Biology subjects

Shang, X.

Publications and source records attributed to Shang, X..

At least 19 recordsLinked to original sources

bioRxiv2026

Engineered CTLA-4 targeting chimeras for targeted protein degradation and immune synapse reprogramming

Despite the success of immune checkpoint blockade in cancer therapy, overcoming the immunosuppressive tumor microenvironment remains a major barrier to durable responses. Here, we introduce cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4)-targeting chimeras (cTACs), a class of synthetic molecules that harness CTLA-4-mediated trans-endocytosis (TE) for targeted protein degradation across cellular boundaries. The lead candidate, cTAC4.0, is composed of the extracellular domain of CD80 fused to an anti-programmed death-ligand 1 (PD-L1) fragment via a human immunoglobulin G4 (IgG4) heavy chain. Unlike existing protein degraders that function within cancer cells via house-keeping pathways, cTAC4.0 operates in trans between immune and tumor cells. It redirects the natural CD80-CTLA-4 internalization to capture and degrade PD-L1 from tumor cells into T cells, while simultaneously converting PD-L1-mediated suppression into CD28-driven co-stimulation, depending on the relative availability of CTLA-4 or CD28. Mechanistically, we demonstrate that cTAC4.0 promotes CTLA-4-dependent internalization and lysosomal degradation of both membrane-bound and soluble PD-L1, reduces surface PD-L1 levels on target cells, and activates T cells in a CD28-dependent manner. Functionally, cTAC4.0 enhances the cytotoxicity of both bispecific T cell engagers and T cell receptor (TCR)-engineered T cells in vitro, and when combined with these engineered T cells, it suppresses tumor growth in vivo in a glioblastoma xenograft model. By coupling immune checkpoint degradation with T cell activation in an immune synapse-enriched manner, cTACs establish a paradigm for intercellular protein degradation systems that simultaneously deplete suppressive ligands and enhance T cell function to overcome resistance in cancer immunotherapy.

immunology↗

bioRxiv2026

Structure-aware deep learning predicts influenza antigenicity and guides vaccine strain recommendation

The continuous accumulation of genetic mutations in influenza A viruses (IAVs) drives antigenic drift, necessitating precise antigenic prediction for optimal vaccine strain selection. While sequence-based methods have advanced antigenic surveillance, they neglect the three-dimensional structural context that fundamentally dictates viral antigenicity. Here, we introduce Vir3D, which leverages ESMFold-derived structural information from amino acid sequences to precisely predict viral antigenicity and guide vaccine strain selection. Across both human H3 and highly pathogenic avian H5 subtypes, Vir3D not only accurately discriminates antigenic variants and infers pairwise antigenic distances, but also mechanistically delineates key structural residues driving viral immune evasion. In a decade-long retrospective analysis, Vir3D-prioritized vaccines consistently achieve broader antigenic coverage of circulating strains than World Health Organization (WHO) recommendations. Crucially, Vir3D successfully predicts that the emerging U.S. dairy cattle H5N1 virus (TX/24) remains antigenically stable relative to clade 2.3.4.4b vaccine strains, and subsequent wet-laboratory validation of hemagglutination inhibition (HI) assays definitively corroborates this finding. Overall, Vir3D establishes a powerful, structure-driven framework for proactive influenza surveillance and pandemic preparedness.

