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.

项目总结/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，以及这些复杂的基因调控机制的时空动力学奠定了基础 对于不同的生物过程，特别是在发展，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; 而研究细胞状态转换过程的时间动力学仍然具有挑战性。我们 假设监管动态是由“写入”和“擦除”之间的平衡塑造的， 表观基因组变量，因此可以从测量维持这些表观基因组变量的连接分子层中推断出。 通过单细胞多组学技术的发展实现监管平衡。研究监管 细胞状态转换的动力学在衰老的大脑中特别具有挑战性：大脑的衰老涉及复杂的 细胞和分子变化，包括某些细胞类型的分子特征的变化， 细胞群组成，并在这个组织中的神经元细胞之间的通信下降， 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 它是多种疾病的最大风险因素，包括神经变性和癌症。 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 目前对衰老的组学分析只能捕捉到个体形态的静态图片， 区分维护良好的组件（年轻）和即将失去保真度的组件（预衰减）， 记录不同分子类型之间的复杂关系。在这一建议中，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，专注于小鼠衰老的大脑 作为一个模型系统，通过开发创新的单细胞基因组学技术， 调节动力学和转录状态。首先，我们将开发一套单细胞多组学工具， 在维持细胞的调节状态中正向和反向反应速率的综合分析， 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。下一步，我们将开发一种技术， 共定位的调控元件及其表观遗传状态与来自单细胞的转录组， 评估细胞的调节功能。最后，我们将开发一个模块化的组织规模的平台， 单细胞高清晰度三维空间配准（AMBER），然后将其与这些单细胞联合收割机组合 多组学工具，以多模式分子谱重建整个组织结构。我们将应用我们的 研究神经系统中衰老的分子变化的方法， 小鼠模型，并相信我们的方法是广泛适用于研究各种监管动态 健康和疾病的生物系统。