Biology subjects

Xu, M.

Publications and source records attributed to Xu, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Molecular grammar of RNA m6A modification in ribonucleoprotein granules

Diverse RNA chemical modifications collectively regulate nearly all aspects of RNA metabolism. Among these, N6-methyladenosine (m6A) is the most prevalent internal modification in eukaryotic messenger RNAs. Recent studies have implicated m6A in the formation and regulation of ribonucleoprotein (RNP) granules--biomolecular condensates that organize RNA metabolism in space and time. However, the underlying biophysical mechanisms remain to be elucidated. Here, building on SMART, a single-molecule platform for quantitatively measuring RNP granule assembly across nanometer-to-mesoscale regimes, we develop SMART-epi to enable precise control of the m6A modification ratio. Using this approach, we show that physiological m6A levels inhibit YTHDF2 RNP granule assembly while directing specific RNA-RNA interaction architectures. Moreover, YTHDF1/2/3 exhibit distinct assembly kinetics, suggesting functional divergence. Together, these findings reveal a molecular grammar by which m6A modulates condensate dynamics and suggest that RNA modifications can encode regulatory information to tune RNP granule behavior, with potential implications for therapeutic intervention.

biophysics↗

bioRxiv2026

Temporal-deviation-driven community detection uncovers early-warning signals for critical transitions in complex diseases

Early detection of critical transitions in complex diseases is crucial for timely clinical intervention. However, as patients often provide only a single snapshot, identifying sample-specific early-warning signals (EWS) from a dynamical evolution perspective remains challenging, coupled with high-dimensional noise amplification. Here, we present TD-COM, a framework for detecting personalized EWS of critical transitions via single-sample community detection. By constructing a temporal perturbation map STDN, TD-COM captures latent dynamical perturbations inferred from static individual profiles. Synergizing these temporaldeviation signals with static topological features, TD-COM implements a multilevel node filtering strategy during community detection, effectively suppressing single-sample noise. Validated on hour-scale, multi-year, and multi-decade transcriptomic data, TD-COM robustly detects critical states preceding clinical deterioration and uncovers their underlying molecular mechanisms. Comparative experiments demonstrate that TD-COM outperforms existing methods in accuracy and topological robustness. Thus, TD-COM provides a generalizable framework for personalized early warning of complex diseases, particularly when longitudinal sampling is infeasible.

bioinformatics↗

bioRxiv2026

The Structural Code of Breast Cancer Proteoform: Alternative Splicing-driven Protein Isoform Variation and Functional Diversification

Alternative splicing generates extensive transcript diversity, but the extent to which this diversity contributes to the translated proteome and reshapes protein function remains unclear. Here, we develop 3DisoGalaxy, an atlas integrating transcript reconstruction, ribosome profiling, structural modelling and regulatory annotation in breast cancer. We find that 58,292 of 90,929 transcripts are linked to translation-supported ORFs, 71.9% of which arise from non-canonical transcript structures. These products are broadly accommodated within established protein fold space, indicating that alternative isoforms frequently preserve recognizable protein architectures despite substantial sequence variation. Functional divergence is instead concentrated in domains, intrinsically disordered regions, localization determinants and regulatory motifs. Motif remodelling is biased towards gains and is more extensive in cancer-driver genes than in other genes, linking isoform-associated regulatory variation to cancer-relevant proteins. As an example of domain remodelling, we identify an AKT1-{Delta}PH isoform that lacks the N-terminal PH domain while retaining the kinase core, with immunoblotting revealing a lower-molecular-weight AKT1 species consistent with this isoform. Together, our results show that alternative transcript diversity extensively enters translation-supported proteoform space and establish a systematic link between transcript variation and protein functional diversification.

cancer biology↗

bioRxiv2026

BioClaw: Human-Bot Research Collaboration Ecosystems in Group Chats

Day-to-day research discussions in group chats often generate hypotheses, analysis requests, and interpretation decisions, yet executing those analyses still requires researchers to leave the conversation and rely on fragmented local tools, databases, visualization software, and literature search engines. In this work, we present BioClaw, a human-bot research collaboration ecosystem that converts natural-language requests in group conversations into tool-grounded analyses executed within isolated Docker containers. Deployed across 8 messaging platforms, BioClaw turns each group chat into a persistent execution workspace. Its design combines multi-channel orchestration, per-group state and workspace management, and isolated containerized execution for reliable shared use over long-lived conversations. To support practical research workflows, BioClaw combines containerized execution with preinstalled 31 biomedical tools and 95+ skills. The application of BioClaw spans various biomedical domains (e.g., genomics, clinics, structural biology) and data modalities (e.g., sequencing data, EHR data, protein structure data). These results establish the viability of embedding executable, tool-rich agent workflows within shared digital workspaces, positioning group chats as a transformative paradigm for collaborative scientific discovery and innovation.

