Biology subjects

Xie, Y.

Publications and source records attributed to Xie, Y..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

A spatially-resolved human brain proteome atlas for understanding function and disease

While the brain performs specialized functions across distinct regions, the spatial organization of the human brain proteome remains largely uncharted. Here we present a comprehensive spatially-resolved proteome atlas of the human brain, analyzing over two thousand MRI- guided locations across four individuals. Proteome analysis integrated with transcriptomics reveals extensive post-transcriptional regulation, with cortical regions showing markedly higher protein diversity than transcript. Unsupervised molecular clustering defines distinct brain territories that transcend anatomical boundaries, instead reflecting metabolic demands and functional specialization patterns. Application to epilepsy brain tissue uncovered disrupted astrocyte metabolism, protein homeostasis and therapeutic targets including the seizure-associated purinergic receptor P2RX7. This resource bridges molecular and systems neuroscience to accelerate neurological drug discovery.

neuroscience↗

bioRxiv2025

Primary metabolism determines the outcome of salicylic acid-mediated immune induction

Controlling the deleterious effects of immune responses is as vital as fighting infection. In plants, this is achieved, in part, by circadian clock-mediated regulation, such as the synthesis of and response to the immune hormone salicylic acid (SA)1,2. Application of SA at the same concentration under light/dark cycles induces immunity with minimal impact on growth, however, prolonged darkness leads to plant death2. To uncover what determines this life-or-death outcome, we identified twenty survival of SA-induced death (ssd) mutants through genetic screening. These mutants are defective in starch, glucose, and nitrate metabolism, and circadian regulation, and accumulate excessive starch and/or glucose. Likewise, glucose application rescues SA-treated plants in prolonged darkness. Surprisingly, SA treatment does not deplete glucose, but instead, induces amino acid and fatty acid catabolism. Through transcriptomic analyses of glucose-rescued WT plants and ssd mutants for shared pathways, we found that SA triggers plant death in darkness by inducing oxidative stress, and water loss, while glucose antagonizes these processes, boosts ER protein processing and re-establishes the anabolism-catabolism balance. Interestingly, the programmed cell death induced by effector-triggered immunity shares common transcriptomic patterns with those observed during SA-induced cell death in darkness and could also be attenuated by glucose treatment. Therefore, coordination with the cellular metabolic context plays a central role in determining immune outcomes and optimizing plant health.

plant biology↗

bioRxiv2025

DNASE1L3 surveils mitochondrial DNA on the surface of distinct mammalian cells

The extracellular space is a critical environment for discriminating self versus non-self nucleic acids and initiating the appropriate immune responses through signaling cascades to relay information about extracellular nucleic acids. Here, we provide evidence that oxidized mitochondrial DNA is tethered to the surface of select mammalian cells through cell surface proteins and heparan sulfate proteoglycans. We demonstrate that cell surface DNA accumulates in large clusters that partially overlap with domains enriched in RNA binding proteins. Finally, we show that human and murine B cell surfaces contain DNA that can be cleared by the secreted nuclease DNASE1L3, and that patients with a DNASE1L3 missense variant associated with increased risk for autoimmune disease harbor increased levels of surface DNA on B and T cells. Taken together, this work expands the scope of cell surface nucleic acid biology and provides a mechanistic link between cell surface molecules and DNA targeting in autoimmune disease.

cell biology↗

bioRxiv2025

scLinguist: A pre-trained hyena-based foundation model for cross-modality translation in single-cell multi-omics

Single-cell multi-omics provides complementary insights into cellular states, heterogeneity, and regulatory programmes. However, paired assays remain costly, low-throughput, and technically challenging, whereas large-scale single-modality data such as scRNA-seq are abundant but do not capture protein-level biology. Here we present scLinguist, a foundation model for cross-modality translation introduces a three-stage framework: self-supervised pretraining on large-scale unimodal datasets to learn modality-specific representations, post-pretraining on limited paired data to capture cross-modality relationships, and inference to predict missing modalities (e.g., protein from RNA) in fine-tuning or zero-shot settings. Systematic benchmarking shows that scLinguist consistently outperforms state-of-the-art methods in predicting protein abundance from RNA across diverse biological contexts. It achieves high predictive performance while preserving cellular heterogeneity and further enables mechanistic and generalizable inference under simulated gene perturbations. Furthermore, scLinguist exhibits strong transferability across health states and datasets. By leveraging abundant unimodal data and minimizing dependence on paired assays, scLinguist establishes a scalable and versatile framework for cross-modality translation in single-cell analysis.

