Biology subjects

Xu, T.

Publications and source records attributed to Xu, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

A distributed neural architecture of sustained affect across external and internal experience

AO_SCPLOWBSTRACTC_SCPLOWSustained affect shapes well-being, yet its neural architecture across externally elicited and internally generated experience remains unclear. Using whole-brain functional connectivity during minutes-long naturalistic movie viewing, we derived positive and negative affective experience signatures and their underlying neural architecture. These signatures predicted valence-specific affective intensity and generalized to independent movie-viewing data and internally generated affect, discriminating sad memory and rumination from neutral distraction while tracking subjective experience. Importantly, their expression showed little relation to vigilance or cognitive demand. Characterization of these signatures revealed coherent community structure and a shared distributed backbone, alongside valence-preferential components, consistent with a partially separable architecture. Extending beyond experimentally evoked states, in four resting-state depression cohorts, these signatures distinguished patients from controls with reduced positive and elevated negative signature expression, and predicted symptom burden and anhedonia. These findings identify a generalizable distributed architecture bridging external and internal affective experience and extending to clinically relevant affective dysregulation.

neuroscience↗

bioRxiv2026

Thermal Conductivity of Artificial Materials Engineered from Plant and Bacterial Cells

Bio-based materials are known for their excellent biodegradability and, in some cases, their potential to fix carbon dioxide. Owing to these properties, they are increasingly being utilized as environmentally friendly alternatives across various applications. In this study, we focused on using living cells themselves as material components, aiming to evaluate their potential as substitutes for conventional plastic-based thermal insulators. We selected two types of cells, photosynthetic purple non-sulfur bacterium Rhodovulum sulfidophilum and tobacco BY-2 plant suspension cells. After optimizing solidification conditions through the addition of pectin and cellulose nanofibers, we measured the thermal conductivity of the solidified cells under atmospheric pressure. The results showed that R. sulfidophilum exhibited 0.0553 W/m{middle dot}K, while BY-2 exhibited a thermal conductivity of 0.043 W/m{middle dot}K. Both values indicate relatively low thermal conductivity compared to existing bio-based materials, suggesting high insulation performance. Among the solidified cells, the solidified BY-2 cells showed minimal variation in thermal insulation performance under pressure changes, and had a low thermal emissivity as revealed by FT-IR analysis. Based on these findings, we propose that cell-derived materials can serve as potentially biodegradable bio-based thermal insulation materials.

bioengineering↗

bioRxiv2026

Interpreting Omics Data Analysis with Large Language Models for Disease Target and Drug Discovery

In biomedical scientific discovery, synthesizing prior knowledge from the literature is an essential component of interpreting numerical omics data analyses for disease target identification and drug discovery. Large language models (LLMs) alone can rapidly retrieve disease mechanisms from biomedical text, but text-only outputs are general and unreliable for target and drug prioritization without cohort-specific quantitative evidence. Herein, we propose a provenance-aware Text-to-Target framework that couples schema-constrained multi-model LLM retrieval with numeric omics data analysis. The key design is a modality-aware fusion step: candidates are partitioned into overlap-supported anchors, retrieval-only hidden hubs, and network-emergent novelty nodes, then propagated into staged hypothesis and strategy generation under topology constraints. We evaluate the model in Alzheimers disease (AD) and pancreatic ductal adenocarcinoma (PDAC). In PDAC, the workflow produced a balanced 75-gene candidate universe and a 23-strategy portfolio, with significant DepMap support at both target level and strategy level. In AD, stricter candidate controls yielded a compact 34-gene universe and 14 strategies; under an expanded CRISPRbrain registry, both target-level axes were significant, with strong strategy-level enrichment. Across both diseases, final strategies preserved full provenance closure to the candidate pool, enabling end-to-end auditability from retrieval artifacts to validation outputs. These results support a transferable discovery architecture in which omics evidence constrains biological activity, LLM retrieval expands mechanistic search space, and network-aware fusion preserves interpretability. The framework provides a reproducible basis for dual-disease target prioritization and motivates continuous literature-mechanism concordance with agentic evidence-refresh loops.