bioinformatics↗

bioRxiv2026

Decoupling Detection and Classification to Improve Morphological Phenotype Analysis of Sickle Red Blood Cells in Full-Scope Microscopy

Microscopy-based analysis of red blood cell (RBC) morphology is widely used to study phenotypes in sickle cell disease (SCD). Although AI models have been developed to automate classification, most are trained on pre-cropped single-cell images and thus struggle with full-scope microscopic images containing densely packed cells and diverse morphologies, which require both accurate detection and fine-grained classification. We propose an end-to-end computational framework to identify individual RBCs in full-scope microscopy images and classify them into five morphological categories: discocytes (DO), echinocytes (E), elongated and sickle-shaped cells (ES), granular cells (G), and reticulocytes (R). We first evaluate advanced detection-classification models, including You Only Look Once (YOLO) and Detection Transformers (DETR), and demonstrate that while these models effectively detect cells, their classification performance falls short of specialized classifiers trained on single-cell images, particularly for minority phenotypes. To address this limitation, we introduce a two-step framework in which a YOLO-based detector localizes and crops individual cells from full-scope images, followed by a fine-tuned DenseNet121 ensemble classifier that assigns each cell to one of the five morphological categories. The proposed framework achieves a detection-level F1-score of 0.9661 and a weighted-average classification F1-score of 0.9708, with an overall classification accuracy of 97.06%. Compared with the single-step YOLO26n baseline, the two-step pipeline yields a macro-average F1-score improvement of +0.1675, with particularly substantial gains for minority classes (E: +0.1623; G: +0.2774; R: +0.2603). Overall, this hybrid framework demonstrates a practical strategy for adapting fast, general-purpose detection models to domain-specific biomedical tasks by combining them with specialized classifiers, delivering both efficiency and high accuracy for scientific and clinical image analysis.

bioengineering↗

bioRxiv2026

PRISM: A High-Throughput Simulation Infrastructure for CADD Agents

Despite rapid progress in AI agents for computer-aided drug design (CADD), protein-ligand simulation workflows remain fragmented across disparate tools, creating a major bottleneck for scalable candidate evaluation. Here, we present PRISM (Protein-Receptor Interaction Simulation Modeler), a Python platform built on GROMACS that unifies ligand parameterization across multiple force fields, automated system construction, enhanced sampling, multi-tier binding free energy estimation, and trajectory analysis within a single workflow. Through the Model Context Protocol (MCP), PRISM further serves as the computational infrastructure for CADD-Agent, an expert-workflow-driven AI agent designed to orchestrate hierarchical drug screening pipelines. As a pilot application, we applied PRISM to riboflavin synthase and demonstrated end-to-end automation from candidate library assembly to binding pocket characterization, identifying a potential allosteric inhibition site at the oligomerization interface. Together, these results establish PRISM as a high-throughput simulation infrastructure for agent-enabled CADD.

biophysics↗

bioRxiv2026

DenMark: A Bayesian Hierarchical Model for Identifying Cell-Density Correlated Genes from Spatial Transcriptomics

Recent advances in single-cell-resolution spatial transcriptomics enable the profiling of gene expression while preserving the precise locations of individual cells, enabling quantitative investigation of how cellular organization relates to molecular state. A fundamental yet under-modeled aspect of organization is local cell density, which varies across microenvironments and can be linked to transcriptional programs. However, rigorous computational frameworks to quantify density-expression correlations remain lacking. Here, we present DenMark (Density-dependent Marked point process framework), a unified statistical framework that jointly models local cell locations and gene expression in single-cell-resolution ST data, enabling identification of density-correlated genes while naturally providing uncertainty quantification. To scale inference, DenMark leverages a Hilbert space Gaussian process approximation. In simulations, DenMark provides an accurate and well-calibrated estimate of density-expression association. Across single-cell ST platforms, including MERFISH and 10x Xenium, and across brain and cancer tissues, DenMark identifies genes whose expression is associated with cellular clustering and reveals density-related biological programs.

