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

摘要 这项提议的长期目标是定量地了解基因调控网络(GRN)是如何 在人脑发育过程中产生细胞类型的多样性。这项提议的重点是 确定在人类中唯一丰富的关键祖细胞类型是如何产生的。这样的一种 理解对于揭示人类发育性疾病的机制是至关重要的。一共有三个 实现这一目标的挑战：1.开发人体组织的伦理问题，2.计算 和实验技术，以确定祖细胞状态和状态转换的序列，从而提供 细胞类型的多样性；3.基因调控网络的定量模型难以建立。 在缺乏数据来确定数千个生化常数的情况下。该提案的方法是 建立必要的计算、数学和实验框架，以克服这些挑战。 为了概括人类早期的大脑发育，该提案将使用体外培养的人类胚胎干细胞 差异化系统。为了获得底层基因调控网络的快照，高通量单个 在这个过程中，细胞测序将被用来获得数千个单细胞的转录图谱 关于发展的问题。将克服推断单元状态和单元状态转换序列的挑战 通过一种新的统计方法获得细胞状态、跃迁序列的联合概率分布 以及一组关键的基因，它们的动态反映了这些状态和转变。这些推论将通过以下方式进行检验 映射到体内数据并使用病毒谱系追踪。前脑和外径向神经胶质细胞的起源 因此，在发育中的人类前脑中独特丰富的祖细胞将被确定。 建立预测模型的挑战将通过使用理论物理和 从统计学中进行集成建模，以建立做出概率预测的模型。通过使用可用的 数据作为模型的约束条件，该框架将提取所有参数的联合概率分布 模特。然后，将使用这些分布函数来产生关于响应的概率预测 潜在的GRN对扰动的影响。高概率预测将通过微扰进行实验检验 在大脑发育早期的基因表达和信号传递，该模型将得到迭代改进。这个 这一提议的成功将导致第一个控制基因调控网络的定量模型 前脑和组织祖细胞的生成。因此，如果这项工作得以实现，将是一项重大的 洞察导致人类大脑不成比例旋转的分子和细胞事件。

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.