My research interests lie in the development and application of innovative deep learning approaches to address complex biological questions. I am primarily focused on large-scale genomics data (e.g. single-cell, spatial genomics, genetic perturbation, genetics and epigenetics). To achieve this, I build models that effectively capture and interpret complex features behind the data, providing new insights into fundamental biological processes and mechanisms. Through my work, I aim to drive significant advances in the field of computational genomics, contributing to a better understanding of human health and disease.

Honors & Awards

  • Outstanding Doctoral Dissertation, Tsinghua University (2020)
  • Outstanding Graduate of Beijing, Beijing Municipal Commission of Education (2020)
  • Outstanding Fellowship, Beijing Advanced Innovation Center of Structure Biology, Tsinghua University (2019)
  • Top 10 Advances of Bioinformatics in China, Genomics, Proteomics & Bioinformatics (2019)
  • Top 10 Algorithms and Tools for Bioinformatics in China, Genomics, Proteomics & Bioinformatics (2019)
  • Innovation Fellowship, Beijing Advanced Innovation Center of Structure Biology, Tsinghua University (2016)
  • Gold Medal, International Genetically Engineered Machine (2013)
  • Student Scholarship, University of Science and Technology of China (2013)
  • Student Scholarship, University of Science and Technology of China (2012)
  • Freshman Scholarship, University of Science and Technology of China (2011)

Professional Education

  • Postdoctoral Scholar, Stanford University, Genetics (2024)
  • Postdoctoral Associate, MIT, Computer Science & Artificial Intelligence Laboratory (2021)
  • Doctor of Philosophy, Tsinghua University, Computational Biolgoy (2020)
  • Bachelor of Science, Universtiy of Science and Technology of China, Biology (2015)

Stanford Advisors

Current Research and Scholarly Interests

My research focuses on develop deep learning methods to
1. Infer macrophage-tumor cells interaction using spatial multi-omics
2. Decipher the cis-regulatory code using a large language models
3. Predict enhancer-promoter interaction
4. Multi-omics integration
5. Build foundational model for single-cell genomics

All Publications

  • Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space. Nature communications Xiong, L., Tian, K., Li, Y., Ning, W., Gao, X., Zhang, Q. C. 2022; 13 (1): 6118


    Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX's advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.

    View details for DOI 10.1038/s41467-022-33758-z

    View details for PubMedID 36253379

    View details for PubMedCentralID PMC9574176

  • SCALE method for single-cell ATAC-seq analysis via latent feature extraction. Nature communications Xiong, L., Xu, K., Tian, K., Shao, Y., Tang, L., Gao, G., Zhang, M., Jiang, T., Zhang, Q. C. 2019; 10 (1): 4576


    Single-cell ATAC-seq (scATAC-seq) profiles the chromatin accessibility landscape at single cell level, thus revealing cell-to-cell variability in gene regulation. However, the high dimensionality and sparsity of scATAC-seq data often complicate the analysis. Here, we introduce a method for analyzing scATAC-seq data, called Single-Cell ATAC-seq analysis via Latent feature Extraction (SCALE). SCALE combines a deep generative framework and a probabilistic Gaussian Mixture Model to learn latent features that accurately characterize scATAC-seq data. We validate SCALE on datasets generated on different platforms with different protocols, and having different overall data qualities. SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation. Importantly, SCALE also generates interpretable features that directly link to cell populations, and can potentially reveal batch effects in scATAC-seq experiments.

    View details for DOI 10.1038/s41467-019-12630-7

    View details for PubMedID 31594952

    View details for PubMedCentralID PMC6783552

  • Tissue-specific silencing of integrated transgenes achieved through endogenous RNA interference inCaenorhabditis elegans. RNA biology Chen, S., Liu, W., Xiong, L., Tao, Z., Zhao, D. 2024; 21 (1): 1-10


