Proteome-Driven Holistic Reconstruction of Organ-Wide Multi-Scale Networks

蛋白质组驱动的全器官多尺度网络的整体重建

• 批准号: 9982025
• 负责人: Kwanghun Chung
• 金额: $ 46.53万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-30 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9982025
• 关键词: 3-Dimensional; Area; Binding; Biology; Brain; Cells; Chemicals; Clinical; Complex; Development; Disease; Engineering; Functional disorder; Health; Human; Human body; Image; Individual; Label; Methodology; Modeling; Molecular; Morphology; Mus; Organ; Organoids; Phenotype; Population; Proteins; Proteome; Proteomics; Reaction; Resolution; Sampling; Synapses; System; Technology; Tissues; Work; biological systems; cell type; complex biological systems; high dimensionality; holistic approach; insight; molecular phenotype; multiple datasets; organ growth; reconstruction; response

Abstract Human organs such as the brain are stunningly complex. They consist of hundreds to thousands of separate functional areas, each containing a comparable number of distinct cell types and innumerable molecules. Understanding how these multi-scale components work together to generate systems-level responses is essential for many fields of biology, but advancement in this area is hampered by the prevalent methodology of dividing biological systems into known cell types and then separately studying each population. Although powerful, this reductionistic approach makes it difficult to interrogate complex interactions at multiple levels — molecular (e.g., proteins), subcellular (e.g., synapses), cellular, and area level. Moreover, this approach could ignore many potentially important but unidentified functional networks. Our inability to thoroughly identify multi- scale functional networks and interrogate their system-wide, multifactorial interactions has limited our ability to understand the function and dysfunction of complex biological systems. Here, we aim to fundamentally transform our approach from a reductionistic to a holistic one by developing cutting-edge platforms for proteomic reconstruction of organ-wide multi-scale networks. Using murine and human clinical samples and organoids as our models, we will develop four broadly applicable cross-disciplinary platforms that integrate chemical and material engineering technologies. These platforms will enable: (1) scalable tissue transformation into an indestructible, proteome-containing three-dimensional (3D) framework; (2) unlimited rounds of molecular phenotyping of a single intact tissue with precise volume co-registration of multiple datasets; (3) rapid, scalable, and uniform tissue labeling by synchronizing target-probe binding reactions organ-wide; (4) superresolution proteomic imaging of intact organs. If successful, our proposed work will enable proteome- driven holistic reconstruction and high-dimensional quantitative phenotyping of intact biological systems at unprecedented resolution. Using the technology platforms and human brain organoid models, both healthy and diseased, we will investigate the following fundamental questions: (Q1) How many cell types/regions exist at different developmental stages of the brain organoids? (Q2) How these cells and regions form networks? (Q3) How proteomic states of subcellular components, individual cells, circuits, and regions change throughout the development. (Q4) How morphological features of cells change? (Q5) how Q1-4 are altered in diseased organoids? This study may provide new insights into understanding human organ development in health and disease.

抽象的 大脑等人体器官极其复杂。它们由数百到数千个独立的 功能区域，每个区域都包含相当数量的不同细胞类型和无数分子。 了解这些多尺度组件如何协同工作以生成系统级响应是 对于生物学的许多领域来说至关重要，但是该领域的进步受到流行方法论的阻碍 将生物系统划分为已知的细胞类型，然后分别研究每个群体。虽然 这种简化方法虽然强大，但很难在多个层面上探究复杂的相互作用—— 分子（例如蛋白质）、亚细胞（例如突触）、细胞和区域水平。此外，这种方法可以 忽略许多潜在重要但未识别的功能网络。我们无法彻底识别多 扩展功能网络并询问其全系统、多因素的相互作用限制了我们的能力 了解复杂生物系统的功能和功能障碍。在这里，我们的目标是从根本上 通过开发尖端平台，将我们的方法从简化方法转变为整体方法 全器官多尺度网络的蛋白质组学重建。使用小鼠和人类临床样本 类器官作为我们的模型，我们将开发四个广泛适用的跨学科平台，这些平台集成 化学和材料工程技术。这些平台将实现：（1）可扩展的组织转化 进入坚不可摧的、包含蛋白质组的三维 (3D) 框架； (2) 无限轮次 通过多个数据集的精确体积共同配准对单个完整组织进行分子表型分析； (3) 通过在器官范围内同步靶标-探针结合反应，实现快速、可扩展且均匀的组织标记； (4) 完整器官的超分辨率蛋白质组成像。如果成功，我们提出的工作将使蛋白质组成为可能 驱动完整生物系统的整体重建和高维定量表型分析 前所未有的决议。使用技术平台和人脑类器官模型，既健康又健康 如果患病，我们将研究以下基本问题：（Q1）存在多少种细胞类型/区域 大脑类器官的不同发育阶段？ (Q2) 这些细胞和区域如何形成网络？ (Q3) 亚细胞成分、单个细胞、电路和区域的蛋白质组状态在整个过程中如何变化 发展。 (Q4) 细胞的形态特征如何变化？ (Q5) Q1-4 在患病时如何改变 类器官？这项研究可能为理解健康和健康方面的人体器官发育提供新的见解。 疾病。

项目成果

Brain-wide mapping reveals that engrams for a single memory are distributed across multiple brain regions.

• DOI: 10.1038/s41467-022-29384-4
• 发表时间: 2022-04-04
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Roy DS;Park YG;Kim ME;Zhang Y;Ogawa SK;DiNapoli N;Gu X;Cho JH;Choi H;Kamentsky L;Martin J;Mosto O;Aida T;Chung K;Tonegawa S
• 通讯作者: Tonegawa S