Spatial Relationships Between Neurons in the CNS

描述（由申请人提供）：最近在发育神经科学领域的进展揭示了许多控制增殖、模式、神经发生、命运决定、迁移、分化、通路导航、突触发生和细胞死亡的机制。然而，一直缺乏的是对控制神经元定位的机制的理解：大脑结构中神经元位置的决定因素是什么，以及一个细胞如何与其他相同或不同类型的细胞的定位相关？目前的探索性建议将创建软件工具来描述组织体积中神经元之间的几何关系，并在三维空间中建模它们的定位。利用已经成功用于描述二维分布的视网膜神经元之间的几何和空间关系的工具，我们将扩展这种基于matlab的脚本，以便在三维中进行相同类型的分析。将从标记的脑组织样本中提取X-Y-Z位置信息，以显示单个神经元和/或胶质细胞类型，并在此基础上执行各种基于Voronoi域的计算，包括测量Voronoi域体积、Delaunay段长度、最近邻距离和Voronoi facet区域。将进行自相关分析，以确定在给定类型的细胞之间的关系中是否存在一致的高阶模式，或者是否存在类似类型细胞之间保持最小距离的隔离区的证据。相互关联分析将确定不同类型细胞之间的关系。自相关图中隔离区的证据表明，细胞之间存在最小距离间隔规则，建模研究将通过将模拟与真实生物数据进行比较来试图定义这些规则。因此，该项目将建立新的工具来描述大脑结构中细胞之间的空间关系，特别是可能控制它们相对定位的间距规则。这些工具将免费提供给科学界，供他们从网站上下载。这将为未来的研究提供基础，在探索细胞间隔背后的生物学机制的过程中，研究人员可以量化和模拟中枢神经系统中细胞之间的空间关系。

DESCRIPTION (provided by applicant): Recent advances in the field of developmental neuroscience have revealed many of the mechanisms controlling proliferation, patterning, neurogenesis, fate determination, migration, differentiation, pathway navigation, synaptogenesis and cell death. What has been lacking, however, is an understanding of the mechanisms that control neuronal positioning: what are the determinants of neuronal position within a brain structure, and how is a cell related to the positioning of other cells of the same or different type? The present exploratory proposal will create software tools for describing the geometrical relationship between neurons in a volume of tissue, and for modeling their positioning in three-dimensional space. Drawing upon such tools that have been successfully employed to describe the geometry and spatial relationships between retinal neurons distributed in two dimensions, we will extend such Matlab-based scripts to enable the same sorts of analyses in three dimensions. X-Y-Z positional information will be extracted from samples of brain tissue labeled to reveal individual types of neuron and/or glial cell, upon which a variety of Voronoi domain-based computations will be performed, including the measurement of Voronoi domain volumes, Delaunay segment lengths, nearest neighbor distances, and Voronoi facet areas. Auto-correlation analysis will be performed to identify whether there is any consistent higher-order patterning in the relationship between cells of a given type, or any evidence of exclusion zones maintaining a minimal distance between like-type cells. Cross-correlation analysis will determine the relationship between different types of cell. Evidence of exclusion zones in the autocorrelograms is suggestive of minimal-distance spacing rules operating between cells, and modeling studies will seek to define those rules by comparing simulations with real biological data. This project will therefore establish new tools for describing the spatial relationship between cells within a brain structure, specifically, the spacing rules that may govern their relative positioning. These tools will be made freely available to the scientific community for downloading from a website. They will provide the basis for future studies in which researchers can quantitate and model the spatial relationships between cells in the CNS in the process of exploring the biological mechanisms underlying intercellular spacing.