High-throughput robotic analysis of integrated neuronal phenotypes

集成神经元表型的高通量机器人分析

• 批准号: 8549259
• 负责人: Edward S. Boyden
• 金额: $ 89.28万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-30 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8549259
• 关键词: Affect; Algorithms; Biology; Biopsy; Brain; Cells; Characteristics; Complex; Coupling; DNA; Data; Data Analyses; Data Set; Development; Devices; Diagnostic; Disease; Drug Targeting; Dyes; Educational process of instructing; Epilepsy; Exhibits; Gene Delivery; Gene Expression Profile; Genes; Goals; Harvest; Health; Heterogeneity; Human; Image; Imagery; Individual; Infusion procedures; Institutes; Label; Life; Malignant Neoplasms; Manuscripts; Maps; Medicine; Messenger RNA; Methods; Molecular; Molecular Target; Morphology; Mus; Neurons; Organ; Patient Care; Pattern; Phenotype; Population; Prefrontal Cortex; Process; Property; RNA Sequences; Robot; Robotics; Sampling; Science; Scientist; Shapes; Slice; Somatosensory Cortex; Speed; Stereotyping; Surveys; Taxonomy; Tea; Techniques; Technology; Therapeutic; Tissues; Transgenic Mice; Variant; Virus; Visualization software; Work; biocytin; blind; body system; brain cell; brain tissue; cell type; electrical property; empowered; experience; forest; improved; in vivo; innovation; multidisciplinary; patch clamp; protein expression; prototype; relating to nervous system; single cell analysis; tissue processing; tool; transcriptomics

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.

描述（由申请人提供）：脑细胞表现出表达基因、形态和电生理学特性的多样性，并且已被分为通过这些特征中的一个或多个来区分的“细胞类型”。然而，细胞类型定义表达基因、形态特征和电生理特性之间不存在一一对应的关系，也没有统一的脑细胞分类法。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，因为它们会因经验而改变其状态。因此，我们建议开发一种功能强大、易于使用的工具，能够对大脑细胞进行综合表型分析，即一个能够以自动化方式同时获取脑组织中单个细胞的基因表达模式、形态和电生理特性的机器人。最近，我们的两个实验室开发了一种原型“自动修补”机器人，可以对活体小鼠大脑中的神经元进行自动全细胞膜片钳记录，从而显着提高了这项极具挑战性的任务的效率。在多学科茶中，我们建议增强该机器人，将其与转录和形态分析策略相结合，从而产生一个用于完整组织中单细胞的综合表征的平台。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，包括以图像引导方式（目标 1）、提取转录组信息（目标 2）以及执行形态填充（目标 3）和向细胞传递基因（目标 5）。我们还将创建大规模并行自动修补机器人（目标 4）。我们将自动修补来自小鼠不同皮质区域的数百至数千个单细胞（目标 6），无论是在体内还是在切片中 广泛地调查细胞，以及针对特定的荧光标记的神经群体。我们将创建可视化软件来帮助分析出现的整合细胞概况，旨在估计“细胞类型空间”的维度，表征细胞间的异质性，并发现用于分子靶向的最佳细胞类型标记。我们的目标是创建一个功能强大、易于使用的工具箱，使全新的科学成为可能，将细胞类型分类和细胞状态表征的关键任务转化为常规、简单的任务。由于我们的目标是开发一个具有非常广泛适用性的工具箱，因此我们的创新不仅注重功能，而且注重易用性，旨在使生物学领域能够在单细胞水平上表征正常和患病器官状态。我们将尽可能免费地分发所有工具、方法和数据集，并教其他人使用这些技术。由于许多疾病不同程度地影响不同的细胞，我们将寻求将我们的工作商业化，以实现直接改善人类健康的诊断或治疗工具。