Defining and perturbing gene regulatory dynamics in the developing human brain

总结 人类大脑的发育也许代表了复杂器官特化的顶峰， 了解1）正常发育如何产生人类所需的所有细胞类型的系统 2）遗传变异如何干扰这一过程并导致疾病。我们将产生大规模的 单细胞数据集，以开发准确的模型，能够预测遗传变化的影响， 调控元件和干扰反式作用调控因子对基因表达的复杂过程中 人类大脑发育的过程。我们将研究两个高度医学相关的，人类， 忠实地概括胎儿分化模式的体外时间动态分化系统：hiPSC- 衍生的大脑皮质和脊髓类器官。对于这些差异化轨迹中的每一个，我们将在 映射、扰动、建模、验证和学习的不同目标：映射：我们将生成 系统性、单细胞多组学（RNA-seq、ATAC-seq和蛋白质定量）数据，以绘制调控 元件，染色质接触，RNA聚合酶，蛋白质结合，以及通过分化的基因表达。 hiPSC到脑组织。扰动：我们将使用基于CRISPR的方法来全面识别TF 所需的分化和映射的单细胞基因调控和表达的影响，扰乱一个子集 这些因素在这些分化轨迹的多个时间点的变化。建模：我们将开发多个 输入核苷酸分辨神经网络，以使用这些映射来学习动态基因调控网络， 扰动数据集。这些模型旨在了解规则和语法的变化情况 的转录因子基序在分化时间，并将预测染色质和基因表达的影响 可能是遗传变异造成的验证：我们将应用我们的网络模型来识别，调查， 通过敲低转录，实验性地测试与理解疾病变异相关的扰动， 因子、干扰调控元件和编辑疾病相关的非编码变体。学习和 比较：最后，我们将从验证的转录因子中提取并测试其功能的分子特性， 模型，并比较实验和建模方法，以更好地了解准确性，优势， 缺点我们的项目的成功完成将为遗传学如何提供机械解释。 变异可能影响大脑发育（通过破坏调节元件，进而破坏基因表达） 发展我们的斯坦福大学团队由一个多元化的研究团队组成， 合作，并在基因组学方法开发（Greenleaf，Engreitz），单细胞方法 和分析（Greenleaf，Pasca），人类大脑的3D细胞模型（Pasca），以及基因组的深度学习 数据集（Kundaje）。这个项目的输出将是一个黄金标准的数据集定义的trans-acting因素 网络驱动发展，以及一个捕捉这些复杂动态的模型，能够定量地将 基因型的变化对脑和脊髓发育中基因组功能和表型的影响。

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.