Spatiotemporal transcriptome of the human brain

By Hyo Jung Kang, Y. Kawasawa, Feng Cheng, Ying Zhu, Xuming Xu, Mingfeng Li, A. Sousa, M. Pletikos, Kyle A. Meyer, G. Sedmak and 16 more. 2011

2,018 citations recorded by the source corpus.

Brain development and function depend on the precise regulation of gene expression. However, our understanding of the complexity and dynamics of the transcriptome of the human brain is incomplete. Here we report the generation and analysis of exon-level transcriptome and associated genotyping data, representing males and females of different ethnicities, from multiple brain regions and neocortical areas of developing and adult post-mortem human brains. We found that 86 per cent of the genes analysed were expressed, and that 90 per cent of these were differentially regulated at the whole-transcript or exon level across brain regions and/or time. The majority of these spatio-temporal differences were detected before birth, with subsequent increases in the similarity among regional transcriptomes. The transcriptome is organized into distinct co-expression networks, and shows sex-biased gene expression and exon usage. We also profiled trajectories of genes associated with neurobiological categories and diseases, and identified associations between single nucleotide polymorphisms and gene expression. This study provides a comprehensive data set on the human brain transcriptome and insights into the transcriptional foundations of human neurodevelopment.

Subfields: Molecular Biology

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Citations in the corpus

Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults (2018, neutral): “To compare laser capture microdissected rhesus macaque samples to gross human hippocampus samples, we calculated average expression over all hippocampus samples for each age 33,34 .”
Comprehensive Analysis of Tissue-wide Gene Expression and Phenotype Data Reveals Tissues Affected in Rare Genetic Disorders (2017, background): “Expanding our approach to organ-specific developmental gene expression profiles such as the HBT dataset (Kang et al., 2011) could inform us of fetal gene programs; however, such datasets are not available for most human tissues.”
Common genetic variants influence human subcortical brain structures (2015, positive): “ADNI2GO, (4) BETULA, (5) BFS, (6) BIG, (7) BIG-Rep, (8) BrainSCALE, (9) BRCDECC, (10) CHARGE, (11) EPIGEN, (12) GIG, (13) GSP, (14) HUBIN, (15) IMAGEN, (16) IMpACT, (17) LBC1936, (18) Lieber, (19) MAS, (20) MCIC, (21) MooDS, (22) MPIP, (23) NCNG, (24) NESDA, (25) neuroIMAGE, (26) neuroIMAGE-Rep, (27”
Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders (2019, background): “(B) Brain developmental expression trajectory displayed for the three groups of genes based on (Kang et al., 2011) The 146 genome-wide significant loci from the cross-disorder meta analysis were clustered into three groups based on predicted disorder-specific associations: (1) no-pleiotopy; (2) plei”
The Cellular and Molecular Landscapes of the Developing Human Central Nervous System (2016, positive): “Of these, 9 out of 10 genes appear to be differentially expressed or spliced across brain regions and/or time (Kang et al., 2011; Mazin et al., 2013).”
Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism (2019, methodology): “To estimate the partial co-expression network, we used the BrainSpan microarray dataset (https://developinghumanbrain.org/; Kang et al., 2011).”
Integrative functional genomic analysis of human brain development and neuropsychiatric risks (2018, background): “of samples in this study across broad developmental phases (embryonic to adulthood), age [5 PCW to 64 PY (19)], and developmental windows”
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, methodology): “Network approaches are particularly useful for relating whole-tissue-level changes to data from these high-resolution experiments 11–13”
NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data (2014, neutral): “Kang et al. (2011) performed weighted gene co-expression network analysis to define modules of co-expressed genes and identified 29 such modules, associated with distinct spatio-temporal expression patterns and biological processes in the brain.”