Biology subjects

Wang, C.

Publications and source records attributed to Wang, C..

At least 181 records · Page 10Linked to original sources

bioRxiv2026

Histone neutralization protects the ischemic brain against stroke-associated pneumonia

Bacterial pneumonia aggravates ischemic stroke via mechanisms that still remain to be determined. In ischemic stroke patients and mice exposed to middle cerebral artery occlusion, we show that stroke-associated pneumonia markedly worsens clinical stroke outcome. In mice, pneumonia induced 3 days after stroke impaired neurological recovery and increased brain neutrophil infiltrates, blood-brain barrier breakdown, cerebral microvascular thrombosis, and progressive brain atrophy. The antibiotic amoxicillin only partially ameliorated pneumonia-associated neurological deficits and neutrophil infiltrates. Neutrophils were critical mediators of pneumonia-induced blood-brain barrier breakdown and microvascular thrombosis. Notably, administration of a neutralizing anti-histone antibody during pneumonia--unlike degradation or blockade of neutrophil extracellular trap formation or myeloperoxidase inhibition--restored long-term neurological recovery and prevented brain atrophy in stroke-associated pneumonia mice. This study identifies extracellular histones as key drivers of secondary inflammatory brain injury and establishes histone neutralization as a therapeutic strategy with an extended treatment window in the post-acute stroke phase. One Sentence SummaryNeutralizing extracellular histones reverses pneumonia-driven secondary brain injury and restores long-term recovery after ischemic stroke.

neuroscience↗

bioRxiv2026

Refinement of Nucleus Accumbens Neuronal Dynamics During Cocaine Self-Administration Training

Drug addiction is an acquired motivational-behavioral state that begins with drug taking, which is comprised of a series of phases, including initial acquisition, stabilization, habituation, and maintenance. In rodent models of cocaine self-administration, the forebrain region nucleus accumbens (NAc) has been critically implicated in the acquisition-maintenance process of drug taking and seeking behaviors. However, it remains unknown how NAc neurons shift their activity patterns in response to these phasic transitions during cocaine taking. To examine this, we used GCaMP6m-based in vivo Ca2+ imaging to monitor activities of principal medium spiny neurons (MSNs) in the NAc across eleven days of cocaine self-administration. Behaviorally, mice exhibited progressive stabilization of operant responding and locomotion across 11 days of cocaine self-administration. During the early training days, we detected a portion of NAc neurons--a potential neuronal ensemble--that exhibited increased activities temporally contingent to the lever-press for cocaine. The number of NAc neurons exhibiting contingent activity increased progressively over the first three training days and then decreased gradually during the later training days, exhibiting expansion-refinement dynamics that may correspond to the acquisition and subsequent stabilization/maintenance of cocaine self-administration. Using a neuron-tracking technique, we found that the lever-press-contingent NAc ensemble exhibited substantial compositional dynamics, with neurons dropping into and out across training days. These activity features of lever-press-contingent neurons may represent key circuit dynamics of the NAc that transition the acquisition toward the maintenance of cocaine-taking behavior.

neuroscience↗

bioRxiv2026

Benchmarking niche identification via domain segmentation for spatial transcriptomics data

Tissue niches are spatially organized microenvironments in which coordinated multicellular interactions shape cellular states and biological functions. Currently, niche identification is routinely performed using domain segmentation frameworks. While interrelated, spatial domains and niches are not fundamentally equivalent. The former emphasizes intra-domain compositional consistency and transcriptomic homogeneity, whereas the latter is defined by the emergent properties of localized signaling gradients and the functional reciprocity between key cell lineages. Here, we present a high-resolution reference by thoroughly annotating single-cell resolution CosMx ST data of a human follicular lymphoid hyperplasia lymph node, a dynamic, non-compartmentalized tissue containing several critical immune niches defined by specific lineage architectures. We systematically benchmarked 16 contemporary domain segmentation algorithms, demonstrating that most methods in their default configurations fail to recapitulate biologically defined niche boundaries. Our analysis reveals that the definitive, disjoint spatial distributions of key functional lineages are frequently obscured by the stochastic infiltration of peripheral cell types. Such reduction in the spatial signal-to-noise ratio represents a primary bottleneck for existing algorithms, which prioritize local transcriptomic variance over global architectural logic. Following this observation, we demonstrate that strategic weighting of core functional lineages can restore the resolution of spatial niches in select domain segmentation frameworks. Cross-comparison against compartmentalized tissues further underscores the unique challenges of niche identification in non-mechanically separated environments and clarifies the fundamental divergence between structural domain segmentation and functional niche discovery. Our work delineates the limitations of current paradigms and advocates for the development of specialized computational approaches tailored specifically to the complexity of functional microenvironments.

