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At least 19 recordsLinked to original sources
PubMed2025

Deep learning-based cell-specific gene regulatory networks inferred from single-cell multiome data.

Gene regulatory networks (GRNs) provide a global representation of how genetic/genomic information is transferred in living systems and are a key component in understanding genome regulation. Single-cell multiome data provide unprecedented opportunities to reconstruct GRNs at fine-grained resolution. However, the inference of GRNs is hindered by insufficient single omic profiles due to the characteristic high loss rate of single-cell sequencing data. In this study, we developed scMultiomeGRN, a deep learning framework to infer transcription factor (TF) regulatory networks via unique integration of single-cell genomic (single-cell RNA sequencing) and epigenomic (single-cell ATAC sequencing) data. We create scMultiomeGRN to elucidate these networks by conceptualizing TF network graph structures. Specifically, we build modality-specific neighbor aggregators and cross-modal attention modules to learn latent representations of TFs from single-cell multi-omics. We demonstrate that scMultiomeGRN outperforms state-of-the-art models on multiple benchmark datasets involved in diseases and health. Via scMultiomeGRN, we identified Alzheimer's disease-relevant regulatory network of SPI1 and RUNX1 for microglia. In summary, scMultiomeGRN offers a deep learning framework to identify cell type-specific gene regulatory network from single-cell multiome data.

Deep Learning↗
PubMed2025

Epigenetic orchestration of cancer-immune dynamics: mechanisms, technologies, and clinical advancements.

BACKGROUND: Epigenetic dysregulation plays a pivotal role in cancer immune evasion by orchestrating tumour antigen silencing, immune cell dysfunction, and the formation of an immunosuppressive microenvironment. By disrupting successive phases of the cancer-immunity cycle-from antigen presentation to T cell exhaustion-these aberrations facilitate immune escape and tumour progression, highlighting the need for targeted epigenetic intervention. AIM OF REVIEW: This review systematically dissects how epigenetic alterations impair anti-tumour immunity at each stage of the CI cycle. It not only integrates fragmented mechanistic evidence but also emphasizes underexplored crosstalk between specific epigenetic regulators and immune cell types. It further highlights emerging technologies-such as single-cell epigenomics, spatial multi-omics, and CRISPR-based screens-that are driving discovery of novel therapeutic targets and refining patient stratification. Key scientific concepts of review. We discuss how epigenetic interventions, alone or in combination with immunotherapies, can reinvigorate immune responses and overcome resistance to current treatments. A particular focus is given to how integrative high-resolution platforms are mapping immunoepigenetic landscapes, enabling mechanism-informed, precision immunotherapy strategies. By bridging epigenetic regulation with translational immuno-oncology, this review outlines a future where epigenetic reprogramming becomes central to overcoming immune evasion in cancer.

Humans↗
PubMed2026

Epigenetic and Transcriptional Regulatory Networks Underlying Psoriasis Pathogenesis.

Psoriasis is a chronic, immune-mediated dermatologic disorder characterized by the hyperproliferation of keratinocytes and dysregulated immune signaling. Although genome-wide association studies have identified susceptibility loci, the multifactorial nature of the disease underlines the importance of nongenetic regulatory mechanisms. Among these epigenetic modifications are those that critically link genetic predisposition with environmental stimuli. This review offers an in-depth overview of the current insights into the role of epigenetic regulation in the pathophysiology of psoriasis. Key mechanisms, including aberrant DNA methylation, histone post-translational modifications (eg, H3K27ac, H3K4me3), and dysregulated noncoding RNAs, are discussed in the context of inflammatory signaling and immune cell function. This review also explores how environmental factors such as UV radiation and air pollution induce the epigenetic reprogramming that perpetuates the proinflammatory state. Furthermore, it highlights the translational potential of targeting epigenetic regulators and epigenome-editing technologies, including clustered regularly interspaced short palindromic repeats (CRISPR) fusion systems, as precision therapeutic strategies. In parallel, advances in single-cell epigenomics, spatial transcriptomics, and the profiling of circulating biomarkers offer novel diagnostic tools. Despite advances, challenges persist, including the limited predictive value of preclinical models and variable epigenetic profiles. Positioning epigenetics as the bridge between genetic risk, environmental triggers, and therapeutic advances, this review presents a framework for precision medicine in psoriasis.

