High Throughput Mapping of Neuronal and Glial Networks

描述（由申请人提供）：复杂的细胞网络是哺乳动物中枢神经系统（CMS）的功能基础。理解这些网络的生理动力学，换句话说，理解相互作用的细胞群之间的信号如何产生和调节有意义的生理信息，将直接有助于我们理解中枢神经系统如何在健康中发挥作用，以及它如何在疾病中失效。目前，我们对神经元和神经胶质网络动力学的机械理解非常有限，即使我们了解其背后的分子功能单位（即突触）。一种方法是应用网络理论来表征神经元和神经胶质网络。网络理论是统计力学的一个分支，它对独立于网络物理细节的复杂网络进行分类，并提供对其动态行为的理解。将网络理论应用于神经元和神经胶质网络需要了解它们的结构或拓扑结构。然而，在目前的技术条件下，对神经元和胶质细胞之间的分子信号进行高通量的密集计算测量，以及对其潜在网络结构的定量信息的提取是不可能的。因此，我们需要的是能够对生理神经元和神经胶质网络进行高通量表征和分析的算法和软件。在这里，我们建议开发计算工具，使我们能够绘制功能性神经元和胶质信号网络的空间和时间拓扑，并在网络理论的背景下对它们进行分类和分析。我们将详细讨论这样做所需的算法和编程，并举例说明这样一个程序的测试版的操作和验证。我们建议使用这种方法，神经元和神经胶质网络可以在已知的数学网络类型中进行分类，并根据它们被分类的网络类型的定量特性进行行为。我们还提供了初步的实验数据，首次显示星形胶质细胞网络中的钙信号，使用我们的软件工具绘制，具有以前未识别的拓扑结构。我们认为，损伤后健康神经元和胶质细胞的网络拓扑结构重塑是神经病理疾病状态诱导和维持的基础，因此这些发现的临床意义以及研究它们所需的计算工具的发展非常重要。

DESCRIPTION (provided by applicant): Complex cellular networks underlie the functional foundation of the mammalian central nervous system (CMS). Understanding the physiological dynamics of these networks, in other words understanding how signaling between interacting groups of cells produce and modulate meaningful physiological information, will directly contribute to our understanding of how the CNS functions in health and how it fails in disease. At present, our mechanistic understanding of the dynamics of neuronal and glial networks is very limited, even though we understand the molecular functional unit that underlies it (i.e., the synapse). One approach is to apply network theory to characterize neuronal and glial networks. Network theory is a branch of statistical mechanics that classifies complex networks independent of the physical details of the network and provides an understanding of its dynamical behavior. Applying network theory to neuronal and glial networks requires knowing their structure or topology. However, high throughput computationally intensive measurements of molecular signaling between neurons and glia, and the extraction of quantitative information about their underlying network structure is not possible given current techniques. What is needed therefore, are algorithms and software that will allow the high throughput characterization and analysis of physiological neuronal and glial networks. Here, we propose to develop computational tools that will allow us to map the spatial and temporal topology of functional neuronal and glial signaling networks, and classify and analyze them within the context of network theory. We present a detailed discussion on the algorithms and programming required to do so, and illustrate the operation and validation of a beta version of such a program. We propose that using this approach, neuronal and glial networks can be classified within known mathematical network types and behave as dictated by the quantitative properties of the network types they are classified into. We also present preliminary experimental data showing for the first time that calcium signaling in astrocyte networks, mapped using our software tools, have a previously unidentified topology. We propose that the network topologies of healthy neurons and glia remodel following injury and underlie the induction and maintenance of neuropathological disease states, making the clinical significance of these findings and the development of the computational tools required to investigate them very important.