bioinformatics↗

bioRxiv2026

Incorporation of native HCV E1E2 into a nanoparticle vaccine platform

Development of an effective HCV vaccine requires the induction of both broadly neutralizing antibodies (bnAbs) and a robust cellular response. One issue that has arisen is that HCV subunit vaccines have limited immunogenicity, thus requiring multivalent formats in order to elicit a robust anti-HCV immune response. Toward that end, nanoparticle vaccines possess the ability to facilitate a controlled multivalent presentation and trafficking to lymph nodes, where they can interact with both arms of the immune system. Here, we used a soluble, secreted form of E1E2 (sE1E2) to assemble native E1E2 into a nanoparticle platform using a post-purification coupling assembly system. Nanoparticles were assembled by purifying sE1E2 containing a C-terminal SpyTag and an mi3-SpyCatcher fusion separately and covalently coupling the components via incubation. Free sE1E2-SpyTag was removed from nanoparticle preparations via gel filtration. The sE1E2-mi3 nanoparticles are fully competent to bind conformation-dependent bnAbs, indicating retention of a native assembly in the nanoparticle format. Electron microscopy analysis showed a clear incorporation of sE1E2 on the surface of the nanoparticle. Immunogenicity of sE1E2-mi3 nanoparticles was examined relative to sE1E2 alone and membrane-bound E1E2 (mbE1E2) following inoculation of groups of CD1 mice. Assessment of the immunogenicity of the sE1E2-mi3 nanoparticles showed that the nanoparticle assembly has a similar immunogenicity profile to that of mbE1E2 after only a prime and one boost, and overall superior to sE1E2. This proof-of-principle study sets the stage for further exploration of nanoparticles and other multivalent platforms for the development of E1E2-based vaccines. ImportanceHepatitis C virus infects approximately 50 million people, and at present no effective HCV vaccine exists. Due to the high sequence variability of HCV and the resulting difficulty in developing a vaccine that elicits a broadly neutralizing response, multiple efforts are underway to enhance the immunogenicity of HCV vaccine candidates. In this study, we incorporated native soluble, secreted E1E2 (sE1E2) into a 60-mer nanoparticle via the SpyTag-SpyCatcher system and covalent isopeptide bond attachment using the purified components. These nanoparticles are antigenically intact and elicit a neutralizing antibody response at an earlier time point in the immunization regimen than the corresponding subunit vaccine. These studies show that a well-characterized sE1E2 platform compatible with multiple genotypes can be coupled to nanoparticles for use as a vaccine candidate.

microbiology↗

bioRxiv2026

Spatial Glyco-Codes Define Human Liver Pathology and Progression

Glycosylation is a fundamental process regulating cellular function, tissue organization, and disease progression. However, comprehensive glycan profiling at single-cell spatial resolution remains largely inaccessible, particularly in clinical archival tissues. Here we develop spatial-GPT, a multimodal platform for simultaneous profiling of glycans, proteins, and/or transcripts in archival formalin-fixed paraffin-embedded (FFPE) tissues. Using a panel of 30 DNA-encoded lectins recognizing major mammalian glycan motifs and structural classes, sequencing-based spatial-GPT (DBiT-GPT) mapped the spatial glycome, proteome, and transcriptome across 16 human liver specimens encompassing steatosis, fibrosis, cirrhosis, and hepatocellular carcinoma (HCC), leading to identification of spatial glyco-codes - combinatorial glycan states associated with distinct cellular identities, tissue features, and pathological processes. Unexpectedly, glyco-codes alone were sufficient to resolve major cell types, disease states, and HCC subtypes, revealing a previously unappreciated level of biological information encoded within the tissue glycome. Spatial glycomics uncovered tumor-like glyco-codes in premalignant regions, suggesting that glycan reprogramming may precede overt malignant transformation. Using imaging-based single-cell spatial glycan-protein profiling (CODEX-GP), we track glyco-codes across the whole-tissue architecture of 3 representative HCC samples. We further examined the glyco-codes across more than 300 patient specimens and quantified cell-type- and disease-specific glyco-codes as well as glycan-defined immune-evasion, T-cell-exhaustion, and steato-fibrotic niches. Together, these findings establish spatial glyco-codes as a previously unrecognized layer of tissue organization that encodes cellular identity, tissue function, and disease progression. The ability of glyco-codes to distinguish major liver pathologies across independent patient cohorts further highlights their potential as a new class of molecular histopathology biomarkers.

