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: 8920676
• 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 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8920676
• 关键词: Address; Adoption; Affect; Afferent Neurons; Algorithms; Anatomic Models; Back; Biological; Biological Models; Biophysical Process; Characteristics; Coal; Collaborations; Collection; Complex; Computer Simulation; Computer Vision Systems; Computer software; Confocal Microscopy; Cytoskeletal Modeling; Cytoskeleton; Data; Data Set; Dendrites; Development; Developmental Biology; Developmental Process; Discipline; Drosophila genus; Drosophila melanogaster; Electrophysiology (science); Elements; F-Actin; Future; Goals; Growth; Growth and Development function; Image; Knowledge; Link; Maps; Measurement; Measures; Microtubules; Minority; Modeling; Molecular; Molecular Biology; Molecular Genetics; Molecular Models; Morphology; Nervous system structure; Neurites; Neuroanatomy; Neurobiology; Neurons; Neurosciences; Neurosciences Research; Positioning Attribute; Principal Investigator; Process; Property; Proteins; Protocols documentation; Research; Research Personnel; Resources; Role; Series; Shapes; Signal Transduction; Staging; Statistical Models; Structure; Students; Synapses; System; Systems Biology; Techniques; Technology; Testing; Time; Transgenic Organisms; Trees; analog; base; computerized tools; data sharing; developmental genetics; developmental neurobiology; digital; feeding; genetic manipulation; graduate student; high school; in vivo; innovation; insight; molecular imaging; molecular modeling; morphometry; neurogenetics; next generation; novel; open source; post-doctoral training; programs; quantitative imaging; reconstruction; relating to nervous system; research study; role model; simulation; 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：树突状乔木的形状和功能特性来自许多复杂的发育过程的相互作用。现在公认的是，多个局部水平的细胞骨架元素的相互作用指导的树突乔木的生长和发育。然而，控制发育获得最终功能树突特性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的数据驱动的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，定量形态测量学和数学模拟之间的紧密合作，可以首次实现大规模的研究，能够实现对乔木新兴特征的机制的整体理解。项目目标：该项目的主要神经科学目标是了解细胞骨架成分在分化过程中的多种局部相互作用如何定义成熟的树突状乔木形状及其功能整合特性，使用果蝇感觉神经元作为模型。该项目的技术目标是开发一种新的调查方法， 扩展了以前从发育生物学和遗传学，体内共聚焦成像和电生理学，计算机视觉和神经解剖建模中分离的方法。具体目标：我们提出了三个紧密结合的具体目标。目标1：使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，作为紧急树突乔木形状和功能的基本决定因素。目标二：实施先进的4D多参数成像协议和自动算法来重建乔木，并量化多个亚细胞成分之间的空间和时间关联。目标三：使用自动重建和测量从目标2，统计特征的树突乔木的结构和细胞骨架的特点，和stochemical模拟解剖学上逼真的虚拟神经元类似物的生长和电紧张性能。来自目标3的数据将反馈新的假设，以供随后重复（目标1 -目标2 -目标3）周期进行检验。方法：我们将集中在一个单一的模型系统-果蝇树突状分支（da）感觉神经元。更具体地说，我们将研究I类和IV类DA神经元树枝状化的基础上，他们根本不同的树突形态（简单与复杂）和潜在的细胞骨架组织。我们将使细胞骨架成分与光谱上不同的荧光蛋白融合。这些将用于转基因果蝇，以定量测量F-肌动蛋白，微管， 和微管极性内的树突乔木在其整个发展在体内使用共聚焦多荧光成像。由此产生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状乔木的拓扑结构，并且随着时间的推移而变化。我们将使用神经解剖学随机模拟产生的地图，以建立在不同发育阶段的树突和特定的细胞骨架功能之间的联系。智力优势：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联系起来。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，推进自动化算法的最新水平，用于描绘神经解剖学（及其形态动力学），并实现自动化重建和模拟技术的有效接口。通过这一创新，模型预测可以通过分子生物学技术进行测试，并且统计模型的结果可以用于告知树突发育的分子模型。教育影响：该项目将对博士后研究员、研究生、本科生和高中实习生进行跨学科培训。它将导致对如何进行尖端系统级科学研究的实际见解，克服学科界限，并使用最好的现有协作工具。该计划的学员将处于独特的地位，以开发更广泛的成像驱动的综合系统神经生物学领域。它将使少数民族和K-12学生接触到一个跨学科研究的新世界，这是未来的指示。更广泛的影响：这项研究的分子、成像和计算工具和数据集的组合体将被广泛传播，并提供给广泛的研究人员，以用于其他主要神经科学问题的研究。该项目将作为计算支持的神经科学研究的新模型，实现湿实验室和计算之间长期期望的协同作用。