Strategy to map electrical synaptic connectivity in neural networks

映射神经网络中电突触连接的策略

• 批准号: 10285599
• 负责人: SUE A AICHER
• 金额: --
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10285599
• 关键词: 3-Dimensional; Algorithms; Antibody Binding Sites; Area; BRAIN initiative; Behavior; Binding Proteins; Brain; Brain Diseases; Brain region; Cells; Confocal Microscopy; Connexins; Connexon; Coupled; Coupling; Data; Data Set; Degenerative Disorder; Development; Diagnostic; Electrical Synapse; Electron Microscopy; Electrophysiology (science); Elements; Ensure; Fostering; Funding Opportunities; Gap Junctions; Health; Human; Image; Individual; Ions; Isometric Exercise; Label; Lead; Location; Maps; Measurement; Measures; Mediating; Membrane; Methods; Mission; Modeling; Morphology; Mus; Nervous system structure; Neurons; Noise; Physiological; Physiology; Primates; Probability; Process; Property; Proteins; Protocols documentation; Public Health; Reporting; Research; Research Design; Resolution; Retina; Scanning; Shapes; Signal Transduction; Structure; Sum; Synapses; Techniques; Time; Tissues; United States National Institutes of Health; connexin 36; gap junction channel; insight; large scale data; neural network; nonhuman primate; novel; novel strategies; patch clamp; programs; reconstruction; response; success; tool

SUMMARY Electrical synapses, also known as gap junctions, occur frequently in all nervous systems, including the human brain. They are composed of connexins, arranged to form intercellular channels between adjacent, coupled cells. Connexin36 (Cx36) is the predominant connexin in the CNS. In many brain and retinal circuits, gap junctions provide direct and specific connections between cells. In addition, electrical synapses mediate network properties such as signal averaging, noise reduction and synchronization. However, because of their small size, gap junctions are not visible in large-scale serial EM data sets. For these reasons, gap junctions tend to be under-reported or simply ignored. The objective of this proposal is to develop a combined approach to image gap junction connectivity in EM datasets and, in addition, to estimate the size, strength, and plasticity of gap junctions. We will study regions of the retina that contain gap junctions of dramatically different sizes and shapes, to allow us to correlate structure and function. Aim 1 will use high-resolution confocal microscopy to determine connexon number at large and small gap junctions. Analyses will determine the number of connexons per gap junction. These methods will provide a general-purpose tool to determine the size of gap junctions for use in all brain regions. Aim 2 will use 3D-EM imaging to allow unambiguous identification of gap junctions in FIB-SEM images, which will follow with first-ever immunogold quantification of a membrane-bound protein in 3D-EM structures. These studies will allow high-resolution quantification of gap junctions and proteins in identified neurons. Aim 3 will use electrophysiological measures to determine coupling conductance and then develop models to calculate the maximal potential coupling conductance from the morphological data by multiplying the number of channels/gap junction [Specific Aim 1] times the connectivity (the number of gap junctions between coupled cells) [Specific Aim 2], times the unitary conductance of Cx36. Using paired recordings, we will obtain direct physiological measures of the junctional conductance between coupled cells. Then, by comparison with the potential maximum calculated from the morphological data, we can calculate the open channel probability and place realistic limits on the operating range. These are the fundamental properties required to understand the function of gap junctions in neuronal microcircuits. This program is an exact match for one of the listed areas, “Tools to identify gap junctions and characterize electrical synapses” in the Funding Opportunity Announcement, RFA-MH-20-135.

总结 电突触，也称为间隙连接，在所有神经系统中频繁发生，包括人类 个脑袋它们由连接蛋白组成，排列形成相邻、偶联的细胞间通道， 细胞连接蛋白36（Cx 36）是中枢神经系统中主要的连接蛋白。在许多大脑和视网膜回路中， 连接提供细胞之间的直接和特异性连接。此外，电突触介导 网络特性，如信号平均、降噪和同步。然而，由于其 小尺寸的间隙连接在大规模连续EM数据集中不可见。由于这些原因，缝隙连接 往往被低估或被忽视。 本建议的目的是开发一种综合的方法，以图像间隙连接的EM 数据集，此外，估计间隙连接的大小，强度和可塑性。我们将研究 视网膜上的缝隙连接具有不同的大小和形状， 结构和功能。目的1将使用高分辨率共聚焦显微镜来确定连接子的数量， 大的和小的缝隙连接。分析将确定每个间隙连接的连接子数量。这些 这些方法将提供一种通用工具来确定用于所有脑区域的间隙连接的大小。 目标2将使用3D-EM成像，以明确识别FIB-SEM图像中的间隙连接， 随后将首次对3D-EM结构中的膜结合蛋白进行免疫金定量。这些 研究将允许高分辨率量化已识别神经元中的缝隙连接和蛋白质。目标3将 使用电生理测量来确定耦合电导，然后开发模型来计算 最大的潜在耦合电导从形态数据，通过乘以的数量， 通道/间隙连接[具体目标1]乘以连接性（耦合通道之间的间隙连接数量） 细胞）[具体目标2]，乘以Cx 36的单位电导。使用配对记录，我们将获得直接 耦合细胞间连接电导的生理学测量。然后，通过与 根据形态学数据计算的潜在最大值，我们可以计算明渠概率， 对操作范围设置实际限制。这些都是理解 缝隙连接在神经元微电路中的功能。 这个程序是一个完全匹配的一个列出的领域，“工具，以确定缝隙连接和表征 电突触”的资助机会公告，RFA-MH-20-135。