Determining structure and organization of neurofilaments in situ using cryo- electron tomography

使用冷冻电子断层扫描原位确定神经丝的结构和组织

• 批准号: 10405027
• 负责人: Muyuan Chen
• 金额: $ 20万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10405027
• 关键词: 3-Dimensional; Actins; Alzheimer' s Disease; Axon; Biochemical; Biological Markers; Cells; Classification; Computational Technique; Computing Methodologies; Cryo-electron tomography; Cytoskeletal Modeling; Cytoskeleton; Data Analyses; Data Set; Development; Disease; Environment; Filament; Future; Goals; Growth; Homology Modeling; Image; In Situ; Individual; Intermediate Filaments; Knowledge; Light; Macromolecular Complexes; Maps; Methods; Microtubules; Modeling; Molecular Structure; Nerve Degeneration; Neurons; Parkinson Disease; Pathogenesis; Pathway interactions; Play; Preparation; Process; Protein Analysis; Proteins; Protocols documentation; Radial; Rattus; Research; Resolution; Role; Sampling; Structure; System; Techniques; Testing; Thick; Tomogram; Transportation; Visualization; Width; Work; axon growth; axon injury; base; density; design; experimental study; feature selection; insight; molecular assembly/self assembly; molecular modeling; nanometer resolution; nervous system disorder; neurofilament; novel; particle; protein complex; sample fixation; support network

Project Summary As one of the major components of the neuronal cytoskeleton network, neurofilaments are essential for the radial growth and conduction velocity of axons. Neurofilaments are involved in the pathogenesis of multiple neurological disorders and serve as important axonal damage biomarkers in neurodegenerative and traumatic diseases. Despite their critical role in the structure and function of axons, little is known about the molecular assembly of neurofilaments. Currently, it is still unclear how individual neurofilament subunits assemble into mature filaments, or how those filaments interact with each other to form the cytoskeleton network that supports the growth of axons. Cryo-electron tomography (CryoET) allows visualization of protein complexes inside cells at nanometer resolution in three dimensions. Detailed structural information of those proteins can be obtained from the tomogram using specialized computational techniques. In this proposal, we aim to study the structure and organization of neurofilaments within axons using CryoET. To achieve this goal, we will image the axon of primary neurons with CryoET and determine the structures of neurofilaments at subnanometer resolution using subtomogram classification and averaging. Molecular models will be constructed from the averaged structures to reveal the in situ assembly of neurofilaments. The structures and models of neurofilaments will be studied in their cellular context to understand how the neuronal cytoskeleton organization evolves, as the axons grow thicker. To make the proposed work possible, novel computational methods will be developed for CryoET data analysis throughout the process. We believe this research will provide the first structural insight into in situ neurofilament assembly and deepen our understanding of neuronal cytoskeleton organization. The structural information acquired from this research will pave the way for future studies of the axon maturation process, as well as the structural basis of various neurological diseases.

项目摘要 神经丝作为神经元细胞骨架网络的主要组成部分之一， 轴突的径向生长和传导速度。神经丝参与了 发病机制的多种神经系统疾病，并作为重要的轴突损伤的生物标志物， 神经变性和创伤性疾病。尽管它们在生物多样性的结构和功能中发挥着关键作用， 轴突，但对神经丝的分子组装知之甚少。目前，尚不清楚如何 单个神经丝亚单位组装成成熟的纤维丝，或者这些纤维丝如何与 它们相互作用形成支持轴突生长的细胞骨架网络。 冷冻电子断层扫描（CryoET）允许在细胞内的蛋白质复合物的可视化， 纳米级的三维分辨率。这些蛋白质的详细结构信息可以 使用专门的计算技术从断层图像获得。在本建议中，我们的目标是 使用CryoET研究轴突内神经丝的结构和组织。 为了实现这一目标，我们将用CryoET对原代神经元的轴突进行成像，并确定轴突的生长情况。 使用亚断层图像分类的亚纳米分辨率的神经丝结构， 平均分子模型将从平均结构构建，以揭示原位 神经丝的组装。神经丝的结构和模型将在他们的 细胞背景，以了解神经元细胞骨架组织如何演变，轴突生长 更厚。为了使拟议的工作成为可能，将开发新的计算方法， 整个过程中的CryoET数据分析。 我们相信这项研究将提供第一个结构洞察原位神经丝组装， 加深我们对神经元细胞骨架组织的理解。获取的结构信息 这项研究将为未来研究轴突成熟过程铺平道路， 各种神经系统疾病的结构基础。

项目成果

Improving resolution and resolvability of single-particle cryoEM structures using Gaussian mixture models.

使用高斯混合模型提高单粒子冷冻电镜结构的分辨率和可分辨性。

• DOI: 10.1038/s41592-023-02082-9
• 发表时间: 2024
• 期刊: Nature methods
• 影响因子: 48
• 作者: Chen,Muyuan;Schmid,MichaelF;Chiu,Wah
• 通讯作者: Chiu,Wah

Genome organization in double-stranded DNA viruses observed by cryoET.

通过 CryoET 观察双链 DNA 病毒的基因组组织。

• DOI: 10.1101/2023.12.15.571939
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Chen,Muyuan;Sahoo,Bibekananda;Mou,Zongjun;Song,Xiyong;Tsai,Tiffany;Dai,Xinghong
• 通讯作者: Dai,Xinghong