Biology subjects

Liu, N.

Publications and source records attributed to Liu, N..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Spatiotemporal unfolding of prefrontal neural response divergence in adolescent depression during naturalistic experience

Depression alters how emotional experience is interpreted and regulated, yet how depression-related neural divergence unfolds during ongoing experience remains poorly understood, leaving unresolved whether such divergence reflects stable trait-like abnormalities or state-dependent responses that emerge only at particular moments. This question is especially consequential in adolescence, when depression often emerges amid heightened social sensitivity and continuing maturation of prefrontal regulatory systems. Here, we used naturalistic film watching fMRI data of 36 adolescents with depression and 36 matched controls to map where, how, and when neural responses diverge between diagnostic groups. Neural polarization analysis localized group-level divergence to two prefrontal hubs, the dmPFC-rACC and left dlPFC, where individual similarity to the depression-group response pattern was associated with depressive symptom severity across the cohort. These local divergence sites were not spatially isolated. Seed-based inter-subject functional connectivity showed that they were embedded within distributed stimulus-locked coupling patterns spanning prefrontal control, salience, and subcortical reward circuits. Time-resolved phase synchrony further revealed that this divergence was not continuous, but arose at specific narrative moments, with the dmPFC-rACC diverging during socio-emotional events and the left dlPFC during cognitively demanding ones. Together, these findings move beyond static localization to reveal depression-related neural divergence as a spatially organized, network-embedded, and temporally gated response pattern shaped by the unfolding content of naturalistic experience.

neuroscience↗

bioRxiv2026

Heat stress induces persistent placental dysfubction and fetal growth restriction via the ERS-MAPK-apoptosis axis

Maternal heat stress (HS) is an emerging risk factor for adverse pregnancy outcomes, yet how gestational heat exposure causes persistent placental dysfunction remains unclear. In the present study, we established a murine HS model (38.5{degrees}C, 2.5 h/day, E0-E12.5) followed by thermal recovery to E17.5. HS reduced fetal weight during early gestation and caused persistent fetal growth restriction after recovery, despite partial placental weight restoration. Histological analyses revealed early reductions in the junctional and labyrinth zones, followed by sustained labyrinthine deficiency and compensatory junctional zone expansion. Consistently, HS impaired placental vascularization, with reduced vessel length and area, decreased CD31 and -SMA abundance, and altered angiogenesis-related gene expression. HS also triggered oxidative stress, weakened antioxidant capacity, disrupted anti-inflammatory signaling, reduced tight junction protein expression, and compromised barrier integrity. Mechanistically, HS induced excessive endoplasmic reticulum stress, accompanied by increased CHOP, phosphorylated ERK, and cleaved caspase-3. In conclusion, our data unveil a heat-induced placental insufficiency program that restricts fetal growth through vascular, redox, barrier, and ERS-MAPK-apoptotic remodeling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/730306v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@c73748org.highwire.dtl.DTLVardef@fe7b3eorg.highwire.dtl.DTLVardef@14d5e6dorg.highwire.dtl.DTLVardef@72572f_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2026

Sex stratified analyses enable new genetic insights into brain imaging phenotypes

Sex differences are commonly observed in neuroimaging phenotypes and in the risk of brain diseases, yet the underlying genetic mechanisms remain poorly understood. We investigated sex differences in the genetic architecture of 805 neuroimaging phenotypes in 22,950 males and 22,950 females matched for sample size and covariates, and systematically compared sex-stratified with sex-combined genetic analyses. We found eight variant-trait associations with significant sex differences, 235 fine-mapped sex-dominant causal associations, 457 sex-dominant colocalizations with sex hormones, and 96 sex-dominant colocalizations with schizophrenia. Compared with sex-combined analysis, sex-stratified analysis identified 47 new genetic associations, 170 new fine-mapped causal associations, 1,019 new colocalizations with sex hormones, and 191 new colocalizations with schizophrenia. Additionally, sex-stratified analysis improved global heritability and genetic-correlation estimates and enhanced polygenic prediction for certain phenotypes. This work highlights the need to routinely perform sex-stratified genetic association analyses to elucidate sex-specific and sex-shared genetic control of neuroimaging phenotypes and related disorders.

genetics↗

bioRxiv2026

cellNexus: Quality control, annotation, aggregation and analytical layers for the Human Cell Atlas data

