Biology subjects

Yang, X.

Publications and source records attributed to Yang, X..

At least 127 records · Page 7Linked to original sources

bioRxiv2026

Complete Telomere-to-Telomere Assembly of the Y Chromosome in the Chinese Quartet

The complete assembly of the human Y chromosome remains a formidable task due to its highly repetitive, palindromic, and heterochromatic architecture. While complete telomere-to-telomere (T2T) assemblies have recently been generated for select individuals, high-quality, gapless Y chromosome references for East Asian lineages--specifically within well-characterized multi-omics reference cohorts--remain scarce. The Chinese Quartet (CQ), comprising monozygotic twin daughters and their parents, represents a premier national reference material system for genomic standardization. However, because the female offspring inherited their father's X chromosome rather than his Y chromosome, previous pedigree-based T2T assemblies (CQ v3.2) lacked a gapless Y chromosome sequence. Here, we present a complete, gapless T2T assembly of the Y chromosome (designated CQ_chrY) from the father of the CQ. This assembly was generated by integrating Oxford Nanopore Technologies ultra-long reads, PacBio high-fidelity reads, and Hi-C data, resulting in a sequence of 68.50 Mb. The assembly shows exceptional base accuracy (QV = 57.54) and structural assembly quality (CRAQ AQI = 99.781). We completely resolved the 40.09 Mb Yq12 heterochromatic region, a ~940 kb centromeric higher-order repeat array, and annotated 96 protein-coding gene models evaluated against GENCODE/T2T-Y standards, alongside 57.59 Mb (84.07%) of repetitive elements. CQ_chrY serves as a complementary paternal resource extending the CQ reference platform, providing a robust foundation for population genetics, structural variation analysis, and East Asian genomic standardization.

genomics↗

bioRxiv2026

Interactions between the myosin Dachs, the adaptor Dlish, and the palmitoyltransferase Approximated mediate Fat-Dachsous signaling

Bidirectional signaling mediated by heterophilic binding between the giant Drosophila protocadherins Fat and Dachsous (Ds) limits growth through the Hippo pathway and, via patterned binding and cell-by-cell polarization, influences planar cell polarity (PCP) and fate choices along major tissue axes. Prior work showed that the signal transduction initiated by the intracellular domains (ICDs) of Fat and Ds changes the localization and levels of three unusual binding partners: the type XX atypical myosin Dachs, the SH3 domain containing adaptor Dlish, and the DHHC palmitoyltransferase Approximated (App). However, the complex interactions between the three proteins and Fat and Ds are less well understood. Our evidence shows that these proteins play overlapping but distinct roles regulating each other and mediating signaling. Dlish localization and activity requires App binding and palmitoylation. Palmitoylated Dlish helps tether Dachs to the cortex but is also needed to couple Dachs to stabilization by the Ds ICD and destabilization by the Fat ICD. In contrast, Dachs does not localize Dlish but protects it from degradation. Our results also indicate that Fat does not act by changing App levels or localization. We discuss an alternative model based on Dlishs proposed role as an adaptor for E3 ubiquitin ligases.

developmental biology↗

bioRxiv2026

Ultra-large targeted DNA integrations in primary human cells

Genetic engineering experiments and therapies are constrained by the size of DNA integrations into human cells genomes. Existing AAV, lentiviral, and non-viral methods rapidly decrease in integration efficiency beyond [~]5kb of sequence. Through systematic evaluation of non-viral DNA template formats, we identified circular ssDNA and dsDNA as capable of mediating >5kb integrations. Large circular DNA delivery efficiency and its impacts on cell viability and payload expression could be significantly improved with small DNA "helper" plasmids, mRNA-encoded nucleases, and sequence design optimizations. Collectively, these modifications enabled ultra-large--up to 10 kb DNA--integrations at >20% efficiency in primary human T cells at the TRAC locus and at >60% efficiency in human iPSCs at the AAVS1 locus. Finally, we demonstrate that GMP clinical-manufactured T cells with ultra-large integrations are functional in vitro and in vivo. Overall, we identified optimal template architectures, delivery modes, and sequence design rules for ultra-large DNA integrations in both research and clinical settings to accelerate basic genetic research and next-generation cellular therapies.

genetics↗

bioRxiv2026

Network-Level Associations in Nonlinear Brain Dynamics Predict Transcendent Thinking in a Diverse Adolescent Sample

