Studying the Regulatory Dynamics with Single-cell Multiomics

用单细胞多组学研究调控动力学

• 批准号: 10686569
• 负责人: Chenxu Zhu
• 金额: $ 173.7万
• 依托单位: NEW YORK GENOME CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10686569
• 关键词: 3-Dimensional; Affect; Aging; Base Excision Repairs; Biological Models; Biological Process; Biology of Aging; Cells; Communication; Complex; DNA Damage; DNA Methylation; Detection; Development; Disease; Epigenetic Process; Equilibrium; Foundations; Gene Expression; Genetic Transcription; Genomics; Health; Individual; Intervention; Joints; Link; Malignant Neoplasms; Measures; Methods; Modality; Molecular; Molecular Profiling; Mus; Nerve Degeneration; Nervous System; Neurons; Population; Process; Reaction; Regulator Genes; Regulatory Element; Risk Factors; Shapes; Structure; Technology; Tissues; Variant; Writing; aging brain; biological systems; body system; brain tissue; cell type; clinical development; demethylation; epigenome; epigenomics; innovation; mouse model; multimodality; multiple omics; oxidative damage; programs; spatiotemporal; tool; transcriptome; transcriptomics

Project Summary/Abstract A multitude of epigenomic variables and mechanisms contribute to cell-type-specific gene expression programs, and the spatiotemporal dynamics of these complex gene regulatory machinery laid the foundations for diverse biological processes, particularly in development, disease, and aging. Single-cell genomics technologies allowed capturing the static snapshots, such as transcriptomic or epigenomic states of the cells; while it remained challenging to study the temporal dynamics of the cell’s state transition processes. We hypothesized that the regulatory dynamics are shaped by the balance between “writing” and “erasing” of epigenomic variables, and thus can be inferred from measuring the linked molecular layers that maintain regulatory equilibriums by the development of single-cell multiomics technologies. Studying the regulatory dynamics of cell state transition is particularly challenging in aging brains: aging of the brain involves complex cellular and molecular changes, including variations in molecular signatures of certain cell types, changes in cell population compositions, and declined communications between neuron cells in this tissue with the most sophisticated cellular composition and spatial organizations. Aging contributes to many diseases that affect all organ systems and is the greatest risk factor for multiple diseases, including neurodegeneration and cancers. Understanding the fundamental biology of aging is essential for the development of clinical interventions. But current omics analysis of aging can only capture the static pictures of individual modalities, which cannot differentiate well-maintained components (young) from those who are about to lose fidelity (pre-decay) nor record the complex relationships between different molecule types. In this proposal, we aim to fundamentally transform our approaches to studying the principles of cell state transition, focusing on the mouse aging brain as a model system, by developing innovative single-cell genomics technologies for joint analysis of the cell’s regulatory dynamics and transcriptional states. Firstly, we will develop a set of single-cell multiomics tools for integrated analysis of the rates of forward and reverse reactions in maintaining the cell’s regulatory states, including epigenome (DNA methylation and active demethylation) and DNA damages (oxidative damages and base excision repair) with the transcriptional states. Next, we will develop a technology for the detection of colocalized regulatory elements and their epigenetic states jointly with transcriptomes from single cells, to evaluate the cell’s regulatory functionality. Finally, we will develop a modularized platform for tissue-scale high-definition 3-D spatial registration of single cells (AMBER) and then combine it with these single-cell multiomics tools to reconstruct the whole tissue structure with multimodal molecular profiles. We will apply our methods to investigate the molecular changes of aging in nervous systems with 3-D spatial information from mouse models, and believe our approach is broadly applicable to studying regulatory dynamics across various biological systems both in health and diseases.

项目概要/摘要 多种表观基因组变量和机制有助于细胞类型特异性基因表达 程序，以及这些复杂基因调控机制的时空动态奠定了基础 用于不同的生物过程，特别是发育、疾病和衰老。单细胞基因组学 技术允许捕获静态快照，例如细胞的转录组或表观基因组状态； 然而研究细胞状态转换过程的时间动态仍然具有挑战性。我们 假设监管动态是由“写入”和“擦除”之间的平衡决定的 表观基因组变量，因此可以通过测量维持的连接分子层来推断 通过单细胞多组学技术的发展实现调节平衡。研究监管 细胞状态转变的动力学对于衰老的大脑来说尤其具有挑战性：大脑的衰老涉及复杂的过程 细胞和分子变化，包括某些细胞类型分子特征的变化、 细胞群组成，并且该组织中神经元细胞之间的通讯下降最多 复杂的细胞组成和空间组织。衰老会导致许多影响所有人的疾病 器官系统，是多种疾病的最大危险因素，包括神经退行性疾病和癌症。 了解衰老的基本生物学对于临床干预措施的发展至关重要。但 目前的衰老组学分析只能捕获个体模式的静态图像，而无法捕捉到衰老的静态图像。 区分维护良好的组件（年轻的）和即将失去保真度的组件（腐烂前），也 记录不同分子类型之间的复杂关系。在本提案中，我们的目标是从根本上 改变我们研究细胞状态转换原理的方法，重点关注小鼠衰老大脑 作为模型系统，通过开发创新的单细胞基因组学技术来联合分析细胞的 调控动态和转录状态。首先，我们将开发一套单细胞多组学工具 对维持细胞调节状态的正向和反向反应速率进行综合分析， 包括表观基因组（DNA甲基化和主动去甲基化）和DNA损伤（氧化损伤和 碱基切除修复）与转录状态。接下来，我们将开发一种检测技术 共定位的调控元件及其表观遗传状态与单细胞的转录组一起， 评估细胞的调节功能。最后，我们将开发一个用于组织规模的模块化平台 单细胞的高清 3D 空间配准 (AMBER)，然后将其与这些单细胞结合 多组学工具利用多模式分子谱重建整个组织结构。我们将应用我们的 利用 3D 空间信息研究神经系统衰老的分子变化的方法 小鼠模型，并相信我们的方法广泛适用于研究各种不同的监管动态 健康和疾病的生物系统。