- Research Article
- 10.1016/j.devcel.2026.01.007
Hypoxia-inducible factor signaling regulates embryonic interneuron development, GRIN2B expression and adult cortical function.
- Apr 08, 2026
- Developmental cell
- I-Ling Lu + 9 more +9
Publications from 2021 to 2026
Showing 10 of 492 papers
Hypoxia-inducible factor signaling regulates embryonic interneuron development, GRIN2B expression and adult cortical function.
Dominant clones leverage developmental epigenomic states to drive ependymoma.
ZFTA-RELA is the most recurrent genetic alteration seen in paediatric supratentorial ependymoma (EPN) and is sufficient to initiate tumours in mice1. Despite its oncogenic potential, ZFTA-RELA (ZR) is observed nearly exclusively in childhood EPN, with tumours located distinctly in the supratentorial brain of the central nervous system1. We proposed that specific chromatin modules accessible during brain development would render distinct cell lineage programs at direct risk of transformation by ZR. To test this hypothesis, we performed combined single-nucleus assay for transposase-accessible chromatin and RNA (snMultiome) sequencing of the developing mouse forebrain compared with ZR-driven mouse and human EPN. We demonstrated that specific developmental lineage programs present in transient progenitor cells and regulated by PLAG/L family transcription factors were at risk of neoplastic transformation. Binding of this chromatin network by ZR or other PLAG/L family motifs targeting fusion oncoproteins led to persistent chromatin accessibility at oncogenic loci and oncogene expression. Cross-species analysis of mouse and human ZR EPN revealed significant cell type heterogeneity indicating incomplete neurogenic and gliogenic differentiation, with a small percentage of cycling progenitor-like or radial glial-like cells that established a putative tumour cell hierarchy. In vivo lineage tracing studies identified neoplastic clones that aggressively dominated tumour growth and established the entire EPN cellular hierarchy. These findings identify developmental epigenomic states that are critical for fusion-oncoprotein-driven transformation and show how these states continue to shape tumour progression.
Read moreA blueprint to accelerate rare pediatric gene therapy approvals.
Engineering Human Retinal Organoids and Eye-on-a-Chip Models for Degenerative Eye Disease.
Degenerative eye diseases are major causes of irreversible vision loss worldwide, but effective treatments remain limited, partly due to the lack of effective human models. Retinal organoids derived from stem cells can recapitulate key structural and physiological features of the human retina, offering powerful tools to study disease mechanisms and develop new therapies. Here, we review recent progress in engineering retinal organoids and eye-on-a-chip models for modeling degenerative eye diseases, with a focus on engineering innovations. We first describe conventional methods for organoid differentiation and characterization along with current outstanding challenges. To better engineer retinal organoids, new strategies that leverage microfluidics and biomaterials have emerged to regulate dynamic and physiologically relevant environments for organoid differentiation. Moreover, the integration of artificial intelligence, multimodal sensing, and data analytics improves the monitoring and prediction of retinal function and therapeutic outcomes. Finally, we discuss future directions in innovating next-generation retinal organoid and eye-on-a-chip models for disease modeling, drug discovery, and vision restoration, highlighting their potential for precision ophthalmology.
Read moreQuantCell: machine learning based cell annotation integrating qualitative and quantitative imaging profiles.
Recent advances in spatial omics enable high-resolution, multiplexed imaging of RNA and protein expression, but cell annotation remains challenging, particularly in complex tissues with numerous markers or rare cell types. Here, we present QuantCell, a machine learning framework that leverages quantitative imaging data to improve annotation derived from qualitative profiles. QuantCell evaluates multiple models and applies a user-defined false discovery rate to ensure high-confidence annotation. Using PhenoCycler imaging of mouse bone marrow, QuantCell increased annotated cells from 33.1% to 90.2% at 5% FDR, achieving 96.5% accuracy. QuantCell supports diverse imaging platforms and robustly detects rare cell populations.
Read moreEngineering CAR T cells to secrete VEGF-neutralizing scFvs enhances antitumor activity against solid tumors.
Chimeric antigen receptor (CAR) T cell therapy has shown limited efficacy against solid tumors, which often reside in highly immunosuppressive tumor microenvironments (TMEs). TMEs can be highly abundant in vascular endothelial growth factor A (VEGF), which contributes to immunosuppression and abnormal tumor vasculature. Here, we found that CAR T cells engineered to secrete an anti-VEGF single-chain variable fragment (CAR-αVEGF T cells) achieved superior antitumor efficacy against multiple in vivo models of ovarian cancer and glioma, outperforming conventional CAR T cells with and without combination anti-VEGF antibody therapy. Microscopy, flow cytometry, and transcriptomic analyses revealed that armoring the CAR T cells with anti-VEGF single-chain variable fragments enhanced their activation and mitochondrial fitness and enriched immune-stimulatory signatures among endogenous immune cells in the tumor-bearing brain. Moreover, CAR-αVEGF T cells circumvented multiple detrimental effects associated with on-target CAR T cell therapy, including infiltration of suppressive myeloid cells, exaggerated vasculature abnormalities, and hypoxia. Together, our results provide rationale for the clinical translation of CAR-αVEGF T cells as a safe and potent therapy for solid tumors characterized by elevated VEGF.