bioinformatics↗

bioRxiv2026

NEURA: An agentic system for autonomous neuroimaging workflows

Neuroimaging research depends on heterogeneous software, multimodal data and multistage statistical workflows. Large language model (LLM)-based agents offer a route to automate these workflows, but their susceptibility to hallucination limits their credibility in scientific use. Here we introduce NEURA, a proof-carrying framework for hallucination-resistant neuroimaging automation. NEURA converts free-text research questions and neuroimaging datasets into executable analysis plans, validated outputs and structured reports. The system combines disease- and tool-aware planning with a deterministic verification layer inspired by formal proof: before any claim is retained for reporting, it must be checked against tool-derived evidence and domain axioms. On NeuroEval, an expert-curated benchmark of 110 neuroimaging tasks, NEURA achieved 89.5% planning accuracy, a 30.5% improvement over direct LLM queries. In a controlled hallucination-injection experiment, the verification layer detected all the injected error classes under the specified axiom bank and trust assumptions, with no false positives. In case studies of spinocerebellar ataxia type 3, NEURA reproduced cerebellar atrophy and abnormal diffusion patterns consistent with established pathology and independent expert analyses. Together, these findings show that coupling domain-grounded agency with proof-carrying verification can turn LLM-driven workflow automation from probabilistic self-checking into auditable scientific computation.

neuroscience↗

bioRxiv2026

Mechanotransduction-Aware Causal Omics on Tissue Scaffolds: A Controlled Mechanochemical Framework for Identifying Disease Genes Beyond Pure Omics Analysis

Omics-based disease-gene discovery is typically performed as if molecular states evolve independently of tissue mechanics. Most current pipelines analyze transcriptomic or multimodal molecular data alone and identify abnormal genes using differential expressions, latent trajectories, or association-based recovery under treatment. However, in mechanically active tissues, gene expression is shaped not only by internal regulatory networks but also by mechanotransduction arising from strain, curvature, force transmission, and scaffold geometry. This raises a fundamental question: should disease-gene identification in tissues be treated as a pure omics association problem, or as a causal mechanochemical inference problem? We introduce a mechanotransduction-aware causal omics framework on a Cosserat tissue scaffold. Gene expression evolves through intrinsic regulatory dynamics, spatial diffusion, external control, and a mechanotransduction term driven by scaffold mechanics. To distinguish causation from association, we define a hidden mechano-drug rescue channel in the true data-generating system and compare predictive models that either include or omit mechanotransduction. We show that association-based rankings can incorrectly elevate downstream homeostatic or repair genes, even when the disease gene is the true direct mechanochemical target. By contrast, a causal ranking based on reconstruction of the direct mechanotransduction intervention effect correctly identifies the disease gene as the strongest beneficiary. These results argue that popular pure-omics analysis is insufficient for disease-gene discovery in mechanically structured tissues. Mechanotransduction should be modeled as part of the causal structure of tissue biology rather than treated as a secondary covariate or omitted entirely.

bioinformatics↗

bioRxiv2026

Temporal Dynamics of BOLD fMRI Predict Intracranially-Confirmed Seizure Onset Zones in Drug-Resistant Epilepsy

ObjectiveIn individuals with drug-resistant epilepsy, accurately identifying the brain regions where seizures originate is a critical prerequisite to guide surgical treatment and achieve seizure freedom. To accomplish this, intracranial EEG is considered the gold standard, providing the spatiotemporal high-resolution data necessary to pinpoint epileptogenic activity. However, this precision is achieved through an invasive procedure with significant patient burden, which is fundamentally limited by the electrode placement and spatial coverage. MethodsIn this study, we investigated the potential utility of preoperative resting-state fMRI to non-invasively map alterations in brain dynamics at the whole brain level. Region-wise brain dynamics were quantified with complementary measures of local autocorrelation decay rates. We assessed the capacity of these derived features to effectively identify intracranial EEG confirmed seizure onset zones in 18 individuals with drug-resistant medial temporal lobe epilepsy. Overall, the study cohort contained 3867 implanted electrodes of which 159 classified as seizure onset zones by two independent board-certified epileptologists. ResultsOverall, our findings reveal more constrained temporal dynamics for brain regions associated with seizure onsets compared to non-seizure onset zones. Individual-level prediction showed a performance better than chance in 15 of the 18 patients. The overall predictive performance across all patients yielded a median AUC of 0.81, a median true positive rate of 0.75, and a median true negative rate of 0.83. Furthermore, in a subset of 13 patients, those with negative seizure outcomes showed higher probabilities of seizure onset zone predictions outside the resection area compared to those with good outcomes. SignificanceOverall, our findings suggest that altered temporal dynamics derived from preoperative resting-state fMRI represent a promising non-invasive approach for delineating epileptogenic tissue, potentially informing intervention strategies and guiding electrode placement.

