Biology subjects

XU, K.

Publications and source records attributed to XU, K..

10 recordsLinked to original sources

bioRxiv2026

GeoSinkhorn Flow: Geometry-Aware Flow Matching for Perturbation-Conditioned Dynamics in Single cell Data Phenoscapes

Single-cell technologies increasingly enable large-scale profiling of complex biological systems across many experimental perturbations, but understanding how a system reorganizes across an entire space of conditions is difficult to interpret from single-cell embeddings alone. Phenoscapes address this by embedding entire cell-state distributions, rather than individual cells, as points in a data-driven landscape. Thus, a single dot represents one experimental condition rather than one cell, yielding a map of the perturbation or patient landscape that reveals how phenotype varies continuously across conditions. Phenoscapes remain a comparatively new and underexplored representation and existing static phenoscapes position experimental conditions relative to one another without specifying trajectories that describe how a cellular population moves from one condition to another. Flow- and trajectory-based models for single-cell data are numerous, but existing approaches operate at the level of individual cells, are not built to respect the geometry of a phenoscape, and typically cannot generalize their predicted flows to conditions not seen during training. Here we introduce GeoSinkhorn Flow, a perturbation-conditioned, geometry-aware flow-matching framework specialized designed to convert a static phenoscape into a dynamic phenoscape, in which each condition-level point is additionally connected by geodesic, condition-aware transport trajectories that are consistent with the phenoscape's underlying geometry. GeoSinkhorn Flow can also model transitions directly between time points without constructing a phenoscape. GeoSinkhorn Flow uses heat-diffusion geometry to define manifold-aware transport costs and trains a geometric autoencoder whose latent Euclidean distances stay aligned with the static phenoscape geometry while extending it to unseen cells. By explicitly conditioning the learned transport dynamics on perturbation-specific gene or drug embeddings, GeoSinkhorn Flow generalizes its trajectories to new measurement conditions, produces intermediate progression states along a trajectory, and extends predictions to unseen cells and held-out perturbations. An unbalanced optimal transport variant further learns growth or transition weights, so that changes in relative cell-state abundance or transition propensity are modeled together with state movement. Across synthetic manifolds, patient-derived organoid drug responses, Perturb-seq knockdowns, and time-resolved CyTOF signaling, GeoSinkhorn Flow recovered intrinsic manifold geometry, inferred biologically coherent response trajectories, and generalized to held-out drug combinations, perturbation genes, and time points. Together, these results establish GeoSinkhorn Flow as a perturbation-conditioned, phenoscape-consistent dynamical framework, distinct from standard flow-matching approaches, for converting static phenoscapes into dynamic ones that generalize across experimental conditions.

bioinformatics↗

bioRxiv2026

Glymphatic System Is Enhanced in Anti-NMDAR Encephalitis and Facilitates Antibody Influx and Clearance in the Brain

ABSTRACT Background: Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is an antibody-mediated neuroinflammatory disease. How pathogenic autoantibodies enter and are cleared from the brain parenchyma remains poorly understood. The glymphatic system (GS) mediates cerebrospinal fluid (CSF)-interstitial fluid (ISF) exchange and waste removal, yet its role in anti-NMDAR encephalitis is undefined. Methods: We used polypeptides GluN1356-385 induced active-immunization anti-NMDAR encephalitis mouse model. Glymphatic influx and efflux were evaluated via fluorescent tracer delivery combined with real-time imaging and ex-vivo quantification. Perivascular aquaporin-4 (AQP4) polarization, brain IgG deposition, NMDAR levels and blood-brain barrier (BBB) integrity were analyzed. GS function was pharmacologically modulated with AER-271 (an AQP4 inhibitor) or dexmedetomidine (Dex), and deep cervical lymphatic vessels (dCLV) were ligated in a passive-transfer model (PTM). Behavioral performance was characterized by multiple neurobehavioral tests. Results: Both glymphatic influx and efflux were enhanced in anti-NMDAR encephalitis mice, accompanied by upregulated antibody entry into and clearance from the brain. However, the cerebral IgG deposition was increased. These changes were associated with increased perivascular AQP4 polarization, with no alterations in overall AQP4 abundance or BBB tight-junction integrity. AQP4 inhibition with AER-271 attenuated glymphatic influx and efflux, reduced hippocampus IgG deposition, and partially ameliorated behavioral deficits without restoring hippocampus NMDAR levels. Conversely, dCLV ligation in the PTM mice increased brain retention of patient-derived IgG and exacerbated anxiety-like behavior. Dex enhanced antibody clearance and reduced hippocampus IgG deposition but did not improve behavioral outcomes. Conclusion: The GS is dynamically upregulated in anti-NMDAR encephalitis, driven by increased perivascular AQP4 polarization. The enhanced glymphatic influx facilitates antibody entry into the brain parenchyma and exacerbates neuronal injury, whereas enhanced efflux provides a compensatory route for antibody clearance. Rather than broadly enhancing glymphatic activity, precise tuning of this pathway may present a promising non-immunological therapeutic strategy for autoimmune encephalitis. Keywords: anti-NMDAR encephalitis; antibody influx and clearance; glymphatic system; AQP4 polarization; AER-271; dexmedetomidine

neuroscience↗

bioRxiv2026

Spatially Confined Phase Separation Promotes Local Tubulin Availability to Stabilize Ciliary Microtubule Growth

