Biology subjects

Xiao, D.

Publications and source records attributed to Xiao, D..

At least 19 recordsLinked to original sources

bioRxiv2026

FLASH X-rays preserve neutrophil chromatin to boost antitumor immunity

Ultrahigh dose-rate FLASH radiotherapy spares normal tissue while preserving antitumor activity, positioning it as a promising technology in radiation oncology, but its immunomodulatory mechanisms remain elusive. Here, using a bilateral subcutaneous tumor model in mice, we show that 8-Gy FLASH X-rays, versus an equal dose of conventional dose-rate (CONV) X-rays, elicit a robust abscopal antitumor effect with anti-PD-1 therapy. Single-cell transcriptome and T cell receptor (TCR) profiling reveal that FLASH X-rays plus anti-PD-1 enhance antitumor immune infiltration and TCR clonal expansion in both irradiated and abscopal tumor lesions. Mechanistically, the ultrashort temporal continuity of FLASH dose delivery conserves chromatin compaction in neutrophils during exposure and inhibits neutrophil extracellular trap (NET) formation, thereby dismantling NET-mediated CD8+ T-cell exclusion within primary tumors. Conversely, CONV or split-dose FLASH X-rays induce marked chromatin modifications prior to dose completion, leading to chromatin hyper-relaxation which promotes NETosis and consequently abrogates systemic antitumor immunity. Notably, this detrimental effect can be reversed by pharmacologically suppressing PARylation-mediated chromatin modification and decondensation in neutrophils. These findings identify radiation-induced aberrant chromatin relaxation in neutrophils as a critical determinant of radioimmunotherapy outcomes, providing a rationale for redefining the FLASH effect and advancing its clinical translation.

cancer biology↗

bioRxiv2026

Plug-and-Play 3D localization microscopy

Point-spread-function engineering by depth-encoding phase masks enables volumetric super-resolution imaging by 3D single-molecule localization microscopy (SMLM) but usually requires cumbersome relay optics. We demonstrate simple 3D SMLM implementation by phase mask insertion directly into the infinity space of a commercial microscope, along with appropriate computational compensation for field-dependence. The entire inserted component, including the phase mask, is 3D printing-based.

bioengineering↗

bioRxiv2026

High-speed volumetric single-molecule imaging using dual-wavelength light sheets and PSF-engineered enhanced biplane detection

Single-molecule localization microscopy (SMLM) enables nanoscale imaging but remains limited in three-dimensional (3D), high-speed, and high-density applications due to background fluorescence, photon inefficiency, and large point-spread function (PSF) footprints. Here, we present single-objective light-sheet microscopy with PSF-engineering enhanced biplane detection (SoLiD-3D), a versatile imaging platform that integrates dual-wavelength light-sheet illumination with dual-color, multi-configuration biplane imaging for parallel acquisition with PSF engineered detection for high-speed volumetric SMLM. Parallelized single-objective light-sheet excitation combined with PSF engineering overcomes key limitations of conventional wide-field and biplane approaches. Independent control of two excitation wavelengths for optical sectioning enables simultaneous dual-target imaging and single-target dual-color imaging with improved contrast and temporal resolution utilizing dynamically displaced light sheets for volumetric coverage. Using SoLiD-3D, we demonstrate high-speed single- and dual-target dual-color imaging that doubles localization density without sacrificing photon efficiency and continuous volumetric imaging via PSF-engineering enhanced biplane detection for whole-cell 3D imaging with improved axial localization performance over extended depth ranges. We further demonstrate improved speed by utilizing the Hummus PSF, a compact engineered PSF that enables high-precision 3D localization with a substantially reduced spatial footprint, for the first time for super-resolution imaging applications. Taken together, SoLiD-3D mitigates the trade-off between axial range and localization precision and offers improved speed compared to conventional 3D SMLM approaches.

biophysics↗

bioRxiv2026

Prenatal e-cigarette exposure induces region- and cell type-specific metabolic and developmental reprogramming