Humans↗
PubMed2026

Plant cis-regulatory grammar: Decoding the multidimensional code of transcriptional regulation for programmable crop engineering.

Cis-regulatory elements (CREs) orchestrate the spatiotemporal precision of gene expression that underlies plant development, adaptation, and domestication. Decoding the cis-regulatory grammar of plant genomes remains a central challenge in modern biology, with profound implications for programmable crop engineering. Here, recent conceptual and technological advances are synthesized to reshape our understanding of plant CREs. This review first argues that CRE function is not only an intrinsic property of DNA sequence alone but also emerges from a multidimensional context, including chromatin accessibility, histone modifications, three-dimensional genome topology, and cell type-specific regulatory landscapes. Furthermore, the convergence of single-cell epigenomics, high-throughput functional assays, and CRISPR-based dissection has begun to unravel this contextual grammar, revealing the computational principles governing transcriptional regulation. Critically, we propose that artificial intelligence (AI) platforms are catalyzing an ongoing transition from descriptive discovery to predictive engineering, wherein these platforms outperform natural evolution in designing synthetic CREs. Finally, a roadmap is outlined toward a plant regulatory grammar foundation model, which will enable truly predictive engineering of gene expression when fine-tuned for specific tasks. Collectively, the integration of single-cell resolution maps, precise genome editing, AI-driven design, and regulatory-compliant delivery systems promises to transform our ability to reprogram plant gene regulation for next-generation agriculture, bridging the gap between foundational regulatory biology and tangible crop improvement.

artificial intelligence↗
PubMed2025

dbscATAC: a resource of single-cell super-enhancers/enhancers and gene markers derived from scATAC-seq data.

MOTIVATION: scATAC-seq enables high-resolution mapping of cis-regulatory elements. It has been widely applied to uncover cell-type-specific regulatory networks and complement scRNA-seq analysis in numerous studies. However, a large number of datasets generated by scATAC-seq remain underutilized due to limited exploration of super-enhancers/typical enhancers and gene markers. A comprehensive resource enabling cell-type-specific annotation of cis-regulatory elements and their dynamic enhancer-gene linkages remains an urgent unmet need for scATAC-seq. RESULTS: We present dbscATAC, a specialized single-cell database for annotating super-enhancers, gene markers, and enhancer-gene interactions derived from scATAC-seq data. Using improved machine learning algorithms, we identified 213 835 super-enhancers across 520 tissue/cell types from three species, as well as 347 484 gene markers, 13 470 526 enhancers, and 10 402 346 enhancer-gene interactions derived from 1 668 076 single cells spanning 1028 tissue/cell types in 13 species. An easy-to-use online platform with multiple analytic modules and hierarchical query options was developed for searching, browsing and visualizing single-cell super-enhancers, enhancers, and gene markers. dbscATAC provides a comprehensive resource to facilitate the exploration of enhancer landscapes, gene regulation, and cell-type-specific characteristics in single-cell epigenomics. AVAILABILITY AND IMPLEMENTATION: The database with all the super-enhancer/enhancer annotation data is available at http://singlecelldb.com/dbscATAC/index.php. And the source code of dbscATAC for prediction of SEs, enhancers, and gene markers are available at https://github.com/EvansGao/dbscATAC. The source code, tissue/cell type description, and data summary can be downloaded at DOI: 10.6084/m9.figshare.28706414.scATAC-seq, Database, Super-enhancers/enhancers, Gene markers.

Enhancer Elements, Genetic↗
PubMed2026

A latent activated olfactory stem cell state revealed by single-cell transcriptomic and epigenomic profiling.

The olfactory epithelium is one of the few regions of the nervous system that sustains neurogenesis throughout life. Its experimental accessibility makes it especially tractable for studying molecular mechanisms that drive neural regeneration in response to injury. In this study, we used single-cell sequencing to identify transcriptional and epigenetic processes involved in determining olfactory epithelial stem cell fate during injury-induced regeneration. By combining gene expression and accessible chromatin profiles of individual lineage-traced olfactory stem cells, we identified transcriptional heterogeneity among activated stem cells at a stage when cell fates are being specified. We further identified a subset of resting cells that appears poised for activation, characterized by accessible chromatin around silent genes prior to their expression in response to injury. These results provide evidence for a latent activated stem cell state in which a subset of quiescent olfactory epithelial stem cells are epigenetically primed to support injury-induced regeneration.

