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

基本信息

批准号：
10380809
负责人：
Sharad Ramanathan
金额：
$ 47.39万
依托单位：
HARVARD UNIVERSITY
依托单位国家：
美国
项目类别：
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

项目摘要

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.

抽象的该提案的长期目标是定量了解基因调节网络（GRNS）如何在人脑发育过程中产生细胞类型的多样性。该提议的重点是确定如何产生在人类中唯一富集的关键祖细胞类型。这样的理解对于揭示人类发育疾病的机制至关重要。有三个实现这一目标的挑战：1。与发展人体组织合作的道德问题，2。计算和实验技术，以确定祖细胞态和状态过渡的序列升至细胞类型的多样性，3。在构建基因调节网络的定量模型方在没有数据的情况下，可以确定数千种生化常数。该提议的方法是建立必要的计算，数学和实验框架来克服这些挑战。为了概括早期人脑发育，该提案将采用体外人类胚胎干细胞分化系统。为了获得基础基因调节网络的快照，高吞吐量单个在课程中，将采用细胞测序来获得数千个单元的转录曲线发展。将克服推断细胞态和细胞状态过渡序列的挑战通过一种新的统计方法来获得细胞态的联合概率分布，过渡序列以及一个动力学反映这些状态和过渡的关键基因集。这些推论将通过映射到体内数据并使用病毒谱系跟踪。前脑和外部径向神经胶质细胞的起源（org）因此，将确定在发展中的人类前脑中独特地富集的祖细胞。建立预测模型的挑战将通过使用理论物理学和从统计学到建立制造概率预测的模型的集合建模。通过使用可用的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模型。然后，这些分布功能将用于产生有关响应的概率预测对扰动的基础。高概率预测将通过扰动进行实验测试早期大脑发育过程中的基因表达和信号传导将得到改善。这该提案的成功将导致控制基因调节网络的第一个定量模型前脑和组织祖细胞的产生。如果实现了，这项工作将代表一个主要对分子和细胞事件的洞察力，导致不成比例的人类大脑。