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PubMed2025

Integrated Multiomics Analyses of the Molecular Landscape of Sarcopenia in Alcohol-Related Liver Disease.

BACKGROUND: Skeletal muscle is a major target for ethanol-induced perturbations, leading to sarcopenia in alcohol-related liver disease (ALD). The complex interactions and pathways involved in adaptive and maladaptive responses to ethanol in skeletal muscle are not well understood. Unlike hypothesis-driven experiments, an integrated multiomics-experimental validation approach provides a comprehensive view of these interactions. METHODS: We performed multiomics analyses with experimental validation to identify novel regulatory mechanisms of sarcopenia in ALD. Studies were done in a comprehensive array of models including ethanol-treated (ET) murine and human-induced pluripotent stem cell-derived myotubes (hiPSCm), skeletal muscle from a mouse model of ALD (mALD) and human patients with alcohol-related cirrhosis and controls. We generated 13 untargeted datasets, including chromatin accessibility (assay for transposase accessible chromatin), RNA sequencing, proteomics, phosphoproteomics, acetylomics and metabolomics, and conducted integrated multiomics analyses using UpSet plots and feature extraction. Key findings were validated using immunoblots, redox measurements (NAD+/NADH ratio), imaging and senescence-associated molecular phenotype (SAMP) assays. Mechanistic studies included mitochondrial-targeted Lactobacillus brevis NADH oxidase (MitoLbNOX) to increase redox ratio and MitoTempo as a mitochondrial free radical scavenger. RESULTS: Multiomics analyses revealed enrichment in mitochondrial oxidative function, protein synthesis and senescence pathways consistent with the known effects of hypoxia-inducible factor 1α (HIF1α) during normoxia. Across preclinical and clinical models, HIF1α targets (n = 32 genes) and signalling genes (n > 100 genes) (n = 3 ATACseq, n = 65 phosphoproteomics, n = 10 acetylomics, n = 6 C2C12 proteomics, n = 106 C2C12 RNAseq, n = 64 hiPSC RNAseq, n = 30 hiPSC proteomics, n = 3 mouse proteomics, n = 25 mouse RNAseq, n = 8 human RNAseq, n = 3 human proteomics) were increased. Stabilization of HIF1α (C2C12, 6hEtOH 0.24 ± 0.09; p = 0.043; mALD 0.32 ± 0.074; p = 0.005; data shown as mean difference ± standard error mean) was accompanied by enrichment in the early transient and late change clusters, -log(p-value) = 1.5-3.8, of the HIF1α signalling pathway. Redox ratio was reduced in ET myotubes (C2C12: 15512 ± 872.1, p < 0.001) and mALD muscle, with decreased expression of electron transport chain components (CI-V, p < 0.05) and Sirt3 (C2C12: 0.067 ± 0.023, p = 0.025; mALD: 0.41 ± 0.12, p = 0.013). Acetylation of mitochondrial proteins was increased in both models (C2C12: 107364 ± 4558, p = 0.03; mALD: 40036 ± 18 987, p = 0.049). Ethanol-induced SAMP was observed across models (P16: C2C12: 0.2845 ± 0.1145, p < 0.05; hiPSCm: 0.2591, p = 0.041). MitoLbNOX treatment reversed redox imbalance, HIF1α stabilization, global acetylation and myostatin expression (p < 0.05). CONCLUSIONS: An integrated multiomics approach, combined with experimental validation, identifies HIF1α stabilization and accelerated post-mitotic senescence as novel mechanisms of sarcopenia in ALD. These findings show the complex molecular interactions leading to mitochondrial dysfunction and progressive sarcopenia in ALD.

Sarcopenia↗

PubMed2026

Integrative Multiomics and Drug Sensitivity Profiling Reveal Potential Biomarkers and Therapeutic Strategies in Pediatric Solid Tumors.

