Bridging the gap: joint modeling of single-cell 1D and 3D genomics

弥合差距：单细胞 1D 和 3D 基因组学联合建模

• 批准号: 10572539
• 负责人: YE ZHENG
• 金额: $ 12.01万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10572539
• 关键词: 3-Dimensional; Acceleration; Acute; Acute Lymphocytic Leukemia; Address; Agreement; Architecture; Award; Benchmarking; Biological; Biological Assay; Blood Component Removal; CAR T cell therapy; CISH gene; Cell Line; Cells; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Chromatin; Chromatin Structure; Computer software; DNA; Data; Development; Distal; Educational Status; Elements; Enhancers; Foundations; Fred Hutchinson Cancer Research Center; Frequencies; Gene Expression; Gene Expression Regulation; Genes; Genome; Genomics; Goals; Hi-C; Histones; Human; Immunology; Immunotherapeutic agent; Immunotherapy; Individual; Investigation; Joints; Machine Learning; Maps; Membrane Proteins; Mentors; Methods; Modality; Modeling; Molecular Conformation; Multiomic Data; Mus; Neighborhoods; Organ; Patients; Peripheral Blood Mononuclear Cell; Polycomb; Regulation; Regulator Genes; Regulatory Element; Repression; Resolution; Role; Sampling; Signal Transduction; Signaling Protein; Specific qualifier value; Statistical Models; Structure; System; Technology; Three-dimensional analysis; Tissues; Training; Turtles; Untranslated RNA; Variant; brain cell; brain tissue; career; career development; cell type; chimeric antigen receptor T cells; collaborative environment; computer framework; computerized tools; epigenome; epigenomic profiling; epigenomics; gene regulatory network; genome-wide; genomic data; histone modification; insight; leukemia/lymphoma; machine learning framework; machine learning model; multimodality; multiple omics; novel; patient response; programs; promoter; research facility; single cell technology; single-cell RNA sequencing; statistical and machine learning; statistical learning; statistics; three-dimensional modeling; tool; transcriptome; transcriptomic profiling; transcriptomics; treatment response

PROJECT SUMMARY/ABSTRACT Advances in single-cell technologies have enabled three-dimensional (3D) genome structure profiling and simultaneous capture of the transcriptome and epigenome within a cell. Quantitative tools are, however, still unable to fully leverage the unprecedented resolution of single-cell high-throughput chromatin conformation (scHi-C) data and integrate it with other single-cell modalities. To address this challenge, I propose to (1) Develop a single-cell gene-body associating domain (scGAD) scoring system to explore single-cell 3D genomics data in units of genes. (2) Construct machine learning-based models to impute histone modification and 3D chromatin interaction for simultaneously profiling of each cell's epigenomic features and 3D chromatin architectures. Subsequently, I will develop an epigenomic regulatory score (ERS) model to infer the cell-type-specific promoter- enhancer regulation programs at the highest single-cell and single-gene resolution. (3) Validate and extend scGAD and ERS pipeline to CAR-T immunotherapy study to gain insights into the impact of distal gene regulation variations on patient responses. In Aim 1, preliminary analysis on human and mouse brain tissues demonstrated that scGAD extracts gene features agreeing well with the scRNA-seq data from the same system. As a result, scGAD facilitates the projection of cells from 3D genomics data onto reference panels constructed by scRNA- seq embeddings with known cell-type annotations. Hence, scGAD provides an unprecedentedly accessible and accurate cell type annotation method based on 3D chromatin architectures. Furthermore, the successful integration of cells from different modalities into the same network facilitates information sharing across 3D chromatin structures, the transcriptome, and the epigenome. Aim 2 leverages such multi-modal networks to build an ERS model. ERS jointly models the histone profiles at the promoter and distal neighborhoods of the target gene and the 3D spatial proximity between them. Therefore, the ERS scores quantify the regulatory effects of distal elements on a per gene and cell basis. Aim 3 will extend the integration framework in Aim 1 and 2 using scRNA-seq as a multi-modality bridge to CITE-seq data for a deeper annotation, especially for the Peripheral Blood Mononuclear Cells. This enables the in-depth investigation of the apheresis samples from the Acute Lymphoma Leukemia patients to gain insight into the roles of distal regulatory elements on gene expression and their impact on the CAR-T cell therapy responses. To succeed in achieving these aims, I will pursue additional training with mentor Dr. Steven Henikoff (epigenomics and gene regulation), co-mentors Dr. Raphael Gottardo (statistics), Dr. Manu Setty (machine learning), Dr. Evan Newell (immunology), and collaborator Dr. Cameron Turtle (CAR-T cell therapy). Fred Hutchinson Cancer Research Center is an ideal institute for multi-omics single- cell study with application to immunotherapy, providing cutting-edge research facilities and opportunities for further career development in a rich interdisciplinary environment. A K99/R00 award will be instrumental in addressing these challenges and furnishing me with high-level training to launch my independent scientific career.

