Regardless of the type of real-time PCR used, proper data analysis is critical to achieving effective experimental results. Here is the knowledge about real-time PCR data analysis.
Before discussing the basic analysis process, let me introduce how to design a good experiment. If you are designing your own primers and probes, it will help you with the next step. But in some cases, it is more convenient to use sequences from published literature. Keep in mind that even the sequence provided by the publication does not guarantee optimal experimental results. And the possibility of typographical errors needs to be taken into account. So use BLAST to verify all sequences before entering the lab to make sure they are correct. Checking the sequence and Tm values ​​of primers and probes before placing an order is a basic requirement for experimental design.
The standard curve is an important means of judging the quality of the experiment. Using a known template, PCR product, synthetic oligonucleotide or transcribed RNA as a standard curve to determine PCR efficiency, sensitivity, dynamic range and other parameters. Use the OD260 template sample when creating a standard curve. The total amount of template is described by the number of DNA molecules, and the formula for converting mass to DNA content is as follows:
(Quality (g) * Avogadro constant) Average mass per base * Length of template.
For example, a single-stranded DNA of 70-mer is synthesized, and the sample mass is 0.8*10ˆ-11gm. Substituting the formula:
(0.8*10ˆ-11*6.023*10ˆ23molecules/mole) 330gm/mole/base*70 base.
If a double-stranded template is used, the average mass of the base is 660 gm/mole/base.
The template used in the standard curve was serially diluted 7 times from 1*10ˆ7 and diluted 10 times each time, resulting in 10 template copies. Such a concentration helps to obtain the highest ΔRn and the lowest Ct. When drawing a curve with Excel, the logarithm of the number of templates is X, and the value of Ct (cycle threshold) is the Y axis. The formula for calculating the standard curve is as follows:
y=mx+b. y is Ct, m is the slope, x=log 10 template amount, b=y-intercept.
The experimental efficiency Efficiency [10ˆ(-1/slope)]-1 was calculated using the slope. Experimental efficiency tells us how the PCR reaction is performing. The identification coefficient rˆ2 is the degree to which the actual result agrees with the theoretical value, indicating the accuracy of dilution and pipetting. Y-intercept specifies the sensitivity of the experiment and the accuracy of the template content.
With known template content, it is possible to calculate how many cycles are required to synthesize a certain DNA content:
n=Log(Nn)-Log(N0)/Log(1+E)
Nn is the template content after n cycles, N0 is the original template content, E is the experimental efficiency, and n is the number of cycles required.
The slope of a perfect experiment is -3.32, the efficiency is 100%, the y-intercept is between 33 and 37 cycles, and r^2 is 1.00. If the efficiency is low, the y-intercept is higher, which means that the DNA content is insufficient at the beginning of the cycle or it takes several more cycles. Experimental results with an efficiency of 95-100% can be accepted, but if the y-intercept is significantly higher than 37 or lower than 33, this indicates that the sample size is not accurately identified. Usually the high y-intercept value is stored at a low concentration, and repeated freezing and thawing results in denaturation of the sample. After validating the experiment with a standard curve, the same specifications can be used for cDNA or RNA to optimize sample preparation.
The data analysis after the equipment is running is recommended to follow the steps below.
1. Amplification curves.
If you don't have this curve or it doesn't look normal, be sure to find out the cause and solve the problem. The first thing to check for the dye layer and the specified reporter is that the seemingly simple method is the most likely explanation. If the curve looks irregular, it may be that there is no fluorescing agent in the sample, or that there is no sample at all in the sample port. Specifically, that situation can be determined after about 40 cycles. The solution is to adjust the device to abandon those useless sample ports to make the curve consistent.
2. Baseline
All real-time PCR uses Baseline to detect background noise and reagents in the fluorescer during the previous cycles. Such an incomplete arc appears before the official readout of the data, approximately between the 1st and 10th cycles. If the minimum value of Ct is less than the upper limit of the baseline, the baseline value should be adjusted. Usually the upper limit of 2-3 loop baselines is set below the Ct minimum. To determine whether the baseline setting is appropriate, you can observe the linear expression of the amplification curve Y-axis (ΔRn) instead of the logarithmic mode. Sometimes the seemingly better trend on the logarithmic curve can reflect the problem on the linear graph. If the upper limit is too high, a very low Ct below the baseline will occur. Adjust the baseline until the straight line portion of the curve is similar to the baseline. Correct adjustment will cause the logarithmic curve of the rainbow-like defect to disappear. Similarly, the upper limit of the limit may be too low, and the linear augmentation curve can also be observed to understand. The effect of adjusting the baseline is mainly on the low Ct sample or the high content of the template. If the test must be repeated, diluting the template twice is equivalent to changing the Ct coefficient by one.