Large-scale single-cell atlases such as the Human Cell Atlas have transformed our understanding of human biology. Yet, the lack of a robust framework that standardises quality control, expands cellular annotation, and adds normalisation and analytical layers, limits multi-study analyses and the usefulness of this resource. Here we present cellNexus, a comprehensive resource that enhances the Human Cell Atlas collection into analysis-ready data by linking quality control layers, metadata enrichment, expression normalisation, analysis and data aggregation. These enhancements enable robust large-scale statistical modelling across studies, exemplified here by a multi-tissue map of immune cell communication during ageing. All harmonised layers are accessible via a public web interface and with R and Python APIs. By providing continuous integration with CELLxGENE releases, cellNexus transforms large cell atlas corpora into an accessible, reproducible, interoperable foundation for large-scale biological discovery and the next generation of single-cell foundation models.

bioinformatics↗

bioRxiv2026

A unified spatial transcriptome profiling of ten mouse organs

Spatial transcriptomics has enabled numerous deep learning models in this area, and training them requires large amounts of high-quality data, especially expression matrices paired with histological images. Here, we present a unified spatial transcriptomic dataset generated using the Stereo-seq platform, covering 10 mouse organs--including brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, and uterus--encompassing 23 tissue sections generated from 21 chips, each with matched ssDNA or H&E staining images. The dataset comprises single-cell-resolution (cell-bin) or square bin-50 (25 {micro}m x 25 {micro}m) expression matrices for each sample, accompanied by corresponding cell type annotations. Annotation robustness was further supported by concordance across different sections of the same tissue and corroboration with canonical marker gene expression patterns. Finally, we compared the characteristics of the cell-bin and bin-50 expression matrices and demonstrated the advantages of cell-bin resolution for cell annotation. This dataset provides a standardized resource for spatial transcriptomics method development, benchmarking, and multimodal analysis.

bioinformatics↗

bioRxiv2026

Copper Import via CTR1 Supports the β3-Adrenergic Thermogenic Program

Adaptive thermogenesis requires coordinated activation of mitochondrial oxidation and metabolic remodeling, yet the signals driving this coordination are incompletely understood. Here, we show that cold exposure and {beta}3-adrenergic receptor ({beta}3-AR) stimulation upregulate the high-affinity copper (Cu) importer CTR1 and promote Cu accumulation in thermogenic adipose tissues. Adipocyte-specific Ctr1 knockout (ACKO) mice exhibit markedly reduced energy expenditure and develop severe hypothermia during acute cold challenge. Proteomic analysis of brown adipose tissue (BAT) from ACKO mice reveals coordinated suppression of oxidative phosphorylation and thermogenic metabolic programs, accompanied by attenuation of lipolytic pathways. Cu deficiency also impairs cold- and {beta}3-AR-induced lipolytic activation, including reduced HSL phosphorylation and lipid clearance in both BAT and inguinal white adipose tissue (iWAT). Although BAT-specific Ctr1 deletion (BCKO) leaves acute {beta}3-adrenergic responses largely intact, these mice still exhibit cold intolerance, indicating that BAT Cu homeostasis is indispensable for sustaining thermogenic capacity during cold challenge. Treatment with the Cu ionophore elesclomol partially restores mitochondrial oxidative capacity and improves cold tolerance in ACKO mice. Together, these findings identify CTR1-dependent Cu import as a dynamically regulated component of the {beta}3-adrenergic thermogenic program and establish intracellular Cu availability as a key determinant of thermogenic capacity during adaptive thermogenesis.

molecular biology↗

bioRxiv2026

BioReason-Pro: Advancing Protein Function Prediction with Multimodal Biological Reasoning

Protein function annotation is fundamental to understanding biological mechanisms, designing therapeutics, and advancing biomedical research. Current computational methods either rely on shallow sequence similarity or treat function prediction as isolated classification tasks, failing to capture the integrative reasoning across sequence, structure, domains, and interactions that expert biologists perform to infer function. We introduce BioReason-Pro, the first multimodal reasoning large language model (LLM) for protein function prediction that integrates protein embeddings with biological context to generate structured reasoning traces. A key input into BioReason-Pro is the set of GO term predictions made by GO-GPT, our autoregressive transformer that captures hierarchical and cross-aspect dependencies of GO terms. BioReason-Pro is trained via supervised fine-tuning on synthetic reasoning traces generated by GPT-5 for over 130K proteins and further optimized through reinforcement learning. It achieves 73.6% Fmax on GO term prediction and an LLM judge score of 8/10 on functional summaries, substantially outperforming previous methods. Evaluations with human protein experts show that BioReason-Pro annotations are preferred over ground truth UniProt annotations in 79% of cases. Remarkably, BioReason-Pro predicted a novel interaction partner for the renal cancer biomarker RCDG1, which we confirmed in the lab by co-immunoprecipitation. In other binding-partner predictions, its per-residue attention localized to the exact contact residues resolved in cryo-EM structures. Together, GO-GPT and BioReason-Pro establish a framework for protein function prediction that combines precise ontology modeling with interpretable biological reasoning.