cell biology↗

bioRxiv2025

Reactivation-coupled brain stimulation enables complete learning generalization

Generalization of learned knowledge to new contexts is essential for adaptive behavior. Despite extensive research on the brain plasticity mechanisms underlying learning specificity, the mechanisms that facilitate generalization remain poorly understood. Here, we investigate whether using brain stimulation to disrupt offline consolidation in visual cortex promotes learning generalization. Separate groups of participants (N = 144) were trained on visual detection tasks using either a reactivation-based protocol or conventional full-practice, combined with anodal or sham transcranial direct current stimulation (tDCS) over the visual cortex. Strikingly, only combination of reactivation-based learning with anodal tDCS produced complete generalization from trained to untrained stimuli, an effect consistently replicated across features (orientation, motion direction). In contrast, reactivation-based learning alone and conventional full-practice - whether with or without brain stimulation - yielded stimulus-specific learning. Importantly, reactivation-coupled brain stimulation achieved generalization with an 80% reduction in training trials while maintaining learning gains comparable to full-practice. These findings demonstrate that reactivation and neuromodulation interact to unlock learning generalization, revealing a key brain plasticity mechanism and offering a rapid, translatable strategy for sensory rehabilitation.

neuroscience↗

bioRxiv2025

A New Aneurysm Rupture-Prone Marfan Mouse Model with FBN1Q2467X Nonsense Mutation Reveals Adventitial Inflammation

Marfan syndrome (MFS) is a genetic disorder caused by mutations in fibrillin-1(FBN1), which encodes FBN1, a key structural component of the extracellular matrix. Mutations in FBN1 influence the severity of aortic disease and therapeutic responses, with aortic aneurysm being the leading cause of mortality in patients with MFS. To investigate the mechanisms driving aneurysm progression, we generated a mouse model (Fbn1Q2469X/+) carrying the FBN1Q2467X nonsense mutation identified in MFS patients. This mutation results in FBN1 deficiency. Although Fbn1Q2469X/+ mice appear normal, showing only mild, nonprogressive dilation of the aortic root and ascending aorta with minor reductions in blood pressure, homozygous Fbn1Q2469X/Q2469X mice develop spontaneous thoracic aortic aneurysms (TAA) that progress to rupture between 10 and 25 days of age, with 100% penetrance. Histopathology shows progressive vessel wall degeneration characterized by disorganized vascular smooth muscle cells, collagen loss, and elastic fiber fragmentation from early to late stages. RNA-seq analysis identifies inflammation as the dominant process in late-stage aneurysms. Immunofluorescence assay reveals inflammatory cells prominently localized to the adventitia near rupture sites, linking adventitial inflammation to aneurysm progression. This genetically modified Fbn1Q2469X/Q2469X mouse model consistently develops progressive aortic aneurysms and provides a reliable, cost-effective platform to investigate the molecular mechanisms of aneurysm progression and to evaluate therapeutic strategies in aneurysm diseases, including Marfan syndrome and related disorders.

genetics↗

bioRxiv2025

TESSA: A unified model to detect trajectory-preserved and spatially-variable genes in spatial transcriptomics

Identifying spatially variable genes (SVGs) has been an essential task in spatial transcriptomics. In addition to SVGs detection, there are genes exhibiting expression patterns that are associated with cellular developmental stages or lineage fates across a tissue section. Identifying such genes could provide novel insights into tumor metastasis. Here, we introduce a unified statistical model, termed TrajEctory-preServed and SpAtially-variable gene detection (TESSA), to detect both types of genes. Moreover, we propose a novel strategy to address the inherent double-dipping issue commonly encountered when assessing temporal gene effects in transcriptomics analysis. We demonstrate the testing performance through extensive simulation studies and real applications to several publicly available datasets. Downstream analyses further highlight the potential of our method in identifying genes associated with tumor progression and enhancing spatial domain detection.

bioinformatics↗

bioRxiv2025

Matrix metalloproteinase-3 promotes arteriovenous fistula failure by regulating FAK-AKT signaling

