CRCNS: Cytoskeletal Mechanisms of Dendrite Arbor Shape Development

CRCNS：树突乔木形状发育的细胞骨架机制

• 批准号: 9310382
• 负责人: Daniel N Cox
• 金额: $ 32.3万
• 依托单位: GEORGIA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-07-15 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9310382
• 关键词: Address; Adoption; Affect; Afferent Neurons; Algorithms; Anatomic Models; Anatomy; Back; Biological; Biological Models; Biophysical Process; Characteristics; Coal; Collaborations; Collection; Complex; Computer Simulation; Computer Vision Systems; Computer software; Confocal Microscopy; Crystallization; Cytoskeletal Modeling; Cytoskeleton; Data; Data Set; Dendrites; Development; Developmental Biology; Developmental Process; Discipline; Drosophila genus; Drosophila melanogaster; Electrophysiology (science); Elements; F-Actin; Future; Genetic; Genetic Structures; Goals; Growth; Growth and Development function; Image; Imaging Device; Internships; Investigation; K-12 student; Knowledge; Link; Maps; Mathematics; Measurement; Measures; Microtubules; Minority; Modeling; Molecular; Molecular Biology; Molecular Genetics; Molecular Models; Morphology; Mosaicism; Nervous system structure; Neurites; Neuroanatomy; Neurobiology; Neurons; Neurosciences; Neurosciences Research; Positioning Attribute; Process; Property; Proteins; Protocols documentation; Research; Research Personnel; Resolution; Resources; Role; Series; Shapes; Signal Transduction; Statistical Data Interpretation; Statistical Models; Structure; Synapses; System; Systems Biology; Techniques; Technology; Testing; Time; Transgenic Organisms; Trees; analog; base; computerized tools; data sharing; developmental genetics; developmental neurobiology; digital; experimental study; feeding; fluorescence imaging; genetic manipulation; graduate student; high school; in vivo; innovation; insight; molecular imaging; molecular modeling; morphometry; neurogenetics; neuroinformatics; next generation; novel; open source; post-doctoral training; programs; reconstruction; relating to nervous system; role model; simulation; spatiotemporal; synergism; tool; undergraduate student; virtual

DESCRIPTION (provided by applicant): Background: Dendritic arbor shape and functional properties emerge from the interaction of many complex developmental processes. It is now accepted that multiple local-level interactions of cytoskeleton elements direct the growth and development of the dendrite arbor. However, the specific mechanisms that control developmental acquisition of final functional dendritic properties are largely unknown. Addressing this fundamental question requires novel data driven systems-biology tools to study developmental and biophysical mechanisms in the same neuronal model. A tightly-knit collaboration between molecular genetics, quantitative morphometry, and mathematical simulation can for the first time enable large-scale studies capable of achieving holistic understanding of the mechanisms underlying emergent features of the arbor. Project Goals: The main neuroscientific goal of this project is to understand how multiple local interactions of cytoskeleton components during differentiation define mature dendritic arbor shape and its functional integrative properties, using Drosophila sensory neurons as a model. The technological goal of this project is to develop a novel investigative approach that integrates and extends previously separate approaches from developmental biology & genetics, in vivo confocal imaging & electrophysiology, computer vision, and neuroanatomical modeling. Specific Aims: We propose 3 tightly integrated specific aims. Aim 1: use genetic manipulations and electrophysiological recordings to model the role of cytoskeletal organization and dynamics as a fundamental determinant of emergent dendrite arbor shape and function. Aim 2: Implement advanced 4D multi-parameter imaging protocols and automated algorithms to reconstruct the arbor, and quantify spatial and temporal associations among multiple sub-cellular components. Aim 3: using automated reconstructions & measurements from aim 2, statistically characterize the structural and cytoskeletal features of dendrite arbors, and stochastically simulate the growth and electrotonic properties of anatomically realistic virtual neuronal analogues. The data from aim 3 will feed back novel hypotheses to be tested by a subsequent repetition of the (aim 1 - aim 2 - aim 3) cycle. Approach: We will focus on a single model system - Drosophila dendritic arborization (da) sensory neurons. More specifically, we will investigate class I and class IV da neuron arborization based upon their radically distinct dendritic morphologies (simple vs. complex) and underlying cytoskeletal organizations. We will make fusion constructs of cytoskeleton components with spectrally distinct fluorescent proteins. These will be used in transgenic Drosophila in order to quantitatively measure the distribution of F-actin, microtubules, and microtubule polarity within the dendrite arbor throughout its development in vivo using confocal multi-fluor imaging. The resulting images will be processed by automated quantitative computer vision algorithms that will accurately extract the topology of the dendritic arbor, and it changes over time. We will use the resulting maps in neuroanatomical stochastic simulations to establish the links between the emergent morphometrics of the dendrite and specific cytoskeleton features at various developmental stages. Intellectual Merit: From a neurogenetics perspective, this project will pioneer the use of cytoskeletal features as putative fundamental determinants in statistical neuroanatomical models. These determinants will be linked to morphological determinants. From a computational perspective, this project will advance the state of the art in automated algorithms for delineating neuroanatomy (and its morphological dynamics) by deploying core technologies for large-scale multi-parameter studies, and result in an effective interfacing of automated reconstruction and simulation technologies. With this innovation, model predictions can be tested by molecular biological techniques, and findings of statistical models can be used to inform molecular models of dendrite arbor development. Educational Impact: This project will result in a cross-disciplinary training of post-doctoral fellows, graduate students, undergraduate students and high school interns. It will result in practical insight on ways to conduct cutting-edge systems-level scientific research overcoming disciplinary boundaries and using best-available collaborative tools. The trainees from this program will be uniquely positioned to develop the broader field of imaging-driven integrative systems neurobiology. It will expose minority and K-12 students to a new world of trans-disciplinary research that is indicative of the future. Broader Impacts: The combined body of molecular, imaging, and computational tools and datasets from this research will be disseminated widely, and made available to a broad class of investigators for adoption in the study of other major neuroscience problems. This project will serve as a new model for computationally enabled neuroscience research that achieves a long-desired synergy between the wet lab and computation.