molecular biology↗

bioRxiv2026

Isoform-resolved spatial transcriptomics on a lab-made high-density array via a single-chip NGS-TGS workflow

High-resolution spatial transcriptomics is still cost-prohibitive and dominated by short-read sequencing, limiting in situ detection of transcript structures. Here we present a low-cost, lab-made, high-density bead-in-microwell chip assembled by routine centrifuge and decoded by a tri-part combinatorial long-barcode strategy. The design provides 5.6x107 barcode combinations across 4.1x10{square} capture sites, reducing collisions while remaining tolerant to third-generation sequencing raw errors. Using a single-chip, dual-platform workflow, we split full-length cDNA for next-generation and third-generation sequencing within the same spatial coordinates. In an incompatible tomato-pepper graft, we identified interface-associated splicing reprogramming, including thousands of unannotated isoforms, enriched novel CaGRP1 isoforms, and SlPIP2 intron retention, supported by individual long-read sequences. In mouse embryos, we further demonstrate cross-species applicability by resolving unannotated Col1a2 isoforms in oligodendrocyte progenitors and spatially restricted Dalrd3 intron retention. This cost-efficient system broadens access to spatial full-length transcriptomics and may support diverse biological studies.

plant biology↗

bioRxiv2026

Subcellular transcriptome sequencing with single cell APEX-seq identifies regulators of cell-cell interactions

Single-cell RNA sequencing has transformed our understanding of tissue complexity and heterogeneous cell states, yet provides little information about the subcellular organization of transcriptomes - despite the central role of RNA localization in splicing, translation, and function. Here we introduce single-cell APEX-seq (scAPEX-seq), a proximity labeling-based method for mapping subcellular transcriptomes at single-cell resolution. Improvements in probe design and RNA recovery enable APEX integration with droplet-based RNA-seq to capture endoplasmic reticulum-associated transcripts from thousands of individual cells. Applied to tumor-macrophage co-cultures, ER-targeted scAPEX-seq revealed interaction-dependent cell states and transcriptomic signatures by enriching for cell surface and secretory transcripts that are poorly resolved by conventional scRNA-seq. We further applied scAPEX-seq to short- and long-term co-cultures of HER2+ tumor cells with human chimeric antigen receptor (CAR) T cells, resolving distinct activated CAR T cell states, including populations characterized by upregulated NT5E or CTSW expression. We showed that overexpression of CTSW, a cathepsin protease, in CAR T cells promotes stem-like phenotypes, long-term proliferation, and sustained tumor cell killing. scAPEX-seq provides a powerful and scalable approach for profiling subcellular RNA populations, enabling the discovery of cell-cell interaction regulators missed by conventional approaches.

cell biology↗

bioRxiv2026

A mouse-adapted Yezo virus model for antiviral testing in immunocompetent mice

Emerging tick-borne orthonairoviruses pose a growing public health threat, yet few animal models fully recapitulate human disease. Here, we report the development of a mouse-adapted Yezo virus (MA-YEZV) strain that causes lethal infection in immunocompetent mice, mirroring key clinical features of human infection, including thrombocytopenia, leukopenia, and severe liver injury. Serial passaging of YEZV in C57BL/6J mice selected for 31 non-synonymous mutations, enhancing viral replication and pathogenicity. MA-YEZV exhibited broad tissue tropism, with the highest viral loads in the liver, and induced a robust inflammatory response marked by elevated proinflammatory cytokines (e.g., IFN-{gamma}, TNF-, IL-6) and inflammasome activation. Ribavirin treatment effectively suppressed viral replication, prevented mortality, and mitigated tissue damage, whereas remdesivir showed no efficacy. This model provides a critical tool for studying YEZV pathogenesis and antiviral development, while the identified mutations offer insights into viral adaptation and virulence mechanisms. Our findings underscore the potential of MA-YEZV as a platform for evaluating countermeasures against emerging orthonairoviruses.