neuroscience↗

bioRxiv2026

Auxin promotes GPI-anchored protein-mediated trafficking of ABP1 to enable cell-surface auxin signaling

Rapid auxin responses are triggered by Auxin-Binding Protein 1 (ABP1) signaling; however, the coupling of hormone perception to signaling activation is unclear. ABP1, a cell surface auxin receptor, is localized to endoplasmic reticulum (ER), raising a fundamental question of where auxin is perceived and how signaling-competent receptors are mobilized to cell surface. Herein, we established a dark-induced auxin synthesis system in Arabidopsis hypocotyl; abp1 mutants showed pronounced elongation. Auxin dose-dependently stimulates LORELEI-like glycosyl-phosphatidylinositol-anchored protein 1 (GPI-AP, LLG1) interaction with ABP1, driving their outward trafficking from ER. However, this complex dissociated in acidic apoplast, facilitating H+-ATPase-mediated auxin signaling. Our findings reveal how intracellular auxin perception is converted into rapid cell-surface signaling, establishing a mechanistic framework for fast auxin responses and uncovering a GPI-AP-mediated trafficking mechanism for ER-retained receptors.

plant biology↗

bioRxiv2026

The stability of thought: using experience sampling and brain imaging to determine the contextually bound nature of human cognition.

Human behavior is highly flexible, allowing efficient performance across a wide range of task contexts. A distributed set of frontal and parietal regions, commonly termed the multiple-demand network (MDN), is consistently engaged during diverse cognitively demanding tasks and is thought to support this flexibility. However, it remains unclear how patterns of MDN engagement relate to the qualitative features of ongoing cognition experienced during task performance. To address this issue, we examined the reliability of self-reported experiential features sampled during performance of a broad range of tasks. Across tasks, we found little evidence that particular patterns of thought were intrinsically more reliable than others, nor that individual tasks were associated with stable, characteristic thought profiles. Instead, the reliability of specific experiential features varied systematically across task contexts, with the same patterns showing high stability in some tasks and low stability in others. We next asked whether stable patterns of thought were associated with distinct neural signatures. We found that patterns of brain activity resembling the MDN tended to be present for tasks in which deliberate task focus was high, and when distraction was lower, adding to an emerging body of research suggesting that coordinated activity within frontal and parietal regions helps to establish a stable goal-focused mode of thoughts and actions.

neuroscience↗

bioRxiv2026

Probiotic Lactiplantibacillus plantarum VB165 improves metabolic disorders in Insulin-Resistant Mice

This study investigated the effects of Lactiplantibacillus plantarum VB165, a probiotic strain with intrinsic -glucosidase inhibitor (AGI) activity, on metabolic disorders in high-fat diet (HFD)-induced insulin-resistant (IR) mice. Male C57BL/6 mice were divided into four groups: normal control diet (NCD), NCD supplemented with VB165, HFD, and HFD supplemented with VB165. After 16 weeks, VB165 supplementation significantly attenuated HFD-induced weight gain and reduced epididymal and inguinal white adipose tissue indices. VB165 also improved glucose intolerance and insulin resistance (IR), as demonstrated by oral glucose tolerance tests (OGTT) and insulin tolerance tests (ITT), and lowered fasting blood glucose, fasting insulin, and Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) levels. Additionally, it ameliorated dyslipidemia by reducing serum total cholesterol, triglycerides, and low-density lipoprotein cholesterol (LDL-C), while alleviating hepatic steatosis and adipocyte hypertrophy. Mechanistically, VB165 enhanced intestinal barrier function by upregulating tight junction proteins (ZO-1 and Occludin), reduced systemic inflammation by lowering LPS, IL-6, and IL-1{beta}. Gut microbiota analysis revealed that VB165 modulated community composition, suppressing HFD-enriched genera (e.g., Ileibacterium and Coriobacteriaceae_UCG_002) and promoting beneficial taxa (e.g., Faecalibaculum and Oscillibacter). These findings demonstrate that L. plantarum VB165 improves HFD-induced metabolic disorders via multi-target mechanisms, highlighting its potential as a probiotic intervention for IR and related metabolic diseases.