pathology↗

bioRxiv2026

DesignMaster: A Multi-Conditional Diffusion Framework for Rational PROTAC Design

MotivationProteolysis-targeting chimeras (PROTACs) enable targeted protein degradation through ternary complex formation with E3 ubiquitin ligase. However, the rational design of PROTACs remains highly challenging due to limited structure-activity relationship data and the vast conformational diversity of linkers. Existing computational approaches can be broadly divided into structure-based ternary modelling methods and fragment-based linker generation models. Although these approaches have advanced PROTAC design, they typically neglect key physicochemical constraints and linker-length control during the generation process, causing the generated PROTACs to lack balanced structural properties required for effective ternary complex formation with drug-like characteristics. ResultsTo address these limitations, we propose DesignMaster, a diffusion-based generative framework that explicitly incorporates linker length and physicochemical properties as controllable conditioning signals. DesignMaster employs an E(3)-equivariant graph Transformer with a gated multi-condition fusion module to inject linker length and physicochemical constraints throughout the diffusion process, enabling fine-grained and constraint-aware molecular generation. Experiments on PROTAC-DB 2.0 and 3.0 demonstrate that DesignMaster outperforms state-of-the-art baselines, with a 3.2% improvement in validity and a 34.4% improvement in recovery. The Case study shows DesignMaster achieves a 51.78% reduction in RMSD when predicting the linker of PROTAC BCPyr targeting 6W7O, highlighting its potential for practical structure-guided PROTAC design. AvailabilityThe source code and datasets are available at https://github.com/ABILiLab/DesignMaster.

bioinformatics↗

bioRxiv2026

SpatialClaw: A Memory-Augmented Autonomous Ecosystem for Spatial Omics Analysis

While the expansion of spatial omics has revolutionized our ability to dissect tissue architecture, the accumulation of incompatible computational methods has heavily fragmented end-to-end analysis, rendering complex workflows irreproducible. Generic conversational agents lack the domain-specific precision necessary to navigate the intricate biological pipelines. To overcome this, we present SpatialClaw, a memory-augmented autonomous ecosystem to unify spatial omics analysis under a single natural-language interaction. SpatialClaw integrates 30 specialized skills, spanning raw data preprocessing, spatial domain identification, deconvolution, spatially variable gene detection, cell-cell communication analysis, multi-sample and cross-modality integration. Distinct from existing agents, SpatialClaw introduces a graph-based persistent memory architecture that stores dataset metadata, analysis lineage, biological insights, and user preferences as versioned nodes and edges across three hierarchical layers (Session, Episodic, and Semantic), governed by a deterministic promotion policy. A Memory-Augmented Reasoning (MAR) Operator bridges the memory store and the main agent, synthesizing retrieved experiences into task-specific guidance for each query. In rigorous benchmarking spanning three memory-sensitive scenarios across 10 spatialomics skills, SpatialClaw outperforms both a standard large language model and the memory-only configuration. Furthermore, we demonstrate its robust biological utility by dissecting the complex tumor microenvironment of a 15-section human triple-negative breast cancer cohort. In merely three conversational turns and with zero direct scripting, SpatialClaw executes a comprehensive end-to-end workflow, yielding standardized output bundles. Ultimately, by synergizing comprehensive analytical tools with structured persistent memory, SpatialClaw elevates spatial omics from disjointed computational stitching to a fully traceable, reproducible, and self-improving discovery ecosystem. SpatialClaw is ready to use at https://github.com/ShangBioLab/SpatialClaw.

bioinformatics↗

bioRxiv2026

Cell Type-informed Characterization of Spatial Niches from Spatial Multimodal and Multi-omics Data

Cell niches play critical roles in tissue organization and orchestrate homeostasis, development, and disease progression. Advances in spatial omics technologies now allow diverse molecular and image-derived data to be jointly captured while preserving spatial context, but deciphering cell niches from such spatial multimodal and multi-omics data remains challenging. Existing computational methods are still limited in their flexibility across variable combinations of spatial modalities and omics data. Here we introduce SpaNECT, a unified and flexible framework designed to accommodate spatial multimodal and multi-omics data for cell niche characterization. SpaNECT further incorporates reference-informed cell-type information to support biologically interpretable niche analysis. Systematic evaluations across diverse tissues, disease conditions, and developmental stages showed that SpaNECT consistently outperformed representative methods in resolving cell niches. In mouse brain spatial multi-omics data, SpaNECT uncovered niche-associated molecular and regulatory programs; in developing chick heart, it tracked cross-stage niche reorganization and progressive remodeling of ventricular-associated cell states during maturation. Overall, SpaNECT establishes a general and robust framework for characterizing cell niches across spatial multimodal and multi-omics data.