Animals↗
PubMed2026

Disentangling covariate effects on single-cell-resolved epigenomes with DeepDive.

Understanding the effects of individual biological factors from single-cell-resolved epigenomic data is hindered by multicollinearity, particularly in human cohorts. We introduce DeepDive, a deep-learning framework designed to systematically disentangle known and unknown sources of variation in single-nucleus ATAC-seq data. DeepDive accurately reconstructs chromatin accessibility, outperforms state-of-the-art methods with incomplete covariate information, and robustly recovers true biological signals from even highly entangled covariates, unlocking counterfactual, "what-if," analyses. Applying DeepDive to pancreatic islet cells, we perform counterfactual analyses to prioritize covariates associated with a type 2 diabetes-linked beta-cell subtype and nominate transcription regulators. DeepDive offers a powerful and unbiased tool for mechanistic discovery in complex human disease cohorts.

disentanglement↗
PubMed2026

Multimodal atlas of human atherosclerosis links granular vascular cell states to coronary artery disease risk.

Advances in single-cell and spatial assays have revolutionized the scale and resolution of molecular tissue profiling. Here we present MetaPlaq, a multimodal atlas of human atherosclerotic arterial beds comprising over a million cells across single-cell transcriptomics, epigenomics and high-resolution spatial expression assays. We map granular cell states and disease-relevant transcriptional programs within the native tissue context of coronary arteries. Furthermore, we map cardiovascular GWAS signals to smooth muscle cells (SMCs) and endothelial cells (ECs) and uncover the cis-regulatory architecture governing their phenotypic transitions. Our comprehensive epigenomic reference allowed us to build cell-specific enhancer-gene link maps and multimodal gene regulatory networks (GRNs) underlying disease-relevant states such as osteogenic SMCs and ECs undergoing mesenchymal transition. We also integrate SMC and EC disease-associated gene sets with GRNs to nominate key transcription factors such as PRRX1, BNC2 and ELK3 regulating atherosclerosis-relevant transcriptional programs. Finally, we layer single-cell and spatial modalities to fine-map GWAS variants with improved cell and anatomical context. We highlight candidate cell-specific regulatory mechanisms at less characterized CAD loci, including FGD5 and MCF2L in ECs. Together, this atlas represents an important step towards fully interpreting genetic risk loci and informing new therapeutic strategies for cardiovascular disease.

Journal Article↗
PubMed2025

Evolutionary fingerprints of epithelial-to-mesenchymal transition.

Mesenchymal plasticity has been extensively described in advanced epithelial cancers; however, its functional role in malignant progression is controversial1-5. The function of epithelial-to-mesenchymal transition (EMT) and cell plasticity in tumour heterogeneity and clonal evolution is poorly understood. Here we clarify the contribution of EMT to malignant progression in pancreatic cancer. We used somatic mosaic genome engineering technologies to trace and ablate malignant mesenchymal lineages along the EMT continuum. The experimental evidence clarifies the essential contribution of mesenchymal lineages to pancreatic cancer evolution. Spatial genomic analysis, single-cell transcriptomic and epigenomic profiling of EMT clarifies its contribution to the emergence of genomic instability, including events of chromothripsis. Genetic ablation of mesenchymal lineages robustly abolished these mutational processes and evolutionary patterns, as confirmed by cross-species analysis of pancreatic and other human solid tumours. Mechanistically, we identified that malignant cells with mesenchymal features display increased chromatin accessibility, particularly in the pericentromeric and centromeric regions, in turn resulting in delayed mitosis and catastrophic cell division. Thus, EMT favours the emergence of genomic-unstable, highly fit tumour cells, which strongly supports the concept of cell-state-restricted patterns of evolution, whereby cancer cell speciation is propagated to progeny within restricted functional compartments. Restraining the evolutionary routes through ablation of clones capable of mesenchymal plasticity, and extinction of the derived lineages, halts the malignant potential of one of the most aggressive forms of human cancer.

Animals↗
PubMed2026

Interpretable data integration for single-cell and spatial multi-omics.

