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:10611419
- 负责人:
- 金额:$ 47.39万
- 依托单位:
- 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
- 来源:
- 关键词:Bar CodesBiochemicalBiological AssayBrainCRISPR/Cas technologyCell LineCellsCellular MorphologyClustered Regularly Interspaced Short Palindromic RepeatsComputer AnalysisComputing MethodologiesCongenital AbnormalityDataDevelopmentDisease modelEthical IssuesEventForebrain DevelopmentFoundationsGene ExpressionGene Expression ProfileGenerationsGenesGeneticGenetic TranscriptionGoalsGuide RNAHumanIn VitroIndividualJointsLHX2 geneLentils - dietaryLibrariesMapsMathematicsMethodsMidbrain structureMitoticModelingMolecularNeocortexNeurogliaNeurologicOrganismPhysicsProbabilityProsencephalonRNARadialRoleSOX11 geneSignal TransductionStatistical MethodsSystemTechniquesTestingViralWNT Signaling PathwayWorkcell typecomputerized toolscourse developmentdevelopmental diseasedirected differentiationgene networkgene regulatory networkglial cell developmenthuman datahuman embryonic stem cellhuman embryonic stem cell linehuman fetus tissuehuman stem cellshuman tissueimprovedin vivoinsightmachine learning methodmodel buildingmodel organismmolecular markerneocorticalnerve stem cellneurodevelopmentneuroregulationnovelpredictive modelingprogenitorresponsesingle cell sequencingstatisticsstem cell differentiationstem cellssubventricular zonesuccesstime intervaltooltranscription factor
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控网络(GRN)如何
在人脑发育过程中产生细胞类型的多样性。该提案的重点是
确定人类中独特富集的关键祖细胞类型是如何产生的。这样一个
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. 开发人体组织的伦理问题,2. 计算
和实验技术来确定祖细胞状态和状态转换的序列,从而给出
细胞类型的多样性,3.基因调控网络定量模型构建的难度
在没有数据的情况下确定数千个生化常数。该提案的做法是
建立必要的计算、数学和实验框架来克服这些挑战。
为了重现人类早期大脑发育,该提案将采用体外人类胚胎干细胞
差异化系统。为了获得底层基因调控网络的快照,高通量单
课程期间将采用细胞测序来获取数千个单细胞的转录谱
的发展。推断细胞状态和细胞状态转换的顺序的挑战将被克服
通过一种新颖的统计方法来获得细胞状态的联合概率分布、转换序列
以及一组关键基因,其动态反映这些状态和转变。推论将被检验
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类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和
从统计数据中进行集成建模,构建进行概率预测的模型。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,这些分布函数将用于生成有关响应的概率预测
潜在的 GRN 的扰动。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,模型将得到迭代改进。这
该提案的成功将产生第一个控制基因调控网络的定量模型
前脑和oRG祖细胞的产生。如果实现的话,这项工作将代表一项重大成果
深入了解导致人类大脑不成比例旋转的分子和细胞事件。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Sharad Ramanathan其他文献
Sharad Ramanathan的其他文献
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{{ truncateString('Sharad Ramanathan', 18)}}的其他基金
Mechanisms of synaptic dopamine signaling in the control of behavior
突触多巴胺信号传导在行为控制中的机制
- 批准号:
10393622 - 财政年份:2020
- 资助金额:
$ 47.39万 - 项目类别:
Mechanisms of synaptic dopamine signaling in the control of behavior
突触多巴胺信号传导在行为控制中的机制
- 批准号:
10206280 - 财政年份:2020
- 资助金额:
$ 47.39万 - 项目类别:
Mechanisms of synaptic dopamine signaling in the control of behavior
突触多巴胺信号传导在行为控制中的机制
- 批准号:
10032939 - 财政年份:2020
- 资助金额:
$ 47.39万 - 项目类别:
Mechanisms of Synaptic Dopamine Signaling in the Control of Behavior
突触多巴胺信号传导在行为控制中的机制
- 批准号:
10605347 - 财政年份:2020
- 资助金额:
$ 47.39万 - 项目类别:
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:
10380809 - 财政年份:2020
- 资助金额:
$ 47.39万 - 项目类别:
Measuring and modeling the dynamics of patterning in human stem cells
人类干细胞模式动态的测量和建模
- 批准号:
10318976 - 财政年份:2019
- 资助金额:
$ 47.39万 - 项目类别:
Measuring and modeling the dynamics ofpatterning in human stem cells
测量和模拟人类干细胞模式的动态
- 批准号:
10734567 - 财政年份:2019
- 资助金额:
$ 47.39万 - 项目类别:
Measuring and modeling the dynamics of patterning in human stem cells
人类干细胞模式动态的测量和建模
- 批准号:
10084170 - 财政年份:2019
- 资助金额:
$ 47.39万 - 项目类别:
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