Experimental Skills | QPCR Experimental Data Analysis

Real-time QPCR, also known as quantitative real-time PCR, is a method of adding fluorophores to DNA amplification reaction to accumulate fluorescence signals to monitor the total amount of products after each PCR cycle in Real time. Then the method of quantitative analysis of the target sequence in the test sample.

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Basic Principle

Before learning about real-time QPCR data processing, we first need to understand its mathematical principles and why Ct values are a key factor in our data processing process. These two graphs show a set of results for real-time QPCR. The graph above is the original linear graph and the graph below is the processed index?graph, but they both represent the same thing. The X-axis represents the number of PCR cycles, and the Y-axis represents the fluorescence value of the amplification reaction, that is, the amount of amplified product. In this figure, there are several concepts such as baseline, threshold, and Ct value, from which we can finally get the content of the target gene in the template.

(1)?So what is a baseline? As indicated by the red box, the baseline is the horizontal part of the amplification curve. In the initial stage of the reaction, although the product exhibits index?growth, due to the small total amount, its fluorescence is at the background level and no increase in fluorescence can be detected. Therefore, the generation of baseline signals is caused by accidental errors in measurement. However, when sufficient amplification products are accumulated to generate detectable fluorescence signals, the value of this signal is called the threshold, as indicated by the red arrow. Usually, the threshold defaults to the standard deviation of the baseline signal×10. Before and after the threshold, the PCR reaction still increases exponentially, so the PCR results at this time are reliable and can accurately reflect the initial amount of templates in the system. The Ct value is the number of cycles required when the signal strength reaches the threshold, which is the intersection of the amplification curve and the threshold, as indicated by the red dot in the figure.

(2)?We can see on the right side of the graph the four stages of the amplification curve: baseline period, index?growth period, linear growth period, and plateau period. During the baseline and index?growth periods, the amplification products grew exponentially, but we were unable to detect them during the baseline period. After the linear and plateau stages, due to the significant differences in amplification efficiency between different genes or under different conditions, it is impossible to calculate the content of the template. Therefore, the Ct value during the index?growth period becomes a key value for calculating template content.

(3) So, why can the initial target gene content be obtained by knowing the Ct value? The graph represents the single reaction amplification curve of a gene. The above formula calculates the number of molecules N of the amplification product, which is equal to the number of molecules of the template multiplied by 1+amplification efficiency to the nth power, where n represents the number of cycles. That is to say, if the amplification efficiency is 100%, the number of product molecules is equal to the number of templates multiplied by 2 to the nth power. However, it is evident that during the linear growth and plateau periods, the amplification efficiency cannot be 100%, so the PCR theoretical equation mentioned above only holds in the index?period, which is the part of the green box.

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Data Processing

1.?The three images here show the three names we mentioned during QPCR: amplification curve, standard curve, and melting curve.

(1) Amplification curve: the amplification curve has two forms of presentation, one is linear and the other is logarithmic. We usually use CT to calculate the concentration of the sample in the sample. The higher the CT value, the lower the template concentration.

(2) Standard curve: A series of Ct values obtained by gradient dilution of samples (standards) with known concentrations for QPCR. By using these Ct values and corresponding to the number of Log templates, a related curve can be obtained, which is called the standard curve. Some parameters in this standard curve can be used to determine the quality of this QPCR system.

(3) Melting curve: Tm value, melting temperature, annealing temperature of PCR double stranded products. These two figures are monitoring the gradual temperature rise of the product after the completion of QPCR. It can be seen that there will be a sudden decrease in fluorescence signal when the unwinding temperature is reached. The curve we will measure is called the melting curve. In theory, if a specific product is obtained by PCR, there is only one Tm value, indicating that only a single peak exists on the dissolution curve. If it is a multiphase peak, it can be determined that the product is not single and has undergone non-specific amplification.

2. Absolute quantification: used to determine the absolute quantity value of a nucleic acid sequence in an unknown sample, commonly known as copy number. Absolute quantification requires a template with known concentrations to establish a standard curve. Absolute quantification is not commonly used in our experiments, so we will only briefly introduce it. It is achieved by comparing the CT value of the sample with the standard curve. The result of the analysis is the amount of nucleic acid (copy number, microgram) in a given number of samples (given number of cells, per microgram of total RNA).

3. Relative quantification: It is used to determine the expression differences between target transcripts of samples subjected to different treatments or the expression differences of target transcripts at different time periods, which is also known as multiple differences.

The above figure is an example, and the analysis result is the relative ratio, i.e. multiple difference, of a target gene between the experimental group and the control group.

Relative quantification can be based on quality units or reference genes, but using reference genes as standards is more common and convenient, so only the latter will be introduced. The advantage of using reference genes is that this method can accurately quantify the content of target genes in the template, especially when the template is difficult to obtain, making it very convenient to conduct relative gene expression analysis experiments. But the premise is that we must find genes that are consistently expressed in different states, and their expression levels are not affected by sample processing methods, such as thermal stimulation, bacterial induction, etc.