Biology subjects

Yuan, Z.

Publications and source records attributed to Yuan, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

DeSPA uncovers genes with cross-state spatial pattern remodeling and coordinated spatial programs across multi-slice spatial transcriptomics atlases

Spatial transcriptomics increasingly generates multi-slice atlases spanning developmental, regenerative, and disease-associated states. Identifying genes with spatial pattern remodeling across these states is essential for understanding tissue reorganization and the underlying gene programs, yet few existing methods systematically detect such changes while accounting for variation among replicate slices. Here we introduce DeSPA, a statistical framework that models gene-specific spatial variation within each slice using a linear mixed model, tests for cross-state remodeling by aggregating evidence across replicate slices, and organizes identified genes into interpretable spatial co-expression modules that reveal coordinated spatial programs. Simulations show that DeSPA achieves high power while controlling the false discovery rate under inter-slice heterogeneity. In mouse embryogenesis, it recovers programs linked to extracellular matrix organization, muscle formation, neural development, and erythrocyte development. In diabetic testis, it reveals disruption of a late-spermatid spatial program consistent with known spermatogenic dysfunction, while maintaining stable gene rankings under coordinate rotation. In axolotl brain, regeneration preserves a developmental-like biosynthetic backbone but reorganizes it into regeneration-specific modules. Across two mouse intracerebral hemorrhage models, DeSPA resolves a conserved spatial injury program alongside model-specific synaptic and tissue-response programs. Together, these findings establish DeSPA as a scalable and interpretable framework for detecting spatial pattern remodeling and resolving coordinated spatial programs across diverse biological states.

bioinformatics↗

bioRxiv2026

Defense-associated reverse transcriptases synthesize DNA in a telomerase-like manner

Defense-associated reverse transcriptases (DRTs) constitute an emerging class of bacterial defense systems that harness reverse-transcriptase activity to generate cDNA products and counter phage infection. A distinct subset of DRT10 systems evolutionarily related to telomerase generates tandem-repeat DNA to confer antiphage immunity. Here, we biochemically characterize the reverse transcription activity of DRT10 and determine cryo-EM structures of DRT10-ncRNA complex in three functional states, revealing an unanticipated 2:1 RT:ncRNA assembly mode. Two DRT10 protomers dimerize through a central pair of interlocked {beta}-hairpins and are further embraced by a single ncRNA. This architecture distinguishes DRT10 from previously characterized DRT complexes, which either lack an ncRNA component or exhibit an equivalent stoichiometry of RT:ncRNA. Strikingly, despite the nearly identical conformations of the two DRT10 protomers, cDNA product density was resolved in only one catalytic pocket, revealing different product occupancy within the dimer. Moreover, along with flanking linkers, the stem loops of the ncRNA as fixed points to supply steric barriers help define the template boundary. These findings reveal a distinctive ncRNA-mediated DRT10 dimeric assembly and establish a mechanistic framework for protein-primed reverse transcription constrained by the length of the RNA flanking linker.

biochemistry↗

bioRxiv2026

Deciphering the gene-centric spatial dynamics across tissue and time with GeneSPOT

Spatial omics technologies provide unprecedented opportunities to investigate how molecular programs are organized within tissues. However, existing analytical frameworks primarily focus on identifying cellular states and characterizing differentially expressed molecular features across these states. The relational spatial organization of molecular features and its dynamics across biological contexts therefore remain largely unexplored. Here, we present GeneSPOT, a gene-centric computational framework for characterizing the spatial relationships among genes and quantifying their reorganization across biological conditions. Although gene-centric in design, GeneSPOT generalizes to other molecular features. Across diverse applications, GeneSPOT resolved tissue structures, integrated paired and unpaired spatial multi-omics data, unveiled the spatial convergence of gene programs during embryonic development, and identified aging-associated spatial reorganization among genes whose expression did not change significantly with age. GeneSPOT thus complements conventional cell-centric analysis, providing a framework for deciphering how spatial relationships among molecular features are reorganized across biological conditions and over time.

bioinformatics↗

bioRxiv2026

Platform-specific count-matrix preprocessing workflows for spatial transcriptomics data analysis

