Transcriptome-based systematic discovery of GABAergic neurons in the neocortex

 描述(由申请人提供)：指导哺乳动物适应行为的综合感觉、运动和认知能力来自新皮质的神经回路操作。要了解大脑皮层回路的组织，需要全面了解基本的细胞成分。新皮质由大约80%的谷氨酸能锥体神经元和20%的GABA能神经元组成。尽管数量很少，但GABA中间神经元是异常多样的，这种多样性可能对调节皮质回路的平衡和功能操作至关重要。然而，对GABA能神经元的系统鉴定、计数和分类一直是一个具有挑战性的目标。我们假设不同的转录程序是GABA原型的同一性和多样性的基础，正如它们的位置、形态和基本神经支配模式所定义的那样。因此，我们认为转录图谱为发现细胞类型提供了一个基本的起点和有效的策略。在这里，我们提出了一种多方面的方法，结合遗传靶向、单细胞转录学、统计和计算分析、形态生理学研究来系统地识别和分类GABA能神经元。我们专注于来自胚胎内侧神经节隆起(MGE)的GABA神经元，它构成了皮质中间神经元的三分之二，我们已经为其建立了有效的遗传工具。我们已经建立了一种强大的单细胞RNAseq(ScRNAseq)方法，该方法可以通过使用核苷酸条形码的单个mRNA计数来进行高分辨率转录组分析。我们将采取两步“靶向饱和”细胞筛选的方法，系统地发现皮质GABA神经元。首先，我们将把scRNAseq应用于一组通过交叉遗传靶向捕获的GABA亚群，并发现它们的不同转录特征。有了这些表型特征的群体，我们磨练了我们的统计分析，以区分生物信号和实验噪声和伪像，并基于生物地面事实形成我们的计算算法。因此，与转录组分析的非监督聚类方法相比，我们纳入了大量的经验信息，从而实现了生物动机的监督方法，其中描述良好的表型扮演着训练算法和分类器的关键角色。其次，我们将把scRNASeq应用于初级运动皮质中越来越广泛的遗传定义的MGE来源的GABA神经元群体。我们将发现转录组预测的细胞类型，并建立第二轮驱动系，以靶向并验证新细胞类型的子集。我们的研究将通过整合转录图谱和基本细胞表型来建立一个主要的皮质GABA能神经元队列的全面目录。这将为研究抑制回路的组织、功能和功能障碍奠定细胞基础。1

 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