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Multiscale transcriptional architecture of the human brain

人脑的多尺度转录结构

基本信息

批准号：
10606491
负责人：
Michael Clark Oldham
金额：
$ 59.41万
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 2025-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10606491
关键词：
Affect Algorithms American Architecture Autopsy Biological Process Bipolar Disorder Brain Cell Nucleus Cells Code Communities Complex Consensus Data Data Set Detection Development 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 biological research biological systems biomarker identification career cell type cloud based design diagnostic criteria differential expression effective therapy genetic architecture genetic variant human data innovation molecular phenotype novel novel strategies predictive modeling single nucleus RNA-sequencing transcriptome

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，将近一半的人在他们生命中的某个阶段会符合精神障碍的诊断标准。