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At least 127 records · Page 7Linked to original sources
PubMed2026

Rbp-Jκ controls NK cell late maturation and migration via chromatin landscape remodeling.

The transcriptional regulator Rbp-Jκ is a pivotal molecular switch in Notch signaling; however, its cell-intrinsic role in natural killer (NK) cell maturation and migration remains incompletely understood. Here, we demonstrate that NK cell-specific deletion of Rbp-Jκ (Ncr1iCre × Rbp-Jκfl/fl, Rbp-JκΔNK) impairs NK cell terminal maturation and migration, as evidenced by increased retention of NK cells in bone marrow, a reduced number of circulating NK cells and decreased expression of migration mediators (CD62L, S1pr5, and Cx3cr1). Despite exhibiting an activated phenotype, Rbp-Jκ-deficient NK cells fail to control B16F10 lung metastases in vivo because of impaired tissue mobilization. Multiomics (scRNA-seq/scATAC-seq, bulk ATAC-seq, and CUT&Tag) reveal that Rbp-Jκ orchestrates chromatin remodeling in NK cells, suppressing the expression of genes related to NK cell activation and cytotoxicity while promoting the expression of genes involved in ribosome and oxidative phosphorylation. Notably, Rbp-Jκ directly binds to the Kruppel-like factor 2 (Klf2) promoter, and loss of Rbp-Jκ reduces both the mRNA and protein levels of Klf2. Klf2 overexpression rescues the decreased expression of CD62L and CX3CR1 in Rbp-Jκ-deficient NK cells. The cooccupancy of Rbp-Jκ and Klf2 at shared genomic loci is confirmed by ChIP-qPCR. In summary, our study reveals that Rbp-Jκ acts as a master regulator of NK cell terminal maturation and tissue homing via chromatin reprogramming, with Klf2 acting as its critical downstream transcription factor.

Animals↗
PubMed2026

In vivo genome-wide CRISPR screens identify FOXR1 as a suppressor of CD8+ T cell antitumor immunity.

T cell dysfunction critically limits the efficacy of T cell-based immunotherapies in solid tumors, yet the intrinsic regulators of T cell dysfunction remain incompletely understood. Through an in vivo genome-wide CRISPR screen in tumor-infiltrating CD8+ T cells, we identified Forkhead Box R1 (FOXR1) as a potent transcriptional suppressor of CD8+ T cell effector functions. Genetic ablation of FOXR1 significantly enhanced cytokine production and cytotoxic capacity in both murine and human CD8+ T cells, whereas its overexpression impaired T cell activation and effector molecule expression. Mechanistically, multiomics integration of RNA-seq, CUT&Tag-seq, and ATAC-seq revealed that FOXR1 binds directly to promoter regions of key effector genes, including IL2, GZMB, and PRF1, and represses their expression. Importantly, FOXR1 deletion in human anti-CD19 CAR T cells improved their efficacy against solid tumors, demonstrating that FOXR1 is a checkpoint of T cell effector function and targeting FOXR1 is a promising strategy to enhance CAR T cell efficacy against solid tumors.

Animals↗
PubMed2026

T-SMmOTE: tweaked synthetic majority minority oversampling technique for data scarcity issue in multi omics studies.

MOTIVATION: Multiomics data offer a rich data mine for modeling complex as well as day-to-day diseases, but their practical deployment is constrained by the limited sample availability. To this end, generating synthetic samples is a viable remedy. Extant schemes operating along this line, however, are mostly limited to augmenting the minority class in imbalanced datasets and often produce synthetic samples that lack sufficient diversity and fail to faithfully capture the underlying data distribution. As a result, the full potential of synthetic augmentation in multi-omics learning remains underexplored. The aim is to address the data scarcity problem in multi-omics domain. We propose a synthetic oversampling framework, which is dedicated to addressing overall data scarcity in multi-omics datasets and the lack of diversity in synthetic samples. Contrary to conventional methods that restrict augmentation to minority classes and rely on interpolation of two neighbors, our method generates diverse yet distribution-aligned synthetic samples by interpolating three neighbors and extends this augmentation paradigm to the majority class. The framework first balances the dataset by generating synthetic minority samples, and subsequently augments the balanced dataset by oversampling both majority and minority classes. RESULTS: Empirical evaluation on multi-omics data obtained from three heterogeneous health scenarios-inflammatory bowel disease, multi-organ dysfunction syndrome, and colorectal cancer-substantiates the utility of the proposed scheme in improving the predictive performance. The models trained on T-SMmOTE-augmented data achieve higher Matthews correlation coefficient values, along with improvedscores for both majority and minority classes. Notably, oversampling of the majority class improves the cognition of the minority class as well. We also explore the consistency of the class distributions between the original and augmented class-specific datasets. These findings confirm the capability of our scheme to learn from small, high-dimensional multi-omics datasets and highlight its potential for non-invasive disease detection. AVAILABILITY AND IMPLEMENTATION: https://github.com/payelu/TSMm.