microbiology↗

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

Stage-adaptive integration of polydopamine promotes hPSC-derived alveolar organoids differentiation and maturation

Human pluripotent stem cell (hPSC)-derived alveolar organoids (ALOs) have emerged as a powerful tool for modeling human lung development and disease, and accelerating respiratory drug discovery. However, achieving the functional maturation of ALOs remains challenging. Polydopamine (PDA) is a mussel-inspired polyphenolic biomaterial with antioxidant and adhesive properties that can be deployed as surface coatings and nanoparticles (NPs) in cell culture systems. Here, we integrate PDA coatings and NPs sequentially in a stage-adaptive manner throughout the hPSC-derived ALOs differentiation system and study their contributions to ALOs maturation. Our results demonstrated PDA coating yielded more anterior foregut endoderm (AFE) spheroids by strengthening the interaction between Matrigel and substrate. Bulk RNA-seq revealed enrichment of cell-cell and cell-extracellular matrix interactions by PDA. The subsequent incorporation of PDA NPs in Matrigel at lung progenitor cells (LPCs) stage significantly mitigated reactive oxygen species (ROS) accumulation and enhanced LPCs generation. Functionally, AT2 cells in ALOs exhibit characteristic lysosome-to-lamellar body (LB) maturation due to the traffic of internalized PDA NPs to endolysosome. Transcriptomics further indicated enrichment of endocytic-phagosome and epithelium development pathways by PDA treatment. Together, our study establishes a stage-adaptive-integrated PDA strategy throughout hPSC-to-ALOs differentiation and demonstrates that PDA robustly enhances ALOs maturation and secretory function. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/708928v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@88208dorg.highwire.dtl.DTLVardef@1111590org.highwire.dtl.DTLVardef@9ea9b0org.highwire.dtl.DTLVardef@969fad_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2026

Unique Amygdala Signatures and Shared Prefrontal Deficits in Autism: Mapping Social Heterogeneity via Naturalistic functional Magnetic Resonance Imaging

BackgroundNaturalistic fMRI provides an ecologically valid window into social brain function, yet binary diagnostic labels may obscure neural signatures linked to the continuous spectrum of social deficits. We investigated whether social brain alterations in autism spectrum disorder (ASD) follow a categorical, dimensional, or "dual-track" architecture. MethodsWe analyzed fMRI data from 428 youth (262 ASD, 166 typically developing; ages 5-22) watching two films: The Present and Despicable Me. Using Principal Component Analysis (PCA) to quantify primary (PC1) and secondary (PC2) synchronization, we employed variance partitioning to disentangle the contributions of categorical diagnosis from continuous symptom severity (Social Responsiveness Scale-2, SRS-2). ResultsDuring The Present, reduced synchronization was widespread. In social-motivational hubs (medial prefrontal cortex, caudate), reductions were largely explained by variance shared between diagnosis and SRS-2 scores. In contrast, the left amygdala exhibited a unique dimensional association with SRS-2 scores independent of categorical diagnosis. Secondary response patterns (PC2), reflecting complex temporal integration, revealed further unique dimensional effects in the cuneus. Notably, these signatures were stimulus-dependent, manifesting during the emotionally complex narrative of The Present but not during the slapstick-oriented Despicable Me. ConclusionsWhile core social-motivational hubs reflect overlapping diagnostic and dimensional deficits, the amygdala and secondary visual patterns provide distinct, dimension-specific signatures of social impairment. This variance partitioning approach supports a Research Domain Criteria (RDoC) framework, highlighting the necessity of integrating dimensional assessments and narrative complexity to characterize the neural architecture of autism.

neuroscience↗

bioRxiv2026

Widespread symbiosis of ciliate epibionts colonizing gills of shrimps inhabiting vents and seeps across the Pacific Ocean

