Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Single-cell epigenomics”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

50 records · Page 3Linked to original sources
PubMed2026

CHCHD10 Mitigates Alzheimer's Disease-Related Phenotypes in Association With Epigenetic Remodeling in Directly Reprogrammed Neurons.

Mitochondrial dysfunction and chromatin dysregulation are interconnected contributors to neuronal vulnerability in Alzheimer's disease (AD), yet the molecular mechanisms linking these processes remain poorly understood. CHCHD10, a mitochondrial intermembrane space protein, has been implicated in neurodegenerative disorders, but its role in AD has not been defined. Here, we identify CHCHD10 as a previously unrecognized modulator of neuronal epigenomic stability in AD. Using direct fibroblast-to-neuron reprogramming, which preserves patient-specific epigenetic signatures, we show that AD neurons recapitulate genome-wide hypomethylation patterns observed in postmortem AD cortex. CHCHD10 expression is significantly reduced in AD neurons and across multiple human brain datasets, including single-cell and bulk RNA sequencing, proteomics, and human cortical tissue analyses. Restoration of CHCHD10 in AD neurons reduces amyloid-β and insoluble tau accumulation while reversing AD-associated differentially methylated regions across CpG islands, promoters, and regulatory elements. CHCHD10-responsive methylation changes overlap with those observed in human AD brain regions and colocalize with significant AD loci and cortex-specific eQTL loci, including MAPT and ABCA7. Finally, we identify KATNAL2 as a CHCHD10-responsive effector whose loss enhances tau phosphorylation and seeding, whereas its restoration mitigates tau pathology. Together, these findings support a CHCHD10-associated neuroprotective pathway linking mitochondrial dysfunction, epigenomic instability, and tau pathology in AD.

Humans↗
PubMed2025

scooby: Modeling multi-modal genomic profiles from DNA sequence at single-cell resolution.

Understanding how regulatory DNA elements shape gene expression across individual cells is a fundamental challenge in genomics. Joint RNA-seq and epigenomic profiling provides opportunities to build unifying models of gene regulation capturing sequence determinants across steps of gene expression. However, current models, developed primarily for bulk omics data, fail to capture the cellular heterogeneity and dynamic processes revealed by single-cell multi-modal technologies. Here, we introduce scooby, the first framework to model scRNA-seq coverage and scATAC-seq insertion profiles along the genome from sequence at single-cell resolution. For this, we leverage the pre-trained multi-omics profile predictor Borzoi as a foundation model, equip it with a cell-specific decoder, and fine-tune its sequence embeddings. Specifically, we condition the decoder on the cell position in a precomputed single-cell embedding resulting in strong generalization capability. Applied to a hematopoiesis dataset, scooby recapitulates cell-specific expression levels of held-out genes, and identifies regulators and their putative target genes through in silico motif deletion. Moreover, accurate variant effect prediction with scooby allows for breaking down bulk eQTL effects into single-cell effects and delineating their impact on chromatin accessibility and gene expression. We anticipate scooby to aid unraveling the complexities of gene regulation at the resolution of individual cells.

Journal Article↗
PubMed2026

Imaging-Guided Omics Technologies for Resolving Rare Cancer States and Advancing Nanomedicine.

The ability to resolve rare and transient cellular states is critical for understanding metastasis, immune evasion, and therapy resistance in cancer, yet these dynamic processes often escape detection by conventional sequencing and imaging approaches. Recent advances at the interface of nanotechnology, high-resolution live-cell imaging, and single-cell/spatial multiomics methods have enabled functional profiling of cells with unprecedented precision within their native microenvironment. In this Mini-Review, we highlight emerging nanoscale platforms that couple real-time phenotypic imaging with molecular readouts, such as FUNseq and CIN-seq, to directly link functional heterogeneity to transcriptomic, proteomic, and epigenomic information. By integrating nanoscale optical imaging, microengineered perturbation tools, and AI-driven computational analysis, these technologies open up new avenues for dissecting rare metastatic, therapy-resistant, or immune-evasive subpopulations. We further discuss how these next-generation imaging-guided single-cell and spatial omics platforms not only advance fundamental cancer biology but also create opportunities to accelerate the development of nanomedicine applications.

Humans↗
PubMed2021

Comparative cellular analysis of motor cortex in human, marmoset and mouse.

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

Animals↗
PubMed2025

Integrating multi-omics approaches in acute myeloid leukemia (AML): Advancements and clinical implications.

