Man in hood using pipetteQuantitative PCR (qPCR) is a powerful tool for analyzing gene expression in all sorts of biological samples, but its interpretation can often be rather confusing.  Even experts don’t always agree on the best approach to analyze the data generated by this widely used technique.

One of the most common approaches used in qPCR analysis is the ΔΔCT method – pronounced “delta-delta-C-T,” and sometimes abbreviated ddCt for convenience.  If all the proper bases are covered (and we’ll talk about these), this approach will generate two key values for each sample – ΔΔCT and Fold Change, which is derived from ΔΔCT and helps to place the data into a biologically relevant framework.

So, first, what do you need to make sure you cover in your samples?  Well, it’s ΔΔCT, so each delta in the name implies a comparison to use in the analysis.  Keep in mind that the basic data of qPCR are threshold cycles (CT), which are the PCR cycles at which the signal for your gene of interest crosses the threshold you have designated (usually with the help of qPCR software).  Starting from there, one delta refers to the difference between the target gene and the assay’s reference gene, often called a housekeeping gene.  Some common housekeeping genes include GAPDH, ACTB, and MRPL19, so if your assay includes one of those, there’s a good chance it’s there as the reference for your first delta, creating a ΔCT.  The ΔCT calculation is carried out to ensure that variation in factors such as the number of cells per sample and the efficiency of cDNA synthesis are controlled for in your final data.

The second delta comes from comparing the calculated ΔCT values to those generated by the results from your reference sample.  These are the ΔCT values from whatever samples you consider your baseline.  In most cases, these will be some sort of control samples – for example, you might use the ΔCT results from biological samples that did not receive test compounds as a means of determining the effects of the compounds, simply by comparison of treated samples to this baseline.  The final ΔΔCT values, then, are the result of two major comparisons:
(1) the target gene to the housekeeping gene, to control for variance among samples
(2) experimental conditions to a control condition, to quantify the experimental effects being observed.

Finally, the most valuable…er, value to come from ΔΔCT analysis is likely to be the fold change that can now be determined using each ΔΔCT.  Fold change is calculated as 2^(-ΔΔCT) – in other words, it doubles with every reduction of a single cycle in ΔCT values.  This may or may not be the exact fold change, as the efficiency of a qPCR reaction isn’t always precisely 100%.  However, because we generally expect a doubling of the number of a target gene in a cDNA sample with each successive PCR cycle, 2^(-ΔΔCT) provides an excellent picture of a gene’s relative expression under different conditions.

Of course, with Visikol’s qPCR services, experienced scientists take care of all these calculations, making it easy to quickly determine the outcome of experiments by evaluating the ΔΔCT and fold change values provided.  Please contact the Visikol team with any questions about our gene expression assays.


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