Read moreMissenseHMM: state-based annotations for missense variants through joint modeling of pathogenicity scores
Many computational predictors of missense variant pathogenicity are available. To capture information across various predictors, we propose MissenseHMM, which learns states corresponding to combinatorial patterns of variant prioritizations. We applied MissenseHMM to 43 predictors, annotating over 70 million missense variants with 20 states that showed distinct predictor scores patterns, amino acid substitutions and other genomic annotation enrichments. MissenseHMM state annotations enhanced individual predictors’ associations with clinical pathogenic variants and deep mutational scanning data, and also provided insight into the performances of various protein language models. Overall, MissenseHMM complements pathogenicity predictors and is an annotation resource for missense variant interpretation.
Read moreChildhood brain tumors instruct cranial hematopoiesis and immunotolerance.
Recent research has challenged a long-held view of the brain as an immune-privileged organ, revealing active immunosurveillance with therapeutic relevance. Using a new genetically engineered mouse model of ZFTA-RELA ependymoma, a childhood brain tumor, we characterized an immune circuit between the tumor and antigen-presenting hematopoietic stem and progenitor cells (HSPCs) in the skull bone marrow. The presentation of antigens by HSPCs to CD4+ T cells biased HSPC lineages toward myelopoiesis and polarized CD4+ T cells to regulatory T cells, culminating in tumor immunotolerance. Remarkably, normalizing hematopoiesis with a single infusion of antibodies directed against cytokines enriched in the cerebrospinal fluid of mice bearing ZFTA-RELA ependymomas, choroid plexus carcinomas or group 3 medulloblastoma-all aggressive childhood brain tumors-disrupted this process and caused profound tumor regression. These findings demonstrate the existence of a skull bone marrow-tumor immunological interface and suggest that modulating the local supply of myeloid cells could represent a less toxic therapeutic strategy for aggressive childhood brain tumors.
Read moreA Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons
SummaryThe human basal ganglia (BG), subcortical nuclei fundamental to motor regulation and cognitive modulation, is constructed from neurons produced during gestation in the adjacent ganglionic eminences (GEs). GEs are transient structures in the ventral prenatal brain that also generate GABAergic inhibitory neurons which migrate to destinations in the BG, cortex and other destinations. This study aims to elucidate the epigenomic and 3D-genomic dynamics involved in the specification and maturation of GEs and GE-derived neurons, using single-nucleus methyl-3C sequencing (snm3C-seq), highly-multiplexed spatial transcriptomics, and chromatin+RNA single-molecule imaging. Our multi-modal data support a heterogeneous temporal progression across GE subregions, with the lateral GE (LGE) showing declining neurogenic activity in mid-gestation and caudal GE (CGE) exhibiting ongoing developmental progression through infancy. We identified regulatory programs that specify subtypes of BG principal cells, medium spiny neurons (MSN), via synchronized maturation of the 3D-epigenome. In infant brains, we found a transient short-range enriched (SE) chromatin conformation during the transition between oligodendrocyte progenitors (OPCs) and oligodendrocytes (ODCs), and a temporary shift toward Long-range Enriched (LE) chromatin conformation in projection neurons, extending previous works showing the differentiation of neurons and glial cells is associated with permanent SE and LE conformation, respectively. Lastly, we found that gene regulatory regions active in MSNs were enriched in loci associated with genetic risk for neuropsychiatric disease. Our study delineates the highly complex, lineage-specific 3D genomic dynamics in ventral progenitors and basal ganglia populations of the perinatal human brain.
Read moreMechanistic Language Modeling and Oxygenated 3D Screening Reveal Berberine and Enzalutamide Synergy in Resistant Prostate Cancer
Resistance to androgen receptor inhibitors remains a primary challenge in prostate cancer treatment, yet identifying synergistic co-therapies is hindered by immense combinatorial search spaces and the limited interpretability of predictive computation models. Here, we developed an integrated discovery-validation axis coupling knowledge-augmented large language models with oxygen-supplemented 3D spheroid assays. By leveraging inherent model stochasticity, our framework measures the degree of consensus across independent predictions to establish a formal metric for predictive accuracy. This principle enables high-throughput assessment of complex signaling crosstalk, yielding mechanistic rationales for all predictions and defining a high-confidence zone that minimizes experimental attrition. Utilizing this approach to screen 3,592 natural products, we identified a previously unrecognized synergy between berberine and enzalutamide that re-sensitizes resistant cells. Validation confirms that berberine perturbs the PI3K/AKT/mTOR and AMPK axes, a finding consistent with the mechanistic rationales computationally derived by the framework. Integrating interpretable AI with physiologically relevant 3D screening provides a scalable methodology for the rational discovery of synergistic therapies.
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