bioinformatics↗

bioRxiv2026

CsFDL1-CsFTL3 complex represses CsFTL3 via negative feedback to fine-tune flowering in Chrysanthemum seticuspe

In many flowering plants, the transition from vegetative growth to reproductive development is regulated by seasonal changes in photoperiod. Under inductive photoperiods, leaves produce the florigen FT (FLOWERING LOCUS T), which is transported to the shoot apex to promote flowering. The photoperiod is known to have a major effect on the flowering of chrysanthemum. In the perennial short-day (SD) plant Chrysanthemum seticuspe, the expression of CsFTL3 (FT-like gene) does not increase immediately after shifting from long-day (LD) to SD conditions but gradually accumulates under continuous SD conditions, peaking during inflorescence development. However, the underlying mechanism remains elusive. We show that CsFDL1 (an ortholog of FD) and CsFTL3 exhibit a significant inverse expression pattern in leaves during the initial stage of short-day inductions. Furthermore, the expression of CsFTL3 is upregulated in the leaves of CsFDL1-knockdown transgenic lines. CsFDL1 is expressed in leaves and forms a complex with CsFTL3 to recognize several TCGA- and ACGT-containing motifs in the CsFTL3 promoter. The CsFTL3-CsFDL1 complex downregulates CsFTL3 expression, thereby preventing its excessive induction by SD signals and inhibiting precocious floral transition. This study reveals that CsFDL1 acts as a key early repressor in the photoperiodic flowering pathway of chrysanthemum leaf, mediating negative feedback regulation by forming a complex with CsFTL3 to achieve precise temporal control of short-day-dependent flowering responses.

plant biology↗

bioRxiv2026

Information Geometry Reconciles Discrete and Continuous Variation in Single-Cell and Spatial Transcriptomic Analysis

Single-cell and spatial transcriptomics provide high-resolution cellular characterization, yet standard analytical approaches remain theoretically misaligned with the probabilistic nature of the data. After UMI normalization, current pipelines rely on Euclidean or log-transformed Euclidean distance for similarity measurement. Both are fundamentally ill-suited to model the multinomial count data. Euclidean distance in normalized space overemphasizes high-variance genes, while log-transformation inverts this bias but at the cost of distorting subtle, continuous expression modulations. Neither approach naturally captures the dual nature of gene expression: both discrete presence/absence transitions and continuous quantitative variation. To overcome these limitations, we introduce GAIA (Geometric Analysis from an Information Aspect), an information-geometric framework for cell representation learning and inter-cell similarity measurement. By anchoring analysis in the true probabilistic model, treating cells as multinomial distributions over genes and projecting cells to a statistical manifold, GAIA organically reconciles both the presence/absence effect and the more continuous expression modulations. Mathematically, GAIA exploits the equivalence between Fisher-Rao distance in multinomial space and geodesic distance on the unit hypersphere, a property that enables both theoretical guarantees and computational efficiency. Experiments in synthetic and real scRNA-seq and spatial transcriptomic datasets demonstrate that GAIA preserves robust and consistent cell-to-cell relationships, delineates biologically nuanced sub-types, mitigates batch effects arising from sequencing depth variation, and eliminates the dependence on knowledge-restricted gene selection for learning meaningful cell representations. Overall, GAIA offers a knowledge-lean, variance-stabilizing framework for analyzing single-cell and spatial transcriptomic data, enhancing discrimination between nuanced cell sub-type and -states.

bioinformatics↗

bioRxiv2026

Single-Cell and Spatial Methods for Multimodal Functional Glycan Profiling in Tissues