Prenatal e-cigarette exposure (PeCE) is increasingly prevalent and has been associated with adverse neurodevelopmental outcomes, yet how maternal vaping perturbs early brain development remains poorly understood. We integrated spatial transcriptomics, snRNA-seq and lipidomics to define neonatal rat brain responses to PeCE in rats at regional and cellular resolution. PeCE induced pronounced spatial heterogeneity in vulnerability, with the striatum exhibiting the strongest developmental transcriptional disruption. PeCE disrupts lipid metabolic homeostasis and induced molecular signatures suggestive of enhance Ca2+ signaling, dopaminergic responsiveness and region-specific synaptic stress programs. In the striatum, PeCE induced transcriptional programs consistent with altered lipid utilization, silent synapse-like molecular features, suppressed dendritic spine development and delayed D1-medium spiny neuron maturation. PeCE-sensitive genes were enriched in human autism spectrum disorders and neurodevelopment risk loci. Together, these findings identify disrupted metabolic and developmental reprogramming as a central feature of neonatal brain vulnerability to maternal vaping and provide a mechanistic framework linking PeCE to neurodevelopmental disease risk.

neuroscience↗

bioRxiv2026

Direct effector recognition by a tandem kinase triggers non-canonical immunity in wheat

Tandem kinase proteins (TKPs) are an important class of disease resistance genes in cereal crops alongside nucleotide-binding (NB-ARC) leucine-rich repeat (LRR) (NLR) receptors. Here, we cloned the wheat (Triticum aestivum) resistance gene Lr41, which encodes a TKP with a C-terminal fusion, and its paired effector gene AvrLr41 from the rust pathogen Puccinia triticina. AvrLr41 is unusually large and highly divergent from most fungal effectors and is directly recognized by Lr41 through its pseudokinase domain. Immune activation mediated by Lr41 require auxiliary helper proteins, including either a minimal coiled-coil (CC) helper protein or its full-length NLR ortholog. These findings reveal a non-canonical immune system in which a kinase-type receptor directly recognizes an effector and recruits either CC or NLR helpers, demonstrating that full-length NLRs are not strictly required for NLR-linked immune execution and expanding the functional repertoire of disease resistance proteins in crops.

plant biology↗

bioRxiv2025

Pan-cancer tumor classification by a holistic tumor microenvironment atlas

The tumor microenvironment (TME) heterogeneity presents a major bottleneck to effective cancer immunotherapies. We address this by establishing a pan-cancer tumor classification system based on the holistic TME cellular components. First, using single-cell transcriptomes from 1,271 patients across 26 cancer types, we compile a TME cellular atlas, leading to the identification of recurrent cell states and multicellular modules across cancer types. Among these, we highlight a type I interferon (IFN-I)-related multicellular module, including IFIT1+ tumor-associated macrophages, as a critical contributor to immune-activation TMEs. From a holistic perspective, pan-cancer TMEs exhibit trichotomous patterns predominantly driven by T, myeloid, and stromal compartments. Leveraging fine-grained TME cellular composition, we further stratify all tumors into 10 stable groups. These groups display varying responses to immune checkpoint blockade (ICB), and each rationally selected therapeutic strategy specifically perturbs the corresponding expression signature. Our pan-cancer tumor classification scheme reveals the underlying patterns of TME heterogeneity and provides a framework for stratifying patients, guiding treatment selection, and developing novel therapeutic options.

cancer biology↗

bioRxiv2025

SwitchClass: dissecting attenuated and escalated molecular features via a label-switch classification framework

Biological systems exhibit complex molecular trajectories in response to perturbations, ranging from changes that revert or attenuate towards homeostasis to alterations that persist or escalate. Capturing these complex patterns is essential for understanding molecular resilience and maladaptive persistence. Here, we introduce SwitchClass, a label-switch classification framework that distinguishes attenuated and escalated molecular features across biological states. By training dual classifiers with inverted outcome labels, SwitchClass computes a differential feature importance score ({delta}) that quantifies directional change in high-dimensional data. Applied to colorectal cancer proteomics spanning healthy, pre-treatment, and post-treatment samples, Switch-Class reveals proteins that normalise after therapy and those remaining dysregulated, uncovering partial molecular recovery. In phosphoproteomics of dietary perturbation and reversal, it uncovers phosphorylation sites linked to incomplete restoration of insulin signalling. In single-cell transcriptomes from COVID-19 patients with varying severities, it identifies cell-type-specific transcripts that mark either the resolution or persistence of inflammatory activity. Together, these analyses establish SwitchClass as a generalisable and interpretable framework for mapping directional molecular changes underlying adaptation, divergence, and disease severity across biological systems. SwitchClass is freely available from https://github.com/PYangLab/SwitchClass.

