Our team is investigating the connections between gene regulation and 13disease development. We focus on cis-regulatory elements (CREs), which are crucial for controlling gene expression. By conducting large-scale profiling of CRE activities among various cell types and disease cohorts, coupled with developing advanced deep learning models, we strive to decipher how genetic variations within CREs affect gene regulation in a cell-type specific manner within disease contexts. We have devised computational methods for identify-ing transcribed CREs (tCREs) at the single-cell level and have constructed an atlas of single-cell tCREs spanning human tissues. We devised an innovative framework that integrates single-cell tCRE data with genome-wide association studies (GWAS) for assessing trait heritability at single-cell resolution, uncover-ing the nuanced trait-gene regulatory relationships across a continuum of cell populations (Figure). Currently, we are extending these single-cell-tCRE-based methods to study large disease cohorts, including single-cells from patients with amyotrophic lateral sclerosis, ulcerative colitis, and acute myeloid leukemia. The goal is to shed light on the role of gene regulation in these diseases. Fur-thermore, we are constructing a deep learning model to predict gene expression directly from DNA sequences. This model integrates large-scale tCRE data with additional epigenomic information, allowing us to simulate mutations through in silico mutagenesis and evaluate their impact on gene expression. With the use of these in silico mutagenesis data, we have developed a reliable method to link distal CREs to their target genes of in a cell-type-specific manner. These methods will enable us to annotate the functional and cellular contexts of CREs in a genome-wide and cell-type-specific manner for understanding their roles in gene regulation and diseases.Figure: Assessing disease heritability at single-cell resolutionWe devised a novel metric called Individual Cell CRE Activity Module (ICE-CREAM) Score to integrate single-cell tCRE data with GWAS for assessing disease herita-bility at single-cell resolution. A) Crohn’s Disease (CD) ICE-CREAM score in meta-cells across our tCRE atlas. B) Ulcerative Colitis (UC) ICE-CREAM score in meta-cells across the atlas. C) Differences between CD and UC ICE-CREAM demonstrating that fibroblasts and T cells are the most enriched in differential heritability between CD and UC.Recent Major PublicationsDou J, Tan Y, Kock KH, Wang J, Cheng X, Tan LM, Han KY, Hon CC, Park WY, Shin JW, Jin H, Wang Y, Chen H, Ding L, Prabhakar S, Navin N, Chen R, Chen K. Single-nucleotide variant calling in single-cell sequencing data with Monopogen. Nat Biotechnol 2023 Aug 17. doi: 10.1038/s41587-023-01873-xHou R, Hon CC, Huang Y. CamoTSS: analysis of alterna-tive transcription start sites for cellular phenotypes and regulatory patterns from 5’ scRNA-seq data. Nat Com-mun 2023 Nov 9;14(1):7240. doi: 10.1038/s41467-023-42636-1Umarov R, Hon CC. Enhancer target prediction: state-of-the-art approaches and future prospects. Biochem Soc Trans 2023 Oct 31;51(5):1975-1988. doi: 10.1042/BST20230917Invited presentationsHon CC. “A single-cell atlas of transcribed cis-regulatory elements in the human genome” HCA Asia 2023 Meet-ing (Kolkata, India) November 2023Hon CC. “A single-cell atlas of transcribed cis-regulatory elements in the human genome” The 44th Lorne Ge-nome conference (Lorne, Australia) February 2023Hon CC. “A single-cell atlas of transcribed cis-regulatory elements in the human genome” The 67th Annual Meet-ing of the Japan Society of Human Genetics (Yokohama, Japan) December 2022Laboratory for Genome Information AnalysisTeam Leader: Chung-Chau Hon
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