Publications

* for equal authorship, # for corresponding authorship.


2021

  1. disent
    Construction of continuously expandable single-cell atlases through integration of heterogeneous datasets in a generalized cell-embedding space.
    L. Xiong*, K. Tian*, Y. Li, and Q. C. Zhang#

    bioRxiv 2021.

    Single-cell RNA-seq and ATAC-seq analyses have been widely applied to decipher cell-type and regulation complexities. However, experimental conditions often confound biological variations when comparing data from different samples. For integrative single-cell data analysis, we have developed SCALEX, a deep generative framework that maps cells into a generalized, batch-invariant cell-embedding space. We demonstrate that SCALEX accurately and efficiently integrates heterogenous single-cell data using multiple benchmarks. It outperforms competing methods, especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We demonstrate the advantages of SCALEX by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19, which were assembled from multiple data sources and can keep growing through the inclusion of new incoming data. Analyses based on these atlases revealed the complex cellular landscapes of human and mouse tissues and identified multiple peripheral immune subtypes associated with COVID-19 disease severity.

2019

  1. disent
    SCALE method for single-cell ATAC-seq analysis via latent feature extraction.
    L. Xiong, K. Xu, K. Tian, Y. Shao, L. Tang, G. Gao, M. Zhang, T. Jiang, and Q. C. Zhang#

    Nat Commun 2019.

    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.

2015

  1. disent
    Molecular basis of ligand recognition and transport by glucose transporters.
    D. Deng, P. Sun, C. Yan, M. Ke, X. Jiang, L. Xiong, W. Ren, K. Hirata, M. Yamamoto, S. Fan, and N. Yan

    Nature 2015.

    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.