Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders.

Gene expression imputation and cell-type deconvolution in human brain with spatiotemporal precision and its implications for brain-related disorders.
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DOI:
10.1101/gr.265769.120
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发表时间:
2021-01
期刊:
影响因子:
7
通讯作者:
Jia P
Jia P
中科院分区:
生物学1区
文献类型:
--
作者:
Pei G;Wang YY;Simon LM;Dai Y;Zhao Z;Jia P

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作为人体最复杂的器官,大脑由不同的区域组成,每个区域都由不同的细胞类型及其各自的细胞相互作用组成。人类大脑的发展涉及一系列精心调整的互动事件。这些包括时空基因表达变化和细胞类型组成的动态改变。然而,由于大脑时空转录组收集的困难,我们对这一过程的理解在很大程度上仍然是不完整的。在这项研究中,我们开发了一种基于张量的方法,在转录组水平上估算基因表达。经过严格的计算基准测试,我们应用我们的方法来推断广泛使用的BrainSpan资源中缺失的数据点,并完成了整个时空转录组学网格。接下来,我们进行了去卷积分析,以全面表征整个BrainSpan资源中的主要细胞类型动态,以估计整个发育过程中的细胞时间变化和不同的新皮层区域。此外,整合这些结果与GWAS汇总统计的13个脑相关性状揭示了多个新的性状细胞类型协会和性状时空关系。总之,我们估算的BrainSpan转录组数据为研究界提供了宝贵的资源,我们的发现有助于进一步研究人类大脑和相关疾病的转录和细胞动力学。
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 owing 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 provide a valuable resource for the research community and our findings help further studies of the transcriptional and cellular dynamics of the human brain and related diseases.
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