UNLABELLED: Cure rates for childhood malignancies using established therapy protocols have increased to an average of 80% but have reached a plateau. Moreover, survival rates are particularly low for some pediatric tumors-such as high-risk group 3 medulloblastomas, osteosarcomas, Ewing sarcomas, high-risk neuroblastomas, and high-grade gliomas-and dismal for patients with relapsed malignancies. A functional drug response profiling platform for pediatric solid and brain tumors has been established within the INFORM program to identify patient-specific vulnerabilities and biomarkers and to unravel molecular mechanisms associated with drug response profiles for clinical translation. In this study, we performed a multiomics analysis using drug sensitivity profiles, as well as genomic and transcriptomic data, of 81 pediatric solid tumor samples. The integrative analysis suggested two multiomics signatures associated with drug sensitivity. One signature distinguished neuroblastoma samples with sensitivity to navitoclax, a BCL2 family inhibitor. A second signature was specific to a subset of Wilms tumors harboring the SIX1 (Q177R) hotspot mutation that displayed high expression of MGAM, PTPN14, STAT4, and KDM2B and high sensitivity to MEK inhibitors. A patient-specific causal interaction network analysis suggested possible molecular interactions between MEK inhibitors and the SIX1 mutation in Wilms tumor samples. In conclusion, the integration of drug sensitivity profiling and multiomics data revealed potential biomarkers that may be associated with drug sensitivity in pediatric solid tumors. Patient-specific causal interaction network analysis further elucidated the interaction between inhibitors and signature biomarkers, providing insights that may inform clinical translation. SIGNIFICANCE: The combination of multiomics analysis and drug sensitivity profiling identified two signatures related to drug sensitivity in pediatric solid tumors, contributing to the advancement of functional precision medicine and personalized treatment strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans↗

PubMed2026

Multiomics: the intersection of personalized nutrition in cardiometabolic diseases.

BACKGROUND: Cardiometabolic diseases are among the leading causes of increasing morbidity and mortality worldwide. However, current population-based dietary recommendations do not sufficiently account for biological differences between individuals and therefore do not have the same effect on everyone. The multiomic approach, which incorporates genomic, epigenomic, transcriptomic, proteomic, metabolomic, and microbiome data, facilitates more accurate classification of disease risk and selection of appropriate nutritional interventions by mapping food-disease relationships across different biological layers. METHODS: Through a narrative synthesis of the current literature, we focused on evidence from multiomic studies to assess their ability to guide personalized nutrition strategies based on individual genetic, metabolic, and microbiome characteristics in cardiometabolic diseases. RESULTS: Recent evidence indicates that metabolomic markers have been reported to provide predictive value in addition to classic risk indicators and to increase the predictive power of models when combined with genetic data. Microbiome research shows that glycemic and lipemic responses can be predicted using algorithms based on gut microbiota. Recent clinical studies show that personalized nutrition plans, which evaluate the microbiome and clinical characteristics together, improve continuous glucose monitoring-based glycemic control, glycated hemoglobin levels, and triglycerides more than the classic Mediterranean diet. CONCLUSION: This review summarizes the current multiomic evidence, discusses the methodological and practical challenges in this field, and highlights future priorities. The integration of digital biomarkers obtained from wearable technologies with multiomic systems and artificial intelligence-supported models, when developed in accordance with ethical and equitable access principles, has the potential to support the transition from the discovery phase to patient-centered clinical applications.

