Methods

Theoretical Notes

 Experimental Design

Data Analysis

Primer Design

 Relative Standard Curve Method:

This method requires running a standard curve for both the gene of interest and the endogeneous control. The template for the standard curve can be any cDNA sample that expresses both the gene of interest and endogeneous control. It is important to note that the expression level of your sample should fall within the limits of your standard curve. In other words, if the sample CT value is outside of the standard curve, you should dilute your sample such that the CT value falls between the highest dilution and the lowest dilution of the standard curve.

Once you obtain the threshold cycle values (CT) for your real time experiment, the data can be exported to Microsoft Excel or any other spreadsheet. First, take the log of the concentration values (either natural log or log base 10). Then fit the data to a line using the linest function on Excel or any other method of your choice. Once you have the values for the slope, intercept, and r-squared, it is a good idea to check the efficiency of your primers via the following equation.

(1)

The next step is to transform your sample data into units defined by your standard curve. In other words, you want to take the CT data obtained from your samples and put them in terms of your standard curve. To do this, use the following equation.
For base 10 logarithm,

(2)

for natural logarithm,
(3)

The next step is to normalize everything to the endogeneous control (e.g. GAPDH). To do this, utilize the following equation.

(4)

where the error noted as the coefficient of variation (standard deviation normalized to the mean) is computed by equation 4a


(4a)

Next, there are two approaches you can take. If expression ratio is desired, simply divide the sample signal that is normalized to the endogeneous control to your control sample signal that is normalized to the endogeneous control.

(5)

If percent regulation is desired, then normalize the difference of expression to the control sample.

(6)

where the error for both equation 5 and 6 noted as the coefficient of variation is computed by equation 6a

(6a)

Sample Calculations for Relative Standard Curve Method:

 

Let us now assume that we have just preformed a real time experiment following the protocol listed under experimental design. In Figure 1, we obtain the following CT values for our experiment.

Figure 1: Real Time experiment performed with protocol listed in experimental design.

The first step is to find the values for the standard curve utilizing the linest function on excel.

Table 2: Raw Data and Standard Curve statistics for sample real time assay.

 

 

Once the statistics for the standard curve are determined, utilize equations 2 or 3 to transform the sample data in terms of the standard curve. For example, HT29 x8 at 5ng of RNA per reaction, we have the following calculation to transform the CT value in terms of the standard curve for GAPDH.

 

(7)

 

Table 3: Samples in terms of parameters defined by standard curve.

Next, normalize your gene of interest with respect to your endogeneous control. Table 4 shows the gene of interest with respect to the endogeneous control.

(8)

Table 4: Gene of interest normalized to endogeneous control for each sample dilution.

Finally, the expression ratio is computed. Table 5 shows the expression ratio for the gene of interest. We can see from these data that our gene of interest is up regulated in HT29 x8 with respect to HT29 parent.

Table 5: Expression ratios of the gene of interest (HT29x8 / HT29 parent)

 

Methods

Theoretical Notes

 Experimental Design

Data Analysis

Primer Design

 
RRC Core Genomics Facility
University of Illinois at Chicago
2003