Acute myeloid leukemia (AML) is a highly heterogeneous and aggressive hematologic malignancy characterized by clonal proliferation of myeloid precursors. Despite significant advancements in genomic profiling and targeted therapies, patient outcomes remain suboptimal due to disease complexity, resistance mechanisms, and high relapse rates. The integration of multi-omics approaches-spanning genomics, epigenomics, transcriptomics, proteomics, and metabolomics-has revolutionized AML research, offering a comprehensive understanding of leukemogenesis, tumor heterogeneity, and therapeutic vulnerabilities. Recent studies leveraging high-throughput sequencing, mass spectrometry, and advanced computational tools have uncovered novel biomarkers, clonal evolution dynamics, and microenvironmental interactions that drive AML progression and resistance. For instance, single-cell multi-omics has revealed chemotherapy-resistant leukemic stem cell populations, while proteogenomic analyses have identified actionable targets such as MCL1 and metabolic dependencies like OXPHOS. Clinically, integrated omics platforms are refining risk stratification, minimal residual disease (MRD) monitoring, and personalized therapy selection. However, challenges such as data integration complexity, cost barriers, and ethical considerations remain. This review highlights the transformative potential of multi-omics in AML, emphasizing recent advancements in technology, biomarker discovery, and therapeutic innovation. By bridging the gap between molecular insights and clinical practice, multi-omics integration promises to redefine AML management, paving the way for precision oncology and improved patient outcomes.

Humans↗
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

Integrated Genomic and Epigenomic Analysis Reveals Epigenetic Plasticity in Disease Progression and Multidrug Resistance in Multiple Myeloma.

UNLABELLED: Multiple myeloma is marked by recurrent cytogenetic abnormalities and mutations that accumulate as the disease progresses. In this study, we sought to elucidate the transitions driving tumorigenesis and therapy resistance in multiple myeloma using a unique cohort of nearly 900 patients spanning premalignant to late-stage refractory multiple myeloma, comprehensively characterized at molecular and clinical levels. Waves of epigenetic dysregulation drove these critical transitions. In this paradigm, genomic and cytogenetic events unlocked epigenetic plasticity, reshaping multiple myeloma cell biology to evade tumor microenvironment constraints and therapeutic pressures. Functional perturbation studies in an isogenic proteasome inhibitor-resistant cell line model demonstrated enhanced reliance on transcriptional cofactors, supporting a mechanistic link between chromatin plasticity and therapy adaptation. Collectively, these findings support a unifying framework in which genomic heterogeneity unlocks gene regulatory plasticity, enabling plasma cells (PC) to evade microenvironmental constraints and therapeutic pressure. These results provide a mechanistic explanation for sequential relapse without new genomic alterations and nominate epigenetic plasticity-mediated PC adaptation as a therapeutic vulnerability in the heterogeneous genetic background of multiple myeloma. SIGNIFICANCE: Assembly and analysis of a multiple myeloma cohort spanning the continuum from premalignant to late relapse that integrates bulk transcriptomics with single-cell multiomic data provides insights into disease progression and epigenetic plasticity.

Multiple Myeloma↗
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↗
PubMed2026

Pan-genomics and multi-omics for deciphering genetic variation and accelerating genetic improvement in ruminant livestock.

Livestock reference genomes have transformed the discovery of variants associated with production, reproduction, health, and environmental adaptation. Nevertheless, a single linear reference represents only one mosaic haplotype and incompletely captures sequence diversity within a species, particularly structural variants, copy-number changes, repeat-rich regions, and breed-specific sequences. Pangenomes address this limitation by integrating multiple high-quality assemblies or population-scale variants into a unified sequence or graph representation. Concurrently, multi-omics approaches connect genomic variation with transcriptomic, epigenomic, manuscriptproteomic, metabolomic, and microbiome responses, thereby improving biological interpretation of genotype-phenotype relationships. This review synthesizes recent progress in livestock pangenomics and multi-omics, with emphasis on cattle, goats, sheep, water buffalo, and chickens. It describes advances in long-read and haplotype-resolved sequencing, graph construction, structural-variant discovery and genotyping, functional annotation, and integrative analysis. Recent pangenome studies have uncovered substantial non-reference sequence, reduced reference bias, identified breed- and population-specific structural variants, and resolved candidate variants underlying pigmentation, body size, tail morphology, cashmere production, altitude adaptation, and other economically relevant traits. However, translation into routine breeding remains constrained by uneven population representation, inconsistent structural-variant definitions, limited functional annotation, computational demands, and insufficient validation across environments. Future progress will depend on diverse near-complete assemblies, graph-aware imputation and genomic prediction, long-read transcriptomics, single-cell and spatial omics, rigorous causal validation, and open, interoperable resources. Together, these developments can support more accurate, resilient, and biologically informed livestock improvement. Importantly, current dairy-cattle evidence indicates that pangenome-derived structural variants can substantially improve variant discovery and functional interpretation while yielding only marginal average gains in routine genomic prediction, favoring targeted augmentation rather than wholesale replacement of established SNP-based evaluations.

