Determining lineage decisions and gene regulatory networks governing the generation of key progenitor cell types during early human brain development

确定人类早期大脑发育过程中控制关键祖细胞类型生成的谱系决策和基因调控网络

• 批准号: 10611419
• 负责人: Sharad Ramanathan
• 金额: $ 47.39万
• 依托单位: HARVARD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-07 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10611419
• 关键词: Bar Codes; Biochemical; Biological Assay; Brain; CRISPR/Cas technology; Cell Line; Cells; Cellular Morphology; Clustered Regularly Interspaced Short Palindromic Repeats; Computer Analysis; Computing Methodologies; Congenital Abnormality; Data; Development; Disease model; Ethical Issues; Event; Forebrain Development; Foundations; Gene Expression; Gene Expression Profile; Generations; Genes; Genetic; Genetic Transcription; Goals; Guide RNA; Human; In Vitro; Individual; Joints; LHX2 gene; Lentils - dietary; Libraries; Maps; Mathematics; Methods; Midbrain structure; Mitotic; Modeling; Molecular; Neocortex; Neuroglia; Neurologic; Organism; Physics; Probability; Prosencephalon; RNA; Radial; Role; SOX11 gene; Signal Transduction; Statistical Methods; System; Techniques; Testing; Viral; WNT Signaling Pathway; Work; cell type; computerized tools; course development; developmental disease; directed differentiation; gene network; gene regulatory network; glial cell development; human data; human embryonic stem cell; human embryonic stem cell line; human fetus tissue; human stem cells; human tissue; improved; in vivo; insight; machine learning method; model building; model organism; molecular marker; neocortical; nerve stem cell; neurodevelopment; neuroregulation; novel; predictive modeling; progenitor; response; single cell sequencing; statistics; stem cell differentiation; stem cells; subventricular zone; success; time interval; tool; transcription factor

Abstract The long term goal of this proposal is to quantitatively understand how gene regulatory networks (GRNs) generate the diversity of cell types during the development of the human brain. The focus of this proposal is to determine how key progenitor cell types that are uniquely enriched in humans are generated. Such an understanding is essential for uncovering the mechanisms of human developmental diseases. There are three challenges to achieving this goal: 1. Ethical issues in working with developing human tissue, 2. Computational and experimental techniques to determine the sequence of progenitor cell states and state transitions that give rise to the diversity of cell types, 3. the difficultly in building quantitative models of the gene regulatory networks in the absence of data to determine the thousands of biochemical constants. The approach of the proposal is to build the necessary computational, mathematical and experimental framework to overcome these challenges. To recapitulate early human brain development, the proposal will employ an in vitro human embryonic stem cell differentiation system. To obtain snapshots of the underlying gene regulatory network, high throughput single cell sequencing will be employed to obtain transcriptional profiles of thousands of single cells during the course of development. The challenge of inferring the sequence of cell states and cell state transitions will be overcome through a novel statistical method to obtain a joint probability distribution of the cell states, sequence of transitions and a key set of genes whose dynamics reflect these states and transitions. The inferences will be tested by mapping to in vivo data and using viral lineage tracing. The origins of forebrain and outer radial glial cells (oRG) progenitors uniquely enriched in the developing human forebrain will thus be determined. The challenge of building predictive models will be overcome by using methods from theoretical physics and ensemble modeling from statistics to build models that make probabilistic predictions. By using the available data as constraints on the model, the framework will extract joint probability distributions of all the parameters of the model. These distribution functions will then be used to produce probabilistic predictions about the responses of the underlying GRNs to perturbations. High probability predictions will be tested experimentally by perturbing gene expression and signaling during early brain development and the model will be iteratively improved. The success of this proposal will result in the first quantitative model of the gene regulatory network controlling the generation of forebrain and the oRG progenitor cells. If achieved, this work therefore would represent a major insight into the molecular and cellular events that give rise to the disproportionately gyrated human brain.

摘要 这项提案的长期目标是定量地了解基因调控网络（GRNs） 在人类大脑发育过程中产生了细胞类型的多样性。本提案的重点是 确定在人类中独特富集的关键祖细胞类型是如何产生的。这样的 理解是揭示人类发育疾病机制的关键。有三 实现这一目标的挑战：1。在开发人体组织工作中的伦理问题，2。计算 和实验技术来确定祖细胞状态和状态转换的顺序， 增加细胞类型的多样性，3.建立基因调控网络定量模型的困难 在缺乏数据来确定数千个生化常数的情况下。建议的方法是 建立必要的计算，数学和实验框架，以克服这些挑战。 为了概括早期人类大脑的发育，该提案将采用体外人类胚胎干细胞 分化系统为了获得潜在基因调控网络的快照，高通量单克隆抗体可以被用来检测基因的表达。 细胞测序将用于获得过程中数千个单细胞的转录谱 发展质量和将克服推断细胞状态和细胞状态转变的序列的挑战 通过一种新的统计方法，得到了细胞状态、跃迁序列 以及一组关键基因，它们的动态反映了这些状态和转变。这些推论将由以下人员进行检验： 映射到体内数据并使用病毒谱系追踪。前脑和外放射状胶质细胞（oRG）的起源 因此将确定在发育中的人前脑中独特富集的祖细胞。 建立预测模型的挑战将通过使用理论物理学方法来克服， 从统计学的集合建模来构建做出概率预测的模型。通过使用可用 数据作为模型的约束，框架将提取所有参数的联合概率分布， 该模型然后，这些分布函数将用于生成关于响应的概率预测 潜在的GRNs的扰动。高概率的预测将通过扰动实验来检验。 基因表达和信号传导，并且该模型将被迭代地改进。的 这一建议的成功将导致控制基因调控网络的第一个定量模型。 前脑和oRG祖细胞的产生。如果能够实现，这项工作将是一项重大的 深入了解导致人类大脑不成比例旋转的分子和细胞事件。