microbiology↗

bioRxiv2026

RankMap: Rank-based reference mapping for fast and robust cell type annotation in spatial and single-cell transcriptomics

Accurate cell type annotation is essential for the analysis of single-cell and spatial transcriptomics data. While reference-based annotation methods have been widely adopted, many existing approaches rely on full-transcriptome profiles and incur substantial computational cost, limiting their applicability to large-scale spatial datasets and platforms with partial gene panels. Here, we present RankMap (https://github.com/jinming-cheng/RankMap), an efficient and flexible R package for reference-based cell type annotation across both single-cell and spatial transcriptomics. RankMap transforms gene expression profiles into rank-based representations using the top expressed genes per cell, improving robustness to platform-specific biases and expression scale differences. A multinomial regression model trained with elastic net regularization is then used to predict cell types and associated confidence scores. We benchmarked RankMap on five spatial transcriptomics datasets, including Xenium, MERFISH, and Stereo-seq, as well as two single-cell datasets, and compared it with established methods such as SingleR, Azimuth, and RCTD. RankMap achieved competitive or superior annotation accuracy while consistently reducing runtime compared to existing methods, particularly for large spatial datasets. These results demonstrate that RankMap provides a scalable and robust solution for reference-based cell type annotation in modern single-cell and spatial transcriptomics studies.

bioinformatics↗

bioRxiv2026

Synora: vector-based boundary detection for spatial omics

Tumor-stroma boundaries are critical microenvironmental niches where malignant and non-malignant cells exchange signals that shape invasion, immune modulation and therapeutic response. Spatial omics platforms now resolve these interfaces at single-cell scale, but computational boundary detection remains challenging because heterogeneous neighborhoods can arise either from true compartment interfaces or from unstructured immune infiltration. Here we present Synora, a modality-agnostic computational framework that identifies tumor boundaries using only cell coordinates and binary tumor/non-tumor annotations, making it readily applicable across a broad range of spatial omics modalities. Synora introduces orientedness, a novel metric that quantifies directional neighborhood asymmetry and distinguishes true boundary cells, where neighbors are spatially segregated by type, from infiltrated regions where cell types intermingle randomly. By integrating orientedness with traditional diversity measures into a unified BoundaryScore, Synora achieves robust boundary identification across synthetic datasets with ground-truth boundaries, maintaining performance under realistic perturbations including 50% missing cells and 25% infiltration. Application to 15 Visium HD spatial transcriptomic datasets across multiple cancer types reveals consistent boundary-enriched gene signatures and cell-type spatial gradients. Validation on a CODEX multiplexed protein dataset demonstrates that Synoras precise boundary identification enables discovery of clinically relevant cellular neighborhoods and disease-associated spatial patterns missed by frequency-based approaches. Synora enables boundary-aware spatial analyses by making tissue interfaces quantifiable from minimal inputs, helping to standardize interface detection and comparison across spatial omics platforms and biological contexts.