bioinformatics↗

bioRxiv2026

Pathway-informed Universal Domain Adaptation for Single-cell RNA-seq Data

The rapid accumulation of single-cell atlases has yielded datasets of unprecedented scale, encompassing samples across diverse platforms, locations and laboratories. This multidimensional complexity drives an urgent need for universal domain adaptation methods capable of achieving precise cell-type annotation. However, existing methods lack computational scalability and fail to integrate biological priors. Here, we develop scPathOT, a pathway-informed universal domain adaptation framework that leverages pathway activation transformations to harmonize single-cell datasets across disparate conditions. We demonstrate the versatility of scPathOT across diverse technological platforms, tissues, disease contexts, cellular senescence and treatment conditions. Crucially, this pathway-informed alignment not only accurately resolves cellular identities but also uncovers functional mechanism. In pancreatic islets, scPathOT delineates a shared stress-repair axis traversed by {beta}-cells prior to their divergence into type 1 and type 2 diabetes-specific states. In aging bone marrow, scPathOT disentangles lineage-specific senescence modules that unexpectedly converge onto unified inflammatory and oxidative-stress programs. Furthermore, application to an in-house pancreatic ductal adenocarcinoma cohort uncovers the mechanistic basis underlying the neoadjuvant chemotherapy-induced reorganization of stromal-immune crosstalk. By coupling biological priors with universal domain adaptation, scPathOT provides a scalable, mechanistically interpretable framework to accelerate biological discovery from atlas-level single-cell data.

bioinformatics↗

bioRxiv2026

CD95/Fas Apoptosis Signal Initiation Depends on the Ligand Oligomerization State and Formation of Small Ligand-Receptor Complexes

The death receptor cluster of differentiation 95 (CD95 / Fas) is an important inducer of apoptotic activity in tumor cells, but may exhibit differential cell responses (ranging from predominantly apoptosis up to proliferation) when stimulated by its CD95 ligand (CD95L/FasL). How ligand form and receptor-ligand stoichiometry shape these divergent outcomes remain unresolved. To define structural and functional determinants of CD95 signaling, we systematically compared the oligomerization and activity of native CD95L with CD95L variants, where ligand trimers were stabilized either by a FLAG-tag for subsequent monoclonal antibody (mAb) crosslinking or by genetically fused isoleucine zippers (IZ). Apoptotic activity varied markedly with both ligand concentration and ligand variant type. To quantify receptor-ligand stoichiometry at the cell membrane, we combined quantitative Stimulated Emission Depletion (qSTED) nanoscopy, simulations, and biochemical assays. Across conditions, signaling complexes consisted of small oligomeric assemblies, with up to three CD95 receptors engaging a trimeric ligand. Despite similar stoichiometry, biochemical measurements revealed substantial differences in ligand oligomerization and binding affinity, with IZ-CD95L exhibiting markedly higher affinity than FLAG-CD95L (KD of 0.81 nM and 18.4 nM, respectively). Dimeric/trimeric CD95 formed by CD95L complexation or bridged CD95 by mAb enabled flexible intracellular FADD linkage for enhanced signaling.These results indicate that apoptotic potency is governed by the proximity of CD95 receptors, achieved through receptor bridging as well as by a stabilized trimeric ligand, both of which enhance receptor recruitment and binding avidity. Our findings provide mechanistic insight into CD95 signaling and suggest strategies for optimized apoptosis-inducing therapeutics. Significance StatementThe CD95/Fas receptor-ligand system is a central regulator of programmed cell death, yet the earliest molecular events of CD95 activation remain unclear. We systematically compared different CD95L/FasL variants and show that ligand architecture critically shapes receptor engagement and death signaling. An engineered FasL with an isoleucine zipper exhibited larger trimeric lig- and fractions, stability, and receptor binding, resulting in stronger apoptotic activity. Super-resolution imaging and simulations indicate that CD95 is mainly monomeric at the membrane and forms small oligomers after ligand binding. The flexibility of intracellular FADD linkage trigerred by dimeric/trimeric or bridged CD95 enables more intense signaling. These findings clarify how ligand structure governs CD95 activation and provide design principles for death receptor-targeted therapeutics.

biophysics↗

bioRxiv2026

FluNexus: a versatile web platform for antigenic prediction and visualization of influenza A viruses

