Developing a one-tube circularized ligation product sequencing (CLP-seq) method for the mapping of 3D genome architecture in small cell populations or single cells.

开发一种单管环化连接产物测序 (CLP-seq) 方法，用于绘制小细胞群或单细胞中的 3D 基因组架构。

• 批准号: 10170405
• 负责人: Fulai Jin
• 金额: $ 46.41万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-09 至 2022-08-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10170405
• 关键词: 3-Dimensional; Address; Affect; Architecture; Automation; Base Pairing; Biochemical Reaction; Biological Assay; Biology; Biotin; Brain; Cells; Chromatin; Chromatin Loop; Chromosome Structures; Colon; Complex; DNA; DNA Folding; Data; Deoxyribonucleases; Development; Diabetes Mellitus; Digestion; Disease; Genes; Genetic Transcription; Genome; Genome Mappings; Goals; Heterogeneity; Hi-C; Human; In Situ; Individual; Islet Cell; Label; Lead; Length; Libraries; Ligation; Malignant Neoplasms; Mammalian Cell; Maps; Marriage; Methods; Molecular; Mutation; Nature; Nucleotides; Obesity; Pathogenicity; Physiological; Play; Population; Protocols documentation; Publishing; Recovery; Regulatory Element; Research; Research Personnel; Resolution; Robotics; Sampling; Series; Streptavidin; Technology; Testing; Time; Tissue Sample; Tissues; Transcriptional Regulation; Tube; Untranslated RNA; Work; autism spectrum disorder; cell type; chromosome conformation capture; cost; data analysis pipeline; data resource; deep sequencing; experience; experimental study; follow-up; genetic variant; genome analysis; genome wide association study; human disease; human embryonic stem cell; human tissue; improved; interest; invention; islet; mammalian genome; new technology; next generation sequencing; professor; programs; scale up; single cell analysis; tool

The 3D architecture of mammalian genome plays a key role in transcription regulation. Through DNA looping, non-coding cis-regulatory elements may regulate target genes from hundreds of kilobases away. Because of this complexity, generating a comprehensive map of long-range DNA looping interactions will greatly facilitate our understanding of genome functions. Our previous work for the first time demonstrated that it is feasible to map the 3D genome in mammalian cells with 3~5 billion Hi-C reads, at a resolution of 5-10kb. At this resolution, interactions between individual cis-regulatory elements can be revealed. Recently, single cell Hi-C approach has also been tested to reveal cell-to-cell variability of chromosome structures. The fast growing field of 3D genome research calls for 3D genome maps in a variety of cell or tissue types under different physiological or pathogenic perturbations. In order to achieve broad applicability, 3D genome mapping technology must address the following challenges: (i) Ability to assay rare bio-samples; (ii) Generating high-quality library for deep sequencing at a level of several billion reads; (iii) The ability to analyze a large number of single cells for the analyses of complex tissue or cellular heterogeneity. However, the library quality from Hi-C and its derivatives is usually poor when the amount of starting material is small. The overall goal of this proposal is to develop a simple and efficient 3C-seq method (Circularized Ligation Products sequencing, or CLP-seq) to generate high-quality libraries suitable for ultra-deep sequencing from a small number of cells. In contrast to Hi-C and its derivatives, CLP-seq is unique because it enriches ligation junction products through a series of enzymatic reactions without the need for biotin labeling and pull-down. From a pilot experiment, we estimate that this new method requires less than 1% of cells as starting material to reach sequencing depth at that level of a billion reads (over 100-fold improvement over Hi-C). Furthermore, because CLP- seq avoids biotin labeling and pull-down, it is amenable to the development of a one-tube single cell CLP- seq protocol (scCLP-seq) for massive scalable single cell analysis. In this project, we will establish and optimize these new technologies, and as proof-of-principle, also produce a significant amount of valuable data resources with these methods in the following three aims. In aim 1, we will optimize CLP-seq protocol to generate high-complexity libraries for ultra-deep sequencing from small cell populations or rare human tissues. In aim 2, we will develop a full-package CLP-seq data analysis pipeline to detect and visualize DNA looping interactions at kilobase resolution. We will generate kilobase-resolution 3D genome maps in a few difficult human tissues and perform preliminary functional annotation of non- coding GWAS SNPs in relevant human diseases. In aim 3, we will further develop a one-tube scCLP- seq protocol for simultaneous analysis of hundreds of single cells. As test cases, we will generate 3D genome data in hundreds of single cells from human islet tissues, and explore strategies to perform subpopulation analysis using clustering methods. We believe this technology advance will expand the field of 3D genome study and eventually benefit our understanding of genome functions and human diseases.

哺乳动物基因组的 3D 结构在转录调控中发挥着关键作用。通过DNA 循环的非编码顺式调控元件可以调控数百个碱基外的靶基因。 由于这种复杂性，生成长程 DNA 循环相互作用的综合图谱将 极大地促进了我们对基因组功能的理解。我们之前的工作还是第一次 证明用 3~50 亿个 Hi-C 读数绘制哺乳动物细胞 3D 基因组图谱是可行的， 分辨率为5-10kb。在此分辨率下，各个顺式调控元件之间的相互作用可以是 揭示了。最近，单细胞 Hi-C 方法也经过测试，揭示了细胞间的变异性 染色体结构。快速发展的 3D 基因组研究领域需要 3D 基因组图谱 不同生理或致病扰动下的各种细胞或组织类型。为了达到 为了获得广泛的适用性，3D 基因组作图技术必须解决以下挑战： (i) 能够 分析稀有生物样本； （二）生成数十亿级深度测序的高质量文库 读； (iii) 分析大量单细胞以分析复杂组织或 细胞异质性。然而，当使用 Hi-C 及其衍生物时，其文库质量通常较差。 起始原料用量少。该提案的总体目标是开发一个简单高效的 3C-seq 方法（环状连接产物测序，或 CLP-seq）生成高质量 适合对少量细胞进行超深度测序的文库。与 Hi-C 及其 衍生物，CLP-seq 是独一无二的，因为它通过一系列 酶促反应无需生物素标记和下拉。通过试点实验，我们 估计这种新方法需要不到1%的细胞作为起始材料即可达到测序 深度达到十亿次读取的水平（比 Hi-C 提高了 100 倍以上）。此外，由于 CLP- seq 避免了生物素标记和 Pull-down，适合开发单管单细胞 CLP- 用于大规模可扩展单细胞分析的 seq 协议 (scCLP-seq)。在这个项目中，我们将建立并 优化这些新技术，并作为原理验证，还产生大量有价值的成果 使用这些方法获取数据资源有以下三个目的。在目标 1 中，我们将优化 CLP-seq 生成高复杂性文库的协议，用于从小细胞群进行超深度测序或 罕见的人体组织。在目标 2 中，我们将开发一个完整的 CLP-seq 数据分析流程来检测 并以千碱基分辨率可视化 DNA 循环相互作用。我们将生成千碱基分辨率的 3D 一些困难的人体组织的基因组图谱，并对非 编码相关人类疾病中的 GWAS SNP。在目标 3 中，我们将进一步开发单管 scCLP- 用于同时分析数百个单细胞的 seq 协议。作为测试用例，我们将生成 3D 来自人类胰岛组织的数百个单细胞的基因组数据，并探索执行策略 使用聚类方法进行亚群分析。我们相信这项技术进步将扩大 3D基因组研究领域，最终有利于我们对基因组功能和人类的理解 疾病。