Spatial Relationships Between Neurons in the CNS

描述(申请人提供)：发育神经科学领域的最新进展揭示了许多控制增殖、构型、神经发生、命运决定、迁移、分化、路径导航、突触发生和细胞死亡的机制。然而，缺乏对控制神经元位置的机制的了解：大脑结构中神经元位置的决定因素是什么，一个细胞与其他相同或不同类型细胞的位置是如何相关的？目前的探索性提案将创建软件工具，用于描述大量组织中神经元之间的几何关系，并对其在三维空间中的位置进行建模。利用这些已被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，以实现三维中相同类型的分析。X-Y-Z位置信息将从标记的脑组织样本中提取，以揭示神经元和/或神经胶质细胞的个别类型，在此基础上将执行各种基于Voronoi域的计算，包括测量Voronoi域体积、Delaunay段长度、最近邻距离和Voronoi小平面面积。将执行自相关分析以识别在给定类型的细胞之间的关系中是否存在任何一致的高阶图案，或者是否有任何证据表明在相似类型的细胞之间保持最小距离的隔离区。互相关分析将确定不同类型细胞之间的关系。自相关图中排除区域的证据暗示了细胞之间的最小距离间隔规则，建模研究将寻求通过将模拟与真实生物数据进行比较来定义这些规则。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，特别是可能支配它们相对位置的间距规则。这些工具将免费提供给科学界，供其从网站下载。它们将为未来的研究提供基础，在探索细胞间距背后的生物学机制的过程中，研究人员可以对中枢神经系统中细胞之间的空间关系进行量化和建模。

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.