How microtubule (MT) growth is locally sustained within cilia remains a central unanswered question in ciliogenesis. Here we identify RAT-1 as a regulator of ciliary tubulin homeostasis and MT growth. Genetic screens in Caenorhabditis elegans show that loss of RAT-1 suppresses the toxic effects of a mutant -tubulin and restores ciliary structure, while RAT-1 deficiency reduces tubulin turnover at the ciliary tip, indicating its physiological role in tuning ciliary MT dynamics. Mechanistically, RAT-1 forms intra-ciliary condensates selectively at the distal region through an auto-inhibitory -helix and phosphorylation by the distally localized ciliary kinase. RAT-1 enriches tubulin within condensates in vivo/vitro, and cooperates with end-binding proteins to facilitate sustained MT growth in vitro. Notably, heterologous expression of worm RAT-1 reveals conserved condensate behavior within mammalian cilia. Our findings reveal a kinase-regulated mechanism controlling RAT-1 condensation within cilia and establish RAT-1 as a regulator of ciliary MT dynamics.

cell biology↗

bioRxiv2026

A Discrete Language of Protein Words for Functional Discovery and Design

Proteins can preserve conserved functions despite extensive sequence and structural divergence, suggesting that functional organization is governed by distributed constraints not captured by conventional representations. Here we develop a hierarchical sequence-based representation framework that compresses proteins into context-dependent latent states while preserving multiscale organizational information. Using this framework, we identified previously uncharacterized ciliary proteins lacking detectable sequence and structure homology, including ADMAP1, which is required for normal sperm axonemal organization and motility in mice. Discrete latent protein states captured species-level organizational signatures correlated with major evolutionary groups and revealed expansion of intrinsically disordered regulatory environments in eukaryotes. Autoregressive sampling within this latent space further enabled design of synthetic actin-remodeling proteins that maintained robust F-actin severing activity despite extensive sequence rewiring across key functional interfaces. These findings demonstrate that distributed protein organization can be inferred directly from sequence, linking functional discovery, evolutionary analysis, and protein design within a shared representational framework.

bioinformatics↗

bioRxiv2025

A comprehensive functional landscape of α-tubulin TUBA1A variants illuminates microtubule biology and refines clinical classification

Missense variant interpretation in highly conserved, paralog-rich gene families remains a critical bottleneck for precision medicine. Here, we developed an integrated experimental-computational platform to systematically assess the functional impact of all possible missense mutations in the -tubulin TUBA1A. Combining high-throughput comprehensive mutagenesis, high-content imaging, convolutional neural network- driven phenotyping, and machine learning-guided prediction, we quantified microtubule assembly phenotypes for every coding variant. This approach outperforms conservation-based predictors and enables functional reinterpretation of disease-associated variants. Structural mapping of the TUBA1A mutational landscape reveals distinct domains critical for GTP binding, chaperone-assisted folding or protofilament interaction, illuminating diverse mechanisms of tubulin-related diseases. Integration with ACMG-AMP guidelines demonstrates this mutational landscape improves clinical variant classification in redundant gene families. This framework is broadly applicable to other structurally conserved proteins, linking variant effect prediction to mechanistic insight and clinical translation.

genetics↗

bioRxiv2025

Interpretable spatial multi-omics data integration and dimension reduction with SpaMV

Spatial multi-omics technologies have revolutionized our understanding of biological systems by providing spatially resolved molecular profiles from multiple perspectives. Existing spatial multi-omics integration methods often assume that data from different modalities share a common underlying distribution, aiming to project them into a single unified latent space. This assumption, however, can obscure the unique insights offered by each modality, thereby limiting the full potential of multi-omics analyses. To address this limitation, we present the Spatial Multi-View (SpaMV) representation learning algorithm, which captures both the shared information across modalities and the distinct, modality-specific information, enabling a more comprehensive and interpretable representation of spatial multi-omics data. Through extensive evaluation on both simulated and real-world datasets, SpaMV demonstrates superior spatial domain clustering performance and provides users with more interpretable dimension reduction for downstream analysis. Moreover, SpaMV effectively annotates cell types within clusters of a mouse thymus dataset, highlighting its effectiveness in interpretable dimensionality reduction.

bioinformatics↗

bioRxiv2025

SynSeg: Generating Synthetic Datasets for Accurate Subcellular Segmentation with U-net

