qPCRis very popular as a qualitative and quantitative detection tool. Often you need to use a standard curve to get reliable, reproducible data. qPCRLet us examine together the whys and hows of the standard curve:
qPCR looks like a very simple technique at first glance, and when optimized, it gives great results in your experiment. To ensure that we get consistent and accurate results that reflect what is really happening in the sample under study, we must use the right controls. One of these basic controls is to create a standard curve plot. The standard curve plot allows us to check the efficiency of our primers and the quality of the DNA.
Perfect qPCR Efficient Primers are Important for their Data:
You have designed a primer on the computer with the help of a bioinformatics software, you have seen with your own eyes that it is specific through the NCBI portal, you have confidently ordered the primers you have designed to be synthesized, you have received your primers, it is imperative to test them with a qPCR experiment to avoid making mistakes, getting wrong results and wasting time! This is a big risk that no researcher wants to take. Don’t be impatient to do the study right away and make sure to set up an experimental design for your first study!
It is important to make sure that the ct values obtained are accurate and reflect reality. The logarithmic curves you get with a single dilution, which you think look good, are not always accurate and do not guarantee efficient replication. This is why you should always test your primers with a standard curve. A standard curve plot can show that the primers bind efficiently and precisely to the target and elongate correctly, which is a critical point.
One qPCR How is the Standard Curve Realized?
We use the quantities of at least 5 different data points of the same DNA sample diluted 10-fold to generate a standard curve plot. Theoretically, correctly designed efficient primers should result in a dose-response curve in direct proportion. You then need to plot the Ct values on the y-axis and the log DNA copies per mL on the x-axis. You can prepare a very simple Excel document for this. Below is an example of a standard curve.
The second important issue is to work in at least 3 replicates so that we can also evaluate reproducibility, it will be much more accurate to obtain precise results. One last footnote is to pipette DNA that you are sure of its purity in each reaction and do not forget to use a negative control to eliminate the contamination problem.
Analyzing Your Standard Curve
Some qPCR software has applications for analyzing the standard curve, but many instruments do not have this option. When analyzing your curve, there are a few things to consider when calculating and evaluating it:
PCR Efficiency
Considering that the DNA molecule doubles every cycle, the PR efficiency range should be between 90% and 110%. Why do we give this range of values? Because reactions are never perfect. If some mistakes are made due to dilutions, the efficiency will go above 100%. For values below 90%, we can interpret that you have inhibitor contamination or low primer yield.
R² Value
The R² value is the correlation coefficient and should > be 0.99 to ensure good confidence in the correlation.
This value allows you to see if there is a good linear relationship between the values of each sample. A low value may indicate a poor serial dilution. To avoid this, when making your serial dilutions, make sure you pipette correctly using well-calibrated pipettes and mix the dilutions thoroughly.
Cq Standard Deviation for Values
Performing the amplification helps to reduce errors and makes your R² and PCR efficiency values more reliable. However, if there is too much variability in your replicates, we cannot say that they are reliable values.
To check how reliable your copies are, calculate the standard deviation (SD). Good copies should be within 0.2 SD units. If they are not, you may need to redo your standard curve.
If you get good data from your qPCR standard curve, you are well on your way to being satisfied with the efficiency of your primers! But what if your standard curve data is not so good? Here are two options:
What if your Primers are not Efficient?
It happens surprisingly often that the qPCR standard curve plot ends up lopsided; each DNA concentration results in approximately the same Ct value. This means that the primers are not binding efficiently to the target.
A Bad One qPCR Standard Curve May Reflect Low DNA Expression
A bad standard curve may not be due to primers. If your target is poorly expressed in your sample, your standard curve may be incorrect. You need to verify whether this target is expressed in the cell type you are working with. If your target is poorly expressed, increase the amount of DNA used for amplification. Or vice versa… This can help you determine the appropriate amount of DNA to use in your next experiments. Why use 10 ng per reaction when you can get good amplification at 1 ng? This way you can use your very valuable DNA samples in your different qPCR runs.
It is often tempting to skip the qPCR standard curve step, but in the long run this step can be good for assessing primer efficiency and DNA quality.
Taking the time at the beginning to ensure your primers are efficient can save a lot of time in the long run and ultimately lead to better results!
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