Transcendent thinking (TT) is an enduring affective and cognitive process characterized by abstract meaning-making, moral reflection, self-referential integration, and strong emotional engagement. Despite growing interest in its developmental and affective significance, the intrinsic neural dynamics that predict individual differences in disposition to TT remain poorly understood. Most prior work has relied on linear functional connectivity measures, which may be insufficient to capture the nonlinear and multiscale nature of brain dynamics underlying higher-order affective dispositions like TT. Here, we introduce a nonlinear functional brain network (FBN) framework based on multiscale entropy (MSE) to investigate whether intrinsic resting-state nonlinear brain dynamics predict disposition to TT in adolescents. Functional connectivity was defined as inter-regional similarity in MSE profiles derived from resting-state fMRI, yielding weighted networks that capture scale-dependent dynamical correspondence rather than linear synchrony. Graph-theoretical, spectral, and information-theoretic measures were computed and evaluated against signal-level and network-level null models. Predictive performance was assessed using machine-learning models and compared with conventional time series-based FBNs. Global intelligence (IQ) was examined as a control cognitive variable. MSE-based network features, particularly spectral energy and Shannon entropy, showed significant associations with TT and enabled reliable prediction of individual differences, whereas time series-based network measures failed to predict TT. No network measures reliably predicted IQ. Overall, these results indicate that intrinsic nonlinear brain dynamics carry predictive information about affective dispositions, rather than domainspecific or network-localized cognitive abilities such as IQ. This work demonstrates that nonlinear, multiscale network representations of resting-state brain activity provide a principled and predictive framework for modeling individual differences in enduring affective dispositions.

neuroscience↗

bioRxiv2026

STDrug enables spatially informed personalized drug repurposing from spatial transcriptomics

Drug repurposing offers a scalable route to accelerate therapeutic discovery, yet existing approaches based on single-cell RNA sequencing (scRNA-seq) often overlook spatial tissue context, limiting their ability to capture microenvironment-dependent drug responses. Here we present STDrug, a spatially informed computational framework that integrates spatial transcriptomics, graph-based modeling, and multimodal learning to enable patient-specific therapeutic prioritization. STDrug identifies and aligns disease and control spatial domains using graph convolutional networks and coherent point drift, and prioritizes candidate drugs through an integrative scoring scheme combining tumor-reversible gene signatures, perturbation-based reversal scores, and knowledge-guided gene weighting within a machine learning framework. By modeling spatial domain interactions alongside predicted drug efficacy and toxicity, STDrug generates robust patient-level drug scores. Across hepatocellular carcinoma and prostate cancer datasets, STDrug outperforms existing single-cell and spatial transcriptomics-based drug repurposing methods, achieving signficantly improved predictive accuracy (AUCs=0.81-0.82) across patients. Validation using large-scale electronic health records and in vitro assays further supports the translational relevance of top-ranked candidates. Taking together, STDrug establishes a generalizable framework for incorporating spatial omics into therapeutic discovery, advancing spatially informed and personalized drug repurposing.

bioinformatics↗

bioRxiv2026

Targeting TPO/MPL Signaling to Mitigate JAK2V617F-driven Cardiac Microvascular Disease

BackgroundIndividuals with JAK2V617F-mutant myeloproliferative neoplasms or clonal hematopoiesis of indeterminate potential have a markedly increased risk of cardiovascular disease, yet the mechanisms by which mutant blood cells drive vascular and cardiac dysfunction remain incompletely understood. Although the thrombopoietin (TPO) receptor MPL is central to hematopoiesis and is expressed in vascular endothelial cells (ECs), its role in JAK2V617F-associated cardiovascular complications is unknown. Methods and ResultsWe generated chimeric mice with JAK2V617F-mutant blood cells and wild-type endothelium by bone marrow transplantation and challenged them with a high-fat/high-cholesterol diet to model cardiometabolic stress. These mice developed a distinct cardiovascular phenotype characterized by microvascular disease, increased left ventricular mass, and relatively preserved left ventricular ejection fraction. Histological analysis revealed coronary arteriole stenosis, perivascular fibrosis, reduced microvascular density, and endocardial injury, without evidence of epicardial coronary stenosis or myocardium infarction. Single-cell RNA sequencing revealed activation of inflammatory, stress-response, and endothelial-to-mesenchymal transition gene signatures in ECs, most prominently within the endocardial ECs. Immunohistochemistry identified MPL expression predominantly in endocardial ECs. TPO/MPL signaling was upregulated in endocardial ECs in mice with JAK2V617F-mutant hematopoiesis, and treatment with an anti-MPL neutralizing antibody markedly improved cardiac pathology, restored endocardial integrity, and increased coronary microvascular density despite persistent systemic inflammation. ConclusionsJAK2V617F-mutant hematopoiesis induces coronary microvascular dysfunction. Endocardial ECs represent a key cellular target under cardiometabolic stress, and endocardial MPL signaling constitutes a potential targetable pathway in JAK2V617F-associated cardiovascular disease. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/715884v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@15dcebdorg.highwire.dtl.DTLVardef@1b00a2eorg.highwire.dtl.DTLVardef@6c6f98org.highwire.dtl.DTLVardef@1128f75_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIJAK2V617F-mutant hematopoiesis induces cardiac microvascular disease C_LIO_LIMPL is expressed in endocardial ECs and MPL inhibition restores endocardial integrity and improves cardiac microvascular function C_LI