Glycans regulate multiple physiological processes, including immune recognition and cancer progression. In disease, altered glycan landscapes are interpreted by human lectins. Functional glycan-lectin interactions are difficult to profile because glycans are not genome-encoded and their changes are poorly captured by existing multimodal methods. We present two platforms, single-cell outlining and transcriptome sequencing (scGOAT-seq) and GlycoScope, which use human lectins to enable functional glycan accessibility into single-cell and spatial multiomic measurements. ScGOAT-seq quantifies lectin-accessible glycan states with gene expression, while GlycoScope enables multiplexed in situ co-detection of glycans and proteins in tissues. Applying these approaches to immune cells, we identify stimulus-specific glycan remodeling and show that distinct Siglec-ligand-defined programs stratify immune activation states not captured by traditional methods; in follicular lymphoma, GlycoScope, resolves spatial glycan programs associated with malignant B cells and localized immune microenvironments. The presented methods provide a general framework for integrating functional glycan accessibility into single-cell and spatial multiomics.

molecular biology↗

bioRxiv2026

MATR3 is essential for oocyte growth and maturation quality through a dual molecular mechanism

The molecular mechanisms governing mRNA accumulation during oocyte growth, essential for developmental competence, remain poorly understood. This study investigates the role of Matrin-3 (MATR3), a highly expressed RNA-binding protein in growing oocytes (GOs), using oocyte-specific knockout mouse models and human oocyte maturation arrest (OMA) samples. The results showed that MATR3 was more abundant in GOs than fully-grown oocytes (FGOs), highly expressed in the nucleus of non-surrounded nucleolus (NSN) oocytes, and exited the nucleus during the NSN-to-surrounded nucleolus (SN) transition. In OMA patients, MATR3 nuclear localization was missed, with smaller oocytes than FGOs. Further, Matr3 deletion in mouse GOs caused restricted oocyte growth, global transcription disorders, follicle development failure, blocked GO-granulosa cell communication (via reduced Gdf9 and Radixin expression), and infertility. Mechanistically, MATR3 regulated transcription by recruiting H3K9me2-demethylating lysine-specific demethylase 3B or binding target gene promoters, like Radixin. These findings reveal a critical role of MATR3 in orchestrating transcription and paracrine signaling during oogenesis and suggest its potential as a diagnostic and therapeutic target for OMA.

physiology↗

bioRxiv2026

Antibody-Dependent Heterotypic Syncytia Drive COVID-19 Inflammation and Disease Progression

Severe COVID-19 is characterized by profound immune dysregulation and excessive inflammation. Aberrant myeloid responses drive hyperinflammation, yet the precise mechanisms remain elusive. Here, we demonstrate that certain spike antibodies promote the formation of heterotypic syncytia between monocytes/macrophages and virus-infected pneumocytes, leading to excessive inflammatory cytokine release. This antibody-dependent syncytium formation requires Fc{gamma}RI (CD64) and ADAM10, as well as the assembly of the six-helix bundle by the spike S2 subunit. Notably, these syncytia exhibit a stronger proinflammatory signature compared to virus-infected epithelial cells. In vivo, sub-neutralizing antibody concentrations amplified inflammation and exacerbated disease in SARS-CoV-2-infected mice without increasing viral burden. Furthermore, scRNA-seq of postmortem lung tissues from COVID-19 patients implicated the monocyte/macrophage-derived syncytia as a key cellular source of inflammatory cytokines. Together, these findings define a novel mechanism of antibody-dependent enhancement of inflammation that helps explain the hyperinflammatory responses in severe COVID-19 and suggest new therapeutic opportunities targeting this pathway.

immunology↗

bioRxiv2026

A multi-agent platform for assessment and improvement of bioinformatics software documentation