Integrating single-cell or spatial transcriptomic and epigenomic data enables scrutinizing the transcriptional regulatory mechanisms controlling cell fate. Current integration methods usually align multi-omics data into a shared latent space but fail to reveal the underlying connections between genes and regulatory elements. The correlation- or regression-based regulatory inference methods cannot dissect different transcriptional regulation codes for cells under different spatial and temporal states. To address both problems, we develop a feature-guided optimal transport (FGOT) method, which simultaneously uncovers cellular heterogeneity and their associated transcriptional regulatory links. FGOT also provides post hoc interpretability for existing integration methods. FGOT is applicable for paired/unpaired single-cell multi-omics data and paired spatial multi-omics data. Benchmarking and validating via histone modification data or three-dimensional (3D) genomics data show good robustness and accuracy in integration and inference of regulatory links. The method allows systematic screening of cell-state and spatial-location-specific regulatory elements in diseases at the single-cell level. A record of this paper's transparent peer review process is included in the supplemental information.

Single-Cell Analysis↗
PubMed2026

Integrative multi-omics reveals a fibroblast-centered, ZFHX3-prioritized regulatory framework linking sick sinus syndrome and atrial fibrillation.

OBJECTIVE: To define shared genetic and multi-scale mechanisms underlying comorbidity between sick sinus syndrome (SSS) and atrial fibrillation (AF). METHODS: We integrated genome-wide association study (GWAS) summary statistics for SSS and AF with Genotype-Tissue Expression (GTEx) expression and splicing quantitative trait loci (eQTL/sQTL), atrial single-cell and spatial transcriptomics, and epigenomics. We identified trait-relevant tissues and pathways, prioritized shared cell types, quantified genome-wide and local genetic sharing, detected joint loci by cross-trait meta-analysis, and linked loci to regulatory programs via colocalization and cell-prioritized co-expression networks. RESULTS: Both traits showed strongest enrichment in cardiac tissue, especially Heart Atrial Appendage. Fibroblasts from the left atrial appendage were consistently prioritized as the key shared cell population. SSS and AF displayed significant positive genome-wide genetic correlation, with multiple locally shared regions, including six major loci. Cross-trait meta-analysis identified eight joint-phenotype SNPs implicating four susceptibility genes. ZFHX3 was the leading tissue-cell-gene candidate, acting as a hub in fibroblast co-expression modules and colocalizing with cardiac regulatory signals. CONCLUSION: Shared liability for SSS and AF is highly tissue- and cell-specific, converging on regulatory networks in atrial appendage fibroblasts, with ZFHX3 serving as a central mechanistic and biomarker node.

Humans↗
PubMed2026

Genome-wide profiling of histone modifications and transcription factor binding at single-cell resolution by DeChIC-seq.

Mapping of protein-DNA interactions at single-cell resolution remains a central challenge in epigenomics, particularly for transcription factors (TFs), whose sparse binding limits reliable detection. Here, we establish DeChIC-seq (DNA Deaminase-based Chromatin Immuno-Conversion sequencing), a conversion-based strategy that uses a protein A-DddAtox fusion to directly record protein-DNA interactions by inducing localized C-to-U conversions near antibody-bound chromatin. Retaining genome-wide background sequence information without immunoprecipitation, DeChIC-seq enables profiling of histone modifications and sensitive detection of TF binding. Integration with single-cell whole-genome amplification extends DeChIC-seq to single-cell applications (scDeChIC-seq), enabling chromatin profiling of individual cells. Applied to mouse embryogenesis, scDeChIC-seq resolves lineage-specific chromatin states through profiling of H3K4me3, CTCF, and RAD21 and sensitively detects TF binding, including that of NR5A2, TFAP2C, and KLF5, from extremely limited blastomere inputs. This underscores its strong potential for detecting TF-binding sites in scarce biological samples. DeChIC-seq establishes a conversion-based framework for chromatin profiling that enables mechanistic dissection of TF-driven gene regulation across rare cells, developmental systems, and disease contexts.

Animals↗
PubMed2026

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗
PubMed2025

scPOEM: robust co-embedding of peaks and genes revealing peak-gene regulation.