cell biology↗

bioRxiv2026

Accurate detection of mosaic mutations at short tandem repeats from bulk sequencing data

Short tandem repeats (STRs) are among the most mutable regions of the human genome, yet their somatic mosaicism remains poorly characterized due to the technical challenges of distinguishing genuine mutations from high intrinsic polymorphism and sequencing noise. Here, we introduce BulkMonSTR, a computational framework that combines STR-specific error modelling with machine-learning classification to enable accurate detection of mosaic STR mutations from bulk next-generation sequencing data. BulkMonSTR identifies nucleotide-resolution mutations--including insertions, deletions, and single-nucleotide variants (SNVs)--and supports both control-independent and case-control study designs. Leveraging a comprehensive training dataset derived from pedigree-based validation and in silico spike-in simulations, our random forest classifier effectively discriminates true mosaic events from germline variants and technical artifacts. Benchmarking on simulated and real datasets demonstrates that BulkMonSTR achieves substantially improved precision and F1 scores across diverse coverages and variant allele frequencies. In normal samples, cancer samples and controlled in silico mixing experiments, BulkMonSTR consistently outperforms existing methods, capturing a broader spectrum of STR mutations--including those arising on non-reference alleles--while achieving high validation rates. By enabling systematic, genome-wide interrogation of STR mosaicism, BulkMonSTR provides a scalable foundation for investigating the contributions of somatic STR mutations to aging and disease.

bioinformatics↗

bioRxiv2026

Single-cell spatiotemporal transcriptomics reveals the developmental dynamics and regulatory network of poplar seed fibers

Poplar seed fibers cause environmental and health concerns, yet their developmental mechanisms remain poorly understood. Here, we constructed a high-resolution spatiotemporal transcriptomic atlas of female poplar capsules by integrating single-nucleus and spatial transcriptomics. We delineated the developmental trajectory of seed fibers, confirming their origin from placenta cells, and identified three functionally distinct fiber cell subtypes involved in initiation, metabolic support, and elongation. Weighted gene co-expression network analysis (WGCNA) identified several hub transcription factors, including PtoMYB, PtoHDT1, PtoEIF6 and PtoPDF2, that may serve as key regulators of fiber development. Our study provides a cellular-resolution framework for understanding trichome development in woody perennials and offers candidate targets for functional characterization toward breeding low-fluff poplar cultivars. HighlightsO_LIA spatiotemporal transcriptomic atlas of poplar capsule development is constructed at single-cell resolution C_LIO_LIFiber cells originate from placenta cells and comprise three functionally distinct subtypes C_LIO_LIProvides molecular targets for breeding low-fluff poplar cultivars to mitigate environmental pollution C_LI

plant biology↗

bioRxiv2026

eBiota: Designing microbial communities from large seed pools with desired function using rapid optimization and deep learning