Objective- Surgically created upper extremity arteriovenous fistulae (AVF) are the preferred vascular access for patients requiring dialysis. It is estimated, however, that 50% of AVF fail within one year due to aggressive neointimal hyperplasia, which significantly increases morbidity and mortality. Matrix metalloproteinase-3 (MMP-3), also known as stromelysin-1, is a member of the metalloproteinase family that plays a critical role in the pathogenesis of many human disorders by degrading extracellular matrix and regulating molecular signaling pathways. The role of MMP- 3 in AVF neointimal failure has not been explored. Approach and Results- We observed that MMP-3 was induced in a time-dependent fashion by fetal bovine serum (FBS) and the growth factor PDGF-BB in cultured venous SMC. MMP-3 was also highly expressed in the neointimal SMCs of the outflow veins and the juxta-anastomotic area in an AVF mouse model, as well as in human AVF specimens. Knockdown of MMP-3 significantly suppressed venous SMC proliferation, whereas overexpression of MMP-3 facilitated cell growth in vitro. Importantly, deficiency of global and SMC-specific MMP-3 significantly reduced neointimal hyperplasia and improved patency after AVF creation. Mechanistic studies showed that MMP-3-mediated SMC proliferation and AVF neointimal hyperplasia were regulated via the FAK-AKT signaling pathway. Conclusions- These data suggest that MMP-3 is a key mediator of AVF neointimal failure. Targeting local MMP-3 activity may be a novel therapeutic strategy to prevent AVF neointimal failure and improve outcomes in patients requiring hemodialysis.

cell biology↗

bioRxiv2025

DeepHalo: Deep Learning-Powered Exploration of Halogenated Metabolites Uncovering Antibacterial Depsipeptides

In the omics era, confident high-throughput analytical tools are crucial for the efficient identification of metabolites. Here, we present DeepHalo, a deep learning-integrated and hierarchically optimized workflow designed for high-throughput exploration of halogenated metabolites from high-resolution mass spectrometry-based metabolomics. DeepHalo leverages deep learning models combined with a comprehensive scoring to enhance the reliability of halogen predictions. It integrates PyOpenMS for fast isotope pattern detection and incorporates a halogen-based dereplication algorithm with GNPS molecular networking to efficiently exploit and annotate halogenates from complex biological matrices. To validate its performance, DeepHalo was applied to explore halogenated metabolites from 1,296 microbial culture crude, leading to the discovery of six families of structurally diverse halogenated molecules. This included a new class of cyclic depsipeptides, aglomycins A-E, featuring rare 3-chloroanthranilic acid and/or epoxyvaline blocks. Additionally, a plausible biosynthetic pathway of aglomycins was proposed through bioinformatics analyses and targeted gene knockout experiments. Bioassays revealed that aglomycin A exhibits synergistic antibacterial activity with linezolid against vancomycin-resistant Enterococcus faecium (VRE) both in vitro and in vivo. We envision that DeepHalo (freely available at https://github.com/xieyying/DeepHalo) will become a powerful tool for accelerating the discovery of halogenated "dark matter".

biochemistry↗

bioRxiv2025

Betrixaban Activates cGAS and ERVs to Promote Dual Nucleic-Sensing Antiviral Immunity

Broad-spectrum host-directed antivirals are urgently needed, as virus-targeted drugs often suffer from narrow specificity and rapid resistance. Here, we reported that Betrixaban (BT), an FDA-approved oral Factor Xa inhibitor, induced a robust antiviral state through dual innate immune pathways. Mechanistically, we identified BT directly bound and activated the DNA sensor cGAS to induce cGAMP production, representing BT as the first small-molecule cGAS agonist. Concurrently, BT inhibited histone deacetylases (HDACs), leading to chromatin de-repression of endogenous retroviruses (ERVs) and production of immunostimulatory double-stranded RNA (dsRNA) that engaged RIG-I/MDA5. These combined signal cascades triggered strong type I interferon responses and conferred broad-spectrum antiviral protection against RNA and DNA viruses in vitro and in vivo. These findings unveil a unique host-directed antiviral strategy wherein a small molecule drug engage dual nucleic acid-sensing pathway, and suggest repurposing BT as an orally broad-spectrum antiviral. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/669242v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@aa153dorg.highwire.dtl.DTLVardef@585d43org.highwire.dtl.DTLVardef@152ec59org.highwire.dtl.DTLVardef@11ada33_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

