Gene expression imputation and cell type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders
Abstract
As the most complex organ of the human body, the brain is composed of diverse regions, each consisting of distinct cell types and their respective cellular interactions. Human brain development involves a finely-tuned cascade of interactive events. These include spatiotemporal gene expression changes and dynamic alterations in cell type composition. However, our understanding of this process is still largely incomplete due to the difficulty of brain spatiotemporal transcriptome collection. In this study, we developed a tensor-based approach to impute gene expression on a transcriptome-wide level. After rigorous computational benchmarking, we applied our approach to infer missing data points in the widely used BrainSpan resource and completed the entire grid of spatiotemporal transcriptomics. Next, we conducted deconvolutional analyses to comprehensively characterize major cell type dynamics across the entire BrainSpan resource to estimate the cellular temporal changes and distinct neocortical areas across development. Moreover, integration of these results with GWAS summary statistics for 13 brain associated traits revealed multiple novel trait-cell type associations and trait-spatiotemporal relationships. In summary, our imputed BrainSpan transcriptomic data provides a valuable resource for the research community and our findings help the further study of transcriptional and cellular dynamics of human brain and the related disease.
- Received May 7, 2020.
- Accepted November 25, 2020.
- Published by Cold Spring Harbor Laboratory Press
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