Journal Article↗
PubMed2025

Integration of multi-source gene interaction networks and omics data with graph attention networks to identify novel disease genes.

MOTIVATION: The pathogenesis of diseases is closely associated with genes, and the discovery of disease genes holds significant importance for understanding disease mechanisms and designing targeted therapeutics. However, biological validation of all genes for diseases is expensive and challenging. RESULTS: In this study, we propose DGP-AMIO, a computational method based on graph attention networks, to rank all unknown genes and identify potential novel disease genes by integrating multi-omics and gene interaction networks from multiple data sources. DGP-AMIO outperforms other methods significantly on 20 disease datasets, with an average AUROC and AUPR exceeding 0.9. The superior performance of DGP-AMIO is attributed to the integration of multiomics and gene interaction networks from multiple databases, as well as triGAT, a proposed GAT-based method that enables precise identification of disease genes in directed gene networks. Enrichment analysis conducted on the top 100 genes predicted by DGP-AMIO and literature research revealed that a majority of enriched GO terms, KEGG pathways and top genes were associated with diseases supported by relevant studies. We believe that our method can serve as an effective tool for identifying disease genes and guiding subsequent experimental validation efforts. AVAILABILITY AND IMPLEMENTATION: DGP-AMIO is publicly available at https://github.com/yangkaiyuan1027/DGP-AMIO.

Gene Regulatory Networks↗
PubMed2026

Mice with the mono-allelic p.R37H Dhdds variant show aberrant glycosylation and interneuron deficits.

Developmental delay and seizures with or without movement abnormalities (OMIM 617836) caused by heterozygous pathogenic variants in the DHDDS gene (DHDDS-CDG) is a rare genetic disease that belongs to the progressive encephalopathy spectrum. It results in cognitive delay in affected children, accompanied by myoclonus, seizures, ataxia and tremor, which worsens over time. DHDDS encodes a subunit of a DHDDS/NUS1 cis-prenyltransferase (cis-PTase), a branch point enzyme of the mevalonate pathway essential for N-linked glycosylation. We describe the first mouse model of this disease, DhddsR37H+/- strain, heterozygous for the human recurrent de novo c.110G>A:p.R37H pathogenic variant. DhddsR37H+/- mice present with seizures, myoclonus and memory deficits associated with reduced density or/and maturity of inhibitory interneurons in the cortex. Multiomics analyses of mouse CNS tissues, together with the enzymatic/structural characterization of the R37H DHDDS mutant protein, reveal that the variant produces a catalytically inactive enzyme and results in a brain dolichol deficit, aberrant glycosylation of brain glycoproteins, including those involved in synaptic transmission and major perturbations in the CNS proteome and lipidome. Acetazolamide, a carbonic anhydrase inhibitor clinically approved for treatment of glaucoma, epilepsy, and intracranial hypertension, and successfully used "off-label" to treat genetic movement disorders, reduces seizure susceptibility to pentylenetetrazol in DhddsR37H+/- mice, suggesting potential therapeutic value of using this drug in human DHDDS-CDG patients. Together, our results define cis-PTase as a master regulator of CNS development and function and establish that its monoallelic debilitating variants cause a novel congenital disorder of glycosylation associated with aberrant levels of neuronal proteins and lipids.

DHDDS↗
PubMed2026

Genomic characterization of aggressiveness in pituitary neuroendocrine tumors.

