qPCR data analysis

qPCR data analysis

Your seniors are under great pressure to graduate and are busy doing experiments and writing papers. They have no time to guide you. Meanwhile, your boss keeps asking for results. The experiments are not going well and the data analysis is confusing. What should you do? Will you feel crazy, frustrated, and tormented?

Don't worry, the editor is here to help you solve your problems. In previous issues, we shared with you the evolution of qPCR, primer design, experimental design, and reverse transcription solutions. This issue will share with you the final content of the qPCR experiment theme - data analysis, which will make your data results more accurate and impress your boss. Are you looking forward to it?

qPCR data analysis can be categorized as relative quantification and absolute quantification, depending on the application. For example, relative quantification measures the fold change in mRNA expression of gene A in treated cells compared to control cells. Absolute quantification measures the number of copies of mRNA expression of gene A in a given number of cells. Relative quantification is the most commonly used method in laboratories. First, let's understand the principles of absolute quantification and the data analysis process.

Principles and methods of absolute quantification

Log (starting concentration) is linearly related to the number of cycles. A standard curve can be drawn using a standard sample with a known starting copy number, which means that the linear relationship of the amplification reaction is obtained.

Log2X0 = Log 2M - Ct Log2 (1+En)

According to the sample Ct value, the amount of template contained in the sample can be calculated.

For absolute quantification we need to determine several factors:

• A target gene - the nucleic acid sequence whose quantity needs to be determined.

• A set of standard samples – used to generate a standard curve. These can be plasmids containing the target gene, PCR products, genomic DNA, etc.

• Replicate Wells – Three or more replicate reactions are recommended for each sample to ensure that erroneous wells due to pipetting errors are distinguished and removed.

• Choice of labeling method—either SYBR Green I or Taqman probe method is acceptable.

• Experimental results display - amplification curve; standard curve; melting curve (not required for probe method).

We take the dye method as an example to show you the analysis process in absolute quantification:

Preparation of plasmid standards:

First, design a pair of primers to amplify a fragment containing the target gene from the genomic DNA. Generally speaking, this fragment should be about 100bp longer than the target fragment amplified by qPCR. Then, connect the amplified fragment to the vector to construct a plasmid vector, and then transfer it to the large intestine for cloning and replication. After plasmid extraction and OD value quantification or qubit quantification (qubit quantification is more accurate than OD value determination in principle, so qubit is recommended for plasmid concentration determination, and the Qihengxing qubit kit is recommended for its superior performance and stability), we can dilute the plasmid gradient to obtain a set of standards.

Calculation and dilution method of plasmid standard copy number

Copy number calculation

1OD260 = 50 ng/μL double-stranded DNA, the concentration of the sample to be tested (ng/μL) = OD260 × 50 (× dilution factor)

The amount of double-stranded DNA standard in 1 μL (mol) = (dilution factor × OD × 50 × 10-9) / (sequence length × 649)

Number of copies of 1 μL double-stranded DNA standard (copies) = (dilution factor × OD × 3 × 1016) / (sequence length × 649)

Plasmid standard serial dilution method:

1V stock solution (standard product I) + 9V dilution buffer to obtain standard product II

1V Standard II + 9V dilution buffer to obtain Standard III

1V Standard III + 9V dilution buffer to obtain Standard IV

1V Standard IV + 9V dilution buffer to obtain Standard V

Absolute Quantification Experiment Example - Absolute Quantification of Gene A in Arabidopsis

Reagents: Qihengxing SYBR Green qPCR Mix

Standards: The plasmid standard was set up with 5 concentration gradients, each with 3 replicates; and a negative control was set up;

Experimental steps: Design of specific primers for gene A qPCR

Extraction of Arabidopsis genomic DNA

Fluorescence quantitative PCR amplification experiment

The results were analyzed and the copy number of gene A in Arabidopsis samples was calculated.

Standard curve preparation: Standard curve can be obtained by plotting Mean Ct

y = -3.481x + 40.123 R2 = 0.9996

Calculation of amplification efficiency (E)

E = 10-1/slope-1

=10-1/-3.481 -1

=1.983-1

=0.938

Correlation coefficient (R2): greater than 0.98. The closer it is to 1, the more reliable the result.

Amplification efficiency (E): 0.8-1.2, the closer to 1, the more ideal.