MOTIVATION: Identifying regulatory elements in various chromosomal regions that influence gene expression is a fundamental challenge in epigenomics, with profound implications for understanding gene regulation and disease mechanisms. The advent of paired single-cell RNA sequencing and single-cell ATAC sequencing has created unprecedented opportunities to address this challenge by enabling simultaneous profiling of gene expression and chromatin accessibility at single-cell resolution. However, the inherent signals between them are weak due to the highly sparse and noisy nature of data. RESULTS: This article proposes single-cell meta-Path based Omics Embedding (scPOEM), a novel embedding method that jointly projects chromatin accessibility peaks and expressed genes into a shared low-dimensional space. By integrating the relationships among peak-peak, peak-gene, and gene-gene interactions, scPOEM assigns closer representations in the embedding space to related peak-gene pairs. Our experiments demonstrate that scPOEM generates stable representations of peaks and genes, outperforms existing methods in recovering biologically meaningful peak-gene regulatory relationships and enables new insights in subgroup and differential analysis of gene regulation. These results highlight its potential to uncover gene regulatory mechanisms and enhance the understanding of transcriptional regulation at single-cell resolution. AVAILABILITY AND IMPLEMENTATION: The source code of scPOEM is available at https://github.com/Houyt23/scPOEM. The datasets can be obtained from the 10× Genomics (https://www.10xgenomics.com/datasets/pbmc-from-a-healthy-donor-granulocytes-removed-through-cell-sorting-10-k-1-standard-1-0-0) and GEO database under access codes GSE194122 and GSE239916.

Gene Expression Regulation↗
PubMed2026

Regulatory Evolution and the Genetic Basis of Human Brain Expansion.

The evolution of the human brain is characterized by profound changes in structure and function, despite relatively limited divergence in protein-coding genes compared to other primates. This paradox has led to increasing recognition of gene regulatory elements (GREs) as primary drivers of evolutionary innovation. In this review, we synthesize current knowledge on the role of conserved noncoding elements (CNEs), human accelerated regions (HARs), and transposable element (TE)-derived sequences in shaping gene regulatory networks (GRNs) underlying brain development. Comparative analyses across humans and closely related primates, including the chimpanzee, gorilla, and orangutan, reveal that while core regulatory architectures are highly conserved, subtle changes in regulatory elements drive species-specific gene expression patterns. We highlight how CNEs provide a stable regulatory framework, whereas HARs and TE-derived elements introduce lineage-specific modifications that fine-tune neurodevelopmental processes. Advances in functional genomics, including CRISPR-based perturbations, massively parallel reporter assays, and single-cell multi-omics, have enabled direct interrogation of regulatory function, linking sequence variation to cellular phenotypes. Furthermore, we discuss how regulatory evolution contributes to both cognitive innovation and susceptibility to neurological disorders. Despite significant progress, challenges remain in establishing causal relationships between regulatory variation and phenotypic outcomes. Future integration of multi-omics data and comparative models will be essential for resolving these complexities. Together, this review provides a comprehensive framework for understanding the molecular basis of primate brain evolution through the lens of gene regulation.

Brain evolution↗
PubMed2026

Livestock Multi-Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation.

Livestock multi-omics integration is key to unraveling complex trait regulation, yet systematic, livestock-specific strategies remain scarce. This review traces the progression from single-omics accumulation to multi-dimensional integration, highlighting how large-scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi-omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls-overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome-host integration lacking environmental context, and systematic neglect of metabolic fluxomics-and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock-adapted three-tier analytical framework: (1) statistical association of cross-omics covariation patterns; (2) machine learning-driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior-knowledge-guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single-cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock-specific benchmarking, and translational pipelines, charting a path from correlation-centric reporting to mechanistic causality and precision breeding.

Animals↗
PubMed2025

Self-organization of mouse embryonic stem cells into reproducible pre-gastrulation embryo models via CRISPRa programming.