Designing microbial communities to generate target products is crucial for biotechnology, agriculture, and disease treatment. However, rationally designing such communities from large seed pools has become a major challenge, as the rapidly expanding number of complete microbial genomes greatly expands the search space and sharply increases the required screening time and computational cost. Here, we introduce eBiota, a platform for ab initio design of microbial communities from a pool of 21,514 strains to generate target products. eBiota not only identifies optimal strain combinations but also simulates community behaviors, including microbial interactions and relative abundances. eBiota integrates three modules: CoreBFS, a graph-based search algorithm that rapidly screens for bacteria with complete metabolic pathways related to the target product; ProdFBA, an extended flux balance analysis that identifies microbial consortia with maximal production efficiency; and DeepCooc, a deep learning model trained on 23,323 microbiome samples across various environments to infer co-occurrence patterns. We validated eBiotas capabilities in microbial community design and production efficiency calculation using public microbiome datasets, ranging from single strains to six-member consortia. Further in vitro experiments involving 94 strains confirmed eBiotas ability to identify species that inhibit pathogen growth and to accurately model the relative abundances within complex microbial communities. As an initial digital twin, eBiota provides a powerful platform for the rational design of functional microbial communities, offering new opportunities for metabolic engineering and synthetic biology.

bioengineering↗

bioRxiv2026

MEIsensor: a deep-learning method for mobile element insertion discovery

Mobile element insertions (MEIs) are a critical source of structural variation in the human genome, yet their accurate detection remains challenging, particularly within repetitive regions and for structurally complex insertions. While long-read sequencing enables the direct recovery of inserted sequences, current analytical pipelines rely heavily on repeat-library alignment, which can limit both computational efficiency and accuracy. Here, we present MEIsensor, a deep learning framework designed to detect and classify MEIs directly from long-read sequencing data. Unlike conventional methodologies, MEIsensor performs direct, sequence-based annotation of Alu, LINE1, and SVA insertions. Evaluated against HGSVC benchmarks using HiFi long-read datasets, MEIsensor consistently outperformed existing tools across major MEI classes while substantially improving computational efficiency, demonstrating particularly pronounced gains for structurally complex SVA insertions. Furthermore, manual curation confirmed that MEIsensor successfully identifies MEIs hidden in highly repetitive genomic regions, including centromeric higher-order repeats, that were previously absent from benchmark datasets. Together, these advancements establish MEIsensor as an efficient framework for MEI analysis that promises to significantly advance our understanding of human genomic architecture and evolution.

bioinformatics↗

bioRxiv2026

An interferon independent innate immune response to double stranded RNA in embryonic stem cells

In vertebrate early embryos and embryonic stem cells, the interferon (IFN)-centered double-stranded RNA (dsRNA) sensing and signaling pathway is markedly suppressed, implying the existence of an alternative repertoire of dsRNA sensors in these stages. We recently reported that dsRNA treatment triggers translation inhibition using Prkra as a sensor in zebrafish and mouse early embryos. Independently, here we show that dsRNA stimulates the expression of a subset of interferon-stimulated genes (ISGs) in the absence of IFN production, establishing a defensive state in mouse embryonic stem cells (mESCs). Upon dsRNA stimulation, the multifunctional DExD/H-box RNA helicase Dhx9 is recruited into dsRNA-induced condensates, where it promotes the recruitment and functional suppression of the Mdm2/Cul4A ubiquitin ligase machinery by excluding their substrate adaptor Ddb1, thereby stabilizing p53 and Stat1. Dhx9, p53 and Stat1 then cooperate to stimulate an ISG response. This signaling is important for the defense against ZIKV infection in mESCs as well as other dsRNA stresses. Such a cascade is conserved in human ESCs, while in zebrafish embryos, p53 but not Stat1 is required for the transcriptional response. Our findings define an immediate, cell-autonomous innate immune pathway operating in ESCs and vertebrate embryos.

immunology↗

bioRxiv2026

Nonlinear trajectories of language network development

How the human brain organizes complex cognitive functions remains unresolved, particularly regarding the debate between localized and distributed architectures. Here, we show that the language network undergoes a non-linear developmental reorganization that reconciles these views. Using multimodal neuroimaging and behavioral measures, we identify a three-stage trajectory: early localization, a transiently distributed state during adolescence marked by a connectivity dip, and a return to refined localization in adulthood. This adolescent dip is behaviorally meaningful and contributes to integrative network architecture. Convergent shifts in functional connectivity and brain-behavior relationships identify adolescence as a critical window for large-scale network remodeling. Our findings provide a unifying framework for language network development and suggest that transient redistribution may represent a general principle of human brain maturation.

neuroscience↗

bioRxiv2026

Complete Genomes of Cultivated Gut Bacteria Reveal Mobile Genetic Element-Driven Functional Diversity with Therapeutic Implications