StCEL-based platform enables detection and sequencing of native sialoglycoRNA

Glycosylated RNAs (glycoRNAs) are an emerging class of biomolecules characterized by glycans attached to RNA molecules. Currently, glycoRNA detection remains largely confined to cellular models, substantially impeding its functional characterization and clinical translation. Here, we developed sialyltransferase-mediated chemical enzymatic labeling (StCEL), in which azido-modified sialic acid was transferred to the natural sialic acid on sialoglycoRNA, enabling the detection of native sialoglycoRNA across diverse biological samples with high specificity and sensitivity. Using StCEL, we found that sialoglycoRNA was significantly upregulated in multiple cancer types. StCEL combined with a microplate reader was applied to detect sialoglycoRNA in large-scale samples, revealing that elevated sialoglycoRNA levels correlate positively with tumor malignancy and poor patient prognosis. Furthermore, StCEL-based sialoglycoRNA enrichment and sequencing identified dysregulated sialoglycoRNAs in tumor tissues. Collectively, StCEL establishes a multifunctional platform that enables sensitive and specific detection of sialoglycoRNA, alongside sequencing analysis, demonstrating significant potential for clinical translation.

molecular biology↗

bioRxiv2025

Exceptional diversification of floral form in a specialized orchid pollination system

Traits that facilitate specialized interactions, such as those in flowers that promote pollination, are often invoked as targets of stabilizing selection across macroevolutionary timescales. However, the diversity of pollination mechanisms across flowering plants begs further investigation into the generality of this pattern. We fit a model of multivariate character evolution on a dataset of 140 orchid species sampled across 65 genera from the diverse neotropical Cymbidieae clade to characterize the role of pollination mode on the pace of flower shape evolution. We find that, contrary to the expectation of pollinator-mediated stabilizing selection causing stasis, orchids pollinated by specialized scent-collecting male euglossine bees ("perfume flowers" sensu (1, 2)) exhibit elevated rates of floral evolution compared to plants utilizing other rewarding or deceptive mechanisms. This pattern is recapitulated across at least 5 independent origins of this pollination system amidst a complex backdrop of background rate evolution. The rapid rates of change we observed in perfume flowers may be facilitated by weak evolutionary coupling between functional regions in their flowers, allowing for independent trajectories of evolution. Our results provide novel insights into the capacity for pollinators to generate selective pressures on flowers at macroevolutionary scales, providing an engine for trait diversification in some of the worlds most floristically rich regions.

evolutionary biology↗

bioRxiv2025

Engineering light robustness: Adaptive evolution uncovers new genetic determinants of HL tolerance in Synechocystis

Excess light absorption causes severe photo-oxidative damage and limits photosynthetic efficiency. Enhancing high-light (HL) tolerance and energy utilization in photosynthetic cyanobacteria holds great potential for sustainable biomanufacturing. Here, using Synechocystis sp. PCC 6803 (Syn6803) as a model, we generated eight independently evolved strains tolerant to hyper-HL (2000 mol photons/m2/s) through approximately two years of natural adaptive laboratory evolution (ALE). Remarkably, these strains circumvented common evolutionary trade-offs; all demonstrated markedly enhanced quantum yields and photosynthetic performance, with four strains exhibiting a 121.71%-168.36% increase in biomass accumulation under HL. When equipped with the heterologous sucrose transporter CscB, sucrose productivity under HL increased by up to 208.33%. Whole-genome resequencing unveiled a total of 77 mutations across the eight lineages, uncovering a diverse polygenic landscape for HL adaptation. Reverse genetics combined with structural modeling highlighted pivotal roles for regulatory and structural loci, including slr0758 (KaiC1) and slr1513 (SbtB), in modulating circadian rhythmicity and inorganic carbon uptake to prevent photo-oxidative overload. Overall, our findings uncover diverse evolutionary routes toward high-light acclimation and provide superior cyanobacterial chassis for photosynthetic biotechnology.

microbiology↗

bioRxiv2025

Impact of Inactivation Methods on Biosafety and Antigen Reactivity of Brucella melitensis from the Perspective of Astral-DIA Proteomics Based on Antibody Immunoprecipitation Mass Spectrometry

