we are working on cervical cancer ,we would see the expression levels of target genes in both control and patient samples. we are planning to use internal control as Coiled-coil-helix-coiled coil-helix domain containing 1 [chchd1] here i attached the chchd1 reference file.
can any one suggest which house keeping gene is best for my studies
can we see the expression levels of target genes in matched samples [i.e ]same patient tissue RNA and same patient blood RNA is this method is valid?
Hi Deepthi,
the reference you provided gives an answer to the first half of your question. Those three suggested genes (CHCHD1, SRSF9, TMBIM6) appear useful for qPCR data normalization of cervical cancer samples. And don't pick just one gene, but better use the geometrical mean of all three to normalize your data.
The second half of your question remains open. The same three normalizers may not work optimally for blood samples. You have to perform test runs with your blood samples and analyze the stability their expression
If the expression of these three is not stabile in blood samples, then the easiest way would be to choose another set of three from housekeeping genes e.g. 18SrRNA, ACTB, B2M, GAPDH, GUSB, HPRT1, HSPCB, PPIA, RPL13A, RPLP0, TFRC, UBC. Look for a set of three genes which demonstrate the most stabile expression in your blood samples. Use the help of geNorm and NormFinder, just as it was done in your reference article. And then use the geometrical mean of these three genes as a normalizing factor. Geometrical mean is less sensitive to outliers than arithmetical mean.
BR, Einari
Hi Deepthi,
the reference you provided gives an answer to the first half of your question. Those three suggested genes (CHCHD1, SRSF9, TMBIM6) appear useful for qPCR data normalization of cervical cancer samples. And don't pick just one gene, but better use the geometrical mean of all three to normalize your data.
The second half of your question remains open. The same three normalizers may not work optimally for blood samples. You have to perform test runs with your blood samples and analyze the stability their expression
If the expression of these three is not stabile in blood samples, then the easiest way would be to choose another set of three from housekeeping genes e.g. 18SrRNA, ACTB, B2M, GAPDH, GUSB, HPRT1, HSPCB, PPIA, RPL13A, RPLP0, TFRC, UBC. Look for a set of three genes which demonstrate the most stabile expression in your blood samples. Use the help of geNorm and NormFinder, just as it was done in your reference article. And then use the geometrical mean of these three genes as a normalizing factor. Geometrical mean is less sensitive to outliers than arithmetical mean.
BR, Einari
Einari is absolutely correct that it is best to use more than one reference gene, and that it is very important to check the stability of potential reference genes and all the sample types you will be studying.
This post provides some useful information on using geNorm https://theaddictivebrain.wordpress.com/2015/08/28/using-genorm-for-determing-stable-reference-gene/. There are also software programs available that will analyze your qPCR data and check reference gene stability for you automatically.
Hi Deepti,
You can try 18s rRNA as this is a good house keeping gene.
Thanks
Kiran
Hi,
Selection of a house keeping /internal control genes that need to be used for the study needed to be selected based on many parameters as mentioned above.
Below are the links for the details that help you doing so:
https://www.qiagen.com/no/spotlight-pages/newsletters-and-magazines/articles/endogenous-controls/
http://www.sciencedirect.com/science/article/pii/S0006291X03025646
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3204251/
Hope these will help.
Thanks
Prasanth
Hello
i think GAPDH and B-actin can be good for you. but it needs to study more.
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