BACKGROUND: Aggressive evolution of PitNETs is rare; metastatic spread is even more. Defining aggressiveness and malignancy is challenging, subsequently hard to predict, and to understand. The aim was to provide a molecular definition of aggressiveness using genomic approaches. METHODS: PitNETs from 206 patients were included. Associations between 9 clinicopathological features of aggressiveness and PitNETs' omics were explored. Omics included transcriptome, DNA methylation, chromosomal alterations, and mutations. Clonal tumor evolution was monitored in 7 patients. RESULTS: Among the 9 clinicopathological features of aggressiveness, only rapid progression, progression after radiotherapy, Ki67/MIB1 proliferation index ≥10%, temozolomide treatment, metastases, and specific death were associated with specific omics signatures, while tumour maximal diameter ≥40 mm, cavernous, and sphenoid invasion were not. The omic signatures associated with these features of aggressiveness overlapped but remained distinct between corticotroph and mammo-somato-thyrotroph lineages. For each lineage, a common signature of aggressiveness was identified, associating a proliferative transcriptome signature and DNA hypermethylation. Alterations in specific genes were associated with aggressive features, including a novel PitNET gene, LRP1B, and known cancer genes (TP53, CDKN2A), while USP8 and GNAS alterations were not. Integration of gene alterations with methylome and transcriptome signatures isolated a subset of molecularly aggressive PitNETs. Molecular signatures were stable during the course of the disease, despite evolution toward aggressiveness and potential clonal divergence. CONCLUSION: This systematic analysis of clinicopathological features of aggressiveness using an integrated multiomic approach establishes a histomolecular definition of aggressiveness in PitNETs. Prospective cohort studies are needed to validate these molecular signatures and establish their prognostic value.

Humans↗
PubMed2026

Natural variants of CsSHN1 orchestrate a temporal regulatory cascade driving fruit skin netting in cucumber.

Fruit skin netting (russeting, Rs) forms when epidermal microcracks are sealed by a suberized periderm, reducing marketability. We previously identified the Rs locus (CsSHN1), which encodes an AP2/ERF transcription factor, as a major determinant of cucumber skin netting, but how fruit growth is temporally coupled to periderm formation remains unclear. Here, we integrated population genomics, time-series multiomics, DNA affinity purification sequencing (DAP-seq), and transgenic assays to decode the CsSHN1-mediated regulatory network. Six functionally relevant CsSHN1 variants were identified across 325 cucumber accessions. Allele distribution and selective sweep analyses revealed breeding-driven selection for smooth fruit skin. Overexpression of a netted allele in a smooth background induced epidermal fissures, altered cell geometry, and increased fruit size, demonstrating a dosage-sensitive effect. Time-series transcriptomics and metabolomics of near-isogenic lines (NILs) defined 3 developmental phases of netting: early suppression of lignin and trehalose genes preceding cracks, growth-driven fissuring accompanied by cell-wall remodeling and defense activation, and maturation-stage cell-wall degradation with strong induction of ligno-suberin biosynthesis. Across the cucumber genome, DAP-seq identified approximately 8,000 in vitro CsSHN1 binding sites. These binding sites were significantly enriched for the GCC-box motif and included genes involved in cutin and suberin biosynthesis. Together, these results show that CsSHN1 orchestrates fruit skin netting through a growth-coupled temporal regulatory cascade, providing a mechanistic framework for manipulating fruit epidermal properties.

Cucumis sativus↗
PubMed2026

Flavonoid biosynthesis mediated by GmF3Hs contributes to drought tolerance in soybean.

Flavonoids are central to abiotic stress responses, yet the specific signaling roles and evolutionary dynamics of flavonoid biosynthetic intermediates in crop drought adaptation remain elusive. Here, we demonstrate that dihydrokaempferol (DHK) and dihydroquercetin (DHQ), specific intermediate products of the soybean flavanone 3-hydroxylases GmF3H1/2, function as potent signaling molecules that mitigate drought stress. Exogenous DHK/DHQ promoted abscisic acid-dependent stomatal closure and enhanced drought tolerance across diverse dicot species, including soybean and tobacco, highlighting a broadly conserved stress-mitigating signaling mechanism. CRISPR/Cas9-generated gmf3hs double mutants exhibited severe drought hypersensitivity due to compromised redox homeostasis and defective stomatal regulation, which could be specifically rescued by DHK/DHQ application. Furthermore, the loss of GmF3H triggered a distinct reproductive trade-off under stress, leading to increased pod initiation but severe filling defects. Multiomics network analysis revealed extensive rewiring of broader stress-responsive pathways and identified upstream transcription factors, among which GmPHL11 directly binds to and activates the GmF3H1 promoter; overexpression of GmPHL11 promoted DHK accumulation and enhanced drought stress tolerance in soybean hairy roots. Finally, population genomic analyses demonstrated that the GmF3H1H1 haplotype, which confers superior enzymatic activity and robust root growth under drought stress, might have undergone positive selection during soybean domestication. Collectively, our findings redefine the role of GmF3H-derived specific intermediates as potent signaling molecules, providing comprehensive mechanistic and evolutionary insights into flavonoid-mediated drought resilience, developmental trade-offs, and molecular breeding in crops.