    Transgene silencing is a common phenomenon observed in Caenorhabditis elegans, particularly in the germline, but the precise mechanisms underlying this process remain elusive. Through an analysis of the transcription factors profile of C. elegans, we discovered that the expression of several transgenic reporter lines exhibited tissue-specific silencing, specifically in the intestine of C. elegans. Notably, this silencing could be reversed in mutants defective in endogenous RNA interference (RNAi). Further investigation using knock-in strains revealed that these intestine-silent genes were indeed expressed in vivo, indicating that the organism itself regulates the intestine-specific silencing. This tissue-specific silencing appears to be mediated through the endo-RNAi pathway, with the main factors of this pathway, mut-2 and mut-16, are significantly enriched in the intestine. Additionally, histone modification factors, such as met-2, are involved in this silencing mechanism. Given the crucial role of the intestine in reproduction alongside the germline, the transgene silencing observed in the intestine reflects the self-protective mechanisms employed by the organisms. In summary, our study proposed that compared to other tissues, the transgenic silencing of intestine is specifically regulated by the endo-RNAi pathway.

    View details for DOI 10.1080/15476286.2024.2332856

    View details for PubMedID 38531838

  • CD127 imprints functional heterogeneity to diversify monocyte responses in inflammatory diseases. The Journal of experimental medicine Zhang, B., Zhang, Y., Xiong, L., Li, Y., Zhang, Y., Zhao, J., Jiang, H., Li, C., Liu, Y., Liu, X., Liu, H., Ping, Y. F., Zhang, Q. C., Zhang, Z., Bian, X. W., Zhao, Y., Hu, X. 2022; 219 (2)


    Inflammatory monocytes are key mediators of acute and chronic inflammation; yet, their functional diversity remains obscure. Single-cell transcriptome analyses of human inflammatory monocytes from COVID-19 and rheumatoid arthritis patients revealed a subset of cells positive for CD127, an IL-7 receptor subunit, and such positivity rendered otherwise inert monocytes responsive to IL-7. Active IL-7 signaling engaged epigenetically coupled, STAT5-coordinated transcriptional programs to restrain inflammatory gene expression, resulting in inverse correlation between CD127 expression and inflammatory phenotypes in a seemingly homogeneous monocyte population. In COVID-19 and rheumatoid arthritis, CD127 marked a subset of monocytes/macrophages that retained hypoinflammatory phenotypes within the highly inflammatory tissue environments. Furthermore, generation of an integrated expression atlas revealed unified features of human inflammatory monocytes across different diseases and different tissues, exemplified by those of the CD127high subset. Overall, we phenotypically and molecularly characterized CD127-imprinted functional heterogeneity of human inflammatory monocytes with direct relevance for inflammatory diseases.

    View details for DOI 10.1084/jem.20211191

    View details for PubMedID 35015026

    View details for PubMedCentralID PMC8757045

  • Molecular basis of ligand recognition and transport by glucose transporters. Nature Deng, D., Sun, P., Yan, C., Ke, M., Jiang, X., Xiong, L., Ren, W., Hirata, K., Yamamoto, M., Fan, S., Yan, N. 2015; 526 (7573): 391-6


    The major facilitator superfamily glucose transporters, exemplified by human GLUT1-4, have been central to the study of solute transport. Using lipidic cubic phase crystallization and microfocus X-ray diffraction, we determined the structure of human GLUT3 in complex with D-glucose at 1.5 Å resolution in an outward-occluded conformation. The high-resolution structure allows discrimination of both α- and β-anomers of D-glucose. Two additional structures of GLUT3 bound to the exofacial inhibitor maltose were obtained at 2.6 Å in the outward-open and 2.4 Å in the outward-occluded states. In all three structures, the ligands are predominantly coordinated by polar residues from the carboxy terminal domain. Conformational transition from outward-open to outward-occluded entails a prominent local rearrangement of the extracellular part of transmembrane segment TM7. Comparison of the outward-facing GLUT3 structures with the inward-open GLUT1 provides insights into the alternating access cycle for GLUTs, whereby the C-terminal domain provides the primary substrate-binding site and the amino-terminal domain undergoes rigid-body rotation with respect to the C-terminal domain. Our studies provide an important framework for the mechanistic and kinetic understanding of GLUTs and shed light on structure-guided ligand design.

    View details for DOI 10.1038/nature14655

    View details for PubMedID 26176916