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.

概括 电突触，也称为间隙连接，频繁出现在所有神经系统中，包括人类 脑。它们由连接蛋白组成，排列成相邻耦合的细胞间通道 细胞。 Connexin36 (Cx36) 是 CNS 中主要的连接蛋白。在许多大脑和视网膜回路中，间隙 连接提供细胞之间直接且特定的连接。此外，电突触介导 网络特性，例如信号平均、降噪和同步。然而，由于他们的 小尺寸的间隙连接在大规模串行 EM 数据集中不可见。由于这些原因，间隙连接 往往被低估或干脆忽略。 该提案的目标是开发一种在 EM 中实现图像间隙连接连接的组合方法 数据集，此外还可以估计间隙连接的尺寸、强度和可塑性。我们将研究以下地区 视网膜包含大小和形状截然不同的间隙连接，使我们能够关联 结构和功能。目标 1 将使用高分辨率共聚焦显微镜来确定连接子数 大间隙和小间隙连接。分析将确定每个间隙连接的连接子数量。这些 方法将提供通用工具来确定用于所有大脑区域的间隙连接的大小。 目标 2 将使用 3D-EM 成像来明确识别 FIB-SEM 图像中的间隙连接，这 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 研究将允许对已识别神经元中的间隙连接和蛋白质进行高分辨率定量。目标3将 使用电生理学测量来确定耦合电导，然后开发模型来计算 形态数据中的最大电势耦合电导乘以 通道/间隙连接 [具体目标 1] 乘以连接性（耦合之间的间隙连接数量） 细胞）[具体目标 2]，乘以 Cx36 的单一电导。使用配对录音，我们将获得直接 耦合细胞之间连接电导的生理测量。那么，通过比较 根据形态数据计算出的潜在最大值，我们可以计算明渠概率 对操作范围设置现实的限制。这些是理解该函数所需的基本属性 间隙连接在神经元微电路中的功能。 该程序与列出的领域之一完全匹配，“识别间隙连接和表征的工具” 资金机会公告 RFA-MH-20-135 中的“电突触”。