Real-time PCR results can be analyzed using two main approaches: absolute quantification and relative quantification. Each method serves a different purpose depending on the experimental goals.
Absolute quantification relies on establishing a linear relationship between the logarithm of the initial DNA concentration and the number of amplification cycles. A standard curve is generated using samples with known copy numbers. By measuring the cycle threshold (Ct) value of the target sample, the original template quantity can be determined accurately.
To prepare the standard curve, a series of dilutions are made from a stock solution. For example:
- 1 volume of the stock solution is mixed with 9 volumes of dilution buffer to create solution II.
- 1 volume of solution II is then mixed with 9 volumes of dilution buffer to create solution III.
- This process continues to produce solutions IV and V, resulting in a tenfold serial dilution.
Once the standard curve is established, the copy number of the sample can be calculated. The formula involves determining the sample concentration (in ng/µL) using optical density at 260 nm (OD260), multiplied by 40 and the dilution factor. The molecular weight of the sample is also considered, along with the number of bases, using the formula:
**Sample concentration (ng/µL) = OD260 × 40 × dilution factor**
**Copy number = (Sample concentration / Molecular weight) × 6 × 10¹â´**
By plotting the Ct values against the standard curve, the exact copy number of the sample can be obtained.
Relative quantification, on the other hand, compares the expression levels of a target gene before and after an experiment. It is often used in gene expression studies where the goal is to measure changes rather than absolute amounts.
There are two common approaches for relative quantification: the double standard curve method and the double Ct method.
The **double standard curve method** involves creating separate standard curves for both the target gene and the reference gene. While this approach is straightforward, it requires generating a new standard curve for each gene and each experimental run. This can be time-consuming and may not always reflect the true amplification behavior of the sample. It is widely used in gene regulation studies.
The **double Ct method** avoids the need for standard curves altogether. Instead, it calculates the difference in Ct values between the target gene and the reference gene. This method assumes that the amplification efficiency is consistent across all samples, ideally around 100%. If the efficiencies differ significantly, the results may be less reliable. However, this technique is commonly used in gene expression analysis due to its simplicity and efficiency.
Both methods have their advantages and limitations, and the choice depends on the specific research question and experimental design. Understanding these techniques helps in selecting the most appropriate strategy for accurate and meaningful real-time PCR data interpretation.
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