RPLP0/TBP are the most stable reference genes for human dental pulp stem cells under osteogenic differentiation

Abstract

BACKGROUND Validation of the reference gene (RG) stability during experimental analyses is essential for correct quantitative real-time polymerase chain reaction (RT-qPCR) data normalisation. Commonly, in an unreliable way, several studies use genes involved in essential cellular functions [glyceraldehyde-3-phosphate dehydrogenase (GAPDH), 18S rRNA, and β-actin] without paying attention to whether they are suitable for such experimental conditions or the reason for choosing such genes. Furthermore, such studies use only one gene when Minimum Information for Publication of Quantitative Real-Time PCR Experiments guidelines recommend two or more genes. It impacts the credibility of these studies and causes distortions in the gene expression findings. For tissue engineering, the accuracy of gene expression drives the best experimental or therapeutical approaches. AIM To verify the most stable RG during osteogenic differentiation of human dental pulp stem cells (DPSCs) by RT-qPCR. METHODS We cultivated DPSCs under two conditions: Undifferentiated and osteogenic differentiation, both for 35 d. We evaluated the gene expression of 10 candidates for RGs [ribosomal protein, large, P0 (RPLP0 ), TATA-binding protein (TBP ), GAPDH , actin beta (ACTB ), tubulin (TUB ), aminolevulinic acid synthase 1 (ALAS1 ), tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein, zeta (YWHAZ ), eukaryotic translational elongation factor 1 alpha (EF1a ), succinate dehydrogenase complex, subunit A, flavoprotein (SDHA ), and beta-2-microglobulin (B2M )] every 7 d (1, 7, 14, 21, 28, and 35 d) by RT-qPCR. The data were analysed by the four main algorithms, ΔCt method, geNorm, NormFinder, and BestKeeper and ranked by the RefFinder method. We subdivided the samples into eight subgroups. RESULTS All of the data sets from clonogenic and osteogenic samples were analysed using the RefFinder algorithm. The final ranking showed RPLP0/TBP as the two most stable RGs and TUB/B2M as the two least stable RGs. Either the ΔCt method or NormFinder analysis showed TBP/RPLP0 as the two most stable genes. However, geNorm analysis showed RPLP0/EF1α in the first place. These algorithms’ two least stable RGs were B2M/GAPDH. For BestKeeper, ALAS1 was ranked as the most stable RG, and SDHA as the least stable RG. The pair RPLP0/TBP was detected in most subgroups as the most stable RGs, following the RefFinfer ranking. CONCLUSION For the first time, we show that RPLP0/TBP are the most stable RGs, whereas TUB/B2M are unstable RGs for long-term osteogenic differentiation of human DPSCs in traditional monolayers.