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: 9364054
• 负责人: Fulai Jin
• 金额: $ 45.69万
• 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 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9364054
• 关键词: Address; Affect; Architecture; Autistic Disorder; Automation; Base Pairing; Biochemical Reaction; Biological Assay; Biology; Biotin; Brain; Cell Count; Cells; Chromatin; Chromatin Loop; Chromosome Structures; Colon; Complex; DNA; DNA Folding; Data; Data Analyses; Deoxyribonucleases; Development; Diabetes Mellitus; Digestion; Disease; Gene Targeting; Genes; Genetic Transcription; Genome; Genome Mappings; Goals; Heterogeneity; 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; cell type; chromosome conformation capture; cost; 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结构在转录调节中起关键作用。通过DNA 循环，非编码顺式调节元件可能会从数百千碱基中调节靶基因。 由于这种复杂性，生成远程DNA循环相互作用的综合图将 极大地促进了我们对基因组功能的理解。我们先前的第一次工作 证明可以在3〜5亿hi-c读取哺乳动物细胞中绘制3D基因组，在 分辨率为5-10kb。在此分辨率下，单个顺式调节元素之间的相互作用可以是 揭示。最近，也已经对单细胞HI-C方法进行了测试，以揭示细胞对细胞的可变性 染色体结构。 3D基因组研究的快速生长领域要求在A中使用3D基因组图 在不同的生理或致病性扰动下的各种细胞或组织类型。为了实现 广泛的适用性，3D基因组映射技术必须应对以下挑战：（i） 测定罕见的生物样本； （ii）生成高质量的库，用于深度测序数十亿个 阅读； （iii）能够分析大量单细胞进行复杂组织的分析或 细胞异质性。但是，当Hi-C及其导数的图书馆质量通常很差 起始材料的数量很小。该提案的总体目标是开发一个简单有效的 3C-Seq方法（循环连接产品测序或CLP-Seq）生成高质量 库适合从少数细胞进行超深测序。与Hi-C及其相反 CLP-Seq衍生物是独一无二的，因为它通过一系列 酶促反应无需生物素标记和下拉。从飞行员实验中，我们 估计这种新方法需要少于1％的细胞作为开始材料才能达到测序 十亿个读取水平的深度（比HI-C提高了100倍）。此外，因为CLP- SEQ避免了生物素标记和下拉，它适合开发单管单细胞CLP- SEQ协议（SCCLP-SEQ）用于大规模可扩展的单细胞分析。在这个项目中，我们将建立和 优化这些新技术，并作为原理证明，还可以产生大量有价值的 在以下三个目标中使用这些方法的数据资源。在AIM 1中，我们将优化CLP-Seq 生成高复杂性库的协议，用于从小细胞种群或 稀有的人体组织。在AIM 2中，我们将开发全包CLP-seq数据分析管道以检测 并在千目标分辨率下可视化DNA循环相互作用。我们将生成千目标分辨率3D 基因组图在一些困难的人体组织中，并进行非 - 的初步功能注释 在相关人类疾病中编码GWAS SNP。在AIM 3中，我们将进一步开发一个单管SCCLP- 用于同时分析数百个单元的SEQ方案。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个细胞中的基因组数据，并探索执行的策略 使用聚类方法的亚群分析。我们相信这项技术的进步将扩大 3D基因组研究领域，最终使我们对基因组功能和人类的理解有利 疾病。