Fluorescent quantitative PCR (qPCR) is a commonly used technique in laboratories, which can be applied to gene expression analysis, genotyping, pathogen detection, SNP analysis, and more. The operation of qPCR is simple, and its principle is easy to understand. Now, facing a pile of messy data, how to analyze the results becomes a daunting task. Today, Xiao Yi will guide you on how to simplify complex processes and easily obtain data that can be published in SCI journals!
Common analysis methods used in qPCR include relative quantification and absolute quantification, and the choice between these methods should be based on different experimental designs. In this issue, we will focus on a common application of qPCR——gene expression analysis, where the relative quantification method is generally selected.
Experimental Design
Suppose we currently need to study the effect of light induction on the expression of the Arabidopsis AtSUC2 gene. The control group would consist of Arabidopsis plants that have not undergone any treatment, while the experimental group would be plants that have been treated with a certain light induction. RNA would be extracted from both groups and reverse-transcribed to obtain cDNA, which would then be used as a template. The Arabidopsis GAPDH gene would be selected as an internal reference for the qPCR experiment.
The qPCR wells that need to be set up are as follows:
1) NTC (No Template Control) is used to verify whether there is contamination in the PCR system.
2) NRT (No Reverse Transcription) refers to using RNA that has not undergone reverse transcription as the template, which serves as a control for gDNA contamination.
3) The internal reference gene is used to correct for differences caused by varying initial concentrations of samples.
4) The selection of control samples generally falls into the following categories:
- To assess the effect of a certain treatment on gene expression, the untreated sample is used as the reference sample.
- To detect the differences in gene expression at different times, the sample at time 0 is used as the reference sample.
- To compare the differences in gene expression among different tissues, one tissue is arbitrarily selected as the reference sample.
One point to note here is that for each material mentioned above, 3 PCR wells (1, 2, 3) have been set up. These are PCR duplicates, also known as technical replicates, which are intended to eliminate operational errors and accurately evaluate amplification efficiency. Additionally, biological replicates need to be established, which involve conducting the same experiment on different materials (different times, plants, batches, reaction plates) to calibrate biological variability and analyze whether the treatment has statistical significance. Specifically in this case, both the experimental group and the control group require at least two more sets of Arabidopsis samples (A, B, C) to be processed. RNA is extracted from each set and reverse-transcribed, followed by qPCR. For statistical analysis, the average of the three biological replicates is used.
Result Analysis — ΔΔCt Method
The characteristic of the ΔΔCt method is that it relies solely on the Ct values for calculation, but the premise is that the amplification efficiency of the target gene and the reference gene should be relatively consistent and both fall within the range of 90-110%.
The specific calculation formula is as follows:
ΔCt = Ct (target gene) - Ct (reference gene)
ΔΔCt = ΔCt (experimental group) - ΔCt (control group)
RQ = 2^(-ΔΔCt)
Assuming the above experiment studying the effect of light induction on the expression of the Arabidopsis AtSUC2 gene, the results of qPCR are as follows:
During the calculation, we first calculate the average of the 2^-△Ct data for the control group (as shown in the figure above, which is 0.00116). Then, we divide each value of 2^-△Ct by this average to obtain the value of 2^-△△Ct. Finally, we organize these values to obtain the mean and standard deviation, indicating that the expression level of the target gene in the experimental group is increased by approximately 9.73-fold compared to the control group.
The results are plotted using Graphpad software as follows:
The P value calculated using the software's t-test is 0.0024, which is less than 0.05, indicating a statistically significant difference and the results are reliable.
Result Analysis — Dual Standard Curve Method
In practical applications, since the amplification efficiency of the target gene and the reference gene is often different, it is necessary to redesign primers and optimize reaction conditions to achieve the same amplification efficiency. However, we can also choose a more convenient method, the dual-standard curve approach.
Here, let's first understand the analysis method of absolute quantification. The specific steps are to amplify the target fragment through PCR, then insert the target fragment into a cloning vector, extract the recombinant plasmid, and after sequencing confirms it is correct, it can be used as a standard. The amount of plasmid DNA can be measured using instruments like Nanodrop, and the copy number is converted into specific plasmid copies using the copy number conversion formula. After that, the DNA is amplified following a series of dilutions. A standard curve is plotted with the logarithm of the standard copy number as the x-axis and the measured Ct values as the y-axis. Based on the Ct value of the unknown sample, its absolute copy number can be calculated.
Copy number calculation formula:
Copy number = (mass ÷ relative molecular mass) × 6.02 × 10^23
The dual-standard curve method involves separately constructing standard samples for the target gene and the reference gene, performing absolute quantification, and creating standard curves. After calculating the absolute copy number of the unknown samples, a comparison is made, thereby offering higher accuracy.
The specific calculation formula is as follows:
Q = Number of target gene copies / Number of reference gene copies
RQ = Q (experimental) / Q (control)
In the experiment mentioned above, which investigates the effect of light induction on the expression of the Arabidopsis AtSUC2 gene, standard samples for both AtSUC2 and GAPDH were constructed, and the following standard curves were prepared:
The Ct values for the target gene and the internal reference gene in the samples to be tested are as follows:
During the calculation, first, the Ct values of the target gene and the internal reference gene are substituted into the linear relationship of the standard curve to obtain the absolute copy numbers of the target gene and the internal reference gene in both the experimental and control groups. Then, using the formula Q = number of target gene copies / number of reference gene copies, the expression level of the target gene is normalized. Finally, according to the formula RQ = Q (experimental) / Q (control), the RQ is calculated to be 9.71, indicating that the expression level of the target gene in the experimental group is 9.71 times higher than that in the control group.
The results are plotted using GraphPad software as follows:
The P value calculated using the software's t-test is 0.0008, which is less than 0.05, indicating a statistically significant difference and the results are reliable.
That concludes the qPCR data analysis for this session. Next, I will recommend a range of cost-effective products to ensure your experimental data is more accurate and reliable. Come and pick them up!
| Product Name | Cat# | Size |
| Hieff UNICON™ Universal Blue qPCR SYBR Green Master Mix | 11184ES08 | 5×1 mL |
| Hifair™ Ⅲ 1st Strand cDNA Synthesis SuperMix for qPCR (gDNA digester plus) | 11141ES60 | 100 T |
| Hifair™ AdvanceFast One-step RT-gDNA Digestion SuperMix for qPCR | 11151ES60 | 100 T |
| Hifair™ AdvanceFast 1st Strand cDNA Synthesis Kit (No Dye) | 11150ES60 | 100 T |