bioinformatics↗

bioRxiv2025

Physics-Informed Generative Model for 3D Localization Microscopy

Localization microscopy has overcome the diffraction limit, i.e. the conventional resolution limit of a microscope, enabling nanoscale biological imaging by precisely determining the positions of individual emitters such as single fluorescent molecules. However, the performance of deep learning methods, commonly applied to these tasks, depends significantly on the quality of training data, typically generated through simulation. Creating simulations that perfectly replicate experimental conditions remains challenging, resulting in a persistent simulation-to-experiment gap. To bridge this gap, we propose a physics-informed generative model leveraging self-supervised learning directly on experimental data. Our model extends the Deep Latent Particles (DLP) framework by incorporating a physical model of the Point Spread Function (PSF; the image of a single point source in the microscope) into the decoder, enabling it to disentangle learned realistic environments from emitters. Trained directly on unlabeled experimental images, our model intrinsically captures realistic background, noise patterns, and emitter characteristics. The decoder thus acts as a high-fidelity generator, producing fully labeled, realistic training images with known emitter locations. Using these generated datasets significantly improves the performance of supervised localization networks, particularly in challenging scenarios such as complex backgrounds and low signal-to-noise ratios. We demonstrate our approach on a variety of experimentally measured microscopy data, including super-resolution imaging in 2D and 3D and particle tracking in live cells, showing substantial improvements in localization precision and emitter detection.

bioengineering↗

bioRxiv2025

Refate identifies chemical compounds to target trans-regulatory networks for cellular conversion

Identifying chemical compounds that target trans-regulatory networks (TRNs) underlying molecular programs of cells for directed cellular conversion (i.e. differentiation, reprogramming, transdifferentiation, and dedifferentiation) is a key step towards advancing regenerative medicine. Recent innovations in single-cell omics technologies enabled high-resolution profiling of TRNs that govern cell identity and cell-fate decisions. Here, we introduce Refate, a computational framework that integrates large-scale multimodal single-cell atlas data to quantify cell propensity of genes, together with six drug databases, to identify chemical compounds that target TRNs for directed cellular conversion. The reconstructed TRNs, including protein-protein interactions and gene regulatory networks, alongside chemical compounds that drive the cellular conversion provide greater biological interpretability and improve efficiency and efficacy. We evaluated Refate by testing its ability to uncover known transcription factors and chemical compounds validated in experimental conversions of various cell types. Furthermore, we experimentally validated the attribute of several novel chemical compounds identified by Refate for enhancing the conversion of human embryonic stem cells to human cranial neural crest cells. Together, these findings demonstrate Refate as an effective tool for discovering chemical compounds that target TRNs to enable cellular conversion, advancing efforts towards regenerative medicine. HighlightsO_LIRefate quantifies genes for cellular conversion using multimodal single-cell atlases C_LIO_LIRefate uncovers trans-regulatory networks (TRNs) underlying cellular conversion C_LIO_LIRefate identifies chemical compounds that target TRNs for cellular conversion C_LIO_LIValidation of Refate identified chemical compounds for hESC to hCNCC conversion C_LI

developmental biology↗

bioRxiv2025

Discovery of D2469079A, A novel selective Toll-Like Receptor 7/8 Antagonist with Brain Penetrance

