## 4 Discussion  
In general, most gene expression studies use only one reference gene, rather than a pair of reference genes (Supplementary Table S1). Using pairs of reference genes is the gold standard because any inaccuracies in the relative concentrations of cDNA can be adjusted for in further analysis. Not only is RNA quantification often prone to error, but also the RNA-to-cDNA reverse transcription efficiency can be variable between samples. In addition, there is evidence that the efficiency of reverse transcription may be gene dependent as well [27]. Interestingly, our work has highlighted that ACTB may be problematic in showing high variability between replicates, possibly due to variability in its ability to be reverse transcribed. The input concentration of cDNA is calculated by assuming a 'perfect' reverse transcription efficiency of 100% for all genes [27]; however, the actual efficiency is unknown and will differ, as already mentioned, between genes and between samples. Knowing therefore that two reference genes are strongly correlated, and using them both as a baseline, gives further confidence that any differences assessed between the reference genes and the genes of interest will be genuine. It is proposed that the \(C_{T}\) values of PPIA and YWHAZ be averaged and then compared with that of a gene of interest. A further advantage of using a pair of reference genes is the ability to quickly identify a scenario in which either gene does change. A similar analysis, as carried out in this study, could then be conducted to find a new, more suitable pair. Many other published reference or 'housekeeping' gene articles use BestKeeper, NormFinder, and also GeNorm [2; 19; 22]. It is acknowledged that a further platform could add greater accuracy in the determination of the most stable reference genes; however, this was achieved using the \(\Delta\Delta C_{T}\) method and stable genes were still able to be confidently identified. Furthermore, it has previously been shown that evaluating the analysis from multiple approaches provides a more accurate estimate of the best reference genes; GeNorm was also determined to be less rigorous than NormFinder, particularly where some genes had a high variability across samples [28]. Of the primers used, three out of the six had efficiencies of over 100%; this means that the rate of doubling was slightly above two per cycle [29]. The \(\Delta\Delta C_{T}\) method calculations rely on the assumption that each cycle is accompanied by a doubling of the number of target sequence molecules; therefore efficiencies of less than 90% are more problematic as the molecules are not achieving said threshold [30; 31]. The seeding densities of 5000 and 10,000 cells/cm\({}^{2}\) have both commonly been used in previous ES studies [13; 14; 32; 33; 34]. The electric field strength was selected primarily because the same ES device had also shown pro-osteogenic effects at 100 mV/mm in the literature, and it falls within the range of physiological EFs that are established in vivo for both development and regeneration [14; 35]. The metabolic activity differences at the higher seeding density do not constitute an unusual finding; increases were observed in rat adipose-derived MSCs in the same ES set-up and in cells seeded on a substrate formed of  
Figure 6: Gene expression of YWHAZ in primary human bone marrow-derived MSCs stimulated for 30 and 60 min per day versus PPIA and the unstimulated control. N = 3 for all groups. hydroxyapatite, respectively [14, 34]. Evidently, the seeding density plays a role in the cells' response to ES. However, a full analysis of this effect falls out of the scope of this study and will be appropriately investigated in future work. In a survey taken of published literature using ES, 70% of the papers cited GAPDH as the reference gene in the study. Based on our findings, the use of GAPDH as the only reference gene in ES studies should be verified before continued use, especially at lower seeding densities.