Rapid advances in bioinformatics have transformed biomedical research in areas such as single-cell and spatial omics, digital pathology, and multi-modal data integration, yet software usability and reproducibility have not kept pace with the growing complexity and proliferation of computational tools. Inconsistent, incomplete, or inaccessible documentation remains a pervasive and underappreciated barrier, limiting tool adoption, hindering reproducibility across laboratories, and reducing the long-term impact of computational methods. Here, we introduce BioGuider, a multi-agent platform designed to systematically evaluate and improve documentation quality in bioinformatics software. Rather than treating documentation as ancillary text, BioGuider models it as a first-class, testable object. The platform implements a modular pipeline for documentation collection, assessment, reporting, and optional correction, with specialized agents that emulate real-world user interactions. BioGuider evaluates documentation against standardized, task-oriented criteria spanning installation, configuration, usage, and tutorials, and supports iterative, constraint-aware refinement while preserving code integrity and biological context. We benchmark BioGuider using a controlled error-injection framework that introduces realistic documentation failures across general, biology-specific, and configuration-related categories. Across multiple large language models, BioGuider demonstrates robust error detection and correction, with strong performance maintained under severe documentation degradation. Applying BioGuider to 47 widely used bioinformatics tools, we observe a positive association between documentation quality and citation frequency, highlighting documentation as a previously under-quantified driver of software adoption and scientific impact.

bioinformatics↗

bioRxiv2026

Unravelling genome-wide mosaic microsatellite mutations at single-cell resolution

Short tandem repeats (STRs), or microsatellites, are highly mutable genomic elements that modulate gene regulations and are implicated in a range of human diseases. However, detecting mosaic STR mutations at single-cell resolution remains challenging due to both technical and biological complexities. To address this, we developed BayesMonSTR, a robust algorithm that enables accurate detection of mosaic STR mutations. Using this tool in single-cell analysis of human tissues, we reveal an accumulation of longer mosaic STR insertions and deletions (indels) in aging mitotic and post-mitotic cells. Strikingly, prefrontal cortex (PFC) neurons accumulate a higher burden of STR mutations than B cells or lung epithelium, with aged neurons exhibiting a particularly pronounced increase in longer STR deletions. These mutations are enriched at transcription start sites (TSSs) and active enhancers of highly expressed genes. Our work establishes a foundation for genome-wide, hypothesis-free discovery of disease-associated mosaic STR mutations and reveals a previously unexplored landscape of mosaic STR variation in development and aging.

bioinformatics↗

bioRxiv2026

PRISM: Niche-informed Deciphering of Incomplete Spatial Multi-Omics Data

Spatial multi-omics data, characterizing the knowledge of diverse molecular layers, have become indispensable for the in situ analysis of tissue architecture and complex biological processes. Nevertheless, current spatial multi-omics sequencing protocols are often hindered by incompatible protocols, resulting in incomplete spatial multi-omics pairing due to inconsistent field-of-view or spatial resolution. To address this, we present PRISM, a novel computational method tailored for this scenario. PRISM leverages a niche-informed graph to propagate information from paired to unpaired regions, jointly achieving the spatial domain identification and spatial omics imputation. Extensive benchmarking on five diverse simulated and real experimental datasets demonstrated that PRISM outperformed existing methods in spatial multi-omics analysis tasks. Application to the human Parkinsons disease data revealed that PRISM accurately recovered dopamine-associated spatial domains and metabolite distributions masked by incomplete data gaps. PRISM offers a robust solution for bridging the integration gap inherent in incompatible sequencing protocols, thereby facilitating more accurate downstream biological interpretation.

bioinformatics↗

bioRxiv2026

AbNovoBench: a resource and benchmarking platform for monoclonal antibody de novo sequencing

Monoclonal antibodies (mAbs) are critical in disease diagnostics and therapeutics, yet the performance of mass spectrometry (MS)-based de novo sequencing remains incompletely characterized due to limited antibody-specific datasets and the absence of a standardized benchmark framework. Here we present AbNovoBench, a comprehensive framework for evaluating data analysis strategies for mAb de novo sequencing. It features the largest high-quality dataset to date, generated in-house, comprising 1,638,248 peptide-spectrum matches from 131 mAbs across six species and 11 proteases, supplemented by eight mAbs with known full-length sequence for end-to-end reconstruction assessment. Employing a unified training dataset, we systematically benchmarked 13 deep learning-based de novo peptide sequencing algorithms and three assembly strategies across peptide sequencing metrics (accuracy, robustness, efficiency, error types) and assembly metrics (coverage depth, assembly score). AbNovoBench (https://abnovobench.com) provides an online platform enriched with curated antibody MS resources and pre-trained models, enabling customizable antibody sequencing workflows, accelerating antibody-specific algorithms development, and improving reproducibility in proteomics.

