Biology subjects

XU, K.

Publications and source records attributed to XU, K..

4 recordsLinked to original sources

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↗