Drought Resistance↗
PubMed2026

An Integrated Proteomics and Genomics Approach to Identify Essential Protein Kinases During Human Trophoblast Development.

In the developing human placenta, three subtypes of trophoblast cells, cytotrophoblasts (CTBs), extravillous trophoblasts (EVTs), and syncytiotrophoblasts (STBs), mediate critical functions essential for a successful pregnancy. CTBs constitute the stem/progenitor compartment and differentiate into STBs and EVTs within the floating and anchoring villi, respectively. STBs establish the maternal-fetal exchange interface and secrete human chorionic gonadotropin (hCG), a hormone vital for the maintenance of early pregnancy. EVTs anchor the maternal endometrium and invade the uterine tissue to remodel maternal cells, supporting implantation and progression of pregnancy. In this study, we used human trophoblast stem cells (hTSCs) as a model system and performed quantitative, label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) to profile the proteome and phosphoproteome in TSC stem state (analogous to undifferentiated CTBs) and following their differentiation to STBs and EVTs. Through a multiomics approach, we integrated our proteomics data with global gene expression profiles to correlate cell-type specific gene and protein expression during human trophoblast development. We also identified global phosphoproteome and analyzed kinases that are specifically active in hTSC stem state, as well as in differentiated STBs and EVTs. We experimentally validated specific kinases, such as BUB1B, PAK6, PKYMT1, and TNIK, that are essential for maintaining the hTSC stem-state. Additionally, atypical protein kinase C isoforms PKCζ are essential for STB development, whereas PTK2B, SRC, TRIO, and LYN are important for EVT development. Our findings highlight key kinases uniquely required for specific stages of trophoblast development during human placentation and suggest that pharmacological inhibition of these kinases could negatively impact the placentation process during pregnancy.

Humans↗
PubMed2025

Pharmacogenomic insights into angiotensin converting enzyme inhibitors and calcium channel blockers for personalized hypertension treatment.

Arterial hypertension is a complex disorder influenced by extensive genetic variability, which contributes to interindividual differences in drug response by altering metabolism, transport, and receptor interaction. Current antihypertensive therapies effectively control arterial hypertension in only about half of patients, emphasizing the need for precise strategies. Genetic variation plays a crucial role in modulating drug response, and integrating this knowledge into clinical practice could significantly transform the management of hypertension through personalized medicine. This review examines the impact of genetic factors on the efficacy of antihypertensive drug classes, including angiotensin converting enzyme inhibitors and calcium channel blockers. It also examines advances in pharmacogenomic research that can aid in tailoring drug selection and dose adjustment based on genetic profiles. Beyond genomics, this review also highlights the impact of multiomics approaches, such as proteomics, metabolomics, and microbiomics, in advancing precision medicine and enabling a comprehensive, personalized approach to hypertension management. Pharmacogenomics can help refine hypertension care, improve patient outcomes, and reduce the burden of the disease. The future of hypertension treatment lies in precision medicine, where therapy is tailored to individual needs for effective and personalized management.

Humans↗
PubMed2026

HIV immunological nonresponders show low SKAP1 concentration and DNA hypermethylation in the SKAP1 promotor region.

OBJECTIVE: The aim of this study was to improve understanding of biological pathways underlying inadequate CD4 + T-cell restoration after initiating antiretroviral treatment as these so called Immunological nonresponders are at increased risk for morbidity and mortality while treatment options are lacking. DESIGN: We compared baseline multiomics data from 88 Immunological nonresponders and 1467 immunological responders that participated in the 2000HIV study, separated into a discovery and validation cohort. METHODS: We measured expression levels of 2367 plasma proteins, comparing the immunological responders and nonresponders. As this highlighted low Src kinase-associated phosphoprotein 1 (SKAP1) levels in Immunological nonresponders, we measured intracellular SKAP1 levels in CD4 + T-cells, investigated DNA methylation and assessed single-nucleotide polymorphisms (SNPs). We also explored whether HIV or CMV infection may influence SKAP1 expression. RESULTS: SKAP1 plasma levels were significantly lower in Immunological nonresponders in both cohorts. SKAP1 plasma concentrations reflected intracellular levels in CD4 + T-cells. DNA methylation analysis showed significant hypermethylation at the SKAP1 promotor region. Three SNPs close to the SKAP1 gene were associated with poor Immunological response. Preliminary data suggest that HIV or CMV may influence SKAP1 levels. CONCLUSION: Our data show decreased SKAP1 concentrations in immunological nonresponders, potentially driven by hypermethylation of the SKAP1 promoter. Downregulation of SKAP1, which is known to play a role in T cell proliferation and migration, may therefore contribute to the poor restoration of CD4 + cell count after ART.