systems biology↗

bioRxiv2026

Global Metabolomic Analysis of Lytic KSHV Infection: Induced Host Nucleotide Metabolism is Required for Infectious Virus Production

Kaposis Sarcoma Herpes Virus (KSHV) is the etiological agent of Kaposis Sarcoma (KS) which is known to cause metabolic stress in infected host cells. KSHV reprograms host metabolic pathways for efficient viral replication and infectious virion production. Here, we report a time-course global metabolomics study conducted in the doxycycline-inducible iSLK.BAC16 cells to compare latent and lytic KSHV infection. Our data show that amino acid, central carbon, and nucleotide metabolic pathways are highly dysregulated upon reactivation to lytic replication. During lytic KSHV infection, pathway enrichment analysis shows that the top two most significantly impacted and dysregulated pathways are purine and pyrimidine metabolism. Further experiments have shown that nucleotide metabolism is required during lytic KSHV infection to produce maximal infectious virus. Treatment with the FDA-approved drug, methotrexate (MTX), a folate antagonist that inhibits cellular DHFR and decreases nucleotide metabolism by reducing tetrahydrofolate cofactors, significantly reduced KSHV late lytic viral gene expression upon reactivation compared to control. Additionally, titering cell-free supernatants from MTX-treated lytic KSHV-infected cells showed a significant reduction in infectious virion production. Furthermore, by adding folinic acid (FA), a downstream metabolite of the MTX-DHFR inhibition step, in the presence of MTX, late lytic gene expression and infectious virion production were significantly rescued. Furthermore, we observed a significant decrease in viral titer of murine herpesvirus 68 (MHV-68), a model virus to study gammaherpesvirus, after MTX treatment. Overall, our study demonstrates that metabolic inhibition during lytic gammaherpesvirus infection decreases productive infection and hence, serves as a potential therapeutic antiviral target.

microbiology↗

bioRxiv2026

Prime-InDel version 1: A web tool providing InDel markers for targeted regions narrowing

While high-density SNP genotyping enables genome-wide assays in rice (Oryza sativa L.) and other crops, PCR-based markers--particularly those derived from insertion-deletion (InDel) variations--remain crucial for fine-mapping. Currently, readily available primer information for high-density InDel rice markers is still limited. We present the first version of PrimeInDel (https://rfgb.rmbreeding.cn/search/variation/primeIndel), an online tool integrating three sets of InDel markers: an 8K-set and a 316K-set from the 3K Rice Genomes (3K-RG) project, and a 22K-set compiled from published literatures. In user cases, primers from PrimeInDel proved highly effective, narrowing a cold-tolerance QTL (qSR2) from 2.7 Mb to 200 kb and a heading-date QTL (qDeh1.1) from 239.5 kb to 83.6 kb. Our work provides the community with a robust, PCR-based InDel primer, accessible via an online data set, which will facilitate germplasm genotyping and gene identification in rice breeding.

plant biology↗

bioRxiv2026

A human-in-the-loop explanation framework for morphologically transparent AI predictions from whole-slide images

Deep learning models enable the prediction of clinical endpoints from whole-slide images (WSIs), but many such models function as "black boxes", lacking transparency about whether and which histomorphological patterns drive their predictions, hindering interpretability and clinical adoption. Here we propose a human-in-the-loop explanation framework, MorphoXAI, which provides both local and global interpretability for deep learning models by incorporating human-expert interpretations. At the global level, it reveals the histomorphological patterns on which the model consistently relies to distinguish between classes of WSIs, as well as the patterns associated with confusion between classes. At the local level, it indicates which of these patterns are used in the prediction of an individual WSI and which regions within the slide correspond to such patterns. We validated our method on a deep learning model trained for ovarian tumor histologic subtype prediction. The results show that our framework generates explanations that accurately reflect the histomorphology underlying the models predictions at both global and local levels. For interpretability and clinical utility in diagnostic contexts, human evaluation results showed that our explanations were easy to interpret, rich in diagnostic features, and directly helpful for diagnostic decision-making, thereby enhancing pathologist-AI collaboration. Our work highlights that unifying global and local explanations and grounding them in expert-interpreted morphology enhances the interpretability and verifiability of deep learning models, thereby facilitating the transparent deployment of such models in clinical practice.