描述（由申请人提供）： 背景：树突乔木的形状和功能特性是由许多复杂的发育过程的相互作用产生的。现在人们普遍认为，细胞骨架元素的多个局部相互作用指导树突乔木的生长和发育。然而，控制最终功能树突特性的发育获得的具体机制在很大程度上是未知的。解决这个基本问题需要新颖的数据驱动系统生物学工具来研究同一神经元模型中的发育和生物物理机制。分子遗传学、定量形态测量和数学模拟之间的紧密合作首次使得大规模研究能够全面了解乔木新兴特征的机制。项目目标：该项目的主要神经科学目标是使用果蝇感觉神经元作为模型，了解分化过程中细胞骨架成分的多重局部相互作用如何定义成熟的树突乔木形状及其功能综合特性。该项目的技术目标是开发一种新颖的研究方法，该方法集成和 扩展了之前发育生物学和遗传学、体内共焦成像和电生理学、计算机视觉和神经解剖学建模的独立方法。具体目标：我们提出了 3 个紧密结合的具体目标。目标 1：利用遗传操作和电生理记录来模拟细胞骨架组织和动力学的作用，作为突现树突乔木形状和功能的基本决定因素。目标 2：实施先进的 4D 多参数成像协议和自动化算法来重建乔木，并量化多个亚细胞成分之间的空间和时间关联。目标 3：使用目标 2 的自动重建和测量，统计表征树突乔木的结构和细胞骨架特征，并随机模拟解剖学上真实的虚拟神经元类似物的生长和电紧张特性。来自目标 3 的数据将反馈新的假设，并通过随后重复的（目标 1 - 目标 2 - 目标 3）循环进行测试。方法：我们将重点关注单一模型系统 - 果蝇树突状树枝化 (da) 感觉神经元。更具体地说，我们将根据其截然不同的树突形态（简单与复杂）和底层细胞骨架组织来研究 I 类和 IV 类神经元树枝化。我们将用光谱不同的荧光蛋白制作细胞骨架成分的融合结构。这些将用于转基因果蝇，以定量测量 F-肌动蛋白、微管、 使用共焦多荧光成像在体内发育过程中观察树突轴内的微管极性和微管极性。生成的图像将通过自动定量计算机视觉算法进行处理，该算法将准确提取树突乔木的拓扑结构，并且它会随着时间的推移而变化。我们将在神经解剖学随机模拟中使用所得的图来建立树突的新兴形态测量与不同发育阶段的特定细胞骨架特征之间的联系。智力优点：从神经遗传学的角度来看，该项目将率先使用细胞骨架特征作为统计神经解剖模型中假定的基本决定因素。这些决定因素将与形态决定因素相关。从计算的角度来看，该项目将通过部署大规模多参数研究的核心技术，推进描绘神经解剖学（及其形态动力学）的自动化算法的最新技术，并实现自动重建和模拟的有效连接技术。通过这项创新，可以通过分子生物学技术来测试模型预测，并且可以使用统计模型的发现来为树突乔木发育的分子模型提供信息。教育影响：该项目将为博士后、研究生、本科生和高中实习生提供跨学科培训。它将产生关于如何克服学科界限并使用最可用的协作工具进行尖端系统级科学研究的实用见解。该项目的学员将拥有独特的优势，能够开发更广泛的成像驱动综合系统神经生物学领域。它将让少数族裔和 K-12 学生接触到一个预示着未来的跨学科研究的新世界。更广泛的影响：这项研究的分子、成像、计算工具和数据集的综合体将被广泛传播，并提供给广大研究人员用于其他主要神经科学问题的研究。该项目将作为计算神经科学研究的新模型，实现湿实验室和计算之间期待已久的协同作用。