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实现了三维（3D）基因组结构分析和 同时捕获细胞内转录组和表观基因组。但是，定量工具仍然是 无法完全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）发展 单细胞基因体关联域（SCGAD）评分系统，以探索单细胞3D基因组学数据 基因单位。 （2）构建基于机器学习的模型以估算组蛋白修饰和3D染色质 同时分析每个细胞的表观基因组特征和3D染色质体系结构的相互作用。 随后，我将开发一个表观基因组调节评分（ERS）模型，以推断细胞类型特异性启动子 - 增强器调节计划的最高单电池和单基因分辨率。 （3）验证并扩展 SCGAD和ERS管道到CAR-T免疫疗法研究，以了解远端基因调节的影响 患者反应的变化。在AIM 1中，对人和小鼠脑组织的初步分析证明了 SCGAD提取基因特征与来自同一系统的SCRNA-SEQ数据非常吻合。因此， SCGAD促进了细胞从3D基因组学数据投射到由SCRNA构建的参考面板上 具有已知细胞类型注释的SEQ嵌入。因此，SCGAD提供了前所未有的可访问和 基于3D染色质体系结构的精确细胞类型注释方法。此外，成功 将不同模态到同一网络的细胞集成，促进了3D的信息共享 染色质结构，转录组和表观基因组。 AIR 2利用此类多模式网络构建 ERS模型。 ERS共同对目标的启动子和远端社区进行建模 基因及其之间的3D空间接近。因此，ERS得分量化了 远端元素以每个基因和细胞为基础。 AIM 3将使用AIM 1和2中的集成框架使用 scrna-seq是一个多模式的桥梁，以引用seq数据，以进行更深的注释，尤其是对于周围 血液单核细胞。这可以深入研究急性的磨牙样品 淋巴瘤白血病患者可以深入了解远端调节元件在基因表达和 它们对CAR-T细胞疗法反应的影响。为了成功实现这些目标，我将追求更多 与导师史蒂芬·亨科夫（Steven Henikoff）博士（表观基因组学和基因调节）培训 （统计），Manu Setty博士（机器学习），Evan Newell博士（免疫学）和Cameron博士 海龟（CAR-T细胞疗法）。弗雷德·哈钦森（Fred Hutchinson）癌症研究中心是多摩斯单一的理想研究所 用于免疫疗法的细胞研究，为尖端的研究设施和机会 在丰富的跨学科环境中进一步的职业发展。 K99/R00奖将在 应对这些挑战并为我提供高级培训，以启动我的独立科学生涯。