What is the Pfaffl method?
The Pfaffl method, named after it’s curator Michael Pfaffl, is used to calculate relative gene expression data while accounting for differences in primer efficiencies. Pfaffl published his formula in the journal Nucleic Acids Research in 2001. Unlike the delta-delta Ct method, which assumes primer efficiencies are similar (usually between 90 – 110%) between the gene of interest (GOI) and the housekeeping gene (HKG), the Pfaffl method accounts for any efficiency differences to increase reproducibility.
A video tutorial on how to use the Pfaffl method for qPCR data analysis can be found in our Mastering qPCR course
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In order to perform the Pfaffl formula, you require primer efficiencies for your GOI and HKG, as well as cycle threshold (Ct) values for your samples.
What is the Pfaffl formula?
The Pfaffl formula is presented below:
Looks scary, doesn’t it? It actually isn’t. I will use an example below and break down the equation so it is easier to understand.
How to use the Pfaffl formula
For this example, I will be using the same dataset as from the delta-delta Ct guide, where I have Ct values (performed in duplicate) for control and treated samples and an HKG and GOI for each. This could be a theoretical example of a cell culture experiment which has been repeated three times, so I have three independent sets of control and treated samples.
The first thing that is required for the Pfaffl method is the primer efficiencies for your GOI and the HKG. How to calculate primer efficiencies has been described in detail previously, so please refer to this post before continuing further.
Once you have the primer efficiencies, these will be in the format of a percentage, for example, 98%. However, this percentage is not entered directly into the Pfaffl equation, rather it needs to be converted.
A converted primer efficiency value of ‘2‘ indicates a 100% efficiency. This is the case when using the delta-delta Ct method. In other words, for every PCR cycle, the amount of DNA will multiply by 2. On the other hand, an efficiency of 90% would give a converted value of ‘1.90‘ and an efficiency of 110% would give a value of ‘2.10‘.
If you are still unsure, an easy way to convert the primer efficiency percentage is to divide the percentage by 100 and add 1.
For this example, I will pretend I have calculated the primer efficiency of my GOI as ‘1.93‘ (93%) and the HKG as ‘2.01‘ (101%).
2. Average your technical replicates
Once you have your primer efficiencies, the next step is to calculate the average Ct values from the technical replicates, in this case, I have duplicates for all the samples.
Using the sample data from above, this is what I get:
The next step is to decide which sample, or group of samples, to use as a calibrator/reference when calculating the ∆Ct values for all the samples. This is the part which confuses a lot of people. Basically, this all depends on your experiment set-up.
A common way of doing this is to just match the experimental samples and determine the relative gene expression ratios separately. This is all well and true for experiments that have matched pairs, such as the case in cell culture experiments. However, this is difficult when the two experimental groups vary in n numbers and do not have matched pairs.
Another way to select a calibrator/reference sample is to pick the sample with the highest Ct value, so the sample with the lowest gene expression. This way, all the results will be relative to this sample.
I personally average the ‘Average Ct’ values of the biological replicates of the control group to create a ‘Control average’. By doing so would mean that the results are presented relative to the control average Ct values.
Whichever sample, or group of samples, you use as your calibrator/reference is fine so long as this is consistent throughout the analyses and is reported in the results so it is clear. Remember, the results produced at the end are relative gene expression values.
With this in mind, we next need to average the ‘average Ct‘ values for the control samples for the HKG and GOI. So, for the HKG this will be the average of ‘17.18‘, ‘16.96‘ and ‘17.11‘, which works out as ‘17.08‘. For the GOI this will be the average of ‘30.55‘, ‘30.55‘ and ‘30.79‘, so ‘30.63‘ will be the average value.
Next, we need to calculate ∆Ct separately for each gene in each sample. To do this, simply subtract the newly created ‘Control average‘ (now acting as the calibrator/reference) value from the ‘Average Ct‘ of each sample (including all of the control samples). By using the ‘Control average‘ this will enable us to create ∆Ct values for each control sample as well as the treated samples.
We now have the ∆Ct values for both genes in all the samples. We also know our primer efficiencies (step 1).
Therefore, we can now enter everything into the Pfaffl equation to get the gene expression ratio.
Reporting the results
The relative gene expression ratio is what you will report. The best way to report this is a simple bar chart showing the control and treated groups. The graph will display the mean gene expression ratio and usually the standard deviation (or standard error) bars. I have done this below for the example dataset.
Analysing qPCR results with multiple reference genes
If you have more than one reference/housekeeping gene, it may be worth checking out the guide on analysing qPCR data with numerous reference genes. This approach is very similar to the Pfaffl method, with a slight difference in that it can handle two or more reference genes to normalise to.
The FREE Pfaffl Method Excel template
For those still struggling with the analysis, or just want an easy template to use to quickly calculate the gene expression ratio using the Pfaffl method, I have created a Microsoft Excel template to freely download. The file is suitable for the above example, i.e. when there are two experimental groups (control and treated, for example) with three biological replicates in each, with a GOI and HKG.
Click here to download the Pfaffl method data analysis template.