RNA regulatory networks in neuronal cell type diversity and function

神经元细胞类型多样性和功能中的 RNA 调控网络

• 批准号: 10342485
• 负责人: Chaolin Zhang
• 金额: $ 62.5万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-01 至 2026-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10342485
• 关键词: ANK3 gene; Action Potentials; Affect; Alternative Splicing; Axon; Behavior; Biological Models; Brain; Cells; Classification; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Computer Analysis; Data; Data Set; Disease; ES Cell Line; Electrophysiology (science); Elements; Exons; Experimental Models; GTP-Binding Protein alpha Subunits, Gs; Ganglia; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Glutamates; Goals; Human; In Vitro; Interneurons; Knock-out; Mammals; Medial; Messenger RNA; Methods; Modeling; Molecular; Molecular Profiling; Morphology; Motor Neurons; Mus; Mutagenesis; Nervous system structure; Neurons; Organ; Population; Process; Property; Protein Family; Protein Isoforms; Proteins; RNA; RNA Splicing; RNA-Binding Proteins; RNA-Protein Interaction; Regulation; Regulator Genes; Reporter; Research; Resources; Rest; Rodent; Role; Sampling; Specific qualifier value; Spinal; Structure; System; Taxonomy; Testing; Tissues; To specify; Transcript; Validation; Variant; Work; base; cell type; clinically relevant; deep learning; deep learning model; driving force; embryonic stem cell; excitatory neuron; in silico; in vivo; in vivo Model; inhibitory neuron; innovation; insight; interdisciplinary approach; mRNA Precursor; machine learning method; molecular marker; mouse model; neural circuit; neurodevelopment; neuronal excitability; novel; programs; single-cell RNA sequencing; statistical and machine learning; stem cell differentiation; success; transcriptome; transcriptome sequencing; transcriptomics; ANK3 gene; Action Potentials; Affect; Alternative Splicing; Axon; Behavior; Biological Models; Brain; Cells; Classification; Clustered Regularly Interspaced Short Palindromic Repeats; Complex; Computer Analysis; Data; Data Set; Disease; ES Cell Line; Electrophysiology (science); Elements; Exons; Experimental Models; GTP-Binding Protein alpha Subunits, Gs; Ganglia; Gene Expression; Gene Expression Regulation; Genes; Genetic Transcription; Glutamates; Goals; Human; In Vitro; Interneurons; Knock-out; Mammals; Medial; Messenger RNA; Methods; Modeling; Molecular; Molecular Profiling; Morphology; Motor Neurons; Mus; Mutagenesis; Nervous system structure; Neurons; Organ; Population; Process; Property; Protein Family; Protein Isoforms; Proteins; RNA; RNA Splicing; RNA-Binding Proteins; RNA-Protein Interaction; Regulation; Regulator Genes; Reporter; Research; Resources; Rest; Rodent; Role; Sampling; Specific qualifier value; Spinal; Structure; System; Taxonomy; Testing; Tissues; To specify; Transcript; Validation; Variant; Work; base; cell type; clinically relevant; deep learning; deep learning model; driving force; embryonic stem cell; excitatory neuron; in silico; in vivo; in vivo Model; inhibitory neuron; innovation; insight; interdisciplinary approach; mRNA Precursor; machine learning method; molecular marker; mouse model; neural circuit; neurodevelopment; neuronal excitability; novel; programs; single-cell RNA sequencing; statistical and machine learning; stem cell differentiation; success; transcriptome; transcriptome sequencing; transcriptomics

