Transcriptome-based systematic discovery of GABAergic neurons in the neocortex

基于转录组的新皮质 GABA 能神经元的系统发现

基本信息

批准号：
10319407
负责人：
Z JOSH HUANG
金额：
$ 58.28万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2023-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10319407
关键词：
Transcriptome based systematic discovery GABAergic

项目摘要

DESCRIPTION (provided by applicant): The integrated sensory, motor, and cognitive abilities that guide adaptive behavior in mammals emerge from neural circuit operations in the neocortex. Understanding the organization of cortical circuits requires comprehensive knowledge of the basic cellular components. The neocortex consists of approximately 80% glutamatergic pyramidal neurons and 20% GABAergic neurons. Although a minority, GABA interneurons are exceptionally diverse, and this diversity may be crucial in regulating the balance and functional operations of cortical circuits. However, systematic identification, enumeration and classification of GABAergic neurons have been a challenging goal. We hypothesize that distinct transcription programs underlie GABA prototype identity and diversity as defined by their position, morphology and basic innervation pattern. Thus we suggest that transcription profiling provides a fundamental starting point and efficient strategy for cell type discovery. Here we propose a multi-faceted approach that integrates genetic targeting, single cell transcriptomics, statistical and computational analysis, morpho-physiological studies to systematically identify and classify GABAergic neurons. We focus on GABA neurons derived from the embryonic medial ganglionic eminence (MGE), which constitute two-third of cortical interneurons, and for which we have built effective genetic tools. We have established a robust single cell RNAseq (scRNAseq) method that allows high resolution transcriptome profiling through single mRNA counting using nucleotide barcodes. We will take a two-step "Targeted-Saturation" cell screen approach toward systematic discovery of cortical GABA neurons. First, we will apply scRNAseq to a set of GABA subpopulations, captured by intersectional genetic targeting, and discover their distinct transcription signatures. With these phenotype- characterized populations, we hone our statistical analysis to distinguish biological signal vs experimental noise and artifacts, and shape our computation algorithm based on biological ground truth. Thus in contrast to a unsupervised clustering approach to transcriptome analysis, we incorporate extensive empirical information that enable a biology-motivated supervised approach, where well-delineated phenotypes play the key role of training the algorithm and classifier. Second, we will apply scRNASeq to increasingly broader genetic-defined populations of MGE-derived GABA neurons in the primary motor cortex. We will discover transcriptome-predicted cell types and build 2nd round driver lines that target and validate a subset of novel cell types. Our study will build a comprehensive catalog of a major cohort of cortical GABAergic neurons by integrating transcription profiles and basic cell phenotypes. This will establish a cellular foundation for studying inhibitory circuit organization, function, and dysfunction. 1

描述（由适用提供）：新皮层神经电路操作中神经电路操作中出现哺乳动物的自适应行为的综合感觉，运动和认知能力。了解皮层电路的组织需要全面了解基本的细胞成分。新皮层由大约80％的谷氨酸能锥体神经元和20％GABA能神经元组成。尽管是少数族裔，但GABA中间神经元异常多样，并且这种多样性对于确定皮质回路的平衡和功能操作可能至关重要。但是，GABA能神经元的系统识别，枚举和分类一直是一个挑战目标。我们假设GABA原型身份和多样性是由其位置，形态和基本神经支配模式定义的。我们建议转录分析为细胞类型发现提供了一个基本的起点和有效的策略。在这里，我们提出了一种多面方法，该方法将遗传靶向，单细胞转录组学，统计和计算分析，形态生理学研究整合，以系统地识别和分类GABA能神经元。我们专注于源自胚胎介质神经节杰出（MGE）的GABA神经元，该神经元构成了皮质中间神经元的三分之二，并为此我们构建了有效的遗传工具。我们已经建立了一种强大的单细胞RNASEQ（SCRNASEQ）方法，该方法允许使用核苷酸条形码通过单个mRNA计数进行高分辨率转录组分析。我们将采用两步的“靶向饱和”细胞筛选方法来系统发现皮质GABA神经元。首先，我们将将scrnaseq应用于一组由跨遗传靶向捕获的GABA亚群，并发现其独特的转录特征。通过这些表型特征的种群，我们磨练了统计分析，以区分生物学信号与实验噪声和伪影，并基于生物基础真理来塑造我们的计算算法。这与无监督的转录组分析的聚类方法相反，我们合并了广泛的经验信息，使生物学动机的监督方法可以进行，在这种方法中，良好的表型在训练算法和分类器的关键作用中起着关键作用。其次，我们将在原发性运动皮层中使用SCRNASEQ对MGE衍生的GABA神经元的遗传定义越来越宽。我们将发现转录组预测的细胞类型，并构建针对新型细胞类型的子集的第二轮驱动线。我们的研究将通过整合转录曲线和基本细胞表型来建立一系列皮质GABA能神经元的全面目录。这将为研究抑制性电路组织，功能和功能障碍建立细胞基础。 1