Discovering the molecular genetic principles of cell type organization through neurobiology-guided computational analysis of single cell multi-omics data sets

通过神经生物学引导的单细胞多组学数据集计算分析发现细胞类型组织的分子遗传学原理

• 批准号: 10189902
• 负责人: Z JOSH HUANG
• 金额: $ 140.14万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10189902
• 关键词: ATAC-seq; Algorithms; Anatomy; Architecture; Area; BRAIN initiative; Biological; Biology; Brain; Brain region; Cell Shape; Cells; Censuses; Classification; Communication; Computer Analysis; DNA; Data Set; Development; Developmental Biology; Developmental Process; Disease; Gene Expression Profile; Genes; Genetic; Genetic Transcription; Glutamates; Human; Joints; Knowledge; Learning; Link; Methodology; Methods; Methylation; Molecular; Molecular Genetics; Multiomic Data; Neurobiology; Neurons; Noise; Output; Pattern; Phenotype; Physiological; Physiological Processes; Plant Roots; Population; Property; Regulator Genes; Scheme; Series; Shapes; Signal Pathway; Signal Transduction; Statistical Methods; Structure of molecular layer of cerebellar cortex; Supervision; Synapses; Synaptic Transmission; System; Taxonomy; Testing; Work; base; cell type; comparative; deep neural network; developmental genetics; epigenome; epigenomics; feature selection; genetic information; genetic signature; high dimensionality; improved; insight; methylome; multimodality; multiple omics; multitask; neural circuit; neurodevelopment; neuropsychiatric disorder; programs; relating to nervous system; single cell analysis; supervised learning; tool; transcription factor; transcriptome; transcriptomics; web portal

ABSTRACT Understanding the biological principles of cell type diversity and organization is necessary for deciphering neural circuits underlying brain function. The recent rapid accumulation of single cell transcriptomic and epigenomic data sets provides unprecedented opportunity to explore the molecular genetic basis of cell type identity, diversity, and organization. However, analysis of multi-omics datasets have been largely driven by statistic methods that typically do not engage the deep knowledge of neurobiology and developmental biology. As such, most statistic methods do not distinguish technical noise and methodological biases from biologically relevant signals and relationships, and have limited power in achieving biological discovery and insight. Neurobiology guided feature selection based on inherent physiological and developmental processes is essential to move beyond simple statistical clustering of molecular types towards achieving multi-modal definition of neuron types and revealing their inherent relationships as a taxonomy. We have discovered that transcriptional architectures of synaptic input/output (I/O) communication may underlie the essence of cortical GABAergic neuron identity. We hypothesize that transcriptional architectures of synaptic communication is a general defining feature for brain neuron types. We will test this hypothesis by performing a series of supervised learning and feature selection analyses of publically available and emerging single sc-transcriptomic data sets across brain areas and systems from the BRIAN Initiative Cell Census Network (BICCN). We further hypothesize that cell type transcriptional signatures of synaptic communication is orchestrated by well-defined gene regulatory programs rooted in epignomic landscape. We will test this hypothesis by joint analysis of sc-transcriptome, ATACseq, and DNA methylome dataset of the same cortical cell populations from BICCN to identify co-expressed gene signatures that reliably define cell identity, focusing on signatures of synaptic communication. Based on transcriptomic and epigenomic architecture of synaptic communication, we will further develop neurobiology guided feature selection algorithms to improve and refine the current statistical clustering the cortical transcriptomic types. In addition, we will generate web portal tools for automated classification of transcriptomic cell types. Our study will establish a unified paradigm of neuronal cell type organization in which epignomic landscape configures core gene regulatory programs to shape synaptic communication properties that define cardinal neuron types. Together, this work will establish a molecular genetic framework for understanding neuronal diversity and achieving a biological classification across brain areas and mammalian species.

摘要 了解细胞类型多样性和组织的生物学原理对于破译是必要的 大脑功能的神经回路。最近单细胞转录和转录水平的快速积累 表观基因组数据集为探索细胞的分子遗传学基础提供了前所未有的机会 类型身份、多样性和组织。然而，对多组学数据集的分析在很大程度上受到了推动 通过通常不涉及神经生物学和发育方面的深入知识的统计方法 生物学。因此，大多数统计方法不区分技术噪声和方法偏差 与生物相关的信号和关系，在实现生物发现和 洞察力。神经生物学引导的基于内在生理和发育的特征选择 过程对于超越简单的分子类型统计聚集性向实现 神经元类型的多模式定义，并揭示其作为一种分类的内在关系。我们有 发现突触输入/输出(I/O)通信的转录架构可能是 皮质GABA能神经元同一性的实质。我们假设突触的转录结构 交流是大脑神经元类型的一般定义特征。我们将通过以下方式验证这一假设 执行一系列监督学习和特征选择分析 来自Brian Initiative Cell的跨大脑区域和系统的新出现的单一sc-转录数据集 普查网络(BICCN)。我们进一步假设突触的细胞类型转录特征 交流是由根植于附着体景观的定义良好的基因调控程序来编排的。 我们将通过联合分析sc-转录组、ATACseq和DNA甲基组数据集来检验这一假设 来自BICCN的相同的皮质细胞群体，以确定可靠地定义 细胞识别，重点是突触交流的特征。基于转录组和表观基因组学 架构，我们将进一步发展神经生物学指导的特征选择 改进和细化了当前皮质转录类型的统计聚类算法。在……里面 此外，我们将生成用于转录细胞类型自动分类的门户网站工具。我们的研究 将建立统一的神经细胞类型组织范例，在该范例中，表观景观将被配置 核心基因调控程序，以塑造定义基本神经元的突触通信特性 类型。总之，这项工作将建立一个了解神经元多样性的分子遗传框架。 以及实现跨大脑区域和哺乳动物物种的生物分类。