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.

项目摘要/摘要 多种表观基因组变量和机制有助于细胞典型基因表达 程序，以及这些XPLEX基因调节机制的空间学奠定了基础 用于多种生物学过程，尤其是在发育，疾病和衰老中 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； 虽然可以解决细胞过渡过程的时间动态 假设调节动力学是由“写作”和“擦除”之间的平衡的形状 表观基因组变量，因此可以通过测量维持蛋白的链接分子层来推断 通过开发单细胞多组学技术的调节平衡。 细胞状态过渡的动力学在衰老的大脑中尤其具有挑战性：大脑的衰老涉及完成 细胞和分子变化，包括某些细胞类型的分子特征的变化，变化 细胞种群组成，并降低了神经元细胞之间的通信 复杂的细胞组成和空间组织。 器官系统和多种疾病，倾斜的神经变性和癌症的最大风险因素。 了解衰老的基本生物学对于发展临床干预至关重要。 当前对衰老的OMICS分析只能是单个方式的静态图片 将维护良好的组件（年轻）与即将失去忠诚的人（年轻）区分开 记录不同分子类型之间的复杂关系。 将我们的征收转换为研究细胞状态过渡的原理，重点是小鼠衰老的大脑 作为模型系统，通过开发创新的单细胞基因组学技术来对细胞的联合分析 监管动力和转录状态。 对维持细胞调节状态的正向和反应的综合分析， 包含表观基因组（DNA甲基化和主动脱甲基化）和DNA损伤，以及 基本切除维修）接下来，我们将开发一种技术来检测 共定位的调节元件及其表观遗传态与单个细胞的转录组共同 评估细胞的功能，我们将开发用于组织尺度的平台 高清单细胞（琥珀色）的高清3-D空间登记，将其与这些细胞结合 多构想以多模式分子曲线重建整个组织结构。 研究神经系统中衰老的分子变化的方法，并通过3D空间信息从 鼠标模型，并认为我们的方法广泛适用于研究各种监管动态 健康和疾病中的生物系统。