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: 9977809
• 负责人: Z JOSH HUANG
• 金额: $ 6.13万
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9977809
• 关键词: Adaptive Behaviors; Anatomy; Axon; Bar Codes; 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; algorithm training; base; cell type; cognitive ability; cohort; flexibility; gamma-Aminobutyric Acid; genetic approach; hippocampal pyramidal neuron; insight; learning algorithm; molecular marker; nerve supply; neural circuit; neuropsychiatric disorder; novel; online resource; operation; programs; prototype; public health relevance; single-cell RNA sequencing; supervised learning; tool; transcriptome; 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、运动和认知能力源自新皮质的神经回路运作。了解皮质回路的组织需要对基本细胞成分的全面了解。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 GABA 中间神经元数量很少，但其多样性却异常丰富，这种多样性对于调节皮质回路的平衡和功能运作可能至关重要。然而，GABA能神经元的系统识别、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 原型身份和多样性的基础，这由它们的位置、形态和基本神经支配模式来定义。因此，我们认为转录分析为细胞类型发现提供了一个基本起点和有效策略。在这里，我们提出了一种多方面的方法，整合了遗传靶向、单细胞转录组学、统计和计算分析、形态生理学研究，以系统地识别和分类 GABA 能神经元。我们重点研究源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，我们为此构建了有效的遗传工具。我们建立了一种强大的单细胞 RNAseq (scRNAseq) 方法，可通过使用核苷酸条形码进行单 mRNA 计数来进行高分辨率转录组分析。我们将采用两步“目标饱和”细胞筛选方法来系统地发现皮质 GABA 神经元。首先，我们将 scRNAseq 应用于通过交叉遗传靶向捕获的一组 GABA 亚群，并发现它们独特的转录特征。通过这些以表型为特征的群体，我们完善了统计分析，以区分生物信号与实验噪声和伪影，并根据生物基本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，我们结合了广泛的经验信息，从而实现了生物学驱动的监督方法，其中明确描述的表型在训练算法和分类器方面发挥着关键作用。其次，我们将 scRNASeq 应用于初级运动皮层中越来越广泛的 MGE 衍生 GABA 神经元的遗传定义群体。我们将发现转录组预测的细胞类型，并构建第二轮驱动线，以靶向和验证新型细胞类型的子集。我们的研究将通过整合转录谱和基本细胞表型，建立皮质 GABA 能神经元主要群体的综合目录。这将为研究抑制回路组织、功能和功能障碍奠定细胞基础。 1