Humans↗

PubMed2026

Exploratory single-nucleus multiomics analysis of myeloid cell states associated with neoadjuvant chemotherapy response in pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) continues to be one of the most lethal human malignancies, with the vast majority of patients ineligible for immunotherapy. Tumour-associated macrophages (TAMs) are key regulators of the PDAC tumour microenvironment (TME), yet their transcriptional and epigenomic heterogeneity in the context of chemotherapy response is poorly understood. Therefore, we performed an exploratory single nucleus multiomics analysis of PDAC tumors stratified by histopathologic response to neoadjuvant chemotherapy. METHODS: Surgical resection specimens from PDAC patients were classified as responders or non-responders using the American College of Pathologists (CAP) histopathologic criteria. Frozen tissue underwent simultaneous snRNA-seq and snATAC-seq on the 10x Genomics Chromium Single Cell Multiome platform, followed by downstream analyses such as differential gene expression, GO and hallmark pathway enrichment, pseudotime trajectory inference and ChromVAR transcription factor motif analysis. RESULTS: Multiomics profiling of 30 840 high-quality nuclei revealed a myeloid compartment that differed in composition and transcriptional state between CAP-defined responders and non-responders in this small cohort. We observed a trend toward higher LAM-like state proportions in the responders than non-responders (38.4% vs. 26.7%), although this disparity did not achieve statistical significance. The transcriptional programs of the responder myeloid cells are associated with phagocytosis and lipid handling. Chromatin accessibility analysis further suggested candidate response-associated transcription factor motif accessibility patterns. CONCLUSIONS: Neoadjuvant-treated PDAC tumours from CAP-defined responders in this cohort myeloid landscape with apparent enrichment of LAM-like states and immune-activating transcriptional/epigenetic programs. However, these findings are preliminary and hypothesis-generating because of the small cohort size, heterogeneous treatment regimens, absence of matched pre-treatment biopsies, and lack of knockout validation. Larger treatment cohorts and functional/mechanistic studies are needed to determine whether LAM-like myeloid programs contribute to chemotherapy response or reflect a consequence of chemotherapy treatment.

Humans↗

PubMed2026

Strategy for Simultaneous Multiomic Survey of N-Glycomic and Extracellular Matrix Proteome by Mass Spectrometry Imaging.

Recent advances in spatially resolved molecular profiling have positioned matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) as a powerful platform for multiomic tissue analyses. However, conventional workflows that sequentially target distinct molecular classes are time- and resource-intensive, requiring repeated sequential sample preparation, imaging, and data integration. Here, we evaluate streamlined strategies for simultaneous or combined acquisition of N-glycan and collagen-derived peptide information using PNGase F and collagenase. In-solution studies demonstrate that simultaneous enzymatic digestion yields comparable peptide identifications and glycan profiles relative to traditional sequential workflows, with minimal impact on enzymatic specificity. On the basis of these findings, we developed and optimized MALDI-MSI protocols enabling either simultaneous enzyme application or sequential enzyme treatment with unified matrix deposition and single-pass imaging. While direct coapplication reduced image uniformity, a hybrid approach that used sequential enzyme deposition with combined imaging preserved spatial fidelity and spectral quality while significantly reducing processing and computational demands. Application to human tissues, including vertebral bone and ocular samples, highlights the utility of this workflow for fragile specimens and exploratory multiomic surveys. Collectively, these results establish a framework for integrated glycomic and proteomic imaging targeting the extracellular microenvironment, expanding multiomic MALDI-MSI analyses.

Spectrometry, Mass, Matrix-Assisted Laser Desorpti↗

PubMed2025

Giotto Suite: a multiscale and technology-agnostic spatial multiomics analysis ecosystem.

Emerging spatial multiomics technologies provide an increasingly large amount of information content at multiple scales. However, it remains challenging to efficiently represent and harmonize diverse spatial datasets. Here we present Giotto Suite, a suite of modular packages that provides scalable and extensible end-to-end solutions for multiscale and multiomic data analysis, integration and visualization. At its core, Giotto Suite is centered around an innovative data framework, allowing the representation and integration of spatial omics data in a technology-agnostic manner. Giotto Suite integrates molecular, morphology, spatial and annotated feature information to create a responsive and flexible workflow, as demonstrated by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science in R, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive and multiscale ecosystem for spatial multiomic data analysis.

Genomics↗

PubMed2026

Metab8D: a metabolic regulome network from multiomics and machine learning.