Aberrant activation of endosomal Toll-like receptors (TLRs) 7 and 8, which recognize single-stranded RNA, is strongly implicated in the pathogenesis of autoimmune diseases such as systemic lupus erythematosus (SLE). And the frequent neuropsychiatric involvement in SLE highlights the need for brain-penetrant therapies. Here, we report the identification and characterization of D2469079A, a novel, potent dual TLR7/TLR8 antagonist (low nM IC50 in humanized HEK-Blue assays) selective against hTLR9. D2469079A demonstrates favorable in vitro properties, including adequate solubility and high microsomal stability. It exhibited desirable pharmacokinetic profiles across multiple preclinical species (mice, rats, dogs) and achieved significant cerebrospinal fluid (CSF) exposure, confirming its high permeability and brain penetration. Consistent in vivo efficacy was observed in both acute and chronic stimulation models. Importantly, D2469079A displayed a favorable safety profile, lacking significant liabilities related to CYP450 inhibition/induction, hERG interaction, or toxicity in a 14-day rat study. These findings position D2469079A as a promising drug candidate with excellent drug-like characteristics for the treatment of TLR7/8-driven autoimmune diseases, potentially including neuropsychiatric manifestations. We are actively seeking partners for collaborative clinical development.

immunology↗

bioRxiv2025

One-click image reconstruction in single-molecule localization microscopy via deep learning

Deep neural networks have led to significant advancements in microscopy image generation and analysis. In single-molecule localization based super-resolution microscopy, neural networks are capable of predicting fluorophore positions from high-density emitter data, thus reducing acquisition time, and increasing imaging throughput. However, neural network-based solutions in localization microscopy require intensive human intervention and computation expertise to address the compromise between model performance and its generalization. For example, researchers manually tune parameters to generate training images that are similar to their experimental data; thus, for every change in the experimental conditions, a new training set should be manually tuned, and a new model should be trained. Here, we introduce AutoDS and AutoDS3D, two software programs for reconstruction of single-molecule super-resolution microscopy data that are based on Deep-STORM and DeepSTORM3D, that significantly reduce human intervention from the analysis process by automatically extracting the experimental parameters from the imaging raw data. In the 2D case, AutoDS selects the optimal model for the analysis out of a set of pre-trained models, hence, completely removing user supervision from the process. In the 3D case, we improve the computation efficiency of DeepSTORM3D and integrate the lengthy workflow into a graphic user interface that enables image reconstruction with a single click. Ultimately, we demonstrate superior performance of both pipelines compared to Deep-STORM and DeepSTORM3D for single-molecule imaging data of complex biological samples, while significantly reducing the manual labor and computation time.

bioengineering↗

bioRxiv2024

Multi-task benchmarking of single-cell multimodal omics integration methods

Single-cell multimodal omics technologies have empowered the profiling of complex biological systems at a resolution and scale that were previously unattainable. These biotechnologies have propelled the fast-paced innovation and development of data integration methods, leading to a critical need for their systematic categorisation, evaluation, and benchmark. Navigating and selecting the most pertinent integration approach poses a significant challenge, contingent upon the tasks relevant to the study goals and the combination of modalities and batches present in the data at hand. Understanding how well each method performs multiple tasks, including dimension reduction, batch correction, cell type classification and clustering, imputation, feature selection, and spatial registration, and at which combinations will help guide this decision. This study develops a much-needed guideline on choosing the most appropriate method for single-cell multimodal omics data analysis through a systematic categorisation and comprehensive benchmarking of current methods. The Stage 1 protocol for this Registered Report was accepted in principle on 30th July 2024. The protocol, as accepted by the journal, can be found at https://springernature.figshare.com/articles/journal_contribution/Multi-task_benchmarking_of_single-cell_multimodal_omics_integration_methods/26789902.

bioinformatics↗

bioRxiv2024

Precious3GPT: Multimodal Multi-Species Multi-Omics Multi-Tissue Transformer for Aging Research and Drug Discovery

We present a multimodal multi-species multi-omics multi-tissue transformer for aging research and drug discovery capable of performing multiple tasks such as age prediction across species, target discovery, tissue, sex, and disease sample classification, drug sensitivity prediction, replication of omics response and prediction of biological and phenotypic response to compound treatment. This model combines textual, tabular, and knowledge graph-derived representations of biological experiments to provide insights into molecular-level biological processes. We demonstrate that P3GPT has developed an intuition for the interactions between compounds, pathologies, and gene regulation in the context of multiple species and tissues. In these areas, it outperforms existing LLMs and we highlight its utility in diverse case studies. P3GPT is a general model that may be used as a target identification tool, aging clock, digital laboratory, and scientific assistant. The model is intended as a community resource available open source as well as via a Discord server.

