Publications

2023

1. bioRxiv

   scART: recognizing cell clusters and constructing trajectory from single-cell epigenomic data

   Jingxin Guo*, Jingyu Li*, Fei Huang, and 2 more authors

   bioRxiv, Apr 2023

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   The development of single-cell assay for transposase-accessible chromatin using sequencing data (scATAC-seq) has allowed the characterization of epigenetic heterogeneity at single-cell resolution. However, the sparse and noisy nature of scATAC-seq data poses unique computational challenges. To address this, we introduce scART, a novel bioinformatics tool specifically designed for scATAC-seq data analysis. scART utilizes analytical methods highly stable for processing sparse and noisy data, such as k-nearest neighbor (KNN) imputation, Term Frequency-Inverse Document Frequency (TF-IDF) weighting scheme, and the cosine similarity metric to identify underlying cellular heterogeneity in scATAC-seq data. It accurately and robustly identifies cell identities, particularly in data with low sequencing depth, and constructs the trajectory of cellular states. As a demonstration of its utility, scART successfully reconstructed the development trajectory of the embryonic mouse forebrain and uncovered the dynamics of layer-specific neurogenesis. scART is available at GitHub.

2022

1. bioRxiv

   Decoding gene regulation in the mouse embryo using single-cell multi-omics

   Ricard Argelaguet, Tim Lohoff, Jingyu Gavin Li, and 6 more authors

   bioRxiv, Jun 2022

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   Following gastrulation, the three primary germ layers develop into the major organs in a process known as organogenesis. Single-cell RNA sequencing has enabled the profiling of the gene expression dynamics of these cell fate decisions, yet a comprehensive map of the interplay between transcription factors and cis-regulatory elements is lacking, as are the underlying gene regulatory networks. Here we generate a multi-omics atlas of mouse early organogenesis by simultaneously profiling gene expression and chromatin accessibility from tens of thousands of single cells. We develop a computational method to leverage the multi-modal readouts to predict transcription factor binding events in cis-regulatory elements, which we then use to infer gene regulatory networks that underpin lineage commitment events. Finally, we show that these models can be used to generate in silico predictions of the effect of transcription factor perturbations. We validate this experimentally by showing that Brachyury is essential for the differentiation of neuromesodermal progenitors to somitic mesoderm fate by priming cis-regulatory elements.

2. Nature Genetics

   Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types

   Jiaqi Li, Jingjing Wang, Peijing Zhang, and 26 more authors

   Nature Genetics, Nov 2022

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   Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila and earthworm. We then integrated cell landscapes from eight representative metazoan species to study gene regulation across evolution. Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in vertebrates and invertebrates. Our work provides a valuable resource and offers a new strategy for studying regulatory grammar in diverse biological systems.

2021

1. Cell Discovery

   High-throughput Microwell-seq 2.0 profiles massively multiplexed chemical perturbation

   Haide Chen, Yuan Liao, Guodong Zhang, and 10 more authors

   Cell Discovery, Nov 2021

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   In summary, these results illustrated the high sensitivity and robustness of Microwell-seq 2.0 in cell-based screening. Our method may pave the way for a more cost-effective multi-dimensional and high-throughput drug screening assay.