The next step is to set the correct threshold. When all the augmented calibration points are in an exponential growth phase, the Y-axis is displayed with a logarithmic value. It is unlikely that thresholds will be set to fit all curves. The use of multiple thresholds in one experiment is only suitable for situations where low mRNA levels result in very low ΔRn. Some people think that the threshold is as good as possible, and some analysis software adjusts the threshold to cause the standard curve to have a high R^2. Others believe that the most accurate Ct comes from choosing the largest curvature of SDM. In fact, there is no optimal threshold, and setting low causes low Ct may be beneficial for certain situations. A difference of 2 times the sample content caused a change in the Ct value by a factor of 1, and the effect on efficiency was close to 100%.
3. No template controls (NTC)
To ensure that the sample is not contaminated, it is recommended to add 2-3 template-free control samples to the well before the official sample. The control group was closed before the formal sample was added, and the 2-3 control sample was also prepared after the completion of the sample addition. This step will reveal if the sample is contaminated and to what extent. NTC shows a Ct value below 40, and you can check the augmentation curve to learn more. If there is no exponential increase in the smooth increase of the curve, or the rate of augmentation is very slow, observe the ΔRn pattern with a linear graph. One case is a falling ROX while the FAM is unchanged. The other is that the FAM rises but the ROX does not change. Use multiple attempts to check all the fluorescent reagents in each well to find out the relationship between the reported signal and the dye. If it is mildly contaminated, the threshold can be adjusted to eliminate the effects or remove the relevant wells. If the NTC exponentially augments, it will be caused by the PCR products left by previous experiments. Treatment: Replace dTTP with dUTP (2'deoxyurindine 5'triphosphate) and enzyme UNG (uracil-N-glycosylase).
4. No reverse transcription control
If real-time PCR is used for mRNA quantification, the total amount of chromosomal contaminants in the sample needs to be assessed. Add a sample without reverse transcriptase (-RT). If the -RT result is positive, the DNase treatment sample can be used to remove major contaminants but reduce RNA production, or design primer/probe cross-intergenic regions, or use reverse transcription control.
Positive control
The best method for positive control is the standard curve. It is used for quantitative analysis, and the slope and y-intercept reflect the quality of the experiment. If an artificial standard curve cannot be used, a standard curve covering small pieces of DNA or full-length RNA can reflect the efficiency of the experiment. If the experiment does not have an augmentation effect, focus on the possible problems with reagents and templates.
5. Experimental sample
Different manufacturers' probes will have different performance and different maximum ΔRn values, which can affect ΔRn substantially. However, they do not hinder exponential augmentation, as long as the threshold-related data is set during the augmentation period and can be used for analysis.
It is possible that when ROX falls steadily, some curves are parallel before exponential expansion. It is also possible that the curve that appears to have an augmentation is actually a fake. The analysis software will do its best to process the data, however if ΔRn is significantly below 1, it should be directly suspected that no real augmentation exists regardless of the curve.
6. CV (coefficient of variance)
CV (coefficient of variance) is the standard deviation divided by the arithmetic mean to measure the reproducibility and experimental changes in the experiment. If the CV value is small, it has no effect on the experiment. If the value of one of the addition ports is significantly different from the others, the repeated experiments still have such a result, and the amplification is checked using a multicomponent view or a raw spectrum. If there is no amplification, check whether the probe fluorescer is added or not. Exclude the possibility of the probe and then check if the template is added. If the Ct of the abnormal filling port is low, the template may be added twice. Repeating the template will only reduce Ct 1. If the CV value is large, this imagination will not be detected. Another speculation is that the container is not well sealed and some of the reagents in the sample or test tube evaporate.
7. Quantitative data
After completing the preliminary experiments and analysis, the next step is to decide how to compare the data meaningfully. Standard curves for quantifying mRNA and DNA are sometimes used as a reference for absolute quantification. The standard curve allows the calculation of samples of unknown total mass on a mass basis, but regardless of the accuracy of the material concentration, the end result is a definition relative to one unit. Most devices' software can calculate the total amount in units specified in advance, or by the following formula:
Log 10 copy number = Ct–y-intercept/slope.
It is important to verify that your reagents give 100% reaction efficiency. There is no need to expect that one of your samples will give accurate measurement of gene expression. The content of normalization and relative quantification is not discussed in this paper.
8. Data statistics
The experiment is complete, the data is analyzed, what else can be done? There are many, real-time PCR statistics related to a large number of parameters, such as cell harvesting, nucleotides, extraction techniques, reverse transcription, PCR conditions and reagents during the experiment. It is necessary to perform statistical sorting before using your data. The expression of the data depends on the purpose of the experiment, such as testing the expression of genes before and after being affected by a certain factor, the influence of normal cells on cancer cells, time, and so on. Another type of microbial content such as food, water, and the environment confirms the results of biochips, siRNA, and the like.
Depending on the type of experiment, the data should be organized according to certain principles to help the reader observe changes, including data meaning, standard deviation, and confidence interval. Some statistics are used to tell the reader the possibility of significant differences. Most real-time PCRs are the result of testing hypotheses, and sometimes these differences are obvious. The statistical steps are just going through the field. But biological systems are constantly changing, inaccurate, and sometimes statistics can explain the exclusivity of data.
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