Humans↗
PubMed2026

From genotype to phenotype: understanding the genetic basis of autism.

PURPOSE OF REVIEW: This paper covers some of the key findings on the topic of genetic influences in autism over the last 12-18 months, which consist of significant conceptual shifts and new insights from recent technological advances. RECENT FINDINGS: Autism is a highly heritable condition, with significant heterogeneity of the genes that influence the development of this condition. The heterogeneity of autism, and multiple pathways contributing to the development of an autistic phenotype, create challenges in our understanding, diagnosis, and management of this condition.Recent studies of common genetic variation and polygenic risk scores have focussed on resolving phenotypic heterogeneity and identifying meaningful autism subtypes. Rare-variant discovery has expanded across ancestries, the X chromosome, noncoding loci, structural variants, and tandem repeats, aided by long-read and pangenome-informed sequencing. Single-cell multiomics, spatial perturbation methods and human organoid models have connected genetic variation to cell-type-specific and developmental phenotypes, while also revealing substantial mutation-specific effects and methodological sensitivity. Genetic testing increasingly provides aetiological diagnoses and informs medical surveillance. Recent developments also illustrate the therapeutic potential of gene-first approaches for selected monogenic neurodevelopmental disorders. SUMMARY: Recent developments have expanded our understanding of the way the genetic basis of autism manifests phenotypically.

autism↗
PubMed2023

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning.

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

Preprint↗
PubMed2025

PARTAGE: Parallel analysis of replication timing and gene expression.

The human genome is partitioned into functional compartments that replicate at specific times during the S-phase. This temporal program, referred to as replication timing (RT), is co-regulated with the 3D genome organization, is cell type-specific, and changes during development in coordination with gene expression. Moreover, RT alterations are linked to abnormal gene expression, genome instability, and structural variation in multiple diseases, including cancer. However, mechanistic links between RT, large-scale 3D genome architecture, and transcriptional regulation remain poorly understood. A major limitation is that current approaches require the separate profiling of RT and transcriptomes from independent batches of samples, obscuring the complex co-regulation between the epigenome and transcriptome. Here, we developed PARTAGE, a multiomics approach that enables joint profiling of copy number variation (CNV), RT, and gene expression from the same sample, providing a more accurate integrative view of the complex relationships between RT and gene regulation.

Journal Article↗
PubMed2026

Unifying multimodal single-cell data with a mixture-of-experts β-variational autoencoder framework.

Multimodal single-cell assays profile complementary layers of cell state, but integration is complicated by modality mismatch, sparsity, and uneven cohort coverage. Here, we present Unified Variational Inference (UniVI), a scalable mixture-of-experts β-variational autoencoder that learns a shared latent space while preserving modality-specific structure. UniVI couples modality-specific encoders/decoders with a shared latent prior and a symmetric cross-modal alignment objective, enabling consistent integration of paired measurements without curated feature-link graphs or preannotated reference atlases; optional supervised heads can be added when labels are available. Across paired RNA-protein (CITE-seq) and RNA-chromatin (10x Genomics Multiome, SHARE-seq) data spanning human PBMCs and mouse back skin-a nonhematopoietic tissue with continuous differentiation hierarchies-UniVI produces coherent embeddings, improves label transfer, and enables cross-modal reconstruction and denoising. Extending to trimodal measurements, UniVI maintains robust three-way alignment among RNA, chromatin accessibility, and surface proteins (TEA-seq), and accommodates DNA methylation in a paired scNMT-seq mouse gastrulation proof-of-concept under beta-binomial likelihoods. Performance degrades gracefully under severe cell type imbalance and in the presence of modality-exclusive populations. In an acute myeloid leukemia mosaic design, a paired RNA-protein bridge anchors independent RNA-only and protein+genotype cohorts, revealing genotype-associated neighborhoods that sharpen with mutation-aware fine-tuning. UniVI thus provides a flexible, interpretable framework for multimodal integration across paired, trimodal, and mosaic study designs and supports practical reference-to-query projection in partially observed studies.

