Defining and perturbing gene regulatory dynamics in the developing human brain

定义和扰乱人类大脑发育中的基因调控动态

基本信息

批准号：
10658683
负责人：
William James Greenleaf
金额：
$ 61.19万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10658683
关键词：
3-Dimensional ATAC-seq Ablation Affect Automobile Driving Binding Binding Proteins Binding Sites Biological Assay Biological Models Brain Brain Diseases CRISPR interference CRISPR/Cas technology Cell model Cells Cerebral cortex ChIP-seq Chromatin Cognition Collaborations Complex Computer Models Cortical Cord DNA Sequence DNA-Directed RNA Polymerase Data Data Set Development Developmental Process Disadvantaged Disease Elements Enhancers Foundations Gene Expression Genes Genetic Genetic Determinism Genetic Transcription Genetic Variation Genome Genomics Genotype Genotype-Tissue Expression Project Goals Human Human Development In Vitro Individual Intellectual functioning disability Joints Learning Link Maps Measures Medical Methods Modality Modeling Molecular Mutation Neural Network Simulation Neurons Nucleic Acid Regulatory Sequences Nucleotides Organ Organoids Output Patients Pattern Phenotype Polymerase Process Productivity Proliferating Property Proteins RNA Recording of previous events Regulation Regulator Genes Regulatory Element Research Personnel Specific qualifier value Spinal Cord System Testing Time Tissue Differentiation Training Trans-Activators Transcription Factor 3 Untranslated RNA Validation Variant Work XCL1 gene autism spectrum disorder brain tissue cell type combinatorial de novo mutation deep learning developmental disease fetal gene regulatory network genetic variant genomic data human model insight knock-down method development multiple input multiple output multiple omics network models neural model neural network neurodevelopment neuropsychiatry predictive modeling programs single cell analysis syntax transcription factor transcriptome sequencing

3-Dimensional ATAC-seq Ablation Affect Automobile Driving Binding Binding Proteins Binding Sites Biological Assay Biological Models Brain Brain Diseases CRISPR interference CRISPR/Cas technology Cell model Cells Cerebral cortex ChIP-seq Chromatin Cognition Collaborations Complex Computer Models Cortical Cord DNA Sequence DNA-Directed RNA Polymerase Data Data Set Development Developmental Process Disadvantaged Disease Elements Enhancers Foundations Gene Expression Genes Genetic Genetic Determinism Genetic Transcription Genetic Variation Genome Genomics Genotype Genotype-Tissue Expression Project Goals Human Human Development In Vitro Individual Intellectual functioning disability Joints Learning Link Maps Measures Medical Methods Modality Modeling Molecular Mutation Neural Network Simulation Neurons Nucleic Acid Regulatory Sequences Nucleotides Organ Organoids Output Patients Pattern Phenotype Polymerase Process Productivity Proliferating Property Proteins RNA Recording of previous events Regulation Regulator Genes Regulatory Element Research Personnel Specific qualifier value Spinal Cord System Testing Time Tissue Differentiation Training Trans-Activators Transcription Factor 3 Untranslated RNA Validation Variant Work XCL1 gene autism spectrum disorder brain tissue cell type combinatorial de novo mutation deep learning developmental disease fetal gene regulatory network genetic variant genomic data human model insight knock-down method development multiple input multiple output multiple omics network models neural model neural network neurodevelopment neuropsychiatry predictive modeling programs single cell analysis syntax transcription factor transcriptome sequencing

