Unraveling the Genetic Programs Engaged in ASD Neurons Through Coupled Transcriptomic and Phenotypic Readouts

通过耦合转录组和表型读数揭示参与自闭症谱系障碍神经元的遗传程序

• 批准号: 10521895
• 负责人: Samouil Farhi
• 金额: $ 76.14万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-10 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10521895
• 关键词: Affect; Binding; Biological Models; Biological Process; CRISPR interference; Cell Culture Techniques; Cell Differentiation process; Cell model; Cells; Child; Clustered Regularly Interspaced Short Palindromic Repeats; Code; Complement; Coupled; Coupling; Data; Data Analyses; Data Set; Disease; Disease model; Electrophysiology (science); Gene Expression; Gene Expression Profile; Genes; Genetic; Genetic Diseases; Genetic Risk; Genetic Transcription; Genetic study; Human; Human Genetics; Intervention; Investigation; Joints; Lead; Link; Machine Learning; Measurement; Measures; Mental disorders; Methods; Molecular; Morphology; Neurobiology; Neurons; Ontology; Optics; Pathway interactions; Patients; Phenotype; Research Personnel; Synapses; System; Techniques; Testing; Undifferentiated; Variant; Work; autism spectrum disorder; autoencoder; case control; clinical development; cohort; data integration; experience; experimental study; follow-up; genetic signature; genetic variant; genomic locus; high dimensionality; human pluripotent stem cell; innovation; innovative technologies; insight; knock-down; multimodality; multiple omics; multiplexed imaging; programs; protein protein interaction; rare variant; recruit; response; risk variant; scale up; stem cells; success; tool; transcriptomics

Autism spectrum disorders (ASD) are genetically diverse, characterized by both rare variants of large effect size and common variants of small effect size. Identifying the molecular mechanisms resulting from these variants presents a key challenge for the development of clinical interventions. Human pluripotent stem-cell derived neurons (hPSC-Ns) allow studies against a human genetic background, and show altered morphology and electrophysiology in ASD conditions. However, identifying mechanisms remains difficult with small numbers of lines, especially for common genetic variants. To overcome this challenge, we will leverage multi-omic characterization of hPSC-Ns perturbed with CRISPRi knockdown of both large effect size ASD risk genes and genes related to neuronal morphology (Aim 1) and electrophysiology (Aim 2). We will complement these screens with a characterization (Aim 3) of a larger, diverse cohort of 46 ASD lines and 46 matched controls which do not harbor coding variants in the genes perturbed in the previous Aims. An integrative analysis of this data (Aim 4) will generate interpretable genetic signatures related to each of these phenotypes and will show how these signatures interact with ASD risk genes. This approach is made possible by new techniques for pooled stem cell culture developed in Dr. Ralda Nehme’s lab, high content optical profiling methods developed by Dr. Samouil Farhi’s team, and data integration tools developed by Dr. Ernest Fraenkel’s group. The overall project will provide a basic neurobiological understanding of hPSC-Ns; provide valuable insight into how both common and rare variants induce observed cell-intrinsic phenotypes; and define an analytic framework and genetic signatures which can be used to understand mechanistic recruitment of new genetic risk loci and other psychiatric diseases.

自闭症谱系障碍 (ASD) 具有遗传多样性，其特点是具有大效应量的罕见变异 以及小效应量的常见变体。识别这些变异产生的分子机制 对临床干预措施的发展提出了关键挑战。人类多能干细胞来源 神经元 (hPSC-Ns) 允许针对人类遗传背景进行研究，并显示形态和结构的改变 ASD 条件下的电生理学。然而，由于数量较少，确定机制仍然很困难。 系，尤其是常见的遗传变异。为了克服这一挑战，我们将利用多组学 CRISPRi 敲除大效应 ASD 风险基因和扰乱 hPSC-N 的特征 与神经元形态（目标 1）和电生理学（目标 2）相关的基因。我们将补充这些屏幕 具有 46 个 ASD 系和 46 个匹配对照的更大、多样化群体的特征（目标 3） 在之前的目标中受到干扰的基因中存在编码变异。对该数据进行综合分析（目标 4） 将生成与每个表型相关的可解释的遗传特征，并将显示这些表型如何 特征与 ASD 风险基因相互作用。这种方法是通过混合干细胞新技术实现的 Ralda Nehme 博士实验室开发的培养物，Samouil 博士开发的高内涵光学分析方法 Farhi 团队以及 Ernest Fraenkel 博士团队开发的数据集成工具。整个项目将提供 对 hPSC-N 的基本神经生物学理解；提供有关常见和罕见的情况的宝贵见解 变异体诱导观察到的细胞固有表型；并定义分析框架和遗传特征 它可用于了解新遗传风险位点和其他精神疾病的机制招募。