To explore multiomic regulation of the metabolome, we used machine learning to predict metabolomic variation across ~1000 different cancer cell lines with matched omics data from eight biomolecular classes: genomic copy number variation, mutations, DNA methylation, histone post-translational modifications (PTMs), transcriptomics and RNA splice variants, non-coding transcriptomics (miRNA and lncRNA), proteomics, and phosphoproteomics. Overall, the metabolome is tightly associated with the transcriptome, with coding and non-coding RNAs emerging as top predictors. Peripheral metabolites are predictable via levels of corresponding enzymes, while those in central metabolism require combinatorial predictors in signaling and redox pathways, and may not reflect corresponding pathway expression. We reconstruct multiomic interaction subnetworks for highly predictable metabolites, and YAP1 signaling emerged as a top global predictor across four omic layers. We prioritize predictive multiomic features for single-cell and spatial metabolomics assays. Top predictors were enriched for synthetic-lethal interactions and synergistic combination therapies that target compensatory metabolic modulators.

Machine Learning↗

PubMed2026

CERTOMICS: trusted single-cell multiomics pipeline for high-resolution profiling of adoptive cellular immunotherapies.

SUMMARY: Adoptive cellular immunontherapies, such as chimeric antigen receptor (CAR) T cell therapy, have transformed cancer treatment, yet challenges such as resistance, relapse, and high costs limit their efficacy and accessibility. A comprehensive understanding of cellular heterogeneity and molecular profiles is essential to improve these therapies. Advanced single-cell multiomics technologies have the power to analyze the complex interactions between CAR-engineered cells, immune cells, and tumor cells. However, standardized single-cell multiomics computational pipelines specifically tailored to CAR-engineered cell products are lacking. Due to the synthetic nature of CAR transgenes, additional steps for reliable identification and characterization of CAR-positive cells are required but not included in existing data-processing workflows. To address this, we present CERTOMICS, a Nextflow-based, CAR-aware pipeline offering enhanced CERTainty in immunophenotyping and data interpretation, tailored for single-cell multiOMICSprofiling of adoptive cellular immunotherapies. The pipeline standardizes processing 10x Genomics single-cell multiomics data and integrates CAR-specific identification and quality control. Additionally, a curated repository of CAR construct sequences and annotation data is provided, serving as an extensible resource to support the analysis and development of CAR T cell therapies. AVAILABILITY AND IMPLEMENTATION: Detailed documentation of this pipeline, along with a resource on latest FDA-approved CAR therapies is available on our website: https://fraunhofer-izi.github.io/Living-Drugs-Wiki/. The data underlying this article are available on GitHub at https://github.com/fraunhofer-izi/CERTOMICS. The code is also published on Zenodo at https://doi.org/10.5281/zenodo.18709693.

Multiomics↗

PubMed2026

map3C: a computational tool for processing multiomic single-cell Hi-C data.

SUMMARY: The emergence of multiomic single-cell Hi-C (scHi-C) methods, which simultaneously profile chromatin conformation and other modalities such as gene expression or DNA methylation, creates tremendous opportunities for studying the genome's structure-function relationships. Existing tools for processing multiomic scHi-C datasets lack certain key functions for downstream bioinformatics analysis. We present map3C, a software tool that incorporates additional key functions. Specifically, we demonstrate that map3C facilitates multiomic scHi-C processing, quality control, and identification of structural variant locations in the genome. AVAILABILITY AND IMPLEMENTATION: map3C is available at https://github.com/luogenomics/map3C and is archived at https://doi.org/10.5281/zenodo.20724719.

Software↗

PubMed2025

Multiomics Analysis Reveals Therapeutic Targets for Chronic Kidney Disease With Sarcopenia.

