How to Select Endogenous Controls for Real-Time PCR

The process of selecting suitable endogenous controls is important, as the expression of commonly used housekeeping genes can vary under different experimental settings. This introduction aims to guide researchers through the key considerations and best practices for selecting and verifying endogenous controls, helping ensure reliable and reproducible gene expression data from qPCR research experiments.

1. Correction for sample variability: Endogenous controls help to account for variations in the amount of starting material, differences in RNA quality, and efficiency of the reverse transcription and PCR processes. By comparing the expression levels of target genes to those of the endogenous controls, researchers can correct for these sources of variability.
2. Accurate quantification: Normalizing against stable endogenous controls allows for more accurate and reliable quantification of gene expression. It help ensures that observed changes in target gene expression are due to experimental conditions rather than technical artifacts.
3. Consistency across samples: Using endogenous controls help ensures consistency and comparability of results across different samples and experiments. This is particularly important in experiments involving multiple conditions, time points, or biological replicates.

Common endogenous controls include genes such as GAPDH (glyceraldehyde-3-phosphate dehydrogenase), ACTB (beta-actin), and B2M (beta-2-microglobulin). Selection of appropriate endogenous controls is important and should be validated for each specific experimental setup to help ensure they remain stable under the given conditions.

qPCR internal controls are integral to the accuracy and reliability of qPCR experiments, providing a means to normalize data and help ensure that the results reflect true biological differences rather than technical variations.

Variations in the quantity of DNA produced in a qPCR assay are not due solely to variations in gene expression. The extent of DNA amplification is influenced heavily by the experimental conditions, particularly the initial amount of cDNA. To get a valid result, you need to start with exactly the same amount of cDNA in the treated and untreated samples, which is difficult to achieve unless you include a second gene known to be unaffected by the treatment in each sample. Thus, any difference in the mRNA detected will be the result of changes in starting cDNA concentration. Compare the patterns of gene expression between the second gene and the gene of interest to work out the ‘true’ fold change. This second gene can be termed a housekeeping gene, endogenous control, normalizer, reference gene, or internal control gene.

Suppose you test one gene under two conditions and end up with C_T values of 28.5 in the ‘treated’ sample and 27.5 in the ‘untreated’ sample. This gives a measured difference of 1 between these values (delta C_T). If you knew that the amount of cDNA in each sample was exactly the same, you could calculate the fold change as 2^(delta C_T), and that 2^1=2. You could then conclude that the expression level in the treated sample was twice that in the untreated sample.

But you still can’t tell whether this is a ‘true’ fold change because of differences in sample input, and this is where the endogenous control comes in. You select a control gene that is expressed consistently across all samples in your study, measure its expression level under each condition, and come up with C_T values of 19.5 and 18.5 for the treated and untreated samples, respectively. Here, the delta C_T value for the control would also be 1. The control—which has stable expression values—has given you the same delta C_T as your gene of interest. This could imply that the measured two-fold difference in expression levels is caused by a two-fold difference in the initial amount of cDNA in the samples and is not treatment-related at all.

Therefore, in relative gene expression, expression level changes are measured as the difference between delta C_T for the tested gene and delta C_T for the endogenous control: delta delta C_T.

In this example: delta delta C_T = (28.5 – 27.5) – (19.5 – 18.5) = 0. You can conclude from this that the treatment has made no difference to the level of gene expression.

In the example above, we assume that the endogenous control gene is expressed at a consistent level in all studied conditions, so any change in control gene expression between the treated and untreated samples will be measured in that gene’s delta C_T value and will contribute to the calculated delta delta C_T. For reliable results, you need to select the correct control.

An endogenous control gene must have stable expression in all samples tested, i.e., the control should not change its expression between treatments, time points, or other test conditions. In practice, zero variation is very rare and endogenous control genes are allowed small differences in C_T values of up to 0.5 C_T. Differences at the top end of this range will introduce imprecisions. From our equation, a difference of 0.5 C_T will equate to a fold change of 2^0.5 or 1.41. However, if we tried a control gene with a difference of 2 C_T between samples, this would equate to a four-fold change in expression levels, making the gene inappropriate as a control.

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