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.

抽象的 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 神经回路的脑功能。单细胞转录组和 表观基因组数据集为探索细胞的分子遗传基础提供了前所未有的机会 类型身份，多样性和组织。但是，对多委员会数据集的分析已在很大程度上驱动 通过统计方法，通常不参与神经生物学和发展的深刻知识 生物学。因此，大多数统计方法没有区分技术噪声和方法论偏见 生物学相关的信号和关系，并且在实现生物学发现和 洞察力。神经生物学指导的特征选择基于固有的生理和发育 流程对于超越分子类型的简单统计聚类至关重要 神经元类型的多模式定义，并揭示其固有的关系作为分类学。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能神经元身份的本质。我们假设突触的转录体系结构 沟通是脑神经元类型的一般定义特征。我们将通过 进行一系列监督的学习和功能选择分析，并进行公开可用的和 来自Brian Initiative Cell的大脑区域和系统的新兴单个SC转录数据集 人口普查网络（BICCN）。我们进一步假设突触的细胞类型转录特征 沟通由定义的基因调节程序精心策划，这些基因调节程序植根于emendomic景观。 我们将通过对SC转录组，ATACSEQ和DNA甲基甲基数据集的联合分析来检验这一假设。 来自BICCN的同一皮质细胞群来识别可靠定义的共表达基因特征 细胞身份，专注于突触通信的签名。基于转录组和表观基因组学 突触传播的架构，我们将进一步发展神经生物学指导性特征选择 改进和完善当前统计聚类的算法，这些统计群体类型的统计聚类。在 此外，我们还将生成用于转录组细胞类型的自动分类的Web门户工具。我们的研究 将建立一个神经元细胞类型组织的统一范式，其中构象景观配置 核心基因调节程序，以塑造定义基本神经元的突触传播特性 类型。这项工作将共同建立一个分子遗传框架，以理解神经元的多样性 并在大脑区域和哺乳动物物种之间实现生物学分类。