bioinformatics↗

bioRxiv2024

A functional medicine and food homology composition discovery based on disease-related target and data mining against cardiac remodeling

BackgroundMedicine and food homological (MFH) products exhibit enhanced safety and tolerability, minimizing notable side effects, making them pivotal for prolonged use in cardiovascular diseases. This study aims to identify functional compounds in MFH based on cardiac remodeling-related target, employing reliable, comprehensive, and high-throughput methods. MethodsBy bioinformatics and in vivo verifications, we initially investigated the key target in the progression of cardiac remodeling. Subsequently, we performed molecular docking among medical homology compound database (MHCD), and then performed drug-likeness evaluations to recognize functional component based on disease-related target. Pharmacological verifications and data mining including cardiac and medullary transcriptomics, neurotransmitter metabolomics, resting-state functional magnetic resonance imaging (rs-fMRI), and correlationship analysis were utilized to define the benefical effects of MFH functional components, as well as its in-depth mechanims. ResultsThe critical roles of oxidative stress and the key target of NRF2 in cardiac remodeling were discovered, and {beta}-ecdysterone was screened as the most promising NRF2 enhancer in MHCD. Dose-dependent efficacy of {beta}-ecdysterone in countering oxidative stress and ameliorating cardiac remodeling were then verfied by in vivo and ex vivo experiments. By data mining, the crosstalk mechanism between cardiac remodeling and neuromodulation was identified, and further unveiled Slc41a3 as a potential key factor influenced by {beta}-ecdysterone. Additionally, {beta}-ecdysterone mitigated increases in norepinephrine (NE) and its metabolites DHPG in the sympathetic nerve center hypothalamic paraventricular (PVN), as indicated by rs-fMRI. Cardiac and medullary transcriptomes revealed central-peripheral regulation signaling pathways during cardiac remodeling with the involvement of core gene of Dhx37. ConclusionsOur study identified {beta}-ecdysterone as a natural MFH functional compound countering cardiac remodeling by targeting NRF2 elevation. It elucidates crosstalk between cardiac remodeling and neuromodulation, facilitating precise drug screening and mechanistic insights, providing substantial evidence for {beta}-ecdysterone application and molecular mechanisms in cardiovascular diseases.

pharmacology and toxicology↗

bioRxiv2024

Chromosome-level de novo genome unveils the evolution of Gleditsia sinensis and thorns development

Gleditsia Linn is widely distributed in the world and Gleditsia sinensis as an important Leguminosae specie, especially its thorns, has been used in the Chinese medicine as a chief ingredient for centuries. While the lack of genome database limits its in-depth research, especially genetic regulation and molecular developmental mechanism. In this investigation, a chromosome-level de novo genome of G. sinensis was assembled through PacBio HiFi, Illumina sequencing as well as those from Hi-C, genetic mapping and K-mer analysis. The G. sinensis harbors 786.13 Mb sized genome (contig N50=1.58 Mb, scaffold N50=51.53 Mb, 2n=28) with 36, 408 protein-coding genes. The full-length transcriptome sequencing of diverse tissues was performed to assist genome functional annotation. The comparative and evolutionary analysis unveiled that G. sinensis diverged from the Cretaceous period approximately 76.31 million years ago (Mya) and the close relationship between G. sinensis and other 8 Leguminosae species. The whole-genome duplication (WGD) analysis indicated G. sinensis underwent three WGD events and might go through another WGD event after differentiating from other Leguminosae plants. The Weighted Gene Coexpression Network Analysis based on phenotype and differentially expressed genes further demonstrated that GsinMYB should be involved in the development of thorns via regulating late thorn differentiation. This investigation provides a high level genome of Gleditsia for Leguminosae species evolution comparison and functional elucidation and also key insights for further study on the molecular regulation mechanisms of the thorns development as a special abnormal stem organ in plants and the molecular breeding of G. sinensis.