Preprocessing of spatial transcriptomics (ST) count matrices is critical for removing technical variation while preserving biological signals, but optimal strategies remain unclear across diverse platforms. We systematically benchmarked 371 preprocessing workflows across 45 data spanning 11 mainstream ST platforms, evaluating their performance using specifically designed complementary metrics. To further assess preprocessing factors upstream of count-matrix generation, we additionally evaluated image preprocessing, including cell segmentation and post-segmentation transcript processing, across representative imaging-based ST platforms. We found that no universal count-matrix preprocessing workflow performed optimally across all platforms, while data from the same platforms exhibited striking consistency in optimal count-matrix preprocessing workflows. We identified the key data structure characteristics and count-matrix preprocessing steps that critically influence preprocessing performance. We developed platform-specific and data structure-guided recommendations to assist users in selecting the optimal count-matrix preprocessing workflow, validated across a broader range of downstream tasks. We demonstrated that our data-driven recommendations uncover subtle biologically meaningful spatial patterns in new data that were obscured by suboptimal count-matrix preprocessing. Our findings suggest best practices for ST data count-matrix preprocessing.

bioinformatics↗

bioRxiv2026

Agentic transcranial functional ultrasound imaging: @fUS

Functional ultrasound (fUS) imaging provides deep, wide-field access to brain function, but instrument cost and complex acquisition and analysis workflows restrict its wider adoption. Here we introduce @fUS, an agentic transcranial imaging platform with an open architecture that integrates purpose-built hardware with executable experimental skills and persistent structured memory. The system combines 128-channel, 16-bit acquisition at 125 MHz with GPU-based processing and enables functional imaging through the intact scalp and skull of mice, with approximately 100-m spatial resolution and 10-Hz temporal sampling, providing a basis for longitudinal studies without cranial-window surgery. Its agent connects scientific objectives to experimental design, direct instrument control and data analysis, retaining context across interactions and supporting inspectable workflows through text and hands-free voice control. Neuroscience trainees with limited engineering experience independently configured the system within 30 min. Agent-assisted exploration of whisker-stimulation datasets revealed low-frequency vascular changes beyond conventional response mapping, supported by complementary two-photon measurements of single-vessel diameter. By combining transcranial imaging with accessible instrument control and analysis, @fUS provides a foundation for wider adoption and larger, more diverse neuroscience datasets. More broadly, it offers a framework for scientific instruments in which measurement, computation and experimental reasoning are developed as parts of the same system.

bioengineering↗

bioRxiv2026

Spatiotemporal atlas resolves the mesenchymal-epithelial stem cell axis governing human hair follicle aging and pathology

How adult stem cell populations coordinate tissue regeneration across physiological and pathological states remains unclear. Hair follicles contain epithelial and mesenchymal stem cells that codetermine hair regeneration. Here, we integrate a multimodal spatiotemporal atlas with functional validation to define the mesenchymal-epithelial stem cell dynamics governing human hair follicle homeostasis, aging, and androgenetic alopecia. Aging is characterized by passive depletion of hDSC-derived regenerative cues, whereas AGA involves a shift of hDSC toward an inhibitory signaling hub that promotes HFSC dysfunction. We further pinpoint hDSC-secreted PTN as a key regenerative factor that promotes hair growth and counteracts androgen-induced growth suppression. This study provides a holistic framework of adult stem cell crosstalk across physiological and pathological states, offering a mechanistic roadmap to reinstate tissue regeneration.

cell biology↗

bioRxiv2026

Unifying physical and molecular coordinate systems across modalities in spatial biology

