Model-based methods for single cell chromatin interactomic data

基于模型的单细胞染色质组间数据方法

• 批准号: 10657750
• 负责人: Ming Hu
• 金额: $ 48.06万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10657750
• 关键词: 3-Dimensional; Applications Grants; Biological Assay; CISH gene; Cell Line; Cell Nucleus; Cells; Chromatin; Chromatin Interaction Analysis by Paired-End Tag Sequencing; Chromatin Loop; Complex; Computing Methodologies; Data; Disease; Gene Expression; Gene Expression Regulation; Genes; Genome; Hi-C; Hybrids; In Situ; Methodology; Methods; Modeling; Play; Regulatory Element; Research; Resolution; Role; Systematic Bias; Technology; Tissues; Variant; cell type; chromosome conformation capture; deep learning; falls; genome wide association study; genome-wide; human disease; mammalian genome; multimodality; single cell technology; tool; trait; user friendly software; user-friendly

PROJECT SUMMARY/ABSTRACT Millions of cis-regulatory elements (CRE) have been identified in mammalian genomes, which harbor large portion of GWAS variants associated with complex human diseases and traits. Interpreting the regulatory target genes of CRE and GWAS variants remains challenging, as majority of genes are not merely regulated by CREs in close one-dimensional (1D) vicinity. Instead, CREs can form DNA loops and regulate the expression of gene(s) from hundreds of kilobases (Kb) away. Thus, deep understanding of chromatin spatial organization can shed light on gene regulation and disease mechanisms. During the last decade, chromatin conformation capture (3C)-derived technologies (e.g., in situ Hi-C, capture Hi-C, ChIA-PET, PLAC-seq and HiChIP) have been widely used to provide a genome-wide view of chromatin spatial organization. However, these technologies are usually applied to bulk tissue or purified cell lines, and cannot reveal cell-type-specific chromatin interactome within complex tissues. Fortunately, harnessing the power of single cell technologies, single cell Hi-C (scHi-C) and scHi-C-derived multi-modal assays, including single cell Methyl-HiC and single- nucleus methyl-3C, have been rapidly advanced to study chromatin interactome at single cell resolution, providing powerful tools to study chromatin spatial organization in complex tissues and disease relevant cell types. While great strides have been made in scHi-C experimental technologies, computational methods for analyzing scHi-C data are largely lagging behind. The methodological gaps fall mainly in three aspects: (1) Current methods are inefficient to enhance resolution from extremely sparse scHi-C data. (2) Few methods exist for removing systematic biases of scHi-C data within each cell, and adjusting for batch effect across different cells. (3) No method is available to detect Kb resolution cell-type-specific chromatin interactions from scHi-C data. To fill in these gaps, I propose major research directions: (1) develop deep learning-based methods to impute sparse chromatin contacts in each cell, (2) develop non-parametric regression models to remove systematic biases within each cell, and to adjust batch effects across different cells, (3) develop a hybrid approach based on both global and local background models to identify cell-type-specific chromatin interactions, and predict putative target genes of GWAS variants associated with complex human diseases and traits, and (4) develop stand-alone, user-friendly software packages to analyze single cell chromatin interactomic data and disseminate results. Completion of the proposed study will provide robust and user friendly computational methods that allow us to analyze 3D genome organization at single cell resolution and interpret their regulatory role on gene expression and complex human diseases.

项目总结/摘要 哺乳动物基因组中已发现数百万个顺式调节元件（CRE），其中含有 大部分GWAS变异与复杂的人类疾病和性状相关。解读监管 CRE和GWAS变体的靶基因仍然具有挑战性，因为大多数基因不仅受到调控， 在近一维（1D）附近的克雷斯。相反，克雷斯可以形成DNA环，并调节细胞的增殖。 来自数百个酶（Kb）的基因的表达。因此，深入了解染色质空间 组织可以揭示基因调控和疾病机制。在过去的十年中，染色质 构象捕获（3C）衍生的技术（例如，原位Hi-C、捕获Hi-C、ChIA-PET、PLAC-seq和 HiChIP）已被广泛用于提供染色质空间组织的全基因组视图。然而，在这方面， 这些技术通常应用于大块组织或纯化的细胞系，并且不能揭示细胞类型特异性 复杂组织中的染色质相互作用组。幸运的是，利用单细胞技术的力量， 单细胞Hi-C（scHi-C）和scHi-C衍生的多模式测定，包括单细胞甲基HiC和单细胞甲基Hi-C。 核甲基-3C，已经迅速发展到在单细胞分辨率下研究染色质相互作用体， 为研究复杂组织和疾病相关细胞的染色质空间结构提供了有力的工具 类型虽然在scHi-C实验技术中已经取得了很大的进步，但是用于 分析scHi-C数据很大程度上落后了。方法上的差距主要体现在三个方面：（1） 目前的方法对于从极其稀疏的scHi-C数据中提高分辨率是低效的。(2)几种方法 存在用于去除每个细胞内的scHi-C数据的系统性偏差，并调整跨细胞的批次效应。 不同的细胞(3)没有方法可用于检测Kb分辨率细胞类型特异性染色质相互作用， scHi-C数据。为了填补这些空白，我提出了主要的研究方向：（1）开发基于深度学习的 方法来估算每个细胞中的稀疏染色质接触，（2）开发非参数回归模型， 消除每个单元内的系统偏差，并调整不同单元间的批次效应，（3）制定一个 基于全局和局部背景模型混合方法识别细胞类型特异性染色质 相互作用，并预测与复杂人类疾病相关的GWAS变体的推定靶基因， 性状，以及（4）开发独立的，用户友好的软件包来分析单细胞染色质 相互作用数据和传播结果。完成拟议的研究将提供强大的和用户 友好的计算方法，使我们能够在单细胞分辨率下分析3D基因组组织， 解释它们对基因表达和复杂人类疾病的调节作用。

项目成果

Mapping chromatin loops in single cells.

• DOI: 10.1016/j.tig.2022.03.007
• 发表时间: 2022-07
• 期刊: TRENDS IN GENETICS
• 影响因子: 11.4
• 作者: Yu, Miao;Li, Yun;Hu, Ming
• 通讯作者: Hu, Ming