Scalable Cell- and Circuit-Targeted Electrophysiology

神经元回路的功能活动和失调严重依赖于神经元的生理功能。 突触，这是具有挑战性的分析，因为他们出现在大量，他们很难 在体内记录，特别是与特定神经元产生的动态神经代码有关。使 事情甚至更复杂：突触是令人难以置信的动态时尚，这取决于最近的历史， 感官刺激、疾病状态和其他行为相关背景。理想的情况是有一种技术 这将允许个体研究者快速分析神经元之间的突触， 在行为环境中编码，这样就有可能理解信息是如何在突触上转换的。 在这里，我们建议开发一个简单的，易于部署的工具箱来实现这一目标，从最近的几个 发现。首先，我们发现（准备中的手稿），可以自动执行整个 细胞膜片钳神经记录的细胞在活的小鼠大脑中已被确定通过双光子 荧光显微术（例如，表达遗传编码荧光团的给定类型的细胞）。我们这里 我建议发明一种多神经元修补版本的“图像匹配”机器人，以实现同时 表征多个神经元中的神经代码，以及它们之间的突触连接 (Aim 1）。我们还将开发能够进行图像匹配的小型化和优化的硬件， 神经外科和膜片钳电极重复使用以提高突触评估的产量和通量。 (Aim 2）。此外，我们还发现，有可能通过物理方式扩展保存的神经回路， 将它们嵌入可膨胀的聚合物中，然后化学膨胀这些聚合物，我们称之为 扩展显微镜（ExM），它能够对三维组织和生物体进行纳米级成像。我们建议 优化ExM用于突触分析（目标3）。我们在这里提出一个快节奏的，4年的赠款，以创建一个 功能强大，易于使用的工具箱，使体内突触生理学的关键任务成为常规， 自动程序。我们将尽可能自由地分发所有工具和数据集，共享所有算法， 电路设计和组装说明，并接待游客学习这些技术-为此，我们有 广泛的记录

The functional activity and dysregulation of neuronal circuits relies critically on the physiology of neuronal synapses, which are challenging to analyze because they appear in great numbers, and they are difficult to record in vivo, especially in relation to the dynamic neural codes generated by specific neurons. To make things even more complex: synapses are incredibly dynamic in fashions that are dependent on recent history, sensory stimuli, disease state, and other behaviorally relevant contexts. Ideally there would be a technology that would allow for individual investigators to rapidly analyze synapses between neurons exhibiting neural codes in a behavioral context, so that it is possible to understand how information is trans formed at synapses. We here propose to develop a simple, easily deployable toolbox for achieving this, building from several recent discoveries. First, we have found (manuscript in preparation) that it is possible to automatically perform whole cell patch clamp neural recording of cells in the living mouse brain that have been identified via two-photon fluorescence microscopy (e.g., cells of a given type that express a genetically encoded fluorophore). We here propose to invent a multiple-neuron patching version of this “imagepatching” robot, to enable the simultaneous characterization of the neural codes in multiple neurons, as well as the synaptic connections between them (Aim 1). We will also develop miniaturized and optimized hardware capable of performing imagepatching, neurosurgery, and patch clamp electrode reuse for improved yield and throughput of synaptic assessment. (Aim 2). Also, we have discovered that it is possible to physically expand preserved neural circuits, by embedding them in swellable polymers, and then chemically expanding those polymers, a technology we call expansion microscopy (ExM), which enables nanoscale imaging of 3-D tissues and organisms. We propose to optimize ExM for the analyses of synapses (Aim 3). We here propose a fast-paced, 4-year grant, to create a powerful, easy-to-use toolbox that makes the critical task of in vivo synaptic physiology into a routine, automated procedure. We will distribute all tools and datasets as freely as possible, sharing all algorithms, circuit designs, and assembly instructions, and hosting visitors to learn these technologies – for which we have an extensive track record.