项目摘要/摘要 单细胞技术的进步已经使得三维（3D）基因组结构分析成为可能， 同时捕获细胞内的转录组和表观基因组。然而，量化工具仍然 无法充分利用单细胞高通量染色质构象的前所未有的分辨率 （scHi-C）数据并将其与其他单细胞模式集成。为了应对这一挑战，我建议（1）制定 单细胞基因体相关结构域（scGAD）评分系统，用于探索单细胞3D基因组学数据， 基因单位(2)构建基于机器学习的模型来估算组蛋白修饰和3D染色质 这是一个非常复杂的过程，可以通过相互作用来同时分析每个细胞的表观基因组特征和3D染色质结构。 随后，我将开发一个表观基因组调控评分（ERS）模型来推断细胞类型特异性启动子- 增强子调控程序在最高的单细胞和单基因分辨率。(3)扩展和扩展 scGAD和ERS管道到CAR-T免疫治疗研究，以深入了解远端基因调控的影响 患者反应的变化。在目标1中，对人类和小鼠脑组织的初步分析表明， scGAD提取的基因特征与同一系统的scRNA-seq数据一致。因此，在本发明中， scGAD有助于将来自3D基因组学数据的细胞投射到由scRNA构建的参考板上。 seq嵌入与已知的单元格类型注释。因此，scGAD提供了一个前所未有的可访问的， 基于3D染色质结构的精确细胞类型注释方法。此外，成功的 将来自不同模态的细胞集成到同一网络中有助于跨3D的信息共享 染色质结构、转录组和表观基因组。Aim 2利用这种多模式网络， 一个ERS模型。ERS联合模拟了目标启动子和远端邻近区域的组蛋白谱 基因和它们之间的3D空间接近度。因此，ERS评分量化了以下方面的监管效果： 在每个基因和细胞的基础上的远端元件。目标3将扩展目标1和目标2中的集成框架， scRNA-seq作为CITE-seq数据的多模态桥梁，用于更深入的注释，特别是对于外围设备 血单核细胞。这使得能够深入研究来自急性血液透析患者的单采样本。 淋巴瘤白血病患者，以深入了解远端调控元件对基因表达的作用， 它们对CAR-T细胞治疗反应的影响。为了实现这些目标，我将继续努力。 与导师Steven Henikoff博士（表观基因组学和基因调控），共同导师Raphael Gottardo博士 （统计学），Manu Setty博士（机器学习），Evan纽韦尔博士（免疫学），以及合作者卡梅隆博士 Turtle（CAR-T细胞疗法）。弗雷德哈钦森癌症研究中心是一个理想的研究所，多组学单， 细胞研究与免疫治疗的应用，提供尖端的研究设施和机会， 在丰富的跨学科环境中进一步发展职业生涯。K99/R 00奖将有助于 应对这些挑战，并为我提供高水平的培训，以启动我的独立科学生涯。