Establishing a unified physical and molecular coordinate system from fragmented multi-modal data is a longstanding challenge in biology. Here, we present MAPS, a modality-agnostic platform for spatial biology comprising (1) MAPS-alignment for ultrafast alignment of any modality, (2) MAPS-integration for both anchored and unanchored integration across orthogonal modalities for 3D multi-modal reconstruction, and (3) MAPS-Explorer for large-scale interactive 3D analysis. MAPS outperformed existing methods across extensive benchmarks on 34 datasets spanning 16 technology platforms and 6 modalities, while delineating fine-grained multi-modal tissue architectures across diverse biological systems in mouse and human. At cross-consortium scale, MAPS integrated 434 slices comprising 21 million cells from 18 atlases and 5 modalities to construct the most comprehensive 3D multi-modal mouse brain atlas. At individual laboratory scale, MAPS empowered routine 2D spatial assays to reconstruct continuous 3D multi-modal landscapes of human hepatocellular carcinoma, revealing the limitations of 2D spatial relationships and uncovering depth-dependent immune-state transitions.

bioinformatics↗

bioRxiv2026

Functional Ultrasound Characterization of Spatiotemporal Cerebral Hemodynamic Responses Evoked by Whisker Stimulation

Functional ultrasound imaging (fUS) measures task-associated changes in cerebral blood volume (CBV) with fine spatial sampling and rapid acquisition, supporting functional mapping and brain-signal decoding. As an indirect measure of neural activity, fUS-CBV signals are shaped by neurovascular coupling and vascular dynamics, which influence the spatial and temporal stimulus information available for decoding. Consequently, the functional distinctions accessible in fUS-CBV signals require characterization under controlled experimental conditions. Here, controlled whisker stimulation in awake, head-fixed mice was used to characterize these decoding-relevant response properties. Whisker identity and number, stimulation duration, and inter-stimulus interval were systematically varied. All-whisker stimulation produced contralaterally dominant responses in the primary somatosensory barrel field and ventral posteromedial thalamic nucleus. Different whisker inputs were associated with distinguishable cortical response distributions, while stimulation involving more whiskers produced broader representative activation patterns. Across durations of 0.5 s to10 s, brief stimulation generated measurable stimulus-associated signals, and response magnitude, persistence, and spatial distribution varied with stimulus duration. For paired 2-s stimuli, trial-level separability was already high at the shortest tested 1-s gap, corresponding to a 3-s onset-to-onset spacing, and reached 100% at gaps of 3 s or longer. Building on the experimentally characterized spatiotemporal response properties of fUS-CBV signals, a proof-of-concept modeling and decoding framework was developed, comprising cross-animal spatial prediction, classification of short versus long stimulation from S1BF {Delta}CBV time courses with known stimulus onset, and modeling of paired-event separability. Together, the results show that fUS-CBV signals retain structured spatial and temporal information about controlled sensory input after neurovascular transformation. These measured response properties characterize the spatiotemporal boundaries within which neural activity can be reliably distinguished using fUS-CBV signals, providing constraints for the development of fUS-based decoding models and the design of stimulation paradigms suitable for reliable fUS decoding.

neuroscience↗

bioRxiv2026

Shared phylogeographic structuring and recurrent hybridization underlie the diversification of Microhyla frog complexes in the Indochinese biodiversity hotspot

Comparative phylogeography provides a powerful framework to identify the historical processes shaping biodiversity hotspots by testing whether co-distributed species exhibit shared patterns of diversification. Southeast Asia harbors exceptional biodiversity, yet the extent to which common paleogeographic and climatic drivers have structured diversification across taxa remains poorly understood. Here, we investigated the evolutionary history of five widespread Microhyla species complexes distributed across the Indochinese Peninsula and adjacent regions using dense mitochondrial sampling (1,388 individuals) combined with genome-scale ddRAD sequencing (280 individuals). Across all complexes, both approaches recovered multiple lineages and remarkably congruent phylogeographic breaks and contact/intergradation zones associated with major Indochinese regions, including Myanmar, the Tenasserim-Malay Peninsula, southern Vietnam, and northern Vietnam-southern China, supporting the hypothesis that Indochina functions as a mosaic of stable biogeographic units. However, lineage divergence varied substantially among complexes, suggesting that common biogeographic drivers interacted with species-specific demographic histories. Phylogenomic analyses and cyto-nuclear discordance further revealed historical introgression in every complex, indicating that diversification involved both long-term allopatric isolation and reticulate evolution. These findings portray the Indochinese biodiversity hotspot as a dynamic evolutionary system where cycles of fragmentation and reconnection have repeatedly reshaped lineage boundaries. Moreover, the complex phylogeographic structure recovered exemplifies the urgent need for taxonomic revisions, for which genome-scale data provide an essential framework to validate mitochondrial hypotheses and assess admixture patterns for species delimitation. Finally, regions such as the southern Annamites and Tenasserim Hills emerged as recurrent hotspots of genetic diversity across independent lineages, highlighting their importance for conserving not only species/lineage richness but also the evolutionary processes and adaptive potential that sustain biodiversity.