pathology↗

bioRxiv2026

DNA-templated Chiral Metamaterial Array as Information Bits

The growth of digital information demands physically secure information bits that combine intrinsic randomness with multi-layered optical readout. Digital metamaterials with physical unclonable function characteristics are promising, but nearly all examples operate at microwave or terahertz frequencies. Extending digital metamaterials to the visible range requires a physical property that allows binary (0/1) encoding via nanoscale geometry and is orthogonal to intensity-or color-based imaging. Chiral plasmonic metamaterials satisfy these criteria perfectly: their broken mirror symmetry yields chirality polarity in the visible spectrum whose sign and magnitude directly encode bit values ("0", spin-up; "1", spin-down), while remaining invisible under linear polarization. Here, we realize visible-range digital metamaterials by programming self-assembled DNA origami templates with asymmetrically placed gold nanorods to create discrete 3D chiral metamolecules. Using on-surface DNA origami assembly and the Silica Microsphere-Assisted Patterning by Liquid Elimination (SiMPLE) method, we fabricate large-scale bit arrays on optical active glass substrate in solution with [~]1.33 m lattice spacing, [~]86% site occupancy, and [~]12-month stability after silicification. These bottom-up fabricated digital metamaterial array exhibits two independent security layers: (1) a macroscopic spatial pattern only visible by dark-field microscopy, and (2) hidden binary information stored in the single-particle chiroptical response, read out by position-resolved circular dichroism spectroscopy. Quantitative analysis confirms reliable bit encoding and high optical randomness arising from slight structural variations. By leveraging the polarity of plasmonic chirality to translate molecular-scale handedness into robust visible-range digital signals, this work establishes a scalable nanophotonic platform for secure optical information storage, authentication, and encryption.

bioengineering↗

bioRxiv2026

Enhancing interpretability of cryo-EM maps with hybrid attention Transformers

Cryo-electron microscopy (cryo-EM) has become a central technique for determining the structures of macromolecular complexes. However, experimental cryo-EM density maps often exhibit limited interpretability due to noise and heterogeneous, anisotropic resolution, complicating downstream atomic model construction. Here we present DEMO-EMReF, a Swin Transformer-based framework specifically designed for cryo-EM density map refinement that integrates channel attention with hybrid spatial attention. By combining local window-based attention with grid-based sparse global attention, DEMO-EMReF captures fine-grained local density features while modeling long-range, cross-region spatial dependencies, enabling more robust density refinement in heterogeneous and anisotropic map regions. DEMO-EMReF was systematically evaluated on large benchmark sets of both primary maps and half-maps spanning resolutions of 3.0-8.0 [A] and was compared against multiple state-of-the-art map enhancement methods. DEMO-EMReF consistently improves resolution-related metrics, map-model correlations, and local atomic resolvability, with particularly robust gains in challenging heterogeneous or anisotropic cases. Importantly, the refined maps facilitate more accurate and efficient automated atomic model building. Together, DEMO-EMReF provides a robust approach for enhancing cryo-EM density maps and enabling more reliable downstream studies.

bioinformatics↗

bioRxiv2026

Wetting-mediated extracellular phase separation drives long-range cell adhesion

Cells must efficiently locate and engage for tissue formation and immune coordination, yet classical receptor-ligand binding is limited to nanometre distances and is inherently slow [1-3]. Here, we uncover a previously unrecognised physical principle, liquid-like adhesion by phase separation (LAPS). This process creates dynamic wetting layers on cell surfaces [4, 5], functioning as "liquid bridges" that enable robust, long-range cell capture across tens of micrometres. Remarkably, this wetting-mediated attraction remains effective at nanomolar concentrations--conditions where bulk phase separation would not be expected--and facilitates high-fidelity cell sorting through competitive wetting. By integrating aqueous two-phase systems, endogenous proteins (Galectin-3, CCL5), and fluid-particle-dynamics simulations [6], we demonstrate that extracellular liquid-liquid phase separation not only mediates long-range cell capture but also acts as a physical catalyst for contact-dependent signaling. These findings establish extracellular phase separation as a key physical principle complementing molecular recognition in multicellular systems, offering new opportunities for understanding immune response, tissue morphogenesis, and therapeutic strategies targeting the extracellular environment [7, 8].

biophysics↗