The spatial and temporal transcriptional landscape of long non-coding RNAs (lncRNAs,) during human brain development remains poorly understood.
Here, we developed a method for the genome-wide lncRNA transcriptional analysis in an extensive series of 1340 post-mortem human brain specimens collected from 16 regions spanning the period from early embryo development to late adulthood.
(1)We discovered that lncRNA transcriptome dramatically changed during fetal development, while transited to a surprisingly relatively stable state after birth till the late adulthood.
(2)We also discovered that the transcription map of lncRNAs was spatially different, and that this spatial difference was developmentally regulated. Of the 16 brain regions explored (cerebellar cortex, thalamus, striatum, amygdala, hippocampus and 11 neocortex areas), cerebellar cortex showed the most distinct lncRNA expression features from all remaining brain regions throughout the whole developmental period, reflecting its unique developmental and functional features.
(3) by characterizing the functional modules and cellular processes of the spatial-temporal dynamic lncRNAs, we found that they were significantly associated with the RNA processing, neuron differentiation and synaptic signal transportation processes.
(4)we found that many lncRNAs associated with the neurodegenerative Alzheimer and Parkinson diseases were co-expressed in the fetal development of the human brain, and affected the convergent biological processes..
(5)our study provides a comprehensive map for lncRNA transcription dynamics in human brain development, which might shed light on the understanding of the molecular underpinnings of human brain function and disease.
The paper is available at https://academic.oup.com/hmg/article-lookup/doi/10.1093/hmg/ddx203 and pubmed
Would the underlying data be publicly accessible (e.g.: some high level data on your institutions webpage, or public repository)? Your analysis on LncRNAs suggests that the underlying datasets, which should also have protein-coding genes, according to your methods description, could also be interesting for other analysis (e.g.: age-related changes of protein-coding genes in different brain regions.