bioinformatics↗

bioRxiv2026

PantheonOS: An Evolvable Multi-Agent Framework for Automatic Genomics Discovery

The convergence of large language model-powered autonomous agent systems and single-cell biology promises a paradigm shift in biomedical discovery. However, existing biological agent systems, building upon single-agent architectures, are narrowly specialized or overly general, limiting applications to routine analyses. We introduce PantheonOS (https://PantheonOS.stanford.edu), an evolvable, privacy-preserving multi-agent framework designed to reconcile generality with domain specificity. Critically, PantheonOS enables agentic code evolution, allowing evolving state-of-the-art batch correction and our reinforcement-learning augmented gene panel selection algorithms to achieve super-human performance. PantheonOS drives biological discoveries across systems: uncovering asymmetric paracrine Cer1-Nodal inhibition in proximal-distal axis formation of novel early mouse embryo 3D data; integrating human fetal heart multi-omics with whole-heart data to reveal molecular programs underpin heart diseases; and adaptively selecting virtual cell models to predict cardiac regulatory and perturbation effects. Together, PantheonOS points towards a future where scientific discoveries are increasingly driven by self-evolving AI systems across biology and beyond. Websitehttps://pantheonos.stanford.edu Ecosystemhttps://github.com/aristoteleo SummaryLarge language model-powered agent systems are driving a paradigm shift in scientific discovery by automating, scaling, and accelerating data analysis. This transformation is particularly profound in biology, where the rapid expansion of single-cell and spatial genomics has effectively reshaped the field into a data-intensive science. However, existing biological agent systems are typically constrained to single-agent designs, are narrowly specialized, or are overly general without sufficient domain expertise, limiting their applicability to routine or shallow analyses. Here, we introduce PantheonOS (https://pantheonos.stanford.edu), an evolvable, privacy-preserving, and general-purpose multi-agent framework designed to reconcile generality with deep domain specificity. PantheonOS provides an abstract, extensible architecture that enables customized agent composition and supports end-to-end single-cell and multi-omics analysis, spanning reinforcement-learning-augmented gene panel design, raw FASTQ processing, multimodal data integration, and three-dimensional spatial genomics reconstruction. Central to this framework, Pantheon-Evolve enables agentic code evolution, allowing the system to autonomously improve state-of-the-art batch-correction algorithms and new reinforcement-learning based gene panel design algorithms, achieving performance beyond manually designed baselines. We demonstrate the power of PantheonOS across multiple biological domains. In early mouse embryogenesis, PantheonOS automatically reconstructs three-dimensional spatial gene expression landscapes and resolves asymmetric Cer1 expression and paracrine Cer1-Nodal inhibition, revealing a robust proximal-distal axis at embryonic day six (E6.0). In human development, PantheonOS integrates fetal heart single-cell multi-omics with whole-heart 3D MERFISH+ data at post-conception week 12, uncovering spatially resolved molecular programs underlying heart disease ontogeny. Finally, an intelligent model-routing mechanism enables PantheonOS to adaptively select optimal virtual cell models across heterogeneous tasks, revealing minimal regulatory networks of cardiogenesis and predicting spatially resolved perturbation effects in the developing heart. Together, PantheonOS establishes a foundation for fully automated, evolvable, and domain-aware agentic analysis in genomics, and points toward a future in which scientific discovery is increasingly driven by self-improving AI systems across biology and beyond.

bioinformatics↗

bioRxiv2026

Graph-based RNA structural representation reveals determinants of subcellular localization

RNA subcellular localization is a key determinant of RNA function and regulation, yet existing computational approaches rely primarily on sequence or simplified structural descriptors, limiting their scalability to long transcripts, their ability to model inter-label dependencies, and their applicability across RNA types. Here, we present GRASP, a unified graph neural network framework for predicting RNA subcellular localization using a heterogeneous graph representation that is RNA substructure-aware. GRASP presents each RNA as a multi-scale graph comprising nucleotide nodes and secondary-structure-derived substructure nodes, connected by relational edges, enabling joint modeling of base-level interactions and regional structural context. The model further incorporates multi-label dependency learning to capture co-localization patterns across cellular compartments within a unified framework. Across multiple benchmark datasets and RNA types, GRASP consistently outperforms state-of-the-art sequence-based and structure-informed methods, achieving substantial improvements in accuracy, F1 score, and AUC while maintaining strong scalability to long transcripts. In addition, the graph-based representation provides biologically interpretable insights into structural determinants of RNA localization. The source code and data are available at https://github.com/ABILiLab/GRASP, and the web server is accessible at http://grasp.biotools.bio.

bioinformatics↗

bioRxiv2026

Super-resolution single-cell spatial atlas of plant de novo regeneration

Plant de novo regeneration, a capacity absent in animals, relies on stem-cell-niche formation rather than pre-existing reservoirs. Growth hormones such as auxin and cytokinin, together with numerous genetic regulators, participate in regeneration, but the architectural principles coordinating extensive cellular reprogramming at the individual-cell scale remain elusive. Using super-resolution multimodal spatial transcriptomics to 1.16 million cells, we tracked tomato regeneration across temporal intervals from wounding to organogenesis. Datasets are available at http://www.single-cell-spatial.com.

cell biology↗

bioRxiv2026

Base-resolution DNA methylome of human MDS hematopoietic stem cell reveals TET2-GFI1 epigenetic axis repressing MDS

