RT-qPCR calculation

qRT-PCR relative quantitative calculation

After transcriptome analysis, qRT-PCR is generally required to verify whether the expression of the transcript obtained by second-generation sequencing is reliable. Fluorescence quantitative PCR is a method of relative expression quantification. There are many calculation methods. The commonly used relative quantitative data analysis methods include double standard curve method, ΔCt method, 2^-ΔΔCt method (Livak method), and ΔCt of reference gene Method and Pfaffl method. Here we mainly explain how to calculate the commonly used 2^-ΔΔCt method (Livak method).

The cDNA of the gene is used as a template for PCR amplification. During the PCR amplification process, fluorescence signals are collected to detect the PCR process in real time. Since in the exponential period of PCR amplification, the Ct value of the template has a linear relationship with the initial copy number of the template, it can be quantified.

What does the Ct value mean?

The meaning of the Ct value is: the number of cycles (cycle) experienced when the fluorescence signal in each reaction tube reaches the set threshold. qRT-PCR will have a plateau during amplification. Before the plateau, PCR amplification is simply exponential growth, that is, 1 becomes 2, 2 becomes 4, 4 becomes 8...amplification. The mathematical form is 2 to the power of ct. At the plateau, the number of amplifications of all genes is the same, and the only difference is the difference in ct values. Therefore, it is not difficult to infer that the smaller the ct value, the fewer cycles required for the reaction amplification to reach the plateau phase, and the higher the initial content of the target gene.

Here, we have a control group, a treatment group, and an internal reference gene and a target gene. We want to see the expression difference of the target gene in the treatment group relative to the control, that is, calculate -ΔΔCt: the data is as follows:

Step 1. Calculate the mean value of the reference gene sgAction Ct for each group.

Step 2. Calculate the first Δct, that is, the Ct value of the target gene to be tested minus the internal reference gene in each group

Step 3. Calculate the mean value of Δct in the control CK group, and then subtract the mean value of Δct in the control CK group just calculated from each Δct in the treatment group to get ΔΔct (red box).

Step 4. Relative expression calculation, that is, relative to the control group: 2^-ΔΔct:

It is not difficult to see that the -ΔΔct here is the same as the log2 (fold change) value in our transcriptome, so if you make a few more genes, you can draw something like the following figure:

Or the correlation point diagram.