genomics↗

bioRxiv2026

Claw4Science: A Dataset and Platform for the OpenClaw Scientific Agent Ecosystem

Large language models have enabled a new class of scientific software in the form of AI agents that can execute research workflows across bioinformatics, drug discovery, and related domains. Among these systems, OpenClaw introduced a skill-based design that allows workflows to be expressed as structured Markdown files, lowering the barrier to contribution and enabling rapid ecosystem growth. However, this growth has led to fragmentation. Projects are distributed across independent repositories, skills vary widely in quality, naming is inconsistent, and there is no unified way to discover or compare available tools. In this work, we construct the first curated dataset of the OpenClaw scientific ecosystem. The dataset includes 91 projects organized by functional role and 2,230 skills spanning 34 scientific categories. Based on this dataset, we perform a systematic analysis of the structure, distribution, and emerging patterns of scientific agent development. To make this ecosystem accessible in practice, we further build Claw4Science, a public platform at https://claw4science.org, which is built on top of our dataset. The platform organizes projects and aggregates distributed skill repositories into a unified interface, with a focus on bioinformatics and scientific workflows, providing a practical entry point for navigating the ecosystem. Our results show that the OpenClaw ecosystem reflects a shift from isolated systems to a more modular and shareable model of scientific computation. At the same time, challenges in evaluation, reproducibility, and governance remain open. We argue that our dataset provides a foundation for future benchmark development and standardized infrastructure for scientific AI agents.

bioinformatics↗

bioRxiv2026

A modular bacterial platform for tunable double-stranded RNA delivery and RNA interference in nematode Caenorhabditis elegans

RNA interference (RNAi), one of the major molecular tools in model nematode Ceanorhabditis elegans, relies on feeding engineered bacteria that express double-stranded RNA (dsRNA), which modulates animal host gene expression in a programmable manner. Currently no synthetic biology toolkit exits in C. elegans RNAi, while dsRNA circuits construction is restricted to one type of architecture. To facilitate systematic strategies for dsRNA circuit design and expression, here, we performed strain-specific screen of synthetic promoters, based on which we developed seventeen modular genetic parts compatible for rapid assembly of dsRNA expression constructs in Escherichia coli HT115(DE3). We validated dsRNA production in vitro and assessed RNAi efficiency in live animals by feeding. As a proof of concept, a constitutive dsRNA circuit achieved rapid and near-complete gene knockdown, whereas a Ptac-driven circuit enabled tunable, partial silencing while minimizing the leakiness commonly observed in standard feeding RNAi systems. Together, this work establishes a synthetic biology toolkit for programmable dsRNA delivery, enabling precise control of RNAi outcomes from partial to complete gene silencing in C. elegans.

bioengineering↗

bioRxiv2026

Identification of a somatic H3K23me3 methyltransferase SET-19 in C. elegans

Histone methylation plays essential roles in modulating chromatin organization and gene expression. H3K23 methylation is a conserved histone modification, yet its biological roles and the enzymes responsible for its deposition remain poorly understood. Here, we show that the loss of set-19 leads to a pronounced reduction in H3K23 methylation in C. elegans, as revealed by quantitative mass spectrometry, western blotting, and immunofluorescence staining. In vitro biochemical assays show that recombinant SET-19 proteins purified from E. coli directly catalyze H3K23 methylation. Genome-wide chromatin immunoprecipitation assays reveal that H3K23me3 is enriched at heterochromatic regions and that loss of set-19 alters H3K23me3 levels, accompanied by derepression of gene expression. Genetic analyses indicate that SET-19 is dispensable for both germline and somatic RNAi as well as transgenerational epigenetic inheritance of RNAi. SET-19 is predominantly expressed in somatic cells and specifically mediates H3K23me3 deposition in somatic tissues. The loss of set-19 causes a developmental delay without affecting fertility. Together, our results identify SET-19 as a somatic H3K23 methyltransferase and link H3K23me3 to gene repression in C. elegans.

genetics↗

bioRxiv2026

Ultra-low-illumination, high-fidelity longitudinal monitoring of cerebral perfusion via deep learning-enhanced laser speckle contrast imaging

Laser Speckle Contrast Imaging (LSCI) is a non-contact, label-free optical technique widely used in biomedical research and clinical applications. It enables real-time visualization and quantification of microvascular blood flow by analyzing the temporal fluctuations of laser speckles induced by moving red blood cells. However, conventional LSCI uses visible or near-infrared illumination, which--during prolonged exposure (e.g., >1{square}hr)--can induce sublethal neural stress and cause signal drift, compromising physiological relevance and raising ethical concerns. To mitigate these limitations, we introduce TunLSCI--a TransUNet-based recovery network designed to reconstruct high-fidelity mouse cerebral blood flow (CBF) indices from ultra-low-illumination LSCI. We train our network on paired ultra-low-illumination (1.27 {micro}W/mm2) and conventional LSCI data ([~]200 {micro}W/mm2 illumination, the latter as reference), and demonstrate that it outperforms the conventional standard analytical LSCI processing pipeline based on stLASCA, particularly in reconstructing fine vasculature from few frames, suppressing speckle noise, and maintaining robustness against exposure variations. We validate that the proposed TunLSCI reduces illumination power density by [~]157-fold compared with conventional stLASCA, well below the safety threshold for cortical exposure in mice and markedly improves stability during a 2-hour continuous mouse CBF monitoring. Our method significantly minimizes the phototoxic burden of LSCI while preserving spatiotemporal fidelity and quantitative accuracy, thus enabling longitudinal, high-biosafety cerebral perfusion tracking in vivo over multi-hours.