Cultivated bacterial isolates are essential for elucidating gut microbiota functions, yet reliance on fragmented draft genomes and limited mobile genetic element (MGE) annotation has constrained high-resolution analyses. Here, we present the Cultivated Complete Genome Reference (CCGR), a comprehensive compendium of 1,150 fully circularized human gut bacterial genomes. Overcoming draft limitations, CCGR achieves chromosome-scale resolution and reveals distinct evolutionary strategies shaping gene topological organization in response to bacterial growth. Furthermore, we decode the dynamic landscape of MGEs, demonstrating that broad-host-range phages follow strict positional patterns by integrating at conserved chromosomal hotspots flanked by nutrient transporter loci. Importantly, we provide mechanistic in vivo evidence that accessory genomic elements drive strain-level functional heterogeneity. Specifically, the plasmid-encoded scrK gene in Levilactobacillus brevis involved in fructose catabolism is required to mitigate fructose-diet-exacerbated colitis. Collectively, CCGR establishes a framework linking chromosomal architecture and MGEs to bacterial evolution and host health, enabling precise, strain-resolved functional investigations.

microbiology↗

bioRxiv2026

Long-read metagenomic sequencing reveals novel lineages and functional diversity in urban soil microbiome

City parks and other urban green spaces can bring significant benefits to the physical and mental health of city residents. However, there is limited knowledge about the microbial communities inhabiting these urban soils. Here, we applied long-read metagenomic sequencing to 58 urban soil samples from two major cities in China, enabling genome-resolved reconstruction of microbial diversity at unprecedented contiguity. We recovered 7,949 medium- and high-quality metagenome-assembled genomes, comprising 4,171 species-level genome bins, of which over 97% represent previously undescribed species. Long-read assemblies revealed extensive secondary metabolic capacity, including more than 30,000 biosynthetic gene clusters, which were highly contiguous compared with those from fragmented short-read assemblies. Beyond secondary metabolism, we uncovered over 2 million small protein families, including hundreds that are strongly enriched in the neighbourhood of defense systems and mobile genetic elements, highlighting their overlooked role in urban soils. These findings expand our understanding of the functional diversity of urban soil microbiomes and provide new insights with implications for urban public health.

bioinformatics↗

bioRxiv2026

Differentiable Gene Set Enrichment Analysis for Pathway-Level Supervision in Transcriptomic Learning

In transcriptomics-driven drug discovery, upstream predictors of chemical-induced transcriptional profiles (CTPs) are typically trained with gene-wise objectives, whereas downstream interpretation relies on pathway-level, rank-based statistics such as Gene Set Enrichment Analysis (GSEA). This objective mismatch destabilizes pathway conclusions under prediction errors: small ranking perturbations can flip enrichment direction or distort pathway ordering. To bridge this gap, we present differentiable GSEA (dGSEA), a training-compatible surrogate that maps predicted gene-level scores to pathway enrichment with well-behaved gradients. Technically, dGSEA replaces discrete ranking operations with temperature-controlled soft sorting, smooth prefix accumulation, and differentiable extremum aggregation. Critically, to preserve the statistical semantics of classical GSEA, we introduce sign-specific robust permutation normalization (dNES) with optional{kappa} -calibration. For computational efficiency, a scalable Nystrom-window approximation (nyswin) reduces the quadratic bottleneck to near-linear complexity, enabling genome-scale evaluation. Empirically, across synthetic benchmarks and LINCS L1000 signatures, dGSEA matches classical GSEA accuracy with improved numerical stability. When incorporated as an auxiliary objective for SMILES-to-transcriptome prediction, dGSEA improves pathway-level agreement (macro correlation 0.257 [->] 0.306; sign accuracy 0.620 [->] 0.641) without compromising gene-level performance, providing a practical mechanism for pathway-aware optimization in transcriptomic prediction pipelines.

bioinformatics↗

bioRxiv2026

Reconstituting Mouse Embryogenesis Ex Utero from Gastrulation to Fetal Development Reveals Maternally Independent Metabolic Programs

