How to properly validate primers for qPCR
TipTipConcept of qPCR
qPCR or quantitative Polymerase Chain Reaction is a method by which DNA can be amplified and quantified using a dye. Logistically, the dye intercalates the DNA as it is amplified, and thus the fluorescence of the dye is directly correlated to the amount of DNA amplified. After each PCR cycle, the genetic content undergoes a doubling and this is quantified by a commensurate increase in dye fluorescence. Therefore, the more cycles that is required to reach a cycle threshold (Ct) (i.e. a cycle when the detection of the fluorescence exceeds the background level), suggests lower starting expression of the gene of interest. Vice versa, less cycles required to reach the cycle threshold suggests a higher starting expression of the gene of interest.
For example, in figure 1 if we designed primers to look at gene A from a wild-type untreated mouse, the Ct value or cycle threshold is approximately 19 cycles. If for example, we treated the mouse with a drug that increased the expression of gene A, this would be represented on this amplification plot as a decrease in Ct (i.e. a leftward shift in the overall curve such that it exceeds the threshold at a lower cycle number). Gene A in the drug treated mouse may have a Ct of 15, thus suggesting that the amount of gene A in the drug treated mouse was higher and only requiring 15 cycles to be amply detected compared to the 19 cycles in the wild type.
Figure 1. Sample amplification plot for gene A. X axis is the cycle number, Y axis is the fluorescence of the dye. The green curve represents the fluorescence of the dye as gene A gets amplified at each cycle [1].
Why validate primers?
Primers that are not screened for efficiency and specificity can bind non-specifically to other genes for example, and thus any values extracted from the qPCR may not be truly representative to your gene of interest. Without screening for primer efficiency and a single peak in the melting curve, it is difficult to ascertain that the primers being used are truly reflecting the proper amounts and proper gene of interest.
Parameters for validating primers
1: Primer Efficiency
One parameter to screen for when validating primers for a qPCR is the efficiency of said primers. This entails performing a serial dilution of the starting template (often cDNA and generally 1:2 dilutions) and assessing whether the detection of the fluorescent dye reflects this dilution and that the template DNA is truly doubling every cycle. Sub-optimal efficiency can be attributed to poorly designed primers or sub-optimal reagents. In general, appropriate primer efficiency falls in the range of 90-110%. An example of an amplification plot with serial dilutions and a standard curve can be found in Figure 2 [2].
Figure 2. Example of an amplification plot and standard curve. Example of an A) amplification plot of serially diluted cDNA and the subsequent B) standard curve including the efficiency calculation of 104% [2].
2: Melting Curve
Incorporating a melting step at the end of the PCR cycle will help determine how strongly the primers have annealed to your gene of interest. In this step, the temperature is incrementally increased and if primers are specific to a single gene of interest, the primers will dissociate at a single temperature. If primers are non-specific or the primers themselves are forming dimers these will manifest as a secondary or tertiary peaks (Figure 3). This is due to the fact that the primers are annealed at different strengths and thus require a commensurate amount of energy (i.e. temperature) to dissociate. If a second peak is detected, a follow-up experiment can be done to confirm if there is 1 or 2 gene products by running the sample on an agarose gel and assessing the presence of multiple bands.
Figure 3. Example melting curve. A sample melting curve where the variety of colored curves represent different genes of interest being assessed. The secondary peak in red represents primer dimers and the secondary peak in green represents non-specific amplification [3].
References
[1] Image used from (https://toptipbio.com/ct-value-qpcr/)
[2] Hajano, J.-U., Zhang, H.B., Ren, Y.D., Lu, C.T. and Wang, X.F. (2016), Screening of rice (Oryza sativa) cultivars for resistance to rice black streaked dwarf virus using quantitative PCR and visual disease assessment. Plant Pathol, 65: 1509-1517. https://doi.org/10.1111/ppa.12534
[3] Gómez RL, SendÃn LN. Relative Expression Analysis of Target Genes by Using Reverse Transcription-Quantitative PCR. Methods Mol Biol. 2020;2072:51-63. doi: 10.1007/978-1-4939-9865-4_6. PMID: 31541438.
