Multiscale transcriptional architecture of the human brain

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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.