bioengineering↗

bioRxiv2026

Multi-omic profiling of human and mouse dorsal root ganglia enables targeted gene delivery to nociceptors

Chronic pain conditions are often driven by hyperexcitability of nociceptors, the peripheral sensory neurons that detect noxious stimuli. Recent single-cell transcriptomic studies have begun to clarify the molecular identity of distinct peripheral sensory neuron subtypes, but tools that restrict transgene expression to nociceptors while sparing other dorsal root ganglion (DRG) subtypes remain limited. Here, we combined single-nucleus multi-omic profiling of human and mouse DRG with in vivo AAV enhancer screening to identify cis-regulatory elements that drive biased AAV expression in mouse DRG nociceptors and human iPSC-derived nociceptors. We then validated that an enhancer AAV designed to express Kir2.1 preferentially in nociceptors reduces DRG neuronal excitability. Leveraging these multi-omic datasets, we trained a sequence-based model to decode the cis-regulatory logic of nociceptors, enabling both the prioritization of native candidate elements and the design of synthetic enhancers with a range of nociceptor targeting properties. Together, these cross-species multi-omic resources define conserved DRG regulatory programs and provide a viral toolkit for pain research with potential translational applications for patients with refractory pain.

neuroscience↗

bioRxiv2026

Cell Type Architecture and Positional Gene Gradients in an Adult Animal at Subcellular Resolution

The spatial organization of cell types and gene expression underlies tissue architecture and organismal physiology. However, resolving complete cellular and molecular landscapes within a three-dimensional context remains challenging, particularly in organisms with high cellular heterogeneity and complex geometry. Here, we developed a semi-supervised workflow to reconstruct high-resolution three-dimensional spatial transcriptomic models of the planarian Schmidtea mediterranea at single-cell resolution. Planarians are basal bilaterians capable of regenerating body structures after injury. Our reconstruction maps the distribution of cell types and spatially patterned gene expression across the intact organism and identifies 119 candidate positional control genes (PCGs). These genes are expressed across muscle, neural, and epidermal populations. Functional perturbation of selected candidates, including pitx3, ptpn11, pi4ka, upf3b, and cul1, revealed roles in regeneration following injury. In addition, we identify intestinal cells as prominent components of the microenvironment surrounding adult tgs1 pluripotent stem cells (neoblasts). Together, these results demonstrate the utility of three-dimensional spatial transcriptomic reconstruction for mapping cellular architecture and positional gene regulation in complex adult organisms.

developmental biology↗

bioRxiv2026

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

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

cell biology↗

bioRxiv2026

4D Single-Cell Spatial Transcriptomics Reveals Dynamic Morphogenetic Gradients and Regenerative Domains in Planarians

Regeneration relies on precise spatiotemporal gene expression and cellular responses to establish tissue identity and body patterning. Using high-resolution Stereo-seq (715 nm) on 353 sections from 16 whole animals at 8 regeneration timepoints, we constructed a 4D spatiotemporal transcriptomic map of planarian regeneration. Our analysis captured 36 refined cell types from 3,508,004 segmented cells, enabling genome-wide transcriptional imputation of gene expression dynamics across body axes at cellular, tissue, and organismal scales. We identified dynamic positional gradients and distinct spatially distributed cell types during regeneration, including an injury-induced Anterior Regenerative Zone (ARZ). The ARZ exhibited enriched positional signals in epidermal, muscle, and neural cells and was regulated by Mediator 8, which is crucial for polarity remodeling and blastema formation. This study provides a comprehensive spatial molecular and cellular map of regenerative processes, highlighting injury-induced spatial domains and key regulatory factors in planarian regeneration. We also provide an interactive web portal, offering a valuable resource for exploring and analyzing regeneration mechanisms in a spatiotemporal context.

developmental biology↗

bioRxiv2026

Mechanism of Gating and Isoform-Specific Inhibition in Renal CLC Chloride Channels