zoology↗

bioRxiv2026

Virtual spatial transcriptomics from histopathology enables prognostic and therapeutic response prediction in cancer

Spatial transcriptomics reveals cellular heterogeneity, intercellular communication, and tissue organization, but its cost and limited accessibility restrict clinical use. Here, we present VISTA, a model that integrates multi-scale histological features and spatial context to infer spatial gene expression from H&E-stained tissue images. Across leave-one-section-out cross-validation and independent validation, VISTA robustly predicted thousands of genes and outperformed state-of-the-art methods. Beyond expression reconstruction, VISTA enabled clinically relevant downstream analyses. In TCGA breast cancer samples, it identified survival-associated genes, stratified prognostic risk groups, and revealed adverse tumor-associated spatial subtypes. In our in-house intrahepatic cholangiocarcinoma cohort, it preserved tumor-normal organization and identified CLDN4 and CYP3A4 as complementary spatial biomarkers. In HER2+ breast cancer, it predicted pathological response to neoadjuvant trastuzumab-based therapy and linked response-associated regions to immune and cytokine-related programs. These results support virtual spatial transcriptomics from routine histopathology for oncology applications.

genomics↗

bioRxiv2026

AI-enabled reconstruction of 3D spatial multi-omics at single-cell resolution

Three-dimensional (3D) spatial multi-omics provides unparalleled insights into biological activities, yet remains technically prohibitive. Here, we introduce Histo3D-MO, a hybrid experimental-computational pipeline for reconstructing single-cell-resolution 3D spatial multi-omics maps. Notably, Histo3D-MO integrates sparse, omics-disjoint spatial measurements with dense Hematoxylin and Eosin (H&E) histology through SPatial multi-Omics from h&E imaGEs (SPONGE), achieving cell-level 3D mapping across multiple omics layers. Validated using held-out slices, SPONGE substantially outperforms existing omics prediction methods. We further developed an algorithmic suite for 3D cell-type propagation and tissue-domain annotation, enabling whole-volume characterization of the tumor microenvironment. Applied to the in-house hepatocellular carcinoma data, Histo3D-MO revealed spatially organized patterns of translation efficiency, volumetric decoupling between malignant cells and monocytes, and depth-associated monocyte differentiation trajectories. Together, these results establish Histo3D-MO as a scalable framework for reconstructing single-cell-resolution 3D spatial multi-omics and interrogating tissue organization across complex biological systems.

bioinformatics↗

bioRxiv2026

Robust integration of weakly anchored spatial multi-omics

Spatial multi-omics holds great promise for dissecting complex biological processes, though inherent technical constraints continue to limit its widespread adoption. Currently, most studies therefore measure distinct omics features on separate tissue sections, necessitating spatial diagonal integration. An emerging practical solution is to leverage hematoxylin and eosin (H&E) images as an integration anchor, given their ubiquity, low cost, and compatibility across tissue preparations. However, this anchor is frequently compromised in real-world settings by variations in H&E staining style, absence of reliable histological landmarks, and mismatches in spatial resolutions across omics modalities. To address this, we introduce SpaWeaver, a computational framework that couples a pathology foundation model with a graph Transformer and a latent feature aligner module, providing a highly robust solution for weakly anchored spatial omics data diagonal integration. Extensive experiments demonstrate that SpaWeaver exhibits superior robustness against isolated or synergistic weak-anchoring factors. The spatial multi-omics profiles generated by SpaWeaver link molecular features originally separated on two sections, unlocking diverse downstream analyses once exclusive to co-assayed spatial multi-omics data, including niche-aware cell-cell communication inference and multi-omics resolved cell state. In this study, it unveils tumor-distance-dependent fibroblast-CD4{square}T-cell signaling in human colon adenocarcinoma and identifies a hypoxic glycolytic tumor state with pyknotic nuclei in human ovarian cancer. Overall, our approach bridges readily accessible single-omics measurements across weakly anchored tissue sections, enabling unified spatial multi-omics characterization and system-level tissue analysis.

