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

• 批准号: 9754666
• 负责人: Z JOSH HUANG
• 金额: $ 78.9万
• 依托单位: COLD SPRING HARBOR LABORATORY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9754666
• 关键词: Adaptive Behaviors; Algorithms; Anatomy; Axon; Biological; Biology; Birth Order; Catalogs; Categories; Cells; Censuses; Classification; Classification Scheme; Computational algorithm; Computer Analysis; Computing Methodologies; Development; Embryo; Equilibrium; Foundations; Functional disorder; Ganglia; Gene Expression Profile; Gene Expression Profiling; Genetic; Genetic Engineering; Genetic Transcription; Glutamates; Goals; Interneurons; Knowledge; Literature; Location; Mammals; Medial; Messenger RNA; Methods; Minority; Molecular; Morphologic artifacts; Morphology; Motor; Motor Cortex; Mus; Neocortex; Neurons; Noise; Nucleotides; Parvalbumins; Pattern; Phenotype; Physiological; Play; Population; Portraits; Positioning Attribute; Property; Resolution; Sensory; Shapes; Signal Transduction; Spatial Distribution; Statistical Algorithm; Statistical Data Interpretation; Supervision; Training; base; cell type; cognitive ability; cohort; flexibility; gamma-Aminobutyric Acid; hippocampal pyramidal neuron; insight; intersectionality; learning algorithm; molecular marker; nerve supply; neural circuit; neuropsychiatric disorder; novel; online resource; operation; programs; prototype; public health relevance; supervised learning; tool; transcriptome; transcriptome sequencing; transcriptomics; unsupervised learning

 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（由申请人提供）：指导哺乳动物适应性行为的综合感觉、运动和认知能力来自新皮层中的神经回路操作。要理解皮层回路的组织结构，需要对基本细胞成分有全面的了解。新皮质由大约80%的多巴胺能锥体神经元和20%的GABA能神经元组成。虽然是少数，但GABA中间神经元异常多样，这种多样性可能在调节皮质回路的平衡和功能操作中至关重要。然而，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，不同的转录程序的基础GABA原型的身份和多样性，定义其位置，形态和基本的神经支配模式。因此，我们认为，转录谱提供了一个基本的出发点和有效的策略，细胞类型的发现。在这里，我们提出了一个多方面的方法，整合遗传靶向，单细胞转录组学，统计和计算分析，形态生理学研究，系统地识别和分类GABA能神经元。我们专注于GABA神经元来源于胚胎内侧神经节隆起（MGE），构成三分之二的皮层中间神经元，我们已经建立了有效的遗传工具。我们已经建立了一种稳健的单细胞RNAseq（scRNAseq）方法，该方法允许通过使用核苷酸条形码的单个mRNA计数进行高分辨率转录组分析。我们将采取两步“靶向饱和”细胞筛选方法系统地发现皮层GABA神经元。首先，我们将scRNAseq应用于一组GABA亚群，通过交叉遗传靶向捕获，并发现它们不同的转录特征。有了这些表型特征化的群体，我们磨练我们的统计分析，以区分生物信号与实验噪声和伪影，并基于生物基础事实塑造我们的计算算法。因此，与转录组分析的无监督聚类方法相比，我们结合了大量的经验信息，这些信息使生物学动机的监督方法成为可能，其中良好描绘的表型在训练算法和分类器中发挥着关键作用。其次，我们将scRNASeq应用于初级运动皮层中越来越广泛的遗传定义的MGE衍生GABA神经元群体。我们将发现转录组预测的细胞类型，并建立第二轮驱动线，靶向和验证一个新的细胞类型的子集。我们的研究将通过整合转录谱和基本细胞表型来建立一个皮质GABA能神经元主要群体的综合目录。这将为研究抑制回路的组织、功能和功能障碍建立细胞基础。1