Developmental regulation of human cortex transcription and its clinical relevance at base resolution

By A. E. Jaffe, Jooheon Shin, L. Collado-Torres, Jeffrey T. Leek, Ran Tao, Chao Li, Yuan Gao, Yankai Jia, Brady Maher, Thomas M. Hyde and 2 more. 2014

1,328 citations recorded by the source corpus.

Transcriptome analysis of human brain provides fundamental insight into development and disease, but it largely relies on existing annotation. We sequenced transcriptomes of 72 prefrontal cortex samples across six life stages and identified 50,650 differentially expression regions (DERs) associated with developmental and aging, agnostic of annotation. While many DERs annotated to non-exonic sequence (41.1%), most were similarly regulated in cytosolic mRNA extracted from independent samples. The DERs were developmentally conserved across 16 brain regions and in the developing mouse cortex, and were expressed in diverse cell and tissue types. The DERs were further enriched for active chromatin marks and clinical risk for neurodevelopmental disorders such as schizophrenia. Lastly, we demonstrate quantitatively that these DERs associate with a changing neuronal phenotype related to differentiation and maturation. These data show conserved molecular signatures of transcriptional dynamics across brain development, have potential clinical relevance and highlight the incomplete annotation of the human brain transcriptome.

Subfields: Molecular Biology

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Citations in the corpus

Cross‐validation failure: Small sample sizes lead to large error bars (2017, background): “In functional imaging, resting-state fMRI acquisition are a promising source of very large data, via post-hoc aggregation (Biswal et al., 2010; Thompson et al., 2014; Di Martino et al., 2014) or large concerted eﬀorts (Miller et al., 2016; Van Essen et al., 2013).”
Genome-wide genetic data on ~500,000 UK Biobank participants (2017, background): “For example, many blood and urine biomarkers are being measured; and medical imaging of brain [4], heart, bones, carotid arteries and abdominal fat is being carried out on a large subset (~100,000) of participants [5].”
The genetic architecture of the human cerebral cortex (2020, background): “L. T. Elliott et al., Genome-wide association studies of brain imaging phenotypes in UK Biobank.”
Metric learning with spectral graph convolutions on brain connectivity networks (2018, methodology): “Miller et al. (2016) further apply an independent component analysis (ICA) based algorithm to automatically identify and remove artefacts.”
Towards continual learning in medical imaging (2018, methodology): “This research has been conducted using the UK Biobank Resource under Application Number 12579.”
Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism (2016, neutral): “Both interact with miRNA processing complexes and are typically down-regulated during development 12 , but are upregulated in ASD cortex (Fig.”
The relation between statistical power and inference in fMRI (2017, neutral): “Other large-scale collaborative efforts include mega-analyses (such as the ENIGMA consortium [97]) and very promising are the various initiatives for open-data sharing [53] such as HCP used in the current paper and the Functional Connectomes Project (FCP/International Neuroimaging Data sharing Initi”
Discovering dynamic brain networks from big data in rest and task (2017, background): “649 identification of risk factors and early biomarkers relating to many diseases (Sudlow et al., 2015; Miller et al., 2016; Alfaro-Almagro et al., 1000).”
Brain Region-specific Organoids using Mini-bioreactors for Modeling ZIKV Exposure (2016, neutral): “Further comparison with transcriptomes from human dorsolateral prefrontal cortex samples across 6 life stages, ranging from fetal development to aged human tissue (Jaffe et al., 2015) showed the highest correlation with fetal brain tissues, with the best correlation for day 100 organoids (Figure S4B”