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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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
OBJECTIVE: 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.
CONCLUSIONS: 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.
摘要：
背景：在实验分析过程中验证参考基因（RG）的稳定性对于正确的定量实时聚合酶链反应（RT-qPCR）数据标准化至关重要。通常,以一种不可靠的方式，一些研究使用参与基本细胞功能的基因[甘油醛-3-磷酸脱氢酶（GAPDH），18SrRNA,和β-肌动蛋白]，而不注意它们是否适合此类实验条件或选择此类基因的原因。此外，此类研究仅使用一个基因，而定量实时PCR发布的最低信息实验指南推荐两个或更多个基因。它会影响这些研究的可信度，并导致基因表达发现的扭曲。对于组织工程，基因表达的准确性驱动着最好的实验或治疗方法。
目的：通过RT-qPCR验证人牙髓干细胞（DPSC）成骨分化过程中最稳定的RG。
方法：我们在两种条件下培养DPSC：未分化和成骨分化，均为35d。我们评估了10个RGs候选物的基因表达[核糖体蛋白，大,P0(RPLP0),TATA结合蛋白(TBP),GAPDH,肌动蛋白β（ACTB），微管蛋白（TUB），氨基乙酰丙酸合成酶1（ALAS1），酪氨酸3-单加氧酶/色氨酸5-单加氧酶激活蛋白，zeta(YWHAZ),真核翻译延伸因子1α（EF1a），琥珀酸脱氢酶复合物，亚基A,黄素蛋白（SDHA），和β-2-微球蛋白（B2M）]每7d（1、7、14、21、28和35d）通过RT-qPCR。通过四种主要算法对数据进行了分析，ΔCt法,geNorm,NormFinder,和BestKeeper，并按RefFinder方法排名。我们将样本细分为八个亚组。
结果：使用RefFinder算法分析来自克隆和成骨样本的所有数据集。最终排名显示RPLP0/TBP为两个最稳定的RG，TUB/B2M为两个最不稳定的RG。ΔCt方法或NormFinder分析显示TBP/RPLP0是两个最稳定的基因。然而,geNorm分析显示RPLP0/EF1α居首位。这些算法\'两个最不稳定的RG是B2M/GAPDH。对BestKeeper来说,ALAS1被评为最稳定的RG，和SDHA作为最不稳定的RG。在大多数亚组中检测到RPLP0/TBP对是最稳定的RGs，遵循RefFinfer排名。
结论：第一次，我们发现RPLP0/TBP是最稳定的RGs，而TUB/B2M是不稳定的RGs，用于传统单层中人DPSC的长期成骨分化。
目的：通过RT-qPCR验证人牙髓干细胞（DPSC）成骨分化过程中最稳定的RG。
方法：我们在两种条件下培养DPSC：未分化和成骨分化，均为35d。我们评估了10个RGs候选物的基因表达[核糖体蛋白，大,P0(RPLP0),TATA结合蛋白(TBP),GAPDH,肌动蛋白β（ACTB），微管蛋白（TUB），氨基乙酰丙酸合成酶1（ALAS1），酪氨酸3-单加氧酶/色氨酸5-单加氧酶激活蛋白，zeta(YWHAZ),真核翻译延伸因子1α（EF1a），琥珀酸脱氢酶复合物，亚基A,黄素蛋白（SDHA），和β-2-微球蛋白（B2M）]每7d（1、7、14、21、28和35d）通过RT-qPCR。通过四种主要算法对数据进行了分析，ΔCt法,geNorm,NormFinder,和BestKeeper，并按RefFinder方法排名。我们将样本细分为八个亚组。
结果：使用RefFinder算法分析来自克隆和成骨样本的所有数据集。最终排名显示RPLP0/TBP为两个最稳定的RG，TUB/B2M为两个最不稳定的RG。ΔCt方法或NormFinder分析显示TBP/RPLP0是两个最稳定的基因。然而,geNorm分析显示RPLP0/EF1α居首位。这些算法\'两个最不稳定的RG是B2M/GAPDH。对BestKeeper来说,ALAS1被评为最稳定的RG，和SDHA作为最不稳定的RG。在大多数亚组中检测到RPLP0/TBP对是最稳定的RGs，遵循RefFinfer排名。
结论：第一次，我们发现RPLP0/TBP是最稳定的RGs，而TUB/B2M是不稳定的RGs，用于传统单层中人DPSC的长期成骨分化。
