Multiscale transcriptional architecture of the human brain

在美国，将近一半的人在一生中的某个时候会达到精神障碍的诊断标准。 虽然大多数精神障碍是中度到高度遗传的，但其潜在的遗传结构是 复杂.这种复杂性阻碍了识别生物标志物和为患者开发诊断测试和治疗策略的努力。尽管存在这种复杂性，但有理由预期， 会改变大脑中的基因表达。许多研究都试图 在尸检脑样本中识别精神障碍的转录表型，但大量组织的使用导致了样本和数据集之间的可变细胞组成，模糊了转录表型及其细胞起源。单细胞（SC）和单核（SN）转录调控的研究进展 分析为解决这一问题提供了一种新的方法，但这些技术也有技术和统计上的局限性。因此，我们对精神障碍风险因素如何干扰 人类大脑中的基因表达。识别这种扰动的一个主要障碍是缺乏 分析框架，用于预测人类大脑中的基因表达，而不管采样策略如何。的 本申请的目的是检验神经生物学转录组的协方差结构提供这样的框架的假设。在目标1中，我们将评估人脑样本批量和SC/SN基因表达数据中细胞类型特征和转录协变的一致性。在目标2中，我们将 阐明最佳的实验和分析参数，以确定可重复的转录签名， 人类大脑样本中罕见的细胞类型/状态。在目标3中，我们将利用大量可重复的转录共变异和SC/SN基因表达数据来鉴定精神障碍的细胞和分子表型 和相应的基因变异。总的来说，拟议的研究将通过促进 通过荟萃分析和预测建模，神经生物学研究的严谨性和可重复性，同时推进识别精神障碍细胞和分子表型的新策略， 可以容易地应用于其它分子种类和病理条件。

In America, nearly half of individuals will meet diagnostic criteria for a mental disorder at some point in their life. Although most mental disorders are moderately to highly heritable, their underlying genetic architecture is complex. This complexity has hindered efforts to identify biomarkers and develop diagnostic tests and therapeutic strategies for patients. Notwithstanding this complexity, it is reasonable to expect that genetic variants that predispose individuals to mental disorders will alter gene expression in the brain. Many studies have tried to identify transcriptional phenotypes of mental disorders in postmortem brain samples, but the use of bulk tissue has resulted in variable cellular composition across samples and datasets, obscuring transcriptional phenotypes and their cellular origins. Recent advances in single-cell (SC) and single-nucleus (SN) transcriptional profiling offer a new approach to this problem, but these techniques also have technical and statistical limitations. As such, there remain critical gaps in our understanding of how risk factors for mental disorders perturb gene expression in the human brain. A major impediment to identifying such perturbations is the absence of an analytical framework for predicting gene expression in the human brain regardless of sampling strategy. The purpose of this application is to test the hypothesis that the covariance structure of neurobiological transcriptomes provides such a framework. In Aim 1, we will assess the concordance of cell-type signatures and transcriptional covariation in bulk and SC/SN gene expression data from human brain samples. In Aim 2, we will clarify optimal experimental and analytical parameters for identifying reproducible transcriptional signatures of rare cell types/states in human brain samples. And in Aim 3, we will exploit reproducible transcriptional covariation in bulk and SC/SN gene expression data to identify cellular and molecular phenotypes of mental disorders and corresponding genetic variants. Collectively, the proposed studies will have a positive impact by promoting rigor and reproducibility in neurobiological research through meta-analysis and predictive modeling, while simultaneously advancing new strategies for identifying cellular and molecular phenotypes of mental disorders that can be readily applied to other molecular species and pathological conditions.