BACKGROUND: The presence of sarcopenia in patients with chronic kidney disease (CKD) is associated with poor prognosis. The mechanism underlying CKD-induced muscle wasting has not yet been fully explored. This study investigates the influence of renal secretions on muscles using multiomics sequencing. METHODS: The kidney transcriptome analysis by RNA-seq and protein profiling by tandem mass tag (TMT), serum TMT and muscle TMT were performed in CKD established using 0.2% adenine and control mice. Spp1 recombinant protein was used to study its effect on myotube atrophy in vitro. In animal experiments on CKD, pharmacological inhibition of Spp1 was used to explore the role of Spp1 in skeletal muscle wasting. Transcriptome analysis was performed to identify differentially expressed genes (DEGs) in the gastrocnemius muscle following Spp1 pharmacological inhibition. RESULTS: In the renal transcriptome and TMT, 503 and 377 proteins/genes respectively were co-upregulated and co-downregulated. In the serum TMT of CKD and normal control (NC) mice, 22 upregulated and 7 downregulated differentially expressed proteins (DEPs) showed the same expression patterns as those in the kidney transcriptome and TMT analysis. Based on bioinformatics analysis and reported studies, we selected Spp1 for further validation. Spp1 recombinant protein was added to C2C12 myotubes in vitro, and the results indicated that Spp1 significantly increased the protein levels of the muscle atrophy marker (Murf-1) and promoted the smaller myotubes (all p < 0.05). Compared with NC mice, Spp1 mRNA and protein levels were significantly upregulated in the kidneys of CKD mice, and the serum concentration of Spp1 was also markedly increased (all p < 0.05). In animal experiments, pharmacological inhibition of Spp1 increased the weights of gastrocnemius and tibialis anterior muscles (p < 0.05) and improved muscle atrophy phenotype. Transcriptome analysis showed that DEGs in the gastrocnemius muscle following Spp1 pharmacological inhibition were enriched in protein digestion and absorption, glucagon signalling pathway, apelin signalling pathway and ECM-receptor interaction pathway. CONCLUSIONS: Our study is the first to establish a regulatory network of kidney-muscle crosstalk to explore the potential mechanism of CKD-related sarcopenia. Employing multiomics analysis, cellular assessment and animal experiments, we have identified that Spp1 could potentialy serve as a promising therapeutic target for CKD patients with sarcopenia.

Sarcopenia↗

PubMed2026

2025 Donald Seldin Lecture: Leveraging Diverse Population Genomics and Multiomics Integration for Gene Discovery of Cardiovascular and Kidney Diseases.

This review discusses the implications of frameworks leveraging genetic admixture and multiomics data for advancing gene discovery in cardiovascular and kidney disease research. By broadening gene discovery efforts to additional populations that have a disproportionately high risk of disease and leveraging genetic diversity in admixed populations, studies can identify population-enriched risk variants that traditionally have been missed in genome-wide association studies. The use of multiomics approaches, including the transcriptome, proteome, and metabolome, advances a mechanistic understanding of disease beyond associations. As single-cell omics technologies continue to improve, their integration into gene discovery may help uncover cell-type-specific regulatory pathways and more precise biological contexts. The full potential of these approaches depends on sustained investment in diverse, well-characterized omics data sets, methodological innovation in multiancestry statistical approaches, and interdisciplinary collaboration bridging genomics, epidemiology, and clinical medicine. These efforts will need to be translated into clinically actionable insights, including ancestry-informed risk stratification and targeted therapeutics, to improve outcomes for cardiovascular and kidney diseases.

Humans↗

PubMed2024

Multiome Perturb-seq unlocks scalable discovery of integrated perturbation effects on the transcriptome and epigenome.

Single-cell CRISPR screens link genetic perturbations to transcriptional states, but high-throughput methods connecting these induced changes to their regulatory foundations are limited. Here, we introduce Multiome Perturb-seq, extending single-cell CRISPR screens to simultaneously measure perturbation-induced changes in gene expression and chromatin accessibility. We apply Multiome Perturb-seq in a CRISPRi screen of 13 chromatin remodelers in human RPE-1 cells, achieving efficient assignment of sgRNA identities to single nuclei via an improved method for capturing barcode transcripts from nuclear RNA. We organize expression and accessibility measurements into coherent programs describing the integrated effects of perturbations on cell state, finding that ARID1A and SUZ12 knockdowns induce programs enriched for developmental features. Modeling of perturbation-induced heterogeneity connects accessibility changes to changes in gene expression, highlighting the value of multimodal profiling. Overall, our method provides a scalable and simply implemented system to dissect the regulatory logic underpinning cell state. A record of this paper's transparent peer review process is included in the supplemental information.

