Elucidating human beta cell transcriptional regulome with low-input genomic technologies

利用低输入基因组技术阐明人类 β 细胞转录调节组

• 批准号: 10400115
• 负责人: Yan Li
• 金额: $ 44.27万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10400115
• 关键词: 3-Dimensional; Affect; Beta Cell; Biological Assay; Biology; CRISPR screen; Cell Nucleus; Cell Separation; Cell physiology; Cells; ChIP-seq; Chromatin Loop; Clinical; Communities; Complex; DNA; Data; Data Set; Development; Diabetes Mellitus; Diagnosis; Disease; Elements; Enhancers; Environmental Risk Factor; Etiology; Gene Expression; Genes; Genetic; Genetic Transcription; Genetic study; Genome; Genome Mappings; Genomics; Goals; Grant; Haplotypes; Hi-C; Human; Human Cell Line; Human body; INS gene; Individual; Insulin; Islet Cell; Knowledge; Laboratories; Lead; Libraries; Maps; Mediating; Medical; Metabolic; Methods; Mosaicism; Obesity; Prevention; Research; Resolution; Resources; Role; Sampling; Structure of beta Cell of islet; Technology; Testing; Time; Tissues; Transcriptional Regulation; Translating; Type 2 diabetic; Untranslated RNA; Validation; Variant; Work; blood glucose regulation; cohesin; cohort; cost; cost effectiveness; data analysis pipeline; diabetes risk; droplet sequencing; epigenome; genetic signature; genome analysis; genome wide association study; genomic tools; human embryonic stem cell; improved; in vivo; insulin regulation; invention; islet; lifestyle factors; new technology; novel; obesity risk; promoter; risk variant; single-cell RNA sequencing; transcriptome; transcriptome sequencing

Genetic studies have revealed numerous non-coding sequence variants associated with diabetes or obesity risk. Generating reference 3D epigenome data in in key metabolic cell or tissue types, such as human pancreatic β cell, is therefore very important for the understanding of disease etiology. Conventional genomic methods to study transcriptional regulation include RNA-seq and ChIP-seq, for the mapping of transcriptome and epigenome, respectively. These technologies typically require ~1 million cells per assay. Hi-C is currently the most popular method to map the 3D genome organization in an unbiased fashion, but tens of millions cells are required to achieve kilobase resolution 3D genome analysis. On the other hand, the β cell research community is relying on islet samples from deceased donors, which are precious and very expensive. Low-input technologies are therefore essential for β cell genomic studies. The overall goal of this project is to combine several state-of-art single-cell and low- input genomic technologies to systematically characterize the β cell 3D enhancer regulome from a cohort of 40 fresh human islet samples; some key technologies are the original inventions from the laboratories of our team. In aim 1, we will use a massively parallel single cell RNA-seq method (Drop-seq) to generate a comprehensive dataset of single islet cell transcriptome, and simultaneously use a low-input ChIPmentation method to map enhancers and promoters from a cohort of 40 human islet donors. This will lead to the identification of diabetes and obesity signature genes and variable enhancer loci (VELs) correlated with disease status. In aim 2, we will map the human β cell 3D genome at kilobase resolution using a highly efficient easy Hi-C (eHi-C) method. The map will reveal all the interactions between individual enhancers and promoters. In aim 3, we will for the first time perform an in-depth study of the 3D regulome at the human INS locus, and quantify the activity of all enhancers near INS using a haplotype-resolved human cell line. We will also test a hypothesis that MAU2-NIPBL cohesin loading complex may regulate insulin gene expressions through mediating enhancer-promoter DNA looping at this locus. Finally, we will use a novel high-throughput Mosaic-seq method to validation the in vivo activity of dozens of β cell enhancers at single cell level. This comprehensive 3D regulome data will provide a key resource for the understanding of the functions of non-coding genome in T2D or obesity.

遗传学研究揭示了许多与糖尿病相关的非编码序列变异

项目成果

Easy Hi-C: A Low-Input Method for Capturing Genome Organization.

• DOI: 10.1007/978-1-0716-2847-8_9
• 发表时间: 2023
• 期刊: Methods in molecular biology (Clifton, N.J.)

Single-cell lineage analysis reveals extensive multimodal transcriptional control during directed beta-cell differentiation.

单细胞谱系分析揭示了在定向β细胞分化过程中广泛的多模式转录控制。

• DOI: 10.1038/s42255-020-00314-2
• 发表时间: 2020-12
• 期刊: Nature metabolism
• 影响因子: 20.8
• 作者: Weng C;Xi J;Li H;Cui J;Gu A;Lai S;Leskov K;Ke L;Jin F;Li Y
• 通讯作者: Li Y

Identifying differential regulatory control of APOE ɛ4 on African versus European haplotypes as potential therapeutic targets.

• DOI: 10.1002/alz.12534
• 发表时间: 2022-10
• 期刊: Alzheimer's & dementia : the journal of the Alzheimer's Association

Novel correlative analysis identifies multiple genomic variations impacting ASD with macrocephaly.

新颖的相关分析确定了影响自闭症谱系障碍和大头畸形的多种基因组变异。

• DOI: 10.1093/hmg/ddac300
• 发表时间: 2023
• 期刊: Human molecular genetics
• 影响因子: 3.5
• 作者: Fu,Chen;Ngo,Justine;Zhang,Shanshan;Lu,Leina;Miron,Alexander;Schafer,Simon;Gage,FredH;Jin,Fulai;Schumacher,FredrickR;Wynshaw-Boris,Anthony
• 通讯作者: Wynshaw-Boris,Anthony