Embryonic stem cells (ESCs) can self-organize into structures with spatial and molecular similarities to natural embryos. During development, embryonic and extraembryonic cells differentiate through activation of endogenous regulatory elements while co-developing via cell-cell interactions. However, engineering regulatory elements to self-organize ESCs into embryo models remains underexplored. Here, we demonstrate that CRISPR activation (CRISPRa) of two regulatory elements near Gata6 and Cdx2 generates embryonic patterns resembling pre-gastrulation mouse embryos. Live single-cell imaging revealed that self-patterning occurs through orchestrated collective movement driven by cell-intrinsic fate induction. In 3D, CRISPRa-programmed embryo models (CPEMs) exhibit morphological and transcriptomic similarity to pre-gastrulation mouse embryos. CPEMs allow versatile perturbations, including dual Cdx2-Elf5 activation to enhance trophoblast differentiation and lineage-specific activation of laminin and matrix metalloproteinases, uncovering their roles in basement membrane remodeling and embryo model morphology. Our findings demonstrate that minimal intrinsic epigenome editing can self-organize ESCs into programmable pre-gastrulation embryo models with robust lineage-specific perturbation capabilities.

Animals↗
PubMed2026

Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.

BACKGROUND: With growing interest in ART, fertility preservation, and postmenopausal health of women, reproductive medicine is increasingly focused on characterizing oocytes and ovarian tissue composition, as well as understanding the molecular mechanisms that guide ovarian function throughout its lifecycle. High-throughput omics technologies have enabled the characterization of different molecular layers, leading to substantial advances in our understanding of their complex dynamics. However, not all molecular aspects are studied equally, and studies examining the same modalities often show inconsistencies, underscoring the need for data standardization and highlighting the potential for using transformative artificial intelligence and machine-learning (AI/ML) methods for ovary studies. OBJECTIVE AND RATIONALE: This study aims to evaluate how multi-omic studies have advanced our understanding of the ovarian lifecycle from fetal development to postmenopause. We systematically reviewed published studies that have investigated molecular/omic layers, including the genome, methylome, transcriptome, and proteome throughout ovarian development and aging. Our analysis identified key molecular and cellular patterns, highlighted inconsistencies across studies and addressed gaps in data analysis, interpretation, and reproducibility to guide future research. SEARCH METHODS: We conducted a systematic literature search of Medline (PubMed), Embase (Ovid), and Web of Science Core Collection (Clarivate) using a combination of controlled and free text terms for human ovary, oogenesis, folliculogenesis, ovary development and (epi)genome, transcriptome, proteome, and multi-omic mechanisms to find relevant articles published before August 2025. To focus the scope of the current review, studies of domesticated and farm animals, rodents and other model organisms, non-human primates, as well as those examining various human ovarian pathologies were excluded. OUTCOMES: The search identified 23 546 studies for screening, of which 637 full-text studies were assessed for eligibility. Subsequently, we extracted data from 121 studies. Most studies analyzed the transcriptome of oocytes, granulosa cells, and ovarian tissue from reproductive-age individuals (n = 91), with fewer studies examining samples from individuals of advanced reproductive age (n = 45) and fetal (n = 16) samples. Transcriptome analyses were most common (n = 103, 85%), followed by proteome (n = 19, 16%) and epigenome (n = 14, 12%) studies. We found substantial variation in how studies defined and reported participants' groups as well as in their sequencing technologies and data analysis methods, with a lack of standardized reporting of background clinical information, data analysis methods, and pipeline details. The key findings underscore the prevailing consensus on genes defining major ovarian cell types and their roles throughout the ovarian lifespan, from prenatal development to postmenopausal transformation. This review highlighted the underrepresentation of certain patient groups, particularly prepubertal and peri-/postmenopausal individuals, among researched populations, due to obvious clinical and ethical reasons. WIDER IMPLICATIONS: This scoping review offers a comprehensive overview and benchmark of the current state of high-throughput omics-based research on ovarian cellular composition and molecular dynamics. To address these shortcomings, we propose general recommendations for multi-omics ovary studies and emphasize the necessity for more thorough multi-omic data integration by effectively applying novel AI/ML approaches. They can potentially improve the quality of multi-omics analyses at both single-cell and tissue levels despite limited sample sizes and enable integration of molecular profiling data with clinical and radiology datasets, enabling a more comprehensive understanding of ovarian biology. Such advancements can enhance reproducibility of research findings and guide future research to deepen our understanding of ovarian biology and ultimately support the development of medical technologies for better preserving fertility and alleviating infertility. REGISTRATION NUMBER: A protocol was published a priori on the Open Science Framework (https://osf.io/z38gb/).

Female↗