Multiscale transcriptional architecture of the human brain

人脑的多尺度转录结构

基本信息

批准号：
10001244
负责人：
Michael Clark Oldham
金额：
$ 61.39万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-01 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10001244
关键词：
Affect Algorithms American Americas Architecture Autopsy Biological Markers Biological Process Bipolar Disorder Brain Cell Nucleus Cells Code Communities Complex Consensus Data Data Set Detection Development Diagnostic Diagnostic tests Disease Event Gene Expression Gene Expression Profile Gene Expression Profiling Genes Genetic Transcription Goals Health Heritability Heterogeneity Histologic Human Individual Life Mental disorders Meta-Analysis Modeling Molecular Molecular Profiling Neurobiology Outcome Pathologic Patients Phenotype Publishing Quantitative Trait Loci Reproducibility Research Resources Risk Factors Sample Size Sampling Schizophrenia Statistical Models Structure Techniques Testing Therapeutic Tissues Validation Variant base biological research biological systems career cell type cloud based design differential expression effective therapy genetic architecture genetic variant human data innovation molecular phenotype novel novel strategies predictive modeling transcriptome transcriptome sequencing

项目摘要

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.

在美国，将近一半的人会在生活中某个时候符合精神障碍的诊断标准。尽管大多数精神障碍都适中至高度遗传，但他们的基本遗传结构是复杂的。这种复杂性阻碍了识别生物标志物并为患者制定诊断测试和治疗策略的努力。尽管有这种复杂性，但可以合理地期望遗传变异倾向于精神障碍的人会改变大脑中的基因表达。许多研究尝试了为了鉴定验尸脑样本中精神障碍的转录表型，但是散装组织的使用导致样品和数据集的细胞组成可变，从而掩盖了转录表型及其细胞起源。单细胞（SC）和单核（SN）转录的最新进展分析为这个问题提供了一种新的方法，但是这些技术也具有技术和统计限制。因此，我们对精神障碍风险因素如何扰动的理解仍然存在关键的差距人脑中的基因表达。识别这种扰动的主要障碍是没有用于预测人脑基因表达的分析框架，无论采样策略如何。这该应用的目的是检验以下假设：神经生物学转录组的协方差结构提供了这样的框架。在AIM 1中，我们将评估来自人脑样品的散装和SC/SN基因表达数据中细胞类型特征和转录协方差的一致性。在AIM 2中，我们将阐明最佳的实验和分析参数，以识别可重复的转录特征人脑样品中的罕见细胞类型/状态。在AIM 3中，我们将利用大量和SC/SN基因表达数据中可重复的转录协方差，以识别精神疾病的细胞和分子表型和相应的遗传变异。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在神经生物学研究中的严格性和可重复性，同时推进了鉴定精神疾病的细胞和分子表型的新策略可以容易地应用于其他分子物种和病理条件。