Influenza A viruses continuously undergo antigenic evolution to escape host immunity induced by previous infections or vaccinations, consequently causing seasonal epidemics and occasional pandemics. Antigenic prediction and visualization of influenza A viruses are crucial for precise vaccine strain selection and robust pandemic preparedness. However, a user-friendly online platform for these capabilities remains notably absent, despite widespread demand. Here, we present FluNexus (https://flunexus.com), the first-of-its-kind, one-stop-shop web platform designed to facilitate the prediction and visualization of the antigenic change in emerging variants. FluNexus features a data preprocessing module for hemagglutinin subunit 1 (HA1) and hemagglutination inhibition (HI) data across three major public health threat subtypes (H1, H3 and H5). Meanwhile, FluNexus provides an interactive interface for online antigenic prediction and offers practical guidance for researchers. Most notably, FluNexus offers the visualization of influenza A virus antigenic evolution, providing intuitive insights into its antigenic dynamics. Specially, FluNexus proposes a novel manifold-based method for positioning antigens and antisera, generating accurate antigenic cartographies even with sparse HI data. By alleviating the programming burden on biologists, FluNexus supports more informed decision-making in vaccine strain selection and strengthens surveillance and pandemic preparedness. HighlightsO_LIFluNexus features a data preprocessing module for HA1 and HI data spanning the H1, H3, and H5 subtypes. C_LIO_LIFluNexus facilitates online antigenic prediction utilizing ten state-of-the-art antigenic prediction tools, and offers practical guidance based on a comparative evaluation of their performance. C_LIO_LIFluNexus provides a visualization module for mapping antigenic evolution of influenza A viruses, incorporating a novel manifold-based method for antigenic cartography. C_LI

bioinformatics↗

bioRxiv2026

An Advanced Humanized Systemic Lupus Erythematosus Model Enables Parallel Profiling of B Cell-Targeted Therapies

B cell-targeted therapies represent a transformative frontier for systemic lupus erythematosus (SLE) intervention, yet clinical recommendation of specific treatment is hampered by the challenge to perform head-to-head comparisons of efficacy-toxicity trade-offs and tissue-specific impacts in patients. To address this gap, an advanced Toll-like receptor 7 (TLR7) agonist-induced SLE model is developed in a NCG-M (NOD-Prkdcem26Cd52Il2rgem26Cd22Rosa26em1Cin(hCSF2&IL3&KITLG)/Gpt) human immune system (HIS) mice. This model enables parallel evaluation of multiple B cell-directed therapies within a controlled cohort study. It recapitulates core SLE pathologies--including immune effector cell augmentation, autoantibody production and glomerulonephritis--within 2 months, demonstrating greater severity and clinical fidelity than conventional pristane-induced systems. Validated through replication of clinical responses to rituximab and belimumab, the platform directly compares two emerging modalities: universal chimeric antigen receptor-T cells (UCAR-T) elicits delayed but profound B-cell reset correlating with superior reduction in renal pathology and autoantibodies, while T cell engagers (TCEs) mediate rapid yet partial B-cell depletion and transient efficacy. UCAR-T concurrently induces greater acute inflammatory responses, aligning with its distinct toxicity profile. By resolving therapy-specific effects on human immune dynamics and symptom control, this study empowers context-specific therapeutic selection for SLE, advancing tailored management strategies.

immunology↗

bioRxiv2025

Efficient Identification of Short Tandem Repeats via Context-Aware Motif Discovery and Ultra-Fast Sequence Alignment

Tandem repeats (TRs) are highly polymorphic genomic elements, associated with diverse molecular traits and implicated in numerous human diseases. However, large-scale analysis of TRs has been limited by computational challenges, including motif recognition, detection in complex regions, and excessive computational cost. Here we present FastSTR, a computationally efficient tool for precise detection and characterization of TRs. FastSTR integrates a context-aware N-gram motif model with a segmented global alignment algorithm to enable accurate motif identification and boundary definition, even for repeat units up to 8 bp. Across 13 species, FastSTR achieved >90% recall and 99% precision, running several times faster than existing methods white outperforming them in both sensitivity and accuracy. Applied to the human genome, FastSTR uncovered previously unannotated HSATII elements, resolved population-specific TR demonstrate, and identified recurrent STR alterations in lung cancer. These results demonstrate FastSTR as a versatile framework for TR annotation and discovery, advancing studies of genome evolution, genetic diversity, and disease.

