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Open Access Highly Accessed Open Badges Research article

Integrating microRNA and mRNA expression profiles of neuronal progenitors to identify regulatory networks underlying the onset of cortical neurogenesis

Joseph A Nielsen12*, Pierre Lau1, Dragan Maric3, Jeffery L Barker3 and Lynn D Hudson1

Author Affiliations

1 Section of Developmental Genetics, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA

2 Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland, USA

3 Laboratory of Neurophysiology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA

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BMC Neuroscience 2009, 10:98  doi:10.1186/1471-2202-10-98

Published: 19 August 2009



Cortical development is a complex process that includes sequential generation of neuronal progenitors, which proliferate and migrate to form the stratified layers of the developing cortex. To identify the individual microRNAs (miRNAs) and mRNAs that may regulate the genetic network guiding the earliest phase of cortical development, the expression profiles of rat neuronal progenitors obtained at embryonic day 11 (E11), E12 and E13 were analyzed.


Neuronal progenitors were purified from telencephalic dissociates by a positive-selection strategy featuring surface labeling with tetanus-toxin and cholera-toxin followed by fluorescence-activated cell sorting. Microarray analyses revealed the fractions of miRNAs and mRNAs that were up-regulated or down-regulated in these neuronal progenitors at the beginning of cortical development. Nearly half of the dynamically expressed miRNAs were negatively correlated with the expression of their predicted target mRNAs.


These data support a regulatory role for miRNAs during the transition from neuronal progenitors into the earliest differentiating cortical neurons. In addition, by supplying a robust data set in which miRNA and mRNA profiles originate from the same purified cell type, this empirical study may facilitate the development of new algorithms to integrate various "-omics" data sets.