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↗
PubMed2026

BACH1 orchestrates macrophage state transitions to coordinate regenerative inflammation.

Efficient tissue regeneration requires the precise coordination of inflammatory and regenerative programs, principally mediated by monocyte-derived macrophages. However, the transcriptional wiring and epigenomic processes behind complex macrophage subtype specification and transition between the different states are not known. Here we have identified the transcriptional repressor BACH1 as a critical, cell-intrinsic regulator of monocyte-derived macrophage specification during skeletal muscle regeneration. Using a myeloid-specific BACH1 knockout mouse model, we demonstrate that BACH1 deficiency disrupts the temporal coordination of monocyte-to-macrophage differentiation, leading to aberrant macrophage subsets with concurrent opposing pro- and anti-inflammatory features. Single-cell RNA-sequencing profiling reveals that BACH1 controls a core transcriptional network, including Nfkb1, Cebpb, and interferon signaling, governing inflammatory resolution and functional macrophage specialization. Mechanistically, BACH1 loss accelerates macrophage differentiation but also affects its core cellular identity, resulting in sustained, rather than declining inflammatory programs including upregulation of Il1b and thus, defective tissue remodeling. These immune alterations compromise the paracrine landscape during regenerative inflammation and impair muscle stem cell differentiation. Our findings establish BACH1 as a molecular tuner or controller that integrates early innate immune signaling with regenerative output, positioning it as a central node linking transcriptional control, immune fate decisions, and tissue repair.

Animals↗
PubMed2026

Beyond Bulk: Cell-Type-Resolved Epigenomics as the Path Forward in Alzheimer's Disease Research.

Alzheimer's disease (AD) is a complex neurodegenerative disorder in which most risk variants are noncoding and are enriched at gene regulatory regions, implicating epigenetic mechanisms as central mediators of disease pathogenesis. For most of the history of AD epigenetics research, bulk tissue analysis has dominated, obscuring the fundamentally distinct epigenomic landscapes of individual brain cell types and masking cell-type-specific contributions to disease. Advances in single-cell and single-nucleus sequencing, fluorescence-activated nuclei sorting and multiplexed epigenomic platforms have transformed this landscape, enabling cell-type-resolved profiling of chromatin accessibility, DNA methylation, histone modifications and transcription across the major neuronal, glial and neurovascular populations of the human brain. Here, we review these advances, structured around the argument that cell-type resolution is not a methodological refinement but a conceptual necessity. We describe the distinct epigenomic programs disrupted in neurons, microglia, astrocytes, oligodendrocytes and neurovascular cells in AD, highlighting how each cell type responds to pathology. We discuss the discovery of epigenomic erosion, the progressive loss of cell-type-specific epigenomic identity across virtually all brain cell populations as AD advances, as a unifying disease mechanism linking chromatin dysregulation to cognitive decline. Finally, we identify critical gaps in current knowledge, including the near-complete absence of cell-type-resolved histone modification and DNA methylation data for most brain cell types, the underrepresentation of rare populations in standard preparations and the untapped potential of metabolic acylation marks as indicators of the epigenome-metabolism interface in neurodegeneration.

Humans↗
PubMed2026

Molecular and Genomic Mechanisms Linking Diabetes Mellitus and Periodontitis: From Pathogenesis to Translational Opportunities.

Diabetes mellitus and periodontitis are bidirectionally associated chronic disorders linked through metabolic dysregulation, host inflammation, microbial dysbiosis, and impaired tissue remodeling. This review summarizes clinical, molecular, cellular, genomic, epigenomic, transcriptomic, and microbial evidence concerning the mechanisms underlying this relationship and their potential translational relevance. Chronic hyperglycemia is associated with advanced glycation end product signaling through the receptor for advanced glycation end products, mitogen-activated protein kinase/nuclear factor-κB activation, reactive oxygen species production, oxidative stress, and NLR family pyrin domain-containing 3 inflammasome activation, which may contribute to enhanced cytokine responses and periodontal tissue injury. Diabetes is also associated with altered neutrophil and macrophage function, increased T helper 17/interleukin-17 signaling, and an elevated receptor activator of nuclear factor-κB ligand/osteoprotegerin ratio, thereby favoring osteoclastogenesis and alveolar bone loss. Conversely, periodontal inflammation and microbial products may contribute to systemic low-grade inflammation, insulin resistance, and metabolic dysregulation. Multi-omics studies have identified shared susceptibility loci, regulatory networks, and disease-associated cell states, although their causal and clinical significance remains incompletely defined. These findings suggest potential roles for integrated medical-dental care, glycemic screening in dental settings, periodontal inflammation control, host-modulatory therapies, and regenerative biomaterials. Further longitudinal and experimental studies are needed to determine their clinical applicability.

Humans↗