Assessing Genomic, Regulatory and Transcriptional Variation at Single Nuclei Resolution in the Brains of Individuals with Autism Spectrum Disorder

摘要 自闭症谱系障碍（ASD）是一种高度遗传性的神经发育障碍，病因不明，与 影响数百万人的有效治疗选择有限。我们的研究团队拥有长期 致力于了解自闭症谱系障碍的原因和大脑发育的分子过程， 功能、病理学。我们将利用这一经验将最新的分子技术应用于来自 新的自闭症谱系障碍患者脑组织存储库，可对自闭症谱系障碍患者的脑组织进行最大、最详细的分析 ASD 的分子后果。基因破坏性从头突变的遗传分析已发现超过 一百个与 ASD 相关的基因，具有三个主要功能组：基因表达的调节、 神经元通讯和细胞骨架。先前对自闭症谱系障碍患者脑组织的分析表明 确定了一组下调的神经元通讯基因，与 ASD 相关基因重叠， 以及一组与 ASD 相关基因或变异体不重叠的上调神经胶质基因。目前还不清楚 这些变化是否反映了细胞组成或细胞功能的改变以及它们与遗传因素的关系。我们 提议分析 40 名自闭症谱系障碍患者和 40 名未受影响的对照者的死后大脑样本，来源 来自自闭症 BrainNet BioBank 的数据，以评估发生的分子变化。我们将使用全基因组 测序以识别先前与自闭症谱系障碍相关的基因中的基因破坏性变异，并识别罕见的和 可能改变基因表达或剪接的常见变异。在组织样本中，前额皮质和纹状体 在 40 个病例和 40 个对照中，我们将使用最近开发的单核方法来进行 RNA-seq 和 单细胞分辨率的 ATAC-seq 可识别特定个体中与 ASD 相关的基因调控和表达变化 细胞类型和大脑区域。对于来自 20 个病例和 20 个对照的前额叶皮层的组织样本，我们将 还使用尖端的单核长读长 RNA-seq (Iso-seq) 以及大量组织 RNA-seq，进行深入研究 分析自闭症谱系障碍病例和对照之间基因亚型的差异。最后，我们将评估单核如何 由经过编辑以包含三种突变的多能干细胞生长的大脑类器官中，基因表达存在差异 ASD 相关基因。整合这些数据，我们将分析与 ASD 相关的分子变化， 评估这些变化如何因细胞类型、大脑区域、年龄、性别、癫痫状态和基因型而异。我们将使用 RNAscope 原位杂交可验证我们观察到的分子和细胞组成变化以及 基于慢病毒的大规模并行报告基因检测，用于测试附近调控区域或变体的功能 对具有 ASD 相关表达差异的基因进行分析，以验证这些影响并评估因果关系。我们希望 这些见解将为理解 ASD 的异质性和神经生物学特征提供基础 并提供可开发为 ASD 模型未来生物标志物的分子特征 系统。

ABSTRACT Autism spectrum disorder (ASD) is a highly heritable neurodevelopmental disorder of unknown etiology and with limited effective therapeutic options that affects millions of individuals. Our research team has a longstanding commitment to understanding the cause of ASD and the molecular processes underlying brain development, function, and pathology. We will use this experience to apply the latest molecular techniques to samples from a new repository of brain tissue from individuals with ASD to create the largest and most detailed analysis of the molecular consequences of ASD. Genetic analyses of gene disrupting de novo mutations have identified over one hundred genes associated with ASD with three main functional groups: regulation of gene expression, neuronal communication, and cytoskeleton. Prior analyses of brain tissue from individuals with ASD have identified a group of downregulated neuronal communication genes, that overlap with ASD-associated genes, and a group of upregulated glial genes that do not overlap with ASD-associated genes or variants. It is unclear if these changes reflect altered cell composition or cell function and how they relate to genetic factors. We propose to analyze post-mortem brain samples from 40 individuals with ASD and 40 unaffected controls, sourced from the Autism BrainNet BioBank, to assess the molecular changes that occur. We will use whole-genome sequencing to identify gene disruptive variants in genes previously associated with ASD and to identify rare and common variants that may alter gene expression or splicing. In tissue samples the prefrontal cortex and striatum in from 40 cases and 40 controls, we will use recently developed single-nuclei methods to perform RNA-seq and ATAC-seq at single-cell resolution to identify ASD-related changes in gene regulation and expression in specific cell types and brain regions. For tissue samples from the prefrontal cortex of 20 cases and 20 controls we will also use cutting-edge single nuclei long-read RNA-seq (Iso-seq), along with bulk tissue RNA-seq, for an in-depth analysis of how gene isoforms differ between ASD cases and controls. Finally, we will assess how single-nuclei gene expression varies in brain organoids grown from pluripotent stem cells edited to contain mutations in three ASD-associated genes. Integrating these data, we will profile the molecular changes associated with ASD and assess how these changes vary by cell type, brain region, age, sex, seizure status, and genotype. We will use RNAscope in situ hybridization to validate the molecular and cell composition changes we observe and a lentivirus-based massively parallel reporter assay to test the function of regulatory regions or variants in proximity to genes with ASD-related differences in expression to validate these effects and assess causality. We hope that these insights will provide a basis for understanding the heterogeneity of ASD and the neurobiological features of this disorder and provide molecular signatures that could be developed into future biomarkers for ASD model systems.