Humans↗

PubMed2026

Multiomics Integration Identifies a Molecular Subtype of Intrahepatic Cholangiocarcinoma With Enhanced Benefit From Adjuvant Therapy.

Intrahepatic cholangiocarcinoma (iCCA) is a molecularly heterogeneous liver cancer with a poor prognosis. Improved stratification is needed to guide postoperative therapy. In this study, we applied integrative multiomics analysis to classify iCCA and identify biomarkers predictive of adjuvant treatment benefit. Using publicly available datasets (including whole exome sequencing, RNA sequencing, proteomics, and phosphoproteomics from FU-iCCA cohort and a transcriptomic cohort GSE244807), we defined 3 robust molecular subtypes of iCCA. These subtypes exhibited distinct genomic alterations, pathway activation, and immune microenvironments, with significant differences in overall survival (OS). Through protein-protein interaction network analysis and consensus feature selection using 10 clustering algorithms, we prioritized 8 marker genes distinguishing the subtypes. A Cox proportional-hazards model constructed from these markers stratified patients into high- and low-risk groups. High-risk iCCA, characterized by elevated expression of markers such as CLDN18, MUC1, and MUC5AC, had significantly worse OS in the absence of adjuvant therapy. Notably, in an independent validation of 174 patients with iCCA who underwent resection (single-center cohort), high expression of any of these 3 markers were associated with markedly prolonged OS in patients who received adjuvant chemotherapy or chemoembolization, compared with those who did not. In contrast, marker-negative patients showed no clear benefit from adjuvant therapy. In conclusion, our multiomics approach identified a high-risk, mucin-enriched subtype of iCCA. CLDN18, MUC1, and MUC5AC emerge as candidate predictive biomarkers for adjuvant chemotherapy benefit in iCCA, warranting prospective validation to improve personalized postoperative management.

Humans↗

PubMed2026

A scalable, low-cost, sample hashing workflow for multiomic single-cell analysis using the Seq-Well S3 platform.

In-depth analyses of clinical samples have the potential to provide unparalleled insights into the cellular mechanisms that underlie both health and disease, as well as therapeutic and prophylactic responses. However, these specimens are often paucicellular, necessitating the use of workflows that maximize the amount of information that can be learned. Here we provide a detailed protocol for generating and analyzing single-cell multiomic data from low-input samples with the Seq-Well S3 platform. We further describe a matched pipeline for sample hashing that reduces costs and sources of technical variation in the resulting data while also enhancing throughput. In brief, our streamlined and efficient methodology involves: (1) optionally staining single-cell suspensions with antibody-oligonucleotide conjugates for cell surface protein quantification and/or sample multiplexing; (2) generating Seq-Well S3 sequencing libraries; (3) optionally producing bulk-RNA sequencing libraries via SMART-seq2 to support genetic demultiplexing; and (4) computationally analyzing the resulting data. Each step herein has been designed to leverage readily available reagents and standard laboratory equipment, substantially lowering barriers to entry for researchers. The overall Protocol can yield high-quality multiomic insights from samples in under a week.

Single-Cell Analysis↗

PubMed2025

Integrating explainable artificial intelligence with multiomics systems biology and electronic health record data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health records data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; 9 tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct subtissues (defined as clusters of samples within a brain tissue that share a specific expression pattern); and gene-gene coexpression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six Food and Drug Administration (FDA)-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large US de-identified insurance-claims database (n = 364 733), exposure to promethazine, one of the candidate drugs, was associated with a 57%-62% lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both P < .001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multiomics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Alzheimer Disease↗

PubMed2026

DIVAS: an R package for identifying shared and individual variations of multiomics data.

