Bio

Dr. Weize Xu is a postdoctoral researcher in Dr. Xiaojie Qiu's laboratory, where he focuses on advancing computational biology and genomics research. He earned his Ph.D. in Dr. Gang Cao's lab, where he made significant contributions to the development of computational methods and pipelines for spatial transcriptomics (MiP-Seq) and single-cell Hi-C (sciDLO Hi-C). His work during this time centered on enhancing data analysis frameworks, providing more precise insights into complex biological systems.

Dr. Xu is also an expert in the development of bioimaging processing softwares. During his Ph.D., he developed the U-FISH method, a deep learning-based approach for detecting signal points in FISH images. This innovative project involved curating a high-quality dataset from diverse sources, ensuring robust performance across various FISH data types. The resulting model demonstrated outstanding generalizability and included a user-friendly Web and LLM interface, making it accessible to researchers worldwide.

In addition to his Ph.D. research, Dr. Xu further honed his skills at SciLifeLab, where he worked under the mentorship of Dr. Wei Ouyang. There, he focused on web programming and developing key components for the Bioimage.IO deep learning platform, gaining valuable experience in creating innovative tools for computational biology.

With a solid foundation in computational biology, deep learning, and bioinformatics, Dr. Xu is passionate about driving cutting-edge research and contributing new perspectives to his field. He brings a unique combination of technical expertise and a collaborative mindset to his role in Dr. Xiaojie Qiu’s lab.

Stanford Advisors

Contact

  • Academic
    wzxu@stanford.edu
    University - Scholar Department: Genetics Position: Postdoctoral Scholar

Additional Info

  • Mail Code: 5120

All Publications

  • Spatial multi-omics at subcellular resolution via high-throughput in situ pairwise sequencing. Nature biomedical engineering Wu, X., Xu, W., Deng, L., Li, Y., Wang, Z., Sun, L., Gao, A., Wang, H., Yang, X., Wu, C., Zou, Y., Yan, K., Liu, Z., Zhang, L., Du, G., Yang, L., Lin, D., Yue, J., Wang, P., Han, Y., Fu, Z., Dai, J., Cao, G. 2024; 8 (7): 872-889

    Abstract

    Technology for spatial multi-omics aids the discovery of new insights into cellular functions and disease mechanisms. Here we report the development and applicability of multi-omics in situ pairwise sequencing (MiP-seq), a method for the simultaneous detection of DNAs, RNAs, proteins and biomolecules at subcellular resolution. Compared with other in situ sequencing methods, MiP-seq enhances decoding capacity and reduces sequencing and imaging costs while maintaining the efficacy of detection of gene mutations, allele-specific expression and RNA modifications. MiP-seq can be integrated with in vivo calcium imaging and Raman imaging, which enabled us to generate a spatial multi-omics atlas of mouse brain tissues and to correlate gene expression with neuronal activity and cellular biochemical fingerprints. We also report a sequential dilution strategy for resolving optically crowded signals during in situ sequencing. High-throughput in situ pairwise sequencing may facilitate the multidimensional analysis of molecular and functional maps of tissues.

    View details for DOI 10.1038/s41551-024-01205-7

    View details for PubMedID 38745110

    View details for PubMedCentralID 5762154

  • Decoding the spatial chromatin organization and dynamic epigenetic landscapes of macrophage cells during differentiation and immune activation. Nature communications Lin, D., Xu, W., Hong, P., Wu, C., Zhang, Z., Zhang, S., Xing, L., Yang, B., Zhou, W., Xiao, Q., Wang, J., Wang, C., He, Y., Chen, X., Cao, X., Man, J., Reheman, A., Wu, X., Hao, X., Hu, Z., Chen, C., Cao, Z., Yin, R., Fu, Z. F., Zhou, R., Teng, Z., Li, G., Cao, G. 2022; 13 (1): 5857

    Abstract

    Immunocytes dynamically reprogram their gene expression profiles during differentiation and immunoresponse. However, the underlying mechanism remains elusive. Here, we develop a single-cell Hi-C method and systematically delineate the 3D genome and dynamic epigenetic atlas of macrophages during these processes. We propose "degree of disorder" to measure genome organizational patterns inside topologically-associated domains, which is correlated with the chromatin epigenetic states, gene expression, and chromatin structure variability in individual cells. Furthermore, we identify that NF-κB initiates systematic chromatin conformation reorganization upon Mycobacterium tuberculosis infection. The integrated Hi-C, eQTL, and GWAS analysis depicts the atlas of the long-range target genes of mycobacterial disease susceptible loci. Among these, the SNP rs1873613 is located in the anchor of a dynamic chromatin loop with LRRK2, whose inhibitor AdoCbl could be an anti-tuberculosis drug candidate. Our study provides comprehensive resources for the 3D genome structure of immunocytes and sheds insights into the order of genome organization and the coordinated gene transcription during immunoresponse.

    View details for DOI 10.1038/s41467-022-33558-5

    View details for PubMedID 36195603

    View details for PubMedCentralID PMC9532393

  • CoolBox: a flexible toolkit for visual analysis of genomics data. BMC bioinformatics Xu, W., Zhong, Q., Lin, D., Zuo, Y., Dai, J., Li, G., Cao, G. 2021; 22 (1): 489

    Abstract

    Data visualization, especially the genome track plots, is crucial for genomics researchers to discover patterns in large-scale sequencing dataset. Although existing tools works well for producing a normal view of the input data, they are not convenient when users want to create customized data representations. Such gap between the visualization and data processing, prevents the users to uncover more hidden structure of the dataset.We developed CoolBox-an open-source toolkit for visual analysis of genomics data. This user-friendly toolkit is highly compatible with the Python ecosystem and customizable with a well-designed user interface. It can be used in various visualization situations like a Swiss army knife. For example, to produce high-quality genome track plots or fetch commonly used genomic data files with a Python script or command line, to explore genomic data interactively within Jupyter environment or web browser. Moreover, owing to the highly extensible Application Programming Interface design, users can customize their own tracks without difficulty, which greatly facilitate analytical, comparative genomic data visualization tasks.CoolBox allows users to produce high-quality visualization plots and explore their data in a flexible, programmable and user-friendly way.

    View details for DOI 10.1186/s12859-021-04408-w

    View details for PubMedID 34629071

    View details for PubMedCentralID PMC8504052