Relative quantification

In contrast with the absolute quantification method, relative quantification does not express the final results in absolute number of copies or length of the telomere sequences, but as a ratio between the concentration of the target template in the sample of interest and that in a reference sample (463). Therefore, relative quantification does not require a calibration curve or standards with known concentrations.

The levels of target template across multiple samples is obtained by comparing their final Ct values with that obtained from the reference and reporting results as fold-changes relative to this reference. Thus the mentioned methods can be summarized as the ΔCt methods (Figure 3.22) (464;465).

Figure 3.17 ΔCt method of relative quantification.

To compare the concentration of the TG in sample A and B their Ct values are normalised for the Ct value of the TG in a reference sample, which remains the same in different PCR reactions. Therefore, the final concentration of the TG in sample A is provided by the difference between Ct value of TG in A and Ct value of TG in the reference sample (ΔCtA). Similar calculation is used to establish the concentration of sample B (ΔCtB). The different concentration of the TG between sample A and B will be then provided by the difference of their Ct values normalised to the reference sample.

Importantly, introducing the same reference in multiple reactions will allow standardization of results between difference PCR runs. But the relative quantification procedure requires a further normalization step which introduces more complexity in the analysis of the final results. To achieve optimal relative expression results, appropriate normalization strategies are required to control for experimental error (466;467), and to ensure identical cycling performance during real-time PCR. These errors can be due to minor differences in the amount of starting DNA/RNA, quality of the DNA/RNA, or difference in PCR amplification efficiencies between samples

(468). Therefore, to ensure identical starting conditions, the relative expression data have to be equilibrated or normalized according to one of the following variables:

 sample size/mass or tissue volume

 total amount of extracted RNA or total amount of genomic DNA

 reference ribosomal RNAs or reference messenger RNAs (mRNA)

 artificial RNA or DNA molecules (= standard material)

Normally, a “housekeeping” gene (HKG) is selected for this further normalization (Figure 3.23). As the HKG expression is expressed at constant levels between different samples or present in single copy within the genome, variation in its amount reflects variations in the concentration of the original RNA/DNA.

Figure 3.18 ΔΔCt Method of relative quantification

This method introduces a further correction which normalises results for the concentration of an housekeeping gene in the target samples as well as in the reference sample. As the HKG should be expressed at constant levels between different samples or present in single copy within the genome, changes in the concentration of the HKG gene will inform on possible differences in the amount of starting DNA/RNA, quality of the DNA/RNA, or PCR amplification efficiencies between samples within the same run or between different PCR runs.

Selection of an appropriate HKG represents one of the most sensitive parts in the set-up of a PCR experiment, particularly when we want to explore different levels of gene expression between different tissues or in different experimental conditions. It is expected that modest changes in the HKG expression between different tissues or before and after any specific treatment can dramatically affect the quality of the normalization and, subsequently, the final results of the experiment (Figure 3.23).

One example could highlight the relevance of this factor.

Example

Aim - to evaluate the difference in expression of a TG between two samples (A and B) assayed in 2 different days.

Step 1 - Obtain the Ct values of the reference gene (Ref) as well as of the TG in the sample A (day 1) and B (day 2) (Figure 3.24A.A and 3.24.A.B, split in two different pages).

Figure 3.19A Establshing Ct values.

Each PCR run will provide the Ct values of the TG in sample A (A) and B (B) as well as that of the reference sample.

Step 2 – Normalise TG for Ref.

As Ref is the same sample in both reactions, its final Ct value should be the same in the run including sample A and B. Therefore, correction for Ref will allow adjustment for possible difference in the PCR instrument setting between the pre- and post-treatment runs. Moreover, it will inform on how many folds the expression of the gene of interest differs between sample A and sample B compared to the constant reference (Figure 3.24B).

A) Sample A (Day 1) B) Sample B (Day 2)

Figure 3.24B Calculating the ΔCt for sample A and B.

Following normalization of the Ct values of the TG in sample A and B, the difference between samples of the TG is calculated with the ΔCt method.

Step 3 - Apply a further correction for the levels of expression of the HKG.

Figure 3.24C From ΔCt to ΔΔCt.

The same samples A and B are run a second time together with the reference sample to establish the concentration of the selected HKG. Normalization of the ΔCt for the TG in sample A and B with the ΔCt for the HKG in the same samples will allow correction for possible difference in amplification efficiency or original DNA concentration between sample A and B.

Assuming levels of expression of the HKG are not affected by treatment, a possible difference of the HKG levels following treatment will suggest that there was a change in one of the following steps between the two time points:

a) the quality of the extracted mRNA

b) the efficiency of the reverse transcription or c) the total cDNA concentration

As each of these factors could equally affect levels of TG expression, correction of TG for HKG will allow correction of the final results for all these possible sources of variability.

In the LTL PCR-based assay, the most widely used HKG are: β-globin, GAPDH and 36B4 (71;82;83). These genes are selected not because their levels of expression are constant in different experimental conditions, but because they are present in single copy in the genome. Therefore, their Ct values at the end of a PCR run provides information on the amount of genomes (e.g. the original amount of cells) that were present in the original sample. Consequently, normalization of the telomere results for these genes allows quantification of the average amount of telomere repetition per copy of genome (i.e. per cell) in the sample.

Selection of one specific HKG to be used in the PCR assay depends on the level of experience of each laboratory. However, the amplification efficiency of the HKG reaction should always completely overlap with that of the telomere reaction.

Selection of primers, the PCR setting and the concentrations of reagents should be optimized to achieve this goal. The amplification efficiency of the HKG and telomere reactions is commonly checked by standard curve analysis (71;82;83). As previously mentioned for the absolute quantification, serial dilutions of a randomly selected

sample are prepared and the telomere and HKG reactions are performed. Following amplification, a standard curve for the telomere and HKG is generated by plotting the log of the initial template concentration against the Ct generated for each dilution. The value of R² will inform on the amplification efficiency of both reactions. This will allow comparison of the amplification efficiencies between HKG and telomere reactions, with optimisation of the primers and PCR setting. Furthermore, it will inform on whether the same amplification efficiency is maintained at different concentrations of the templates during the HKG and telomere reaction.