bioinformatics↗

bioRxiv2025

LoFT-TCR: A LoRA-based Fine-tuning Framework for TCR-Antigen Binding Prediction

T cells recognize and eliminate diseased cells by binding their T cell receptors (TCRs) to short endogenous peptides (antigens) presented on the cell surface. Such interactions are central to adaptive immunity, yet current experimental approaches to identify TCR-antigen binding pairs remain labor-intensive and constrained by limited reagents. Here, we propose LoFT-TCR, a low-rank adaptation (LoRA)-based fine-tuning framework designed for TCR-antigen binding prediction. To capture precise and informative sequence representations, we first fine-tuned the protein large language model ESM-2 on antigen-specific TCR datasets using LoRA. Subsequently, we constructed a heterogeneous interaction graph where nodes encode sequence features and edges indicate interaction relationships. By leveraging a graph learning framework, LoFT-TCR effectively integrates sequence and topological information to enhance prediction capability. Systematic experiments validated that fine-tuning ESM-2 significantly enhanced the models capability to extract discriminative sequence representations, which are critical for accurate TCR specificity prediction. Moreover, LoFT-TCR consistently achieved superior performance compared to state-of-the-art methods on both TCR-antigen binding prediction and TCR specificity discrimination tasks. Experimental results demonstrate that LoFT-TCR achieves substantial improvements in predictive accuracy and holds potential for advancing personalized T cell-based immunotherapy.

immunology↗

bioRxiv2025

QuantaMind MD enables protein modeling with ab initio accuracy

Accurate biomolecular simulations are essential for understanding chemical processes and advancing applications such as protein engineering and drug design. While quantum mechanical (QM) methods can provide chemical accuracy, their computational cost limits their scalability. Machine learning force fields (MLFFs) offer a promising alternative by achieving similar accuracy with vastly improved efficiency, but their effectiveness is often constrained by limited conformational and chemical diversity in training. We introduce QuantaMind, a robust MLFF workflow designed to extend chemical and conformational coverage, particularly in transition-state regions. QuantaMind enables quantum-accurate molecular dynamics simulations, successfully capturing complex phenomena such as proton diffusion, water dissociation, and acid-base neutralization. We show that QuantaMind can be applied to large biomolecular systems, enabling accurate protein structure optimization and improving the prediction of residue contacts and hydrogen bonds. A pocket-centric simulation strategy further allows QuantaMind to efficiently model protein-ligand interactions with high structural accuracy. These results establish QuantaMind as a versatile and scalable tool for atomistic simulations at ab initio accuracy.

biochemistry↗

bioRxiv2025

TridentTCR: Predicting T Cell Receptor Specificity for Antigens and Autoimmune-Related Targets via a Topology-Aware Graph and Large Language Model

T cells recognize and eliminate diseased cells by binding their T cell receptors (TCRs) to short endogenous peptides presented on the cell surface, commonly referred to as antigens. Accurate prediction of antigen-TCR specificity is essential for advancing T cell-based immunotherapies while minimizing autoimmune toxicity. However, current computational methods struggle to generalize across diverse TCR repertoires and reliably differentiate therapeutic antigens from autoimmune-related antigens (arAgs). Here, we introduce TridentTCR, a computational framework integrating a pretrained large language model (LLM) for high-fidelity sequence embedding with a topology-aware graph neural network to capture structural insights from the antigen-TCR interaction network. TridentTCR significantly advances TCR specificity prediction by enabling robust trinary classification, distinguishing TCR interactions involving general antigens, arAgs, and non-binding. Additionally, TridentTCR outperforms existing state-of-the-art models in binary classification (binding vs. non-binding). Validation on independent clinical datasets demonstrated strong generalization capability for unseen TCRs and antigens. In a disease-specific context, TridentTCR identifies notable cross-reactivity between ZnT8186-194-specific TCRs and a mimotope from Bacteroides stercoris, suggesting that T cell cross-reactivity may contribute to the initiation of type 1 diabetes. Furthermore, we introduced a quantitative metric, antigenic immune response entropy (AIRE), which leverages TridentTCR predictions alongside clonotype frequencies and repertoire diversity to precisely quantify antigen-specific immune responses from single-cell profiling data. Collectively, TridentTCR provides an interpretable and clinically relevant tool, enabling improved understanding of TCR specificity, cross-reactivity, and off-target autoimmune risks in clinical immunotherapy.