The standard deviation STD is generally required to be less than 0.5. In more rigorous experiments, it is required to be less than 0.2.

Calculation of the copy number of gene A in the sample:

Substitute the Ct value into the linear equation:

18.45 = -3.481 X +40.123, the copy number of the unknown sample can be calculated;

Substitute the Ct value into the linear equation:

18.45 = -3.481 X +40.123

Relative quantification

Compare the changes in gene expression between two or more samples, or between the treatment group and the control group

This method is suitable for those who only care about the difference in gene expression between samples and are not concerned with the absolute expression level of a gene.

Similarly, for relative quantification, we also need to pay attention to several factors:

• A reference sample

• One or more unknown samples

• Target gene

• One or two housekeeping genes – used to calibrate the actual expression levels of the target gene between different samples (the selection of housekeeping genes was discussed in our previous article on laboratory design. You can refer to our previous article for more information)

• Replicate wells – Three or more replicate reactions are recommended for each sample to ensure that erroneous wells due to pipetting errors are distinguished and removed.

• Choice of labeling method – either SYBR Green or probe-based

• Experimental results display - amplification curve; standard curve (not required for some analysis methods); melting curve (not required for probe method)

•Standard samples (not required for some analytical methods)

For relative quantification, there are two most commonly used methods:

(1) Double standard curve method

The target gene and housekeeping gene of the control sample and the test sample were absolutely quantified using the standard curve, and then the relative value was calculated according to the calculation formula, which is the relative expression level.

Double standard curve method experiment example - relative quantification of gene A in salt-treated rapeseed

Reagents: Qihengxing SYBR Green qPCR Mix

Standards: Five concentration gradients of plasmid standards were set, each with three biological replicates; three biological replicates were also set for each sample, and a negative control was set;

Experimental steps: Design of specific primers for gene A qPCR

RNA was extracted from the treatment group and the control group and reverse transcribed into cDNA

Fluorescence quantitative PCR amplification experiment

Analyze the results and calculate whether the expression level of target gene A in test samples 1 and 2 increased or decreased compared with the control group?

Experimental data

From the quantitative results, it can be seen that the expression levels of target gene A in test samples 1 and 2 are decreased compared with the control sample.

Summarize:

Advantages of the double standard curve method: The data is the most accurate, and there is no need to consider the impact of poor amplification efficiency; the analysis is simple, and the experimental optimization is relatively simple

Disadvantages of the double standard curve method: A standard curve must be made for each gene and each round of experiment

Application: One of the two most commonly used and recognized relative quantitative methods in gene expression regulation research

(II) Relative quantitative analysis - 2-△△Ct method

formula

2-△△Ct method experimental example - relative quantification of A gene in rapeseed after stress treatment

2 - △△Ct method: Assume that the amplification efficiency of the target gene and the reference gene are close to 100%

△Ct (before treatment) = 15-13 = 2 △Ct (after treatment) = 18-20 = -2

△△Ct=△Ct(after treatment)-△Ct(before treatment)=-2-2=-4

Ratio (after treatment/before treatment) = 2 - ΔΔCt = 2 - (-4) = 16

Therefore, the expression level of the target gene after treatment is 16 times that before treatment.

summary

•Advantages: No need to make a standard curve, the experimental process is relatively simple;

•shortcoming:

Assuming a 100% amplification efficiency;

Assume that the standard curve and the efficiency between each amplification are consistent;

Optimization of experimental conditions is relatively complex.

Therefore, when using this method for calculation and analysis, if we do not know the amplification efficiency, the results we obtain may deviate significantly from the actual results. However, it can also be corrected by drawing a standard curve to observe the amplification efficiency. Therefore, in actual operation, it is recommended that you draw a standard curve to observe the actual amplification efficiency and then correct the results to ensure more accurate results.

Correction method: If we know that the target gene and the reference gene have the same amplification efficiency, but the amplification efficiency is not equal to 2, then 2-△△Ct can be corrected to: E-△△Ct. For example, if the amplification efficiency is 1.95, then the calculation formula can be corrected to

1.95－△△Ct

The 2-△△Ct method is one of the two most commonly used and recognized relative quantitative methods in gene expression regulation research.

After understanding the analysis method of qPCR, do your ideas become clearer? I hope you will have a good harvest. This is the end of this issue. Remember to follow us for more exciting content!