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: 9097814
• 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/9097814
• 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; Genetic; Genetic Structures; Goals; Growth; Growth and Development function; Image; Internships; K-12 student; 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; Process; Property; Proteins; Protocols documentation; Research; Research Personnel; Resolution; Resources; Role; Series; Shapes; Signal Transduction; Staging; 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; 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; 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.

背景：树突乔木的形状和功能特性是许多复杂发育过程相互作用的结果。现在人们认为，细胞骨架元素的多种局部相互作用指导了树突乔木的生长和发育。然而，控制最终功能性树突特性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-生物学工具来研究相同神经元模型中的发育和生物物理机制。分子遗传学、定量形态计量学和数学模拟之间的紧密合作，首次使大规模研究能够全面了解乔木出现特征的机制。项目目标：本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树突乔木形状及其功能整合特性，以果蝇感觉神经元为模型。这个项目的技术目标是开发一种新颖的调查方法，集成和