Hyponatremia is a prevalent disorder marked by excess water retention and substantial morbidity, motivating interest in the CLC-Ka chloride channel as a therapeutic target. Selectively inhibiting CLC-Ka without affecting the closely related CLC-Kb is essential for preventing serious side effects. However, developing isoform-selective inhibitors has been challenging because most small molecules do not distinguish between CLC-Ka and CLC-Kb, and the basis for selectivity in the few known exceptions remains unclear. The small molecule BIM1 preferentially inhibits CLC-Ka over CLC-Kb, providing an opportunity to dissect isoform-specific pharmacology. To investigate this mechanism, we determined cryo-EM structures of BIM1 and BIM15, a related nonselective analog, bound to a CLC-K variant engineered to match the human CLC-Ka binding pocket. Structural and computational analyses reveal that inhibition and isoform selectivity are anchored by interactions with a conserved lysine, with surrounding binding-site residues subtly tuning the local electrostatic environment to promote or disfavor these contacts. These analyses further identify a dynamic extracellular loop that intermittently occludes the shared pathway accessing the inhibitor-binding site and pore. Bound BIM15 engages this gating loop more extensively than BIM1, suggesting that differential loop engagement contributes to inhibitor selectivity, a prediction validated by mutagenesis. Because loop dynamics block the pore, we examined the structural impact of Ca{superscript 2}, which favors opening, and found the gating loop ordered and withdrawn from the pathway. Together, these findings define how binding-site microenvironments and gating-loop dynamics shape isoform-specific inhibition and pore access in CLC-K channels. Significance StatementHyponatremia is a major clinical problem with limited therapeutic options. The kidney chloride channel CLC-Ka is an attractive drug target, but its high sequence identity to CLC-Kb has hindered the development of isoform-selective inhibitors needed for safe therapy. A low-micromolar CLC-Ka-selective inhibitor had been identified, providing a foothold for drug development, but the structural basis of its selectivity was unknown. Here, by integrating cryo-EM structures with molecular dynamics simulations, we define the inhibitor-binding site and reveal the mechanism that enables preferential CLC-Ka inhibition. We further show that a dynamic extracellular loop functions as a gating element shaping inhibitor access and engagement. These findings establish a mechanistic foundation for developing improved treatments for hyponatremia.

biophysics↗

bioRxiv2026

The kinetics of ribonucleoprotein granules

Ribonucleoprotein (RNP) granules are biomolecular condensates composed of diverse RNAs and RNA-binding proteins that play essential roles in regulating multiple aspects of RNA biology. As complex biological systems, the condensate functions of RNP granules emerge from the higher-order kinetic assembly of individual molecules. Here, we develop an in vitro single-molecule approach--termed SMART (group single-molecule assay for ribonucleoprotein granules)--that enables direct and quantitative measurements of RNP granule assembly and disassembly kinetics across nanoscale to mesoscale regimes. Using SMART, we reveal that RNP granule assembly is strongly pathway dependent. In parallel, we develop a minimal two-state mathematical model that faithfully reproduces these kinetic behaviors. This model enables quantification of multivalent interaction timescales, reconstruction of the underlying free-energy landscape governing RNP granule dynamics, and demonstrates that this assembly process is intrinsically non-equilibrium. Understanding the condensate functions of RNP granules suggests new strategies for their rational manipulation in health and disease.

biophysics↗

bioRxiv2026

Ultra-flexible PINE arrays for month-long, continuous intracellular ion flux monitoring in plants with nanomolar accuracy

High-precision in vivo monitoring of ion fluxes is essential yet challenging studying plant electrophysiology such as growth regulation, signal transduction and stress responses. Existing methods for probing ion dynamics are limited by low sensitivity, high invasiveness that interferes physiological processes, and the inability to accurately resolve ion homeostasis with required spatial and temporal resolution. Here, we introduce ultraflexible, plant implantable nanoelectrode (PINE) arrays manufactured on 1.2-m-thick polymer substrates, which enable ultrasensitive and selective measurement of ionic current for month-long via scalable nanofabrication techniques. The fabricated PINE arrays have a smaller dimension than typical plant cells as well as less stiffness, facilitating minimally invasive integration with living plant cells. This subcellular-scale plant-electronic interface allows for reliable, selective detection of K+ flux with a detection limit of [~]10- M, and thus allows continuous, stable monitoring of tomato stem cells over six weeks, capturing dynamic potassium fluctuations during all key growth stages. More importantly, the method permits long-term, real-time tracking of ion-specific dynamics without disrupting plant cellular structure or altering endogenous ion concentrations. Therefore, PINE provides unprecedented access to ion homeostasis and signaling networks, making it an excellent platform for precision agriculture and a foundational tool for future digital plant engineering.

plant biology↗