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结构在转录调控中起着关键作用。通过DNA 环状的非编码顺式调控元件可能会调控数百个碱基以外的靶基因。 由于这种复杂性，生成远程DNA环相互作用的全面地图将 极大地促进了我们对基因组功能的理解。我们之前的工作是第一次 证明了用30~50亿个Hi-C读数在哺乳动物细胞中绘制3D基因组图是可行的， 5-10kb的分辨率。在这一决议下，各个顺式调控元件之间的相互作用可以 被揭露了。最近，单细胞Hi-C方法也被测试以揭示细胞间的可变性 染色体结构。快速发展的三维基因组研究领域要求在一个 细胞或组织类型在不同的生理或致病扰动下的多样性。为了实现 广泛的适用性，3D基因组图谱技术必须解决以下挑战：(I)能够 分析稀有生物样本；(Ii)生成高质量的文库，用于数十亿级别的深度测序 读取；(3)分析大量单个细胞以分析复杂组织或 细胞异质性。然而，Hi-C及其衍生品的文库质量通常较差 起始料量小。这项提议的总体目标是开发一种简单而高效的 3C-SEQ方法(环状连接产物测序，或CLP-SEQ)以生成高质量 适合从少量细胞中进行超深测序的文库。与Hi-C及其 作为衍生产品，CLP-seq是独特的，因为它通过一系列 酶促反应，无需生物素标记和下拉。从一项试点实验中，我们 估计这种新方法只需要不到1%的细胞作为起始材料就可以进行测序 10亿次阅读量的深度(比Hi-C提高100倍以上)。此外，由于中电- SEQ避免了生物素标记和下拉，有利于单管单细胞CLP的开发- 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，我们将建立和 优化这些新技术，并作为原则证明，还可以产生大量有价值的 数据资源利用这些方法主要有以下三个目的。在目标1中，我们将优化CLP-SEQ 用于从小细胞群体中生成用于超深测序的高复杂性文库的协议或 稀有的人体组织。在目标2中，我们将开发一条完整的CLP-SEQ数据分析管道，以检测 并以千碱基的分辨率可视化DNA环路相互作用。我们将生成千基分辨率的3D 在几个困难的人类组织中绘制基因组图谱，并对非 编码相关人类疾病中的GWASSNPs。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 人类胰岛组织中数百个单细胞的基因组数据，并探索执行 使用聚类法进行亚群分析。我们相信，这一技术进步将扩大 3D基因组研究领域，并最终有助于我们对基因组功能和人类 疾病。