New genes for accurate normalization of qRT-PCR results in study of iPS and iPS-derived cells

Publication date: 30 August 2017
Source:Gene, Volume 626
Author(s): A.S. Artyukhov, E.B. Dashinimaev, V.O. Tsvetkov, A.P. Bolshakov, E.V. Konovalova, S.N. Kolbaev, E.A. Vorotelyak, A.V. Vasiliev
iPSC-derived cells (from induced pluripotent stem cells) are a useful source that provide a powerful and widely accepted tool for the study of various types of human cells in vitro. Indeed, iPSC-derived cells from patients with hereditary diseases have been shown to reproduce the hallmarks of these diseases in vitro, phenotypes that can then also be manipulated in vitro. Quantitative reverse transcription PCR (qRT-PCR) is often used to characterize the progress of iPSC differentiation, validate mature cell types and to determine levels of pathological markers. Quantitative reverse transcription PCR (qRT-PCR) is used to quantify mRNA levels. This method requires some way of normalizing the data, typically by relating the obtained levels of gene expression to the levels of expression of a “house keeping gene”, a gene whose expression is presumed not to change during manipulation of the cells. In the literature, typically only one such reference gene is used and its stability of expression during cell manipulation is not demonstrated. We are not aware of any study systematically looking at the expression of such genes in human iPSC or during their differentiation into neurons. Here we compare the expression of 16 reference genes in iPSC, neural stem cells (NSC) and neurons derived from iPSC. The applications GeNorm and NormFinder were used to identify the most suitable reference genes. We showed that ACTb, C1orf43, PSMB4, GAPDH and HMBS have the most stable expression. The use of these reference genes allows an accurate normalization of qRT-PCR results in all the cell types discussed above. We hope that this report will help to enable the performance of proper qRT-PCR results normalization in studies with iPSC-derived cells and in disease-modeling reports.



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