A. MIQE checklist
A.8. Data analysis
CFX Manager™ Software version 2.1 (Bio-Rad) was used to analyse qPCR data. Cq values for each experiment were determined by fluorescence readings at the 0.2 RFU baseline. This baseline was standardised across all experimental runs, for both EvaGreen and SYBR Green reactions. Outliers or Cq values that did not meet the criteria were excluded from analyses.
Criteria for the exclusion of reactions are shown in Fig. 5.11. The NTC and no-RT controls for each qPCR target were determined to either result in the formation of primer dimers, or have no product at all (see Fig. 5.13 and Fig. 5.15). Primer dimer formation is verified by a melt temperature lower than 80°C, and by agarose gel electrophoresis.
A.8.1. Justification for use of reference genes
Two reference genes were used for qPCR analyses. GAPDH and β-actin are both well-known reference genes that have been widely used in experimental research. Alt et al. (2010) conducted a study in which RT-PCR was performed using primers for GAPDH, β-actin and GRα.
GRα levels remained stable whether analysed against GAPDH or β-actin. Therefore results obtained relative to GAPDH and/or β-actin are comparable. β-Actin was however ultimately identified as the more stable reference gene (Alt et al., 2010).
A.8.2. The 2-∆∆CT method
Reactions for each target gene in each treatment/control were repeated in triplicate in a single qPCR experimental run. The average Cq for each target in each treatment was then normalised against GAPDH and β-actin individually, as well as the geometric mean of the two reference genes (Vandesompele et al., 2002) (see Fig. 5.10). The fold change values were calculated by use of the 2-∆∆CT method, which uses the following equations:
Average Cq (gene of interest) – average Cq (reference gene) = ΔCT
ΔΔCT = ΔCT (treatment) – ΔCT (control)
Thus, ΔΔCT (control) = ΔCT (control) – ΔCT (control) = 0
Therefore 2-∆∆CT of control = 20 = 1 and for each treatment the fold change value = 2–(ΔCT (treatment) - ΔCT (control))
Example:
Figure 5.10. Example of the 2-∆∆CT equations used to calculate fold change values.
A.8.3. Statistical analyses
The statistical significance of the relative fold change values for GRα expression of the treatments relative to the control were determined by one-way ANOVA. IBM SPSS Statistics Version 21 was the statistical analysis software package used. Two assumptions of the one- way ANOVA must be satisfied in order for the ANOVA results to be valid. The assumption of the ANOVA that the residuals from the analysis are normally distributed, was determined by the one-sample Kolmogorov-Smirnov test. The other assumption that the residuals must be homoscedastic, was tested with the Levene’s test. The Bonferroni t-test was the multiple comparisons analysis performed to determine whether there were significant differences between the GRα expression of the control and the GRα expression of the treatments. p < 0.05 was considered statistically significant. Fold change values were presented as bar graphs along with the standard error of the mean fold change values (see Results section 3.2).
Cq values of each technical replicate
Geometric mean of GAPDH and β-actin Average Cq
values
ΔCT (av. GRα – av. GAPDH) ΔCT (av. GRα –
av. β-actin)
ΔCT (av. GRα – Geomean)
ΔΔCT (av. ΔCT – av. ΔCT control)
Normalised fold change expression
relative to control
Figure 5.11. The parameters used to exclude reactions from 2-∆∆CT analyses, as shown in the CFX Manager™ Software program.
A.8.4. NTC data
The relevant NTC data for GRα, GAPDH and β-actin are shown below. The GRα NTC quantitation data (Fig. 5.12) showed that NTC samples had Cq values > 10, whereas GAPDH and β-actin had Cq values > 21 and 30 respectively.
However the melt curve analyses for the GRα, GAPDH and β-actin NTC samples (Fig. 5.13) showed that no product was amplified by these primer sets. The melt curve analyses and NTC quantitation data thus suggest that there is no contamination present or that if there is, it cannot be amplified by the GRα, GAPDH and β-actin primer sets.
Figure 5.12. Quantitation data of GRα, GAPDH and β-actin NTC reactions.
Figure 5.13. Melt curve analyses for GRα, GAPDH and β-actin NTC samples. Amplification of the NTC samples resulted in the formation of either primer dimers, or there was no amplification at all.
A.8.5. No-RT data
The relevant no-RT data for GRα, GAPDH and β-actin are shown below. The no-RT control quantitation data for GRα (Fig. 5.14) showed that no-RT samples had a either a Cq value > 36, or one that was not applicable. No-RT GAPDH samples had a Cq value > 35, or one that was not applicable, whereas β-actin no-RT samples had Cq values > 31 (Fig. 5.14). The melt curve analyses for the GRα, GAPDH and β-actin no-RTs (Fig. 5.15) also show either the formation of primer dimers or no amplification at all. The melt curve analyses and no-RT quantitation data thus suggest that there is no contamination with genomic DNA or that if there is, it cannot be amplified by the GRα, GAPDH and β-actin primer sets.
Figure 5.14. Quantitation data of GRα, GAPDH and β-actin no-RT samples. Cq values were detected as >
36 or not applicable, indicating that there was no target gene amplification.
Figure 5.15. Melt curve analyses for GRα, GAPDH and β-actin no-RT samples across the various treatments. Amplification of the no-RT samples resulted in the formation of either primer dimers, or there was no amplification at all.