PROJECT SUMMARY RNA regulatory networks in neuronal cell type diversity and function The mammalian brain is probably the most complex organ in the body and its proper function requires coordination of many diverse types of excitatory and inhibitory neurons that form distinct functional circuitries. Characterization of neuronal cell types is fundamental to understand not only how the brain works, but also how specific cell types are selectively affected in multiple neuronal disorders and how this process can be reversed. A large number of neuronal cell types were recently defined based on their gene expression profiles in bulk and single cells, and these cell types are organized into a hierarchical cell taxonomy. Alternative splicing is a mechanism to generate multiple transcript and protein variants with distinct functions, thus providing a major driving force of the molecular diversity in mammals. The overarching goal of my research group is to understand the contribution of alternative splicing and the underlying RNA-regulatory networks in the brain and brain-related disorders. Previous work from multiple groups, including ours, unambiguously demonstrated that the brain has unique splicing-regulatory programs compared to non-neuronal tissues. Our studies also revealed the establishment of a pan-neuronal splicing program regulated by multiple RNA-binding proteins (RBPs) during neural development, and demonstrated the important role of the Rbfox protein family in regulating axonal maturation and neuronal excitability. However, how alternative splicing contributes to the distinct molecular profiles of diverse neuronal cell types in the cortex is currently poorly understood. We hypothesize that the transcriptome diversity generated by highly regulated alternative exons is a major component that specifies neuronal cell type identity and function. In this application, we describe our preliminary analysis of neuronal cell type-specific alternative splicing regulation in mouse cortex, which provides strong support for the following specific aims we would like to pursue: 1) Perform systematic analysis of neuronal cell type-specific alternative splicing to identify novel neuronal subclasses and regulators using deep sc-RNA-seq data. 2) Validate our computational predictions and characterize mechanisms of neuronal cell type-specific splicing regulation and function using two complementary model systems: the distinction of two major subclasses of GABAergic interneurons originating from caudal (CGE) and medial (MGE) ganglionic eminences, and a GABAergic neuron- specific microexon in Ank3/Ankyrin G. To achieve our goal, we will use a multidisciplinary approach that integrates cutting-edge statistical an machine learning methods and multiple in vitro and in vivo experimental models. This study will generate a global and mechanistic view of precise alternative splicing regulation across diverse neuronal cell types and illuminate its impact on neuronal structural and functional properties.

项目摘要 神经元细胞类型多样性和功能的RNA调节网络 哺乳动物的大脑可能是体内最复杂的器官，其适当功能需要 形成不同功能电路的许多不同类型的兴奋性和抑制神经元的协调。 神经元细胞类型的表征不仅要了解大脑的工作原理，而且了解如何理解 特定的细胞类型在多种神经元疾病中有选择性影响，以及如何逆转此过程。 最近，根据其在大量和 单细胞和这些细胞类型被组织为分层细胞分类。替代剪接是 生成具有不同功能的多个转录本和蛋白质变体的机制，从而提供了主要的功能 哺乳动物分子多样性的驱动力。我的研究小组的总体目标是了解 大脑和大脑相关的替代剪接和潜在的RNA调控网络的贡献 疾病。来自包括我们在内的多个小组的先前工作明确证明了大脑 与非神经组织相比，独特的剪接调节程序。我们的研究还揭示了 建立一个由多种RNA结合蛋白（RBP）调节的泛神经元素剪接程序 神经发育，并证明了RBFOX蛋白家族在调节轴突中的重要作用 成熟和神经元兴奋性。但是，替代剪接如何有助于不同的分子 目前对皮质中各种神经元细胞类型的特征很了解。我们假设 高度调节的替代外显子产生的转录组多样性是指定的主要组成部分 神经元细胞类型的身份和功能。在此应用中，我们描述了我们对神经元细胞的初步分析 小鼠皮层中特定于类型的替代剪接调节，为以下提供了强有力的支持 我们要追求的具体目标：1）对神经元细胞类型特异性替代方案进行系统分析 使用深层SC-RNA-seq数据识别新型神经元亚类和调节剂的剪接。 2）验证我们的 计算预测和表征神经元细胞类型特异性剪接调控的机制和 使用两个互补模型系统的功能：两个主要子类的区别 源自尾骨（CGE）和内侧（MGE）神经节象征的神经元，以及GABA能神经元 - 在ANK3/Ankyrin G中的特定微饰面。为了实现我们的目标，我们将使用一种多学科的方法 整合了机器学习方法和多种体外和体内实验的尖端统计方法 型号。这项研究将对整个跨越精确的替代剪接调节产生全球和机械的看法 多种神经元细胞类型，并阐明了其对神经元结构和功能特性的影响。