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）是一种病因不明的高度遗传性神经发育障碍， 有限的有效治疗选择，影响数百万人。我们的研究团队长期以来 致力于了解ASD的原因和大脑发育的分子过程， 功能和病理学。我们将利用这一经验，将最新的分子技术应用于 新的ASD患者脑组织库，以创建最大和最详细的分析， ASD的分子后果。基因破坏性从头突变的遗传分析已经确定了超过 与ASD相关的100个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ASD患者脑组织的分析 确定了一组下调的神经元通讯基因，与ASD相关基因重叠， 以及一组上调的神经胶质基因，其不与ASD相关基因或变体重叠。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 我建议分析40名ASD患者和40名未受影响的对照者的死后大脑样本， 从自闭症大脑网络生物库，以评估发生的分子变化。我们将使用全基因组 测序以鉴定先前与ASD相关的基因中的基因破坏性变体，并鉴定罕见的和 可能改变基因表达或剪接的常见变异。在组织样本中， 在40例病例和40例对照中，我们将使用最近开发的单核方法进行RNA-seq， ATAC-seq在单细胞分辨率下鉴定ASD相关的基因调控和表达变化， 细胞类型和大脑区域。对于20例病例和20例对照的前额叶皮层组织样本，我们将 我还使用尖端的单核长读RNA-seq（Iso-seq），沿着批量组织RNA-seq，进行深入研究。 分析ASD病例和对照组之间基因亚型的差异。最后，我们将评估单核 基因表达在从多能干细胞生长的脑类器官中变化，这些干细胞被编辑为包含三个突变 ASD相关基因整合这些数据，我们将描绘与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、癫痫发作状态和基因型而变化。我们将使用 RNAscope原位杂交验证了我们观察到的分子和细胞组成的变化， 基于慢病毒的大规模平行报告分析，以测试邻近的调节区或变体的功能 与ASD相关的基因表达差异，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 并提供可能发展成为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.