bioinformatics↗

bioRxiv2026

Decoding Hierarchical Cell-Cell Communication in Spatial Multi-Omics with CellSTIC

Cell-cell communication coordinates tissue development, homeostasis, and immunity, yet defining signaling interactions within intact tissues remains challenging. Single-cell transcriptomics enables systematic ligand- receptor inference, but tissue dissociation removes spatial context and obscures local and region-specific signaling. Spatial transcriptomics and spatial multi-omics can recover communication in situ, although existing methods often incompletely integrate heterogeneous data or produce poorly interpretable ligand-receptor lists. Here we present CellSTIC, a framework that resolves cell-cell communication in spatial multi-omics as structured programs grounded in tissue architecture. CellSTIC integrates multimodal evidence from local neighborhoods and organizes interactions into a hierarchical semantic representation that remains traceable to underlying molecules. This enables analysis from individual ligand-receptor pairs to functional modules comparable across tissues, regions, and states. In simulations and diverse tissue datasets, CellSTIC recovered spatially coherent communication structures and domains, revealing immune, brain, developmental, and regenerative programs, and providing a general approach for generating mechanistic hypotheses in situ. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/728114v2_ufig1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@a04e82org.highwire.dtl.DTLVardef@826435org.highwire.dtl.DTLVardef@81092dorg.highwire.dtl.DTLVardef@1818e8b_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

SpatialDataAgent: Autonomous Spatial Omics Data Curation at Decade Scale

Fragmented metadata in spatial omics archives has rendered large volumes of multimodal molecular-histological data inaccessible as dark data. Here, we introduce SpatialDataAgent, an agentic workflow for autonomous spatial omics data curation, combining schema-constrained evidence evaluation with a self-refining standardization agent. Applied to a decade of GEO records, SpatialDataAgent identified 769 paired H&E-spatial transcriptomics (ST) datasets, representing a 6.4-fold scale expansion over existing manually curated baselines. Within the benchmarking window, the framework achieved a 141% increase in high-confidence Class A paired datasets. We further assembled a recent high-confidence subset into HESRT, a standardized datalake containing 29.2 million spots/cells, establishing a blueprint for evidence-grounded autonomous curation of multimodal biomedical archives.

bioinformatics↗

bioRxiv2026

A Decade of Deep Learning-based Biomedical Image Segmentation

Biomedical image segmentation is a fundamental problem in computational biomedicine that aims to precisely delineate anatomical and biological structures, tissue types, or pathological regions in biomedical images. Accurate segmentation is essential for interpretation, decision-making, and quantitative analysis across a wide range of biological and medical applications. Over the past decade, the field has undergone a profound paradigm shift, evolving from task-specific specialist models to universal foundation models. This review provides an in-depth analysis of the evolution, tracing how the limitations of local discriminative learning drove the transition toward transformer-based global modeling, and large-scale generative pre-training. To help navigate the diverse landscape of interaction paradigms, we introduce the first systematic taxonomy of promptable biomedical image segmentation, categorizing existing methods into six distinct types, enabling users to intuitively select appropriate prompting strategies based on visual demonstrations and quickly pinpoint relevant literature (Prompt Type Visualization). Beyond model architectures, we discuss parallel advancements in dataset development, evaluation protocols, and application-specific adaptations across radiology, pathology, and biology. Integrating these powerful foundation models with rigorous domain-specific adaptation has great potential to improve patient outcomes and healthcare efficiency. Finally, we highlight key challenges in trustworthiness and clinical integration that must be overcome to realize the potential of the next generation of biological and medical generalists.