Effective Brucella inactivation is imperative for safe vaccine development, diagnostics, and sample handling, particularly in resource-limited regions lacking high-level containment facilities. This study investigated inactivation methods using the Rev.1 vaccine strain and three Brucella melitensis field isolates from Gansu (GS-XG, GS-SN, GS-MQ). Heat (80{degrees}C/95{degrees}C, 10-20 min) and formaldehyde (0.4%/0.6%, 48-72 h) inactivation were evaluated for biosafety and antigenicity. Rev.1 was completely inactivated by 80{degrees}C/95{degrees}C for 20 min or 0.6% formaldehyde for 48-72 h. Superior antigenicity, compared to phenol inactivation, was confirmed by ELISA and Western Blot. However, field isolates demonstrated greater resistance, surviving 80{degrees}C for 20 min and 0.4% formaldehyde for 72 h, necessitating stricter conditions (95{degrees}C, 20 min; 0.6% formaldehyde, 72 h) for their complete inactivation. Astral-DIA proteomics analyzed approximately 60% of the proteome ([~]2000/3300 proteins), revealing 256, 311, and 318 differentially expressed proteins between 80{degrees}C/95{degrees}C heat, 48-h/72-h formaldehyde, and heat/formaldehyde methods, respectively. Gene Ontology and KEGG analyses indicated heat inactivation upregulated cellular structure proteins but downregulated metabolic pathways, with 95{degrees}C potentially damaging conformational epitopes. Formaldehyde treatment (48 h) stabilized soluble antigens, preserving ribosomal and regulatory protein epitopes, while 72-h treatment induced organelle disintegration. Protein-Protein Interaction networks suggested heat inactivation enhanced immunogenicity of membrane and stress proteins, suitable for targeted studies, whereas formaldehyde preserved broader epitopes, beneficial for whole-cell vaccines and multi-epitope screening. Inactivation methods must be tailored to specific strain characteristics and applications. Astral-DIA provides molecular insights into antigenicity loss, guiding future research on protein functions and epitope dynamics for precise brucellosis control.

microbiology↗

bioRxiv2025

DANCE 2.0: Transforming single-cell analysis from black box to transparent workflow

Preprocessing is a critical step in single-cell data analysis, yet current practices remain largely a black-box, trial-and-error process driven by user intuition, legacy defaults, and ad hoc heuristics. The optimal combination of steps such as normalization, gene selection, and dimensionality reduction varies across tasks, model architectures, and dataset characteristics, hindering reproducibility and method development. We present DANCE 2.0, an automated and interpretable preprocessing platform featuring two key modules: the Method-Aware Preprocessing (MAP) module, which discovers optimal pipelines for task-specific methods via hierarchical search, and the Dataset-Aware Preprocessing (DAP) module, which recommends pipelines for new datasets via similarity-based matching to a reference atlas. Together, MAP and DAP execute over 325,000 pipeline searches across six major tasks - clustering, cell type annotation, imputation, joint embedding, spatial domain identification, and cell type deconvolution - yielding robust and generalizable recommendations. MAP-recommended pipelines consistently outperform original method defaults, with substantial gains across all tasks. Beyond automation, DANCE 2.0 reveals interpretable preprocessing patterns across tasks, methods, and datasets, transforming preprocessing into a transparent, data-driven process. All resources are openly available at https://github.com/OmicsML/dance to support broad community adoption and future methodological advances.

bioinformatics↗

bioRxiv2025

Neuron-derived extracellular vesicles in plasma present a potential non-invasive biomarker for Huntingtin protein and RNA assessment in Huntington disease