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项目摘要

SUMMARY Human brain development represents perhaps the pinnacle of complex organ specification, and an ideal model system for understanding 1) how normal development can produce all the cell types necessary for human cognition and 2) how genetic variation can perturb this process and lead to disease. We will generate large-scale single cell data sets to develop accurate models capable of predicting the effects of both genetic changes to regulatory elements and perturbations to trans-acting regulatory factors on gene expression during the complex developmental process of human brain development. We will study two highly medically relevant, human, in vitro, temporally dynamic differentiation systems that faithfully recapitulate fetal differentiation patterns: hiPSC- derived cerebral cortical and spinal cord organoids. For each of these differentiation trajectories, we will work in distinct aims toward mapping, perturbing, modeling, validating, and learning: Mapping: we will generate systematic, single cell multi-omic (RNA-seq, ATAC-seq, and protein quantification) data to map regulatory elements, chromatin contacts, RNA polymerase, protein binding, and gene expression through differentiation of hiPSCs to brain tissue. Perturbing: We will use CRISPR-based methods to comprehensively identify TFs required for differentiation and map the single-cell gene regulatory and expression impact of perturbing a subset of these factors at multiple time points across these differentiation trajectories. Modeling: We will develop multi- input nucleotide-resolved neural networks to learn dynamic gene regulatory networks using these mapping and perturbation data sets. These models will aim to understand the changing landscape of regulation and grammars of transcription factor motifs over differentiation time, and will predict both chromatin and gene expression effects expected from genetic perturbations. Validating: We will apply our network models to identify, investigate, and experimentally test perturbations relevant to understanding disease variation, by knocking down transcription factors, perturbing regulatory elements, and editing disease-associated noncoding variants. Learning and comparing: Finally, we will extract and test molecular properties of transcription factor function from validated models, and compare experimental and modeling approaches to better understand accuracy, advantages, and disadvantages. Successful completion of our project will provide mechanistic interpretations for how genetic variants may impact development (by disrupting regulatory element that in turn disrupt gene expression) in brain development. Our Stanford team comprises a diverse team of investigators with a history of productive collaboration, and with expertise in genomics methods development (Greenleaf, Engreitz), single cell methods and analysis (Greenleaf, Pasca), 3D cellular models of human brain (Pasca), and deep learning for genomic data sets (Kundaje). The output of this project will be a gold-standard data set defining the trans-acting factor network driving development, and a model capturing these complex dynamics capable of quantitatively linking changes in genotype to effects on genome function and phenotype in brain and spinal cord development.

概括人脑发育可能代表了复杂器官规格的顶峰，也是理想的模型理解的系统1）正常开发如何产生人类所需的所有细胞类型认知和2）遗传变异如何扰动这一过程并导致疾病。我们将产生大规模单细胞数据集以开发能够预测两种遗传变化对的准确模型调节元素和对复合物过程中基因表达的跨作用调节因素的扰动人脑发育的发展过程。我们将研究两个高度相关的人类忠实地概括胎儿分化模式的体外动态分化系统：hipsc- 衍生的脑皮质和脊髓器官。对于这些差异轨迹中的每一个，我们将在绘制，扰动，建模，验证和学习的不同目标：映射：我们将生成系统的，单细胞多OMIC（RNA-SEQ，ATAC-SEQ和蛋白质定量）数据以绘制调节元素，染色质接触，RNA聚合酶，蛋白质结合和基因表达通过分化 HIPSC到脑组织。扰动：我们将使用基于CRISPR的方法全面识别TF 分化和绘制单细胞基因调节和表达影响的影响这些因素在这些分化轨迹的多个时间点上。建模：我们将开发多种输入核苷酸分辨神经网络，使用这些映射和扰动数据集。这些模型将旨在了解监管和语法的不断变化的格局在分化时间内转录因子基序的基序，并将预测染色质和基因表达效应预期的是遗传扰动。验证：我们将应用我们的网络模型来识别，调查和通过击倒转录，实验测试与理解疾病变异有关的扰动因素，扰动调节元素以及编辑与疾病相关的非编码变体。学习和比较：最后，我们将从经过验证的转录因子函数提取和测试分子特性模型，并比较实验和建模方法，以更好地了解准确性，优势和缺点。成功完成我们项目将为遗传如何提供机械解释变体可能会影响大脑中大脑中基因表达的调节元件的发展（通过破坏调节元件）发展。我们的斯坦福大学团队组成了一个多元化的调查员团队，具有富有成效的历史合作，以及基因组方法开发方面的专业知识（Greenleaf，Engreitz），单细胞方法和分析（Greenleaf，Pasca），人脑的3D细胞模型（PASCA）和基因组深度学习数据集（Kundaje）。该项目的输出将是定义反式因子的金标准数据集网络驱动开发以及捕获这些复杂动力学的模型，能够定量链接基因型对大脑和脊髓发育中基因组功能和表型影响的影响的变化。