immunology↗

bioRxiv2025

Spatial geometry-aware deep learning for deciphering tissue structure from spatially resolved transcriptomics

Recent advances in spatially resolved transcriptomics have enabled high-throughput gene expression profiling while preserving spatial context, thereby facilitating the investigation of spatial heterogeneity within tissues. Here, we present SpatialGEO, a spatial geometry-aware deep learning framework designed to decipher tissue organizational structures through dual-encoder feature extraction and geometric graph learning. Experimental results demonstrate the superior accuracy of SpatialGEO in identifying spatial regions across datasets encompassing diverse biological contexts and spatial resolutions. Moreover, SpatialGEO uncovers key mechanisms of immune evasion and regulation within the tumor microenvironment and provides novel biological insights into mouse embryonic development.

bioinformatics↗

bioRxiv2025

A Multimodal Graph Learning Framework for Versatile Spatial Transcriptomics Analysis with SpatialModal

The development of spatial transcriptomics(ST) technologies enables the exploration of tissue structure and cellular function within a spatial context. However, mainstream analytical methods primarily focus on the gene expression modality itself and struggle to fully leverage auxiliary information from histological images, which limits precise dissection of complex biological structures. To address this, we propose SpatialModal, a multimodal graph learning framework that effectively integrates gene expression data and histological images from spatial transcriptomics to construct unified spatial representations. SpatialModal effectively captures synergistic interactions between modalities by jointly modeling intra- and inter-modal complementary features while incorporating spatial adjacency information. Furthermore, it employs a dual contrastive learning strategy to enhance the discriminative power of representations, thereby enabling efficient and robust analysis of tissue structures. We validate the effectiveness of this approach on multiple public datasets covering diverse tissue types, species, and resolutions. Experiments demonstrate that SpatialModal exhibits significant advantages in downstream tasks, including spatial domain identification, gene expression reconstruction, and pseudotime inference, and accurately elucidates the hierarchical structure of the mouse cortex as well as the metabolic-immune dynamic boundaries in the breast cancer microenvironment. Additionally, SpatialModal shows exceptional robustness in single-cell resolution and multi-slice integration tasks, providing an efficient and broadly applicable analytical tool for spatial transcriptomics research.

bioinformatics↗

bioRxiv2025

Exploring Population Differences in the Human Gut Microbiome: from Microbial Abundance to Single Nucleotide Polymorphisms

The human gut microbiota exhibits significant diversity across populations, influenced by factors such as geography, diet, and lifestyle, particularly between the Han Chinese and non-Chinese populations. While previous studies have predominantly focused on the taxonomic abundance of the gut microbiome, the impact of single nucleotide polymorphisms (SNPs) in driving population-specific differences remains largely underexplored. In this study, we systematically investigated gut microbial differences between the Han Chinese and non-Chinese populations using the Human Gut Microbiome Reference Genome Catalog (HGMRGC). HGMRGC includes 271,480 non-redundant genomes from 5,785 prokaryotic species, which was generated by metagenomic sequencing data from 9,320 publicly available samples across 22 countries and 3,584 newly sequenced samples from Han Chinese individuals across 29 provinces and regions in China. We observed geography was the primary driver of microbial variation of abundance and SNPs. We identified 625 novel population-specific genome clusters from HGMRGC with functional differences in carbohydrate utilization and 126 species exhibiting distinct prevalence related to vitamin biosynthesis, antibiotic resistance, and carbohydrate metabolism. Beta diversity analysis highlighted significant inter-population differences in both microbial abundance and SNPs, while alpha diversity analysis revealed that non-Chinese populations exhibited higher diversity in microbial abundance, and Han Chinese populations displayed greater diversity in SNPs. These results provide valuable insights into population-specific microbial diversity, laying the groundwork for future research on its functional and health implications.

microbiology↗