Accurate segmentation of subcellular components is crucial for understanding cellular processes, but traditional methods struggle with noise and complex structures. Convolutional neural networks improve accuracy but require large, time-consuming, and biased manually annotated datasets. Here, we developed SynSeg, a pipeline that generates synthetic training data to train a U-net model for subcellular structure segmentation, eliminating the need for manual annotation. SynSeg leverages synthetic datasets with variations in intensity, morphology, and signal distribution to deliver context-aware segmentations, even in challenging imaging conditions. We demonstrate SynSegs superior performance in segmenting vesicles and cytoskeletal filaments from culture cells and live C. elegans, outperforming traditional methods such as Otsus thresholding, ILEE, and FilamentSensor 2.0. Additionally, SynSeg effectively quantified disease-associated microtubule morphology in live cells, uncovering structural defects caused by mutant Tau proteins linked to neurodegenerative diseases. These results highlight the potential of synthetic data-driven approaches to advance biological segmentation and enhance microscopy techniques. Significance StatementThis study introduces a novel approach for accurately segmenting cellular structures, such as microtubules and vesicles, using synthetic datasets and advanced deep learning techniques. By leveraging a U-Net model trained on thousands of artificially generated images, our method eliminates the need for labor-intensive experimental data and simplifies the data creation process. Importantly, it incorporates noise and variability into the training datasets to make the model more robust and biologically relevant. Our findings demonstrate that the model can successfully identify cellular components, paving the way for its application in real-world microscopy images. This innovation has the potential to accelerate discoveries in cell biology by providing an efficient, scalable tool for analyzing complex cellular structures, even in challenging imaging conditions.

bioinformatics↗

bioRxiv2025

stDyer-image improves clustering analysis of spatially resolved transcriptomics and proteomics with morphological images

Spatially resolved transcriptomics (SRT) and spatially resolved proteomics (SRP) data enable the study of gene expression and protein abundances within their precise spatial and cellular contexts in tissues. Certain SRT and SRP tech-nologies also capture corresponding morphology images, adding another layer of valuable information. However, few existing methods developed for SRT data effectively leverage these supplementary images to enhance clustering performance. Here, we introduce stDyer-image, an end-to-end deep learning framework designed for clustering for SRT and SRP datasets with images. Unlike existing methods that utilize images to complement gene expression data, stDyer-image directly links image features to cluster labels. This approach draws inspiration from pathologists, who can visually identify specific cell types or tumor regions from morphological images without relying on gene expression or protein abundances. Benchmarks against state-of-the-art tools demonstrate that stDyer-image achieves superior performance in clustering. Moreover, it is capable of handling large-scale datasets across diverse technologies, making it a versatile and powerful tool for spatial omics analysis.

bioinformatics↗

bioRxiv2024

stDyer enables spatial domain clustering with dynamic graph embedding

Spatially resolved transcriptomics (SRT) data provide critical insights into gene expression patterns within tissue contexts, necessitating effective methods for identifying spatial domains. Traditional clustering techniques often over-look spatial information, leading to disjointed domains. Current computational approaches integrate spatial information but still face challenges in recognizing domain boundaries, scalability, and the need of independent clustering steps. We introduce stDyer, an end-to-end deep learning framework designed for spatial domain clustering in SRT data. stDyer combines a Gaussian Mixture Variational AutoEncoder (GMVAE) with graph attention networks (GATs) to simultaneously learn deep representations and perform clustering for units. A unique feature of stDyer is the dynamic graphs it adopts, which adaptively links units based on Gaussian Mixture assignments in the latent space, thereby improving spatial domain clustering and producing smoother domain boundaries. Additionally, stDyers mini-batch neighbor sampling strategy facilitates scalability to large datasets and enables multi-GPU training. Benchmarking against state-of-the-art tools across various SRT technologies, stDyer demonstrates superior performance in spatial domain clustering, multi-slice analysis, and large-scale dataset handling.

bioinformatics↗

bioRxiv2024

Artificial Intelligence-Enabled AlphaFold II Pipeline Guides Functional Fluorescence Labeling of Tubulin Across Species

Dynamic properties are essential for microtubule (MT) physiology. Current techniques for in vivo imaging of MTs present intrinsic limitations in elucidating the isotype-specific nuances of tubulins, which contribute to their versatile functions. Harnessing the power of AlphaFold II pipeline, we engineered a strategy for the minimally invasive fluorescence labeling of endogenous tubulin isotypes or those harboring missense mutations. We demonstrated that a specifically designed 16-amino acid linker, coupled with sfGFP11 from the split-sfGFP system and integration into the H1-S2 loop of tubulin, facilitated tubulin labeling without compromising MT dynamics, embryonic development, or ciliogenesis in C. elegans. Extending this technique to human cells and murine oocytes, we visualized MTs with the minimal background fluorescence and a pathogenic tubulin isoform with fidelity. The utility of our approach across biological contexts and species set an additional paradigm for studying tubulin dynamics and functional specificity, with implications for understanding tubulin-related diseases known as tubulinopathies.

cell biology↗