MOTIVATION: Multiomics data integration aims to identify biological patterns shared across molecular modalities. Most existing methods detect either jointly shared variation, across all modalities, or individual variation, unique to a single modality, but overlook partially shared variation, shared by only a subset of modalities. This is a critical limitation, because many biological mechanisms manifest in some but not all molecular modalities. RESULTS: We present an open-source R package implementing data integration via analysis of subspaces (DIVAS), a framework for systematically identifying jointly shared, partially shared and individual variations across multiple data types. DIVAS combines angle-based subspace analysis with inference through rotational bootstrap, hierarchically searching all combinations of modalities to decompose multiomics data into interpretable components with scores and loadings. In simulations with a known sharing structure, DIVAS recovered every component across a wide range of noise levels, whereas existing methods did not. Applied to multi-modal COVID-19 data, it reveals partially shared immune and metabolic dysregulation patterns underpinning disease severity that conventional approaches would miss. AVAILABILITY AND IMPLEMENTATION: DIVAS is available at https://github.com/ByronSyun/DIVAS, with documentation and vignettes. The COVID-19 case study vignette is available at https://byronsyun.github.io/DIVAS_COVID19_CaseStudy/.

Multiomics↗

PubMed2026

Plasma Multiomics Links Early-Life Adversity to Disease and Cardiometabolic Health: A Population-Based Cohort Study.

BACKGROUND: Childhood adversity (CA) is associated with increased cardiovascular and cardiometabolic risk, but the molecular mechanisms remain unclear. We aimed to identify CA-related metabolomic and proteomic signatures and evaluate their roles in linking CA to incident diseases. METHODS: This prospective cohort study included 153 225 participants aged 48 to 64 years. CA was assessed using the Childhood Trauma Screener-5, capturing cumulative (0-5 domains) and individual adversity exposures. Plasma metabolomics and proteomics data were integrated to derive CA-related molecular signatures. Cox proportional hazards model was used to evaluate associations between CA, molecular signatures, and 58 incident diseases and mortality. Mediation analyses quantified the role of multiomics signatures. RESULTS: Each additional CA domain was associated with higher risk of 49 of 58 incident diseases (hazard ratios [HRs], 1.024-1.338) and a 7.3% higher all-cause mortality risk. Focusing on cardiometabolic health, the CA-related metabolic and proteomic signatures were independently associated with incident disease. Per 1-SD increase in the cumulative CA metabolic signature, the highest observed HR was 1.313 (95% CI, 1.278-1.349) for incident diabetes, and the risk for hypertension was also increased (HR, 1.136 95% CI, 1.114-1.159). Similarly, the proteomic signature was strongly associated with incident diabetes (HR, 1.645 95% CI, 1.489-1.818) and hypertension (HR, 1.223 95% CI, 1.155-1.295). The cumulative CA metabolic and proteomic signatures mediated up to 25.5% and 46.8% of the association with hypertension, respectively. CONCLUSIONS: CA is associated with a broad spectrum of diseases and mortality, with particularly strong links to cardiometabolic health. Multiomics signatures partially mediated these associations.

Humans↗

PubMed2026

Spatial multiomics in biomedical research: advances beyond transcriptomics.

Coordinated changes in gene expression, epigenetic regulation, protein and metabolic activities together drive disease progression and determine clinical outcomes. While spatially resolved transcriptomics has been widely adopted across biomedical fields, it offers an incomplete picture limited to transcriptomic levels. Here, we survey the latest developments in spatial multiomics technologies, with particular emphasis on platforms that extend beyond conventional transcriptomics and profile genomics, epigenomics, proteomics, or metabolomics within intact tissues. These approaches are rapidly becoming commercialized, and here we highlight major technical breakthroughs, enhanced sample compatibility, emerging applications, and computational tools for data analysis. This Review aims to equip researchers with a clear understanding of the current technological landscape and to accelerate the adoption of spatial multiomics methods in biomedical research.

Humans↗