bioengineering↗

bioRxiv2026

H2O: A Foundation Model Bridging Histopathology to Spatial Multi-Omics Profiling

Spatial omics technologies have revolutionized the molecular profiling of tissues but remain constrained by high costs and limited scalability. While hematoxylin and eosin (H&E) staining is ubiquitous, it lacks molecular specificity. Here, we present H2O (Histopathology to Omics), a generalist AI framework that bridges the modality gap between histopathology and spatial multi-omics, enabling the direct inference of spatial transcriptomics (ST) and proteomics (SP) landscapes from routine H&E images. H2O integrates Vision Transformers (ViT) with Large Language Models (LLM) via contrastive learning to align histological morphology with semantic molecular knowledge. This cross-modal approach allows the model to incorporate spatial expression profiles into histological pattern recognition, effectively decoding the molecular heterogeneity underlying tissue morphology. Trained on a pan-tissue dataset of 1.3 million paired H&E-spatial patches across 25 organs and cancer types, H2O predicts spatial omics expression from histology with high concordance to sequenced measurements and consistently outperforms state-of-the-art models across three cancer benchmarks. Notably, H2O recovers the MIF-CD74/CD44 signaling axis directly from H&E images, highlighting its capacity to infer biologically meaningful cell-cell communication without molecular profiling. Applying on three additional public cohorts covering fetal and paediatric thymus tissues, human metastatic lymph node, and breast cancer, encompassing human development, 3D spatial frameworks, and integrative multi-omics, H2O yields biologically concordant insights, demonstrating superior accuracy, robustness, and generalizability across real-world applications in diverse scenarios. H2O converts routine histopathology into a portal for spatially resolved multi-omics profiling by computationally generating transcriptomic and proteomic landscapes, thereby enhancing tissue phenotyping and enabling scalable, integrative tissue-atlas construction.

bioinformatics↗

bioRxiv2026

Fatty acid scavenging enables cancer escape from KRAS inhibition

Although inhibitors of oncogenic KRAS have shown clinical efficacy1, resistance to KRAS inhibition is common2, and its molecular basis remains unclear. Here we show that KRASi-resistant cancer cells sustain mitochondrial bioenergetics through enhanced fatty acid (FA) metabolism, despite suppression of canonical KRAS signaling. Specifically, KRASi-resistant pancreatic cancer cells exploit macropinocytosis to scavenge FA released from adipose tissue, fueling beta-oxidation independently of KRAS-PI3K signaling. This adaptive metabolic program is driven by the adhesion G protein-coupled receptor ADGRB1, which activates non-canonical PI3K{gamma}-PAK1 signaling to stimulate macropinocytosis and maintain metabolic homeostasis under KRASi. Disruption of ADGRB1-PI3K{gamma} signaling dismantles this metabolic program and restores KRASi sensitivity. This pathway operates across multiple KRAS-mutated cancers and is associated with poor therapeutic response and outcome. These findings offer a promising strategy for overcoming KRASi resistance.

cancer biology↗

bioRxiv2026

Inflammatory memory disables a DDR1 degradation checkpoint to enable pancreatic cancer growth

Inflammation and stroma remodeling regulate pancreatic ductal adenocarcinoma (PDAC), but how or if these cues are integrated at the molecular level remains unclear. Here, we identify a metabolic checkpoint that controls the stability of the collagen receptor DDR1, and subsequent tumorigenesis. We show that defective Col-I remodeling deprives PDAC cells of the high affinity DDR1 ligand, [3/4]Col-I, resulting in reduced ATP and activation of AMPK. AMPK phosphorylates DDR1 at T519, promoting its recognition by the E3 ubiquitin ligase adaptor FBXW2 and subsequent degradation. Importantly, this degradation pathway can be disabled by inflammatory signaling. Exposure to inflammatory cytokines induces methylation-dependent silencing of FBXW2, which establishes an inflammatory memory that preserves DDR1 stability, enabling sustained ligand-triggered receptor oligomerization and downstream NF-{kappa}B-NRF2 signaling even in restrictive stromal environments. Together, these findings identify regulated receptor turnover as a mechanism through which stromal architecture, metabolic state, and inflammatory memory are integrated to control PDAC progression.

cancer biology↗