Huntington disease (HD) is a neurodegenerative disease caused by a trinucleotide repeat expansion in the HTT gene encoding an elongated polyglutamine tract in the huntingtin (HTT) protein. The use of biomarkers has become a major component in preclinical studies focusing on HTT lowering strategies. Quantification of soluble mutant HTT (mHTT) in cerebrospinal fluid (CSF) has served as a pharmacodynamic readout and as potential disease progression biomarker. However, development of future assays for HTT measurement from other biofluids, such as blood, will facilitate the access to human samples since CSF collection is an invasive outpatient procedure. Brain cells, in particular neurons, secrete extracellular vesicles (EVs) that cross the blood-brain barrier and circulate in blood. Importantly, EVs have been identified to be involved in HTT export from cells to the extracellular space. However, it is unknow which vesicle subtype correlates better with HD progression. Our work investigates the potential of EVs as non-invasive sources of clinical biomarkers in liquid biopsies. We developed an optimized ultracentrifugation protocol for the purification of ectosomes and exosomes from human samples and plasma of humanized HD mouse models. Ectosomes are larger vesicles that bud from the plasma membrane of cells, whereas exosomes originate from multivesicular bodies and are afterwards released to the extracellular space. Consistent with previous published data in other model systems, ectosomes isolated from plasma of the Hu97/18 mouse model contain both wild-type (wt) and mHTT in higher levels than in exosomes. Similar results were observed in media from HD induced pluripotent stem cells (iPSCs)-differentiated neurons and in Hu97/18 primary neuronal cultures. Interestingly, we also found higher levels of HTT transcripts in this EV subtype. We further demonstrate that initial storage of the samples using a slow freezing protocol preserves HTT and EV protein marker levels, highlighting the importance of sample preparation for EV isolation and analysis. Our results also show that plasma contains vesicles originated from neuronal cells that can be isolated using neuron-specific markers, such as ATPase Na+/K+ transporting subunit alpha 3 (ATP1A3), allowing the evaluation of HTT levels in the brain through vesicles circulating in the blood. Overall, our results demonstrate that HTT protein measurement from EVs isolated from blood can be a potential less-invasive disease biomarker. We also demonstrate that EVs subtypes contain different HTT protein and RNA levels, important for the development of consistent and reliable biomarkers. Further characterization of neuron-specific EVs content from patient-derived biofluids will lead to the development of novel clinical biomarkers and for evaluation of therapeutic strategies.

neuroscience↗

bioRxiv2025

Decoding functional genes across species with annotation-independent machine learning

Dissecting the genetic basis of complex traits across species is a challenge for traditional low-throughput, species-specific methods. To overcome these limitations, we introduce a computational framework that integrates cross-species comparative genomics and machine learning to identify functional genes for shared phenotypes, independent of prior annotation. Our method links orthologous gene group (OG) profiles to specific traits, enabling the powerful discovery of functional genes across diverse evolutionary clades. For plant-arbuscular mycorrhizal symbiosis, a notoriously difficult system, our model pinpointed a regulatory network by identifying 27 core plant-AM symbiosis genes among its top 50 candidate OGs, including the critical receptor SYMRK. The approach also proved highly effective for identifying functional genes related to the tested vertebrate skeletal development and multiple bacterial traits. Most notably, for bacterial motility, our model not only identified 63 known motility genes from the top 100 candidate OGs (of which 78 are present in Escherichia coli) but also guided the experimental validation of three novel essential genes. This annotation-independent strategy represents a paradigm shift in functional genomics, offering a scalable and universal engine to decode the genetic architecture of complex traits and illuminate the vast functional dark matter across the tree of life.

genomics↗

bioRxiv2025

Single-Cell and Spatial Profiling of Tumor Microenvironment Heterogeneity in Human Osteosarcomas

Osteosarcoma, a highly heterogeneous malignant bone tumor, exhibits substantial interpatient and intratumoral heterogeneity. Utilizing integrated single-cell RNA sequencing and spatial transcriptomics, we uncovered distinct tumor microenvironment (TME) transcriptional profiles across patients, highlighting profound interpatient heterogeneity. Intratumorally, malignant cells primarily followed two differentiation trajectories, converging into osteoblastic and chondroblastic functional states, and hypoxia may be the influencer; notably, within individual patients, tumor cells exhibited greater transcriptional similarity driven by specific transcription factors, despite these divergent states. Spatial analysis revealed patient-specific TME cellular co-localization patterns, alongside conserved spatial relationships: vascular components (endothelial cells, pericytes) demonstrated strong co-localization, while immune cells (T cells, myeloid cells) clustered within shared regions. Crucially, these functional states occupied distinct microniches: osteoblastic regions were enriched with osteoclasts, vascular components, and immune cells, whereas chondroblastic regions displayed the opposite composition and were preferentially located in hypoxic, vascular-poor niches, exhibiting significant enrichment of hypoxia-related signaling pathways. Furthermore, our data suggest osteosarcoma cells may activate fibroblasts via the SPP1 signaling pathway, indicating fibroblasts act as key intermediaries in tumor-directed TME modulation. This study comprehensively delineates the intricate landscape of osteosarcoma heterogeneity, defining distinct functional cellular states, their spatially organized TME niches, and a potential SPP1-mediated tumor-fibroblast regulatory axis. This comprehensive analysis elucidates the intricate interpatient and intratumoral heterogeneity in osteosarcoma, revealing functional and spatial organization within the tumor and its microenvironment.

cancer biology↗