genomics↗

bioRxiv2023

Universal inverse modelling of point spread functions for SMLM localization and microscope characterization

The point spread function (PSF) of a microscope describes the image of a point emitter. Knowing the accurate PSF model is essential for various imaging tasks, including single molecule localization, aberration correction and deconvolution. Here we present uiPSF (universal inverse modelling of Point Spread Functions), a toolbox to infer accurate PSF models from microscopy data, using either image stacks of fluorescent beads or directly images of blinking fluorophores, the raw data in single molecule localization microscopy (SMLM). The resulting PSF model enables accurate 3D super-resolution imaging using SMLM. Additionally, uiPSF can be used to characterize and optimize a microscope system by quantifying the aberrations, including field-dependent aberrations, and resolutions. Our modular framework is applicable to a variety of microscope modalities and the PSF model incorporates system or sample specific characteristics, e.g., the bead size, depth dependent aberrations and transformations among channels. We demonstrate its application in single or multiple channels or large field-of-view SMLM systems, 4Pi-SMLM, and lattice light-sheet microscopes using either bead data or single molecule blinking data.

biophysics↗

bioRxiv2023

Asynchronous Detection of Erroneous Behaviors in Human-Robot Interaction with EEG: A Comparative Analysis of Machine Learning Models.

We present a comparative analysis of two distinct machine-learning models designed to detect asynchronous errors during Human-Robot Interaction (HRI). The models under scrutiny are a customized ResNet model and an ensemble model, both trained and validated using EEG data. The ResNet model is a unique adaptation of the Residual Network, comprising a one-dimensional convolutional layer followed by batch normalization and ReLU activation. It also features a custom Residual Block structure and an adaptive average pooling layer, concluding with a fully connected linear layer for binary classification. The ensemble model, on the other hand, incorporates various machine learning models such as MLP, logistic regression, SVM, random forest, and XGBoost, unified in a pipeline with feature extraction and transformation steps. A critical component of our research is the innovative probability map, which maintains a granularity of 0.1 seconds. This map forecasts the likelihood of forthcoming one-second intervals being classified as either Error or Non-error. Our comparative analysis reveals significant variations in the performance of the two models, both of which exhibit promising results in detecting erroneous behaviors during HRI. We provide detailed validation results, including the accuracy, F1 score, and confusion matrix for each model. This study offers valuable insights into the potential of machine learning in enhancing HRI efficiency and accuracy, indicating promising directions for future research.

neuroscience↗

bioRxiv2023

NGAL contributes to hemostasis and thrombosis by potentiating or suppressing coagulant or anticoagulant factors

Coagulation is related to inflammation, but the key pathways, especially innate immunity inflammatory response-coagulation, hemostasis, and thrombosis regulation is poorly understood and need to be further explored. In the current study, we showed that innate immunity inflammatory mediator neutrophil gelatinase-associated apolipoprotein (NGAL) which was upregulated in plasma of deep vein thrombosis patients interacted with and potentiated thrombin, kallikrein, FXIa, and FVIIa and suppressed antithrombin to induce coagulation, hemostasis, and thrombosis. Furthermore, NGAL can augment thrombin-induced platelet aggregation. In multiple mice hemostasis and thrombosis models, NGAL overexpression or intravenous administration promoted coagulation and hemostasis and aggravated thrombus, whereas NGAL knockout or treatment with anti-NGAL monoclonal antibody significantly prolonged bleeding time and alleviated thrombus formation. Notably, NGAL knockout prolonged both mice tail bleeding time and artery occlusion time to over 40 min, resembling uncontrollable bleeding and clotting disorder seen in Hemophilia mice. Furthermore, anti-NGAL monoclonal antibody treatment markedly reduced the formation of blood clots in a mouse-tail thrombosis model induced by carrageenan, which is linked to inflammation. Collectively, these findings suggest NGAL is a crucial coagulation regulator and mediates the crosstalk between innate immunity inflammation and coagulation, hemostasis, and thrombus, and provide new target and strategy for the development of innovative antithrombotic drugs.

biochemistry↗