Analysis of Housekeeping Genes for Accurate Normalization of qPCR Data During Early Postnatal Brain Development
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Maturation of the neocortex during the first three postnatal weeks is a complex process that is characterized by different time courses of maturation of different areas of the neocortex. Analysis of gene expression using quantitative PCR after reverse transcription during this period of ontogeny and comparison of different cortical areas require optimal selection of reference genes for correct normalization of the data. Here, we compared expression of nine reference genes in the somatosensory and visual areas of the neocortex at the age of 5, 8, 10, 13, and 20 days. Using widely used GeNorm and NormFinder applications, as well as a novel approach, we compared stability of expression of GAPDH, YWHAZ, TFR1, RPS18, Rn18S, HPRT1, KIF5C, OSBP, and UQCRFS1. We found that, in both neocortical areas studied, YWHAZ and UQCRFS1 are the best reference genes whereas GAPDH and TFR1 are also stably expressed in the somatosensory cortex and OSBP is stable in the visual cortex. Additionally, analysis of stability of expression of these genes by a novel approach showed that the expression of these genes is stable during the entire period from the 5th to the 20th postnatal days.
KeywordsqPCR Neocortex Normalization Neurons Reference genes
Compliance with Ethical Standards
All experiments were conducted in accordance with the guidelines of the animal ethics committee of the Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences following the Directive 2010/63/EU of the European Parliament and the EU Council from Sept. 22, 2010.
- Andersen C, Jensen J, Orntoft T (2004) Normalization of realtime quantitative reverse transcriptionPCR data: a modelbased variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res 64:5245–5250. https://doi.org/10.1158/0008 CrossRefPubMedGoogle Scholar
- Colonnese MT, Kaminska A, Minlebaev M, Milh M, Bloem B, Lescure S, Moriette G, Chiron C, Ben-Ari Y, Khazipov R (2010) A conserved switch in sensory processing prepares developing neocortex for vision. Neuron 67:480–498. https://doi.org/10.1016/j.neuron.2010.07.015 CrossRefPubMedPubMedCentralGoogle Scholar
- Han D, Lerner AG, Vande Walle L, Upton JP, Xu W, Hagen A, Backes BJ, Oakes SA, Papa FR (2009) IRE1alpha kinase activation modes control alternate endoribonuclease outputs to determine divergent cell fates. Cell 138:562–575. https://doi.org/10.1016/j.cell.2009.07.017 CrossRefPubMedPubMedCentralGoogle Scholar
- Khazipov R, Minlebaev M, Valeeva G (2013) Early gamma oscillations. Neuroscience 250:240–252. https://doi.org/10.1016/j.neuroscience.2013.07.019 CrossRefPubMedGoogle Scholar
- Kroeze Y, Oti M, Beusekom E Van, et al (2017) Transcriptome analysis identifies multifaceted regulatory mechanisms dictating a genetic switch from neuronal network establishment to maintenance during postnatal prefrontal cortex development. 1–19. doi: https://doi.org/10.1093/cercor/bhw407
- Peregud DI, Panchenko LF, Gulyaeva NV (2015) Elevation of BDNF exon I-specific transcripts in the frontal cortex and midbrain of rat during spontaneous morphine withdrawal is accompanied by enhanced pCreb1 occupancy at the corresponding promoter. Neurochem Res 40:130–138. https://doi.org/10.1007/s11064-014-1476-y CrossRefPubMedGoogle Scholar
- Vandesompele J, Preter K, De PB et al (2002) Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control. genes:1–12Google Scholar
- Zhang Y, Chen K, Sloan SA, Bennett ML, Scholze AR, O’Keeffe S, Phatnani HP, Guarnieri P, Caneda C, Ruderisch N, Deng S, Liddelow SA, Zhang C, Daneman R, Maniatis T, Barres BA, Wu JQ (2014) An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J Neurosci 34:11929–11947. https://doi.org/10.1523/JNEUROSCI.1860-14.2014 CrossRefPubMedPubMedCentralGoogle Scholar