Although bacterial symbiosis is well-documented in chemosynthetic-based ecosystems, associations with microeukaryotes remain overlooked. In this study, using scanning electron microscopy and 18S rDNA barcoding, we investigate the presence, diversity, and biogeographic patterns of ciliate epibionts associated with two deep-sea caridean shrimp families: Alvinocarididae, and Thoridae. We identified a widespread lineage of ciliates colonizing the gills of different alvinocaridid species, extending their previously known distribution in freshwater and coastal habitats to deep-sea areas down to 3388 m. These ciliates form a distinct clade related to coastal Chonotrichia, but showing clear genetic divergence from the previously-described species. Geographic divergence of these ciliate populations was observed across the Pacific Ocean, with no evident structure related to their host species. These chonotrichian ciliates exhibited variation occurrence across host species, individuals, and regions, indicating a facultative association with their hosts. In contrast, the thorid shrimps harbored rare and phylogenetically diverse ciliates. More rarely, we found ciliates related to known parasitic lineages hosted by both shrimp families, with signs of immune response - black gills - in some individuals colonized by these ciliates. Our results reveal previously overlooked protist-crustacean associations in chemosynthetic ecosystems and highlight the ecological and biogeographic importance of this group in the deep ocean.

microbiology↗

bioRxiv2026

Human Cerebral Cortex Organization Characterized by Functional PET-FDG "Metabolic Connectivity"

PurposeIn this study, we characterize the spatiotemporal organization of resting-state metabolic connectivity (RSMC) in the human brain, as measured by [18F]- fluorodeoxyglucose (FDG) functional PET (fPET-FDG). We examine the relationship between RSMC organization and resting-state functional connectivity (RSFC) derived from functional magnetic resonance imaging and other known cortical organizational principles. MethodsResting-state fPET-FDG data from 24 individuals were obtained from a publicly available repository. We characterized local metabolic organization using connectivity-based boundary mapping, with adaptations to account for the low signal-to-noise ratio of fPET-FDG data. We then estimated global metabolic organization through community detection-based network and principal gradient analyses. Furthermore, we examined how metabolic connectivity is shaped by temporal-frequency-specific components of fPET-FDG signal. Finally, we contextualized metabolic organization by relating metabolic gradients to anatomical, functional, and energetic reference measures. ResultsAt the local scale, boundary mapping results indicated structured transitions shaped by a combination of both fast and slow fPET-FDG signals, partly overlapping with RSFC boundary maps. Globally, RSMC analyses revealed a robust metabolic structure organized along a superior-inferior cortical gradient. This pattern remained consistent across network community detection and principal gradient analyses and was primarily driven by low-frequency, minute-scale fPET-FDG dynamics. The identified large-scale metabolic profile aligns closely with several known anatomical and energetic constraints. ConclusionThis study characterizes the spatiotemporal organizational principles of RSMC, deepening insight into the brains energetic framework and providing a basis for future cognitive and clinical investigations of metabolic connectivity organization.

neuroscience↗

bioRxiv2026

Regional heterogeneity shapes macroscopic wave dynamics of the human and non-human primate cortex

Growing evidence indicates that macroscopic cortical activity is dominated by propagating waves of excitation. However, many computational models of such wave dynamics assume that the cortex is a spatially homogeneous medium, ignoring the rich regional variations in cellular, molecular, and physiological properties that are known to shape how brain activity evolves through both space and time. Here, we develop a general framework grounded in neural field theory to model how regional heterogeneities in diverse cortical properties shape spatiotemporal brain activity evolving on cortical surface meshes. This enables high resolution, vertex-level simulations without requiring predefined parcellations. The model requires only a standard mesh model of the cortical manifold and a spatial heterogeneity map, providing a biologically grounded and computationally efficient framework that can be generalised to human and non-human species. Using multiple cellular, molecular, and physiological maps in humans--and analogous maps in macaques and marmosets--we show that our model can consistently recapitulate known relationships between regional heterogeneities and variations in cortical wave speed. In particular, we find that models parameterised by heterogeneities in intracortical myelin and excitation-inhibition balance yield the largest performance improvements relative to spatially homogeneous models. Our results identify a key role for regional variations in myelin, receptor, and genetic architecture in shaping the spatial patterning of macroscale cortex-wide activity that is conserved across primate species with diverse cortical geometries.

neuroscience↗

bioRxiv2026

Multi-Omics Reveals Activated Fibroblasts with Dual Functional Roles in Repair and Negative Barrier in the CD8+ T Cell Cytotoxic Niche for Lung Cancer Neoadjuvant Therapy