Journal Article↗
PubMed2025

Mechanistic roles of GmSWEET10a/b and GmSUT1 in the oil-protein balance in soybean mature seeds at transcriptional and metabolic levels.

Previous investigations indicated that the soybean (Glycine max) SUGARS WILL EVENTUALLY BE EXPORTED TRANSPORTER10a/b (GmSWEET10a/b) genes promote oil accumulation, while inhibiting protein accumulation in seeds. To clarify the mechanisms modulated by GmSWEET10a/b in mediating the oil and protein accumulations in soybean seeds, an integrated comparative multiomics was conducted using the double gmsweet10a,b mutant and wild-type (WT) embryos. Spatial metabolomic analysis revealed that gmsweet10a,b embryos were surrounded by a sugar-reduced seed coat and experienced a sugar-starvation state in embryonic tissues in vivo. The decreased sugar content in the gmsweet10a,b embryos reduced the availability of carbon skeletons required for oil synthesis and was associated with decreased expression levels of genes involved in sucrose metabolism, fatty acid biosynthesis, and triacylglycerol assembly. Meanwhile, the expression of genes encoding storage protein was induced in gmsweet10a,b embryos, when compared with WT. These changes resulted in decreased oil content and increased protein content in gmsweet10a,b embryos versus WT. In vitro sugar-starvation assay also supported the suppression of fatty acid biosynthesis and the enhanced storage protein accumulation in developmental embryo under sugar-starved conditions. Furthermore, the knockout of SUCROSE TRANSPORTER 1 (GmSUT1), which was upregulated in gmsweet10a,b embryos, significantly decreased the sugar level, resulting in lower oil content but higher protein content in gmsut1 embryos than WT ones. Our findings provided a mechanistic understanding of the modulation of sugar transport between seed coat to embryo by both GmSWEET10a/b and GmSUT1, which plays a pivotal role in balancing oil and protein accumulations in soybean mature seeds.

Seeds↗
PubMed2026

MYC-bound enhancer RNAs in cis regulate gene transcription and tumorigenesis.

Emerging evidence suggests that MYC binds RNAs, but its functional consequences remain unclear. Here, we integrate multiomics data and reveal that MYC broadly binds enhancer RNAs (eRNAs), which exhibit high cancer- and tissue-specific expression in cancer cell lines and patient tumors. Moreover, we developed a computational pipeline to identify potential cis-regulatory MYC-eRNA target genes, with most predicted eRNA-target pairs supported by RNA polymerase II-mediated chromatin interaction data. Among these, we functionally characterized MERG1 as an oncogenic eRNA that promotes breast cancer tumorigenesis. Mechanistically, MERG1 interacts with MYC to enhance its occupancy at the GREB1 promoter, driving chromatin remodeling and epigenetic activation. This process specifically amplifies GREB1 expression and promotes tumor progression. Last, nanoparticle-mediated delivery of antisense oligonucleotides targeting MERG1 suppresses MYC-mediated breast cancer growth. These results advance our understanding of the enhancer-driven regulation of gene expression and tumorigenesis and provide insights into the regulatory landscape of MYC in cancer.

Humans↗
PubMed2025

Stereo-cell: Spatial enhanced-resolution single-cell sequencing with high-density DNA nanoball-patterned arrays.

Single-cell sequencing technologies have advanced our understanding of cellular heterogeneity and biological complexity. However, existing methods face limitations in throughput, capture uniformity, cell size flexibility, and technical extensibility. We present Stereo-cell, a spatial enhanced-resolution single-cell sequencing platform based on high-density DNA nanoball (DNB)-patterned arrays, which enables scalable and unbiased cell capture at a wide input range and supports high-fidelity transcriptome profiling. Stereo-cell further allows integration with imaging-based modalities and multiomics strategies, including immunofluorescence and epitope profiling. This platform is also compatible with profiling extracellular vesicles, microstructures, and large cells, whereas its spatial resolution facilitates in situ analysis of cell-cell interactions, cellular microenvironments, and subcellular transcript localization. Together, Stereo-cell provides a flexible framework for expanding single-cell research applications.

Animals↗