Mammalian development takes place inside the maternal uterus, creating technological constraints that make difficult the study of embryogenesis in live developing embryos. A central challenge for understanding the role of metabolism in mammalian development is discriminating placental and uterine-regulated signals from embryo-intrinsic processes independent of maternal influence, a process that until now has remained inseparable during gastrulation and organogenesis1-3. Ex utero culture systems allowing continuous growth of embryos during pre-gastrulation to organogenesis4,5 offer a promising solution to this challenge. Here, we present optimized ex utero culture platforms that support faithful development of mouse embryos from gastrulation (embryonic day 6.5/7.5) through the fetal period (embryonic day [~]12.5) and harnessed these platforms for dissecting metabolic transitions in vivo during embryogenesis independently of uterus and placenta. We characterized the metabolome of in utero and ex utero whole embryos, fetal organs and culture medium between embryonic days E6.5 and E12.5 by liquid chromatography mass-spectrometry (LC-MS) metabolomics, isotope tracing, and single cell transcriptomics. These datasets present a comprehensive overview of the dynamic embryonic metabolism during gastrulation and organogenesis in utero and ex utero. This analysis revealed that the midgestational metabolic switch occurring at E10.5-E11.5 is faithfully recapitulated ex utero, indicating that this transition is intrinsically programmed in embryonic tissues and does not require direct maternal or placental cues. Notably, oxygen availability modulated the extent of this transition, but elevated oxygen was insufficient to induce it prematurely, demonstrating that the switch is developmentally timed and only partially environmental-responsive. We further harnessed the ex utero platform for identifying and perturbing a mitochondrial redox shift at E7.5-E8.5 that is critical for developmental progress after gastrulation. These findings uncover the remarkable metabolic plasticity of the mammalian embryo, demonstrating its capacity to sustain growth independently of maternal inputs from the establishment of the body plan through the onset of the fetal period. Moreover, they highlight the use of long-term ex utero culture as a unique framework for dissecting the mechanisms that shape embryogenesis under physiological and experimentally perturbed conditions, while functionally uncoupling embryonic programs from maternal and placental influences.

developmental biology↗

bioRxiv2026

Integrated single-cell and spatial transcriptomic analysis of T cell exhaustion and immunometabolic remodeling in HPV-positive oropharyngeal squamous cell carcinoma

HPV-positive oropharyngeal squamous cell carcinoma (OPSCC) harbors dense lymphocytic infiltration yet responds poorly to immune checkpoint blockade (ICB), a paradox whose mechanistic basis remains unresolved. Here we constructed an integrated single-cell and spatial transcriptomic atlas of HPV OPSCC (23,119 cells, 24 cell types), complemented by CRISPR-Cas9 functional validation in two HPV cell lines and multiplex immunofluorescence across independent cases. Pseudotime trajectory analysis placed TOXCD8 T cells, rather than canonically defined exhausted cells, at the terminal exhaustion endpoint, and uncovered a previously uncharacterized IEGCD8 effector memory population at a fate-decision juncture between activation and irreversible exhaustion. Actively cycling Tregs with broad co-stimulatory and co-inhibitory receptor expression underwent intratumoral immunosuppressive differentiation. Compartmentalized LDHA/MCT4 expression in cancer cells versus LDHB/MCT1 in T cells established a directional lactate flux model, and spatial deconvolution confirmed peripheral immune exclusion. We identified a KMT2D-KLF7-PD-L1 regulatory axis: KMT2D sustains KLF7 transcription through H3K4me1-dependent enhancer activation; KLF7 concurrently drives neural crest differentiation and upregulates PD-L1, thereby linking epigenetic remodeling to both neural programming and immune checkpoint expression. These findings define converging multi-scale mechanisms underlying ICB resistance in HPV OPSCC and nominate the KMT2D-KLF7-PD-L1 axis as a combinatorial therapeutic target.

cancer biology↗

bioRxiv2026

Single-cell transcriptomic atlas of mouse oocyte development from growth to ovulation

Oocyte growth and maturation depend on tightly coordinated programs within oocytes and their surrounding granulosa cells, yet defining the transcriptional continuum of growing oocytes has been challenging due to their large size and the limitations of current droplet-based and spatial transcriptomic platforms. Here, we optimized a high-density array-based platform called Stereo-cell for high-throughput dual-modality profiling of large mouse oocytes, allowing for both the preservation of morphology and transcript capture. By integrating unsupervised transcriptomic clustering with cell morphological features, we delineated successive temporal windows from growing oocytes to metaphase II and uncovered stage-linked shifts from early programs toward later programs. We also profiled the ovarian somatic fraction, reconstructed granulosa-cell subtype relationships, and placed ovarian cell states in tissue context using single-cell-resolution spatial data.

developmental biology↗