High-throughput robotic analysis of integrated neuronal phenotypes

描述（由申请人提供）：脑细胞表现出表达基因、形态和电生理特性的多样性，并已被分组为“细胞类型”，通过这些特征中的一个或多个来区分。然而，定义细胞类型的表达基因、形态特征和电生理特性之间没有一对一的对应关系，也没有统一的脑细胞分类。此外，由于发育、可塑性或疾病，细胞通常会改变其表达的基因、形态和电生理特性，这就提出了如何对细胞类型进行分类的问题，因为它们会因经历而改变其状态。因此，我们建议开发一种功能强大，易于使用的工具，使大脑细胞的综合表型-即，一个机器人，可以同时获得基因表达模式，形态，以及大脑组织中单个细胞的电生理特性，以自动化的方式。最近，我们的两个实验室开发了一种原型“自动贴片”机器人，可以实现对活体小鼠大脑神经元的全细胞贴片钳自动记录，显著提高了这项极具挑战性的任务的效率。在一个多学科的研究中，我们建议增强这个机器人，将其与转录和形态分析策略结合起来，为完整组织中单细胞的综合表征提供一个平台。我们将开发机器人的变体及其算法，使其能够修补大脑切片，包括以图像引导的方式（目标1），提取转录组信息（目标2），并执行形态学填充（目标3）和基因传递到细胞（目标5）。我们还将创建大规模并行自动贴片机器人（目标4）。我们将在体内和切片中对来自小鼠不同皮质区域的成百上千个单细胞进行自贴（Aim 6）

DESCRIPTION (provided by applicant): The cells of the brain exhibit a diversity of expressed genes, morphologies, and electrophysiological properties, and have come to be grouped into "cell types" that are distinguished by one or more of these characteristics. However, there is no one-to-one correspondence between cell type-defining expressed genes, morphological characteristics, and electrophysiological properties and no unified taxonomy of brain cells. Furthermore, cells routinely change their expressed genes, morphologies, and electrophysiological properties, as a result of development, plasticity, or disease, raising the question of how to categorize cell types as they change their states as a result of experience. Accordingly, we propose to develop a powerful, easy-to-use tool that enables the integrative phenotyping of cells of the brain - namely, a robot that can acquire simultaneously the gene expression patterns, morphologies, and electrophysiological properties of single cells in brain tissue, in an automated fashion. Recently, two of our labs developed a prototype "autopatching" robot that enables automated whole-cell patch clamp recording of neurons in living mouse brain, significantly increasing the efficiency of this highly challenging task. In a multidisciplinary tea, we propose to augment this robot, coupling it to transcriptional and morphological analysis strategies, yielding a platform for the comprehensive characterization of single cells in intact tissues. We will develop variants of the robot and its algorithms to enable it to patch in brain slices, including in an image guided fashion (Aim 1), to extract transcriptomic information (Aim 2), and to perform morphological fills (Aim 3) and gene delivery to cells (Aim 5). We will also create massively parallel autopatching robots (Aim 4). We will autopatch hundreds to thousands of single cells from different cortical regions of mice (Aim 6), in vivo as well as in slices, both broadly surveying cells, as well as targeting specific fluorescently labeled neural populations. We will create visualization software to help with analysis of the integrated cell profiles that emerge, aiming to estimate the dimensionality of "cell type space", characterize cell- to-cell heterogeneity, and discover optimal cell type markers for molecular targeting. Our goal is to create a powerful, easy-to-use toolbox that makes fundamentally new kinds of science possible, converting the critical tasks of categorizing cell types, and characterizing cell states, into routne, simple tasks. As our goal is to develop a toolbox which will have very broad applicability, we are focusing our innovation not only on power, but ease of use, aiming to enable fields across biology to characterize normal and diseased organ states at the single cell level. We will distribute all tools, methods, and datasets as freely as possible, and teach others to use these technologies. As many diseases affect different cells to different extents, we will seek to commercialize our work to enable diagnostic or therapeutic tools that directly improve human health.