A major challenge in elucidating immune activation and tolerance is that single-omics technologies are inherently limited. Since single-cell transcriptomics lacks spatial information and spatial transcriptomics lacks resolution or depth, neither can adequately model the multidimensional tumor immune microenvironment (TiME) features. We present NICHE (Niche Integrated Cellular Heterogeneity Elucidator), an integrative multi-omics framework. By aligning physical cell-cell interactions with single-cell ligand-receptor (L-R) expression, NICHE leverages single-cell transcriptomics, spatial proteomics, and AI modeling to systematically decode the composition, spatial interactions, and communications within immune functional niches. To validate NICHEs capabilities, we applied it to human tonsil tissue. The framework successfully identified key structural and functional units and deeply elucidated the cell-cell interactions and molecular crosstalk within them. Furthermore, these results were validated through orthogonal methods, including single-cell spatial transcriptomics and multiplexed immunofluorescence, which confirmed the accuracy and reproducibility of our analyses. In non-small cell lung cancer (NSCLC) patients undergoing neoadjuvant immunotherapy, CD8+ T cell cytotoxic niches mediate tumor clearance with variable outcomes, to define the cellular and molecular drivers behind this functional spectrum, we utilized NICHE to analyze the TiME from patients with complete versus partial pathological response, both pre- and post-treatment. We found that neoadjuvant immunotherapy may induce the activation of CXCL12+, NECTIN2+, POSTN+, and COL6A1+ fibroblasts through the activation of cytotoxic T cells. Paradoxically, these fibroblasts support tissue repair but also establish an immunosuppressive niche that protects residual tumor cells, ultimately attenuating treatment efficacy. Our work establishes a multidimensional framework for dissecting dynamic immune activity, with direct implications for understanding tumor immunology and identifying novel therapeutic targets.

immunology↗

bioRxiv2026

Multiplexed measurements of protein-protein interactions and protein abundance across cellular conditions using Prod&PQ-seq

Methods to profile protein-protein interactions (PPIs) have limited scalability and can only study a handful of conditions and/or targets. Here, we introduce Prod&PQ-seq, a framework for multiplexed detection and quantification of PPIs and proteins. Our framework uses cross-linked cells, antibody-oligonucleotide conjugates (ab-oligos), and captures PPIs by the DNA-caliper, a specialized oligonucleotide for bidirectional priming of proximal ab-oligos. We benchmarked Prod&PQ-seq using recombinant complexes, titrations and cell mixture experiments and show that our framework is quantitative, reproducible, sensitive and specific. Applying Prod&PQ-seq to study Polycomb Repressive Complex 2 (PRC2) shows that EZH2 inhibition and expression of the oncohistone H3.3K27M weakens both PRC2-H3K27me3 interactions and PPIs within PRC2. Further, H3.1K27M and H3.3K27M variants lead to distinct PPI profiles such as the intensity of H3K27ac-K27M or H3K27ac-EED. Together, Prod&PQ-seq enables detection of changes in PPI composition and intensity and protein quantification across biological conditions, small molecule inhibition and genetic perturbations. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC="FIGDIR/small/697286v2_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@1ed0805org.highwire.dtl.DTLVardef@a9c0d9org.highwire.dtl.DTLVardef@b411fborg.highwire.dtl.DTLVardef@8a12e_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2025

Integrative spatial multi-omics reveals prognostic tumor niches in female genital tumors

Female genital tumors (FGTs), including ovarian, endometrial, and cervical cancers, pose a major global health challenge, yet their spatial and molecular determinants of progression and therapy resistance remain poorly understood. Here we present a large-scale integrative spatial and multi-omics atlas of FGTs, combining exome, transcriptome, methylome, and miRNA sequencing with high-resolution Visium HD spatial transcriptomics, profiling over 62 million cells from 100 tumors. We show that HPV-driven suppression of TGF{beta} signaling in cervical cancer disrupts myofibroblast (myCAF) formation and reshapes immune infiltration, while serous tumors exhibit extensive oncogene amplifications that activate MYC and PI3K pathways, linking chromosomal instability (CIN) to proliferative, immune-cold phenotypes. In high-grade serous ovarian cancer, we identify a previously unrecognized malignant domain, the Proliferating Core Desert (PCD) niche, defined by CIN-high epithelial enrichment, immune exclusion, stromal protection, and chemotherapy resistance, maintained through crosstalk with macrophages and fibroblasts. Finally, we develop POWER, a pathology foundation model that predicts PCD niche abundance from routine H&E slides, enabling robust stratification of prognosis and anti-angiogenic therapy response.

genomics↗