Dysfunctional hematopoietic stem cells (HSCs) drive the initiation of myelodysplastic syndromes (MDS), yet the genome-wide DNA methylation landscape in MDS primitive HSCs and its mechanistic contribution to disease pathogenesis remain poorly defined. Here, we establish single-base resolution DNA methylomes of bone marrow HSCs from MDS patients and healthy donors. We uncover widespread hypermethylation within CpG islands, alongside hypomethylation in repetitive elements such as Alu. Differentially methylated regions (DMRs) are enriched for genes involved in cancer-related pathways, as well as extrinsic signaling pathways and intrinsic transcriptional networks essential for HSC function. Among these, we identify GFI1 and BMI1 as key targets of DNA methylation dysregulation in MDS. Notably, using either MDS or Tet2-deficient mouse model, we demonstrate that loss of Tet2, a frequently mutated epigenetic regulator in MDS, induces promoter hypermethylation and transcriptional repression of Gfi1, contributing to expansion of the MDS or aged hematopoietic stem and progenitor cell pool. Our study not only charts the base-resolution DNA methylome of human MDS HSCs but also reveals a TET2-GFI1 axis that safeguards HSC homeostasis. These findings provide mechanistic insight into how aberrant DNA methylation drives HSC dysfunction in MDS and offer an epigenomic resource for discovering novel regulators and therapeutic targets at the stem cell level.

immunology↗

bioRxiv2026

MtDNA heteroplasmy controls tumor immune reprogramming through mitochondrial translation

Mitochondrial DNA (mtDNA) mutations are among the most frequent genetic alterations in human cancers, yet their molecular mechanisms in shaping tumor progression and metastasis under physiological conditions remain poorly understood. Here, we show that mtDNA heteroplasmy acts as a tumor cell-intrinsic rheostat that orchestrates fate decisions between progression and suppression. Moderate heteroplasmy induces a ribosomal storm of mitochondrial translation and ribosome biogenesis, driven by leucyl-tRNA synthetase 2 (LARS2). This response enhances metabolic fitness and licenses the secretion of S100A8/A9, enabling tumor cells to actively remodel the immune microenvironment through recruitment of myeloid-derived suppressor cells and exclusion of CD8 T cells, thereby promoting immune evasion and metastatic progression. In contrast, high heteroplasmy impairs tumorigenesis due to oxidative phosphorylation collapse. Disruption of LARS2, mitochondrial translation, or S100A9 signalling abrogates tumor progression. Our findings establish mtDNA heteroplasmy as a key regulator of cancer evolution, linking mitochondrial genetic variation to immune modulation, and reveal the LARS2-mitochondrial translation-S100A8/A9 axis as a therapeutically targetable programme within the pro-tumorigenic window of mtDNA heteroplasmy.

cancer biology↗

bioRxiv2026

Spatial pharmaco-multiomics reveals drug distribution, metabolic niches, and spatially constrained resistance in medulloblastoma

Single-cell and spatial transcriptomic studies have provided insights into the developmental origins and intratumoural heterogeneity of SHH medulloblastoma (SHH-MB) and suggested how targeted drugs such as CDK4/6 inhibitors remodel tumour ecosystems, yet the interplay between local drug exposure, metabolism, cell state, and drug resistance remains poorly understood. Here we developed a same-section spatial pharmaco-multiomics framework that integrates MALDI-MSI-based spatial metabolomics with Visium whole-transcriptome profiling and high-resolution Xenium imaging to map palbociclib distribution, metabolite landscapes, and transcriptional programs within the same histological contexts of an SHH-MB PDOX model and primary human tumours. Palbociclib-rich tumour bulk exhibited broad suppression of E2F-driven proliferation and a shift toward neuronal differentiation, corroborating and extending prior findings. In contrast, drug-poor tumour-brain interfaces and perivascular regions retained E2F-high proliferative states and were enriched for mesenchymal-like stromal cells and ECM-remodelling genes, indicating anatomically constrained reservoirs of tolerance. Spatial metabolomics linked these interface niches to ganglioside (GM2) and sphingomyelin enrichment, while differentiated, drug-exposed regions displayed phosphatidylcholine, phosphatidic-acid signatures consistent with neuronal maturation. Integrated pathway analysis further revealed a "mitochondrial tuning" program, with upregulation of histidine, folate/one-carbon, CoA, and lipoate metabolism with redox and oxidative-phosphorylation support. These signatures were specific to therapy-exposed border cells. Rare palbociclib-positive, E2F-high resistant spots additionally exhibited mitotic checkpoint and DNA-repair signatures, implying a drug-induced resistance axis independent of scarcity. Together, our study provides a generalisable same-section spatial pharmaco-multiomics pipeline and a spatially resolved model of CDK4/6 response, nominating interface-focused metabolic and cell-intrinsic vulnerabilities for combination therapy.

cancer biology↗