I am doing qPCR using SYBR Green fluorescent dye. I am looking at gene expression of some biomarkers in cancer cells. I have GAPDH or L37 as reference gene and also a normal cell line related to the cancer model. Now what is the best way to represent the quantitative PCR data in this case? For example, many studies follow the relative quantitation using the delta-deltaCt method or normalized expression by the control sample in addition to reference gene. Some strongly prefer this two layer of normalization - one with reference gene and another with the expression in control/normal sample. Is it always recommended as the standard for comparative gene expression analysis? or should I do the relative quantitation after determining the efficiency of the assay and compare the expression level in cancer cell to normal cell? Can anyone give better explanation for this?
The best method for your type of research is the Delta-Delta Ct. It is a good way to show changes between treated vs control. In your case cancer cells vs normal cells. The efficiency of assay is more useful when you are doing experiments on Gene copy number etc. There are some videos on Bio-Rad that explains the differences and use between the three methods. Hope this is helpful. Good luck.
Ideally you should use more than one reference/housekeeping gene and take the geometric mean of the values of your gene of interest relative to each reference gene. In my experience, the efficiency of reactions for each primer pair varies on a day to day basis, so I would run a standard curve for each gene of interest and each reference gene for every experiment. This also allows you to dispense with the delta-delta Ct method of calculation as a numerical value for each gene of interest and each reference gene can be taken from the standard curve. This makes calculations of data easier and allows for fluctuations in the efficiency of each PCR reaction between individual experiments to be taken into account. In addition, if the same mRNA/cDNA is used to construct the standard curves for a protracted study of gene expression over a period of time, the standard curve approach allows a direct comparison of data between individual RT-QPCR experiments carried out at different times.
Thanks for your suggestions Sean Wyatt.
Right now I am using two reference genes and finally their mean values to create standard curve. I have used relatively higher amount of mRNA (1ug) and then done serial dilution of my cDNA starting from 100ng to picograms. Should I scale down the mRNA amount? I work with some tumor suppressor genes and most cell lines express in a low level.
I also use a high amount of RNA for the RT to produce the cDNA to make the standard curve and dilute the cDNA over a long concentration range. The important thing is that the standard curve encompasses the range of cDNA concentrations of your genes of interest and ref cDNAs in your unknown/experimental samples. i.e. all your unknown samples should fall within the standard curve range for each cDNA being assayed.
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