript level alone does not always reliably predict protein abundance at the population scale, and protein abundance variation may play an important role in orchestrating the biological functions of genes involved in the same biological pathways. pQTL analysis is therefore necessary to fully elucidate the molecular basis of kernel-related phenotypes. The x-axis indicates the physical positions (Mb) of the pQTLs across ten maize chromosomes, and the heatmap shows the density of these pQTLs across the maize genome (window size = 10 Mb). (f) Flowchart of data analysis. (g) Candidate gene-based association mapping and pairwise linkage disequilibrium analysis of the local pQTL for P1107. [...]the correlation among different levels is low because the flow of information from DNA to phenotype is a signal propagation process. [...]this integrative strategy does not apply to functionally unrelated QTLs that co-segregate, such as distant pQTLs.

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 Tau neurofibrillary pathology is a hallmark of Alzheimer's disease (AD) and can be quantified in vivo using tau‐selective positron emission tomography (tau PET). Tau PET signal closely correlates with cognitive decline and disease stage, yet the molecular networks underpinning tau accumulation remain incompletely defined. METHODS We performed multi‐omics integration of proteomics, transcriptomics, and tau PET standardized uptake value ratios (SUVRs), and clinical assessments data from cognitively normal and cognitively impaired individuals. Using Light Gradient Boosting Machine (LightGBM), two‐way orthogonal partial least squares, and network‐based approaches, we explored key tau‐associated proteomic signatures and constructed protein–protein interaction (PPI) modules. Module activities were quantified by gene set variation analysis and related to tau PET and cognition. RESULTS Among 60 regions, 15 tau PET imaging biomarkers were selected based on group differences, LightGBM importance, and cognitive relevance. Fifty key tau‐associated proteins were identified and organized into four functional modules. PPI modules 1 (metabolic‐cytoskeletal) and 3 (adhesion‐nutrient sensing) exhibited strong associations with elevated tau PET uptake across selected cortical and limbic regions, as well as with cognitive impairment. CONCLUSION Distinct modules reflected regional tau PET burden and cognitive outcomes in AD, highlighting convergent disruptions in energy metabolism, cytoskeletal stability, and intercellular signaling. Highlights Integration of proteomics, transcriptomics, tau positron emission tomography (PET) imaging, and cognition in Alzheimer's disease. Fifteen key tau PET imaging biomarkers were prioritized. Fifty key tau‐associated proteins were identified. Four distinct molecular networks contribute to regional tau pathology and cognition. Modules 1 (metabolic‐cytoskeletal) and 3 (adhesion‐nutrient sensing) strongly associated with tau PET burden and cognitive impairment.

In recent years, the integration of single‐cell multi‐omics data has provided a more comprehensive understanding of cell functions and internal regulatory mechanisms from a non‐single omics perspective, but it still suffers many challenges, such as omics‐variance, sparsity, cell heterogeneity, and confounding factors. As it is known, the cell cycle is regarded as a confounder when analyzing other factors in single‐cell RNA‐seq data, but it is not clear how it will work on the integrated single‐cell multi‐omics data. Here, a cell cycle‐aware network (CCAN) is developed to remove cell cycle effects from the integrated single‐cell multi‐omics data while keeping the cell type‐specific variations. This is the first computational model to study the cell‐cycle effects in the integration of single‐cell multi‐omics data. Validations on several benchmark datasets show the outstanding performance of CCAN in a variety of downstream analyses and applications, including removing cell cycle effects and batch effects of scRNA‐seq datasets from different protocols, integrating paired and unpaired scRNA‐seq and scATAC‐seq data, accurately transferring cell type labels from scRNA‐seq to scATAC‐seq data, and characterizing the differentiation process from hematopoietic stem cells to different lineages in the integration of differentiation data. This study introduces a novel data integration method applicable to three types of single‐cell multi‐omics integration: intra‐modality, paired inter‐modality, and unpaired inter‐modality. This method uses a domain separation network to extract intrinsic biological signals masked by context‐specific patterns (i.e., cell type‐specific heterogeneity) and confounding factors (i.e., cell cycle effects and batch effects). This method demonstrates superior performance across multiple tasks.