Dear scientists,
i have a major problem with the evaluation of my qPCR results.
First of all my experiment set up:
12 plates of cultured human granulosa cells. Each plate comes from another patient!
4 plates were cultured for 1 day , 4 for 3 days and 4 for 5 days.
Extracted RNA from each plate was analyzed in an RT-qPCR approach in terms of a gene of interest (GOI) and a reference gene (ref.).
Now my results:
I have Ct (Cq) values of each amplification product and plate.
After i calculated: 2^( Ct[GOI] - Ct[ref.] )
i get the difference between Ct values to the power of 2.
Now my question:
How do i get a relative expression ratio, with which i can tell that my GOI is expressed two fold higher on day 3 compared to day 1 for example.
I would really appreciate any help!
You're evaluating your results by the Delta-Delta Ct method but you forgot 1 Delta in your procedure and one negative .
Ithink that among your 12 samples you have et least 1 sample from Control subject and samples from Patients.
1/ after the qPCR
You wil have Ct[GOI] from control = Ct[GOI]c
Ct[ref) from control = Ct[ref]c
and the same things from patients => Ct[GOI]p & Ct[ref]p
2/ calculate the Delta (diofferences) between GOI and ref
DCtc (= Delta from Control) = Ct[GOI]c - Ct[ref.]c
DCtp (= Delta from Patient) = Ct[GOI]p - Ct[ref.]p
3/ Calculate the Delta Delta = the difference between Patient and Control
DDCt = DCtp - DCtc
4/ Then get tour relative expression with -DDCt to the power of 2
relative expression of GOI from patients versus Control = 2^-DDCt
Regards
Hello Francesco Lo Presti,
thank you very much for the fast response!
I also thought about that way. Is it allowed to use a patient as a control? What if this patient already has an abnormal behavior in terms of mRNA expression?
In my current evaluation I used the mean of all patients on day 1 as a control for other patients and days. Is that also a possible way?
Best regards,
Konstantin
Hi Konstantin,
Reading your question, it appears that you want to see the kinetic of expression of the genes over the time period 1d, 3d, & 5d.
I would analyse results patient per patient and take day 1 as control for each patient. In this case you will see the evolution of gene expression for cells belonging to each patients and i'm pretty sure you will see large variations among the patients with some genes up-regulated in one patient while down-regulated in others for the same genes. By using this method you can display your vfinal values as boxplots allowing you to see the exact variability of the expressions (and it's potential significance).
As you said, you can do it also with the mean day 1 for all patients, but in this case do not forget to test each patient cells individually at day 1 vs the mean day 1 (as control) to evaluate the variation you can have. In this latter case, at day one you can imagine to have a mean expression level of gene ABCD at 1 (arbitrarily) while the expression of the same gene ABCD for patient 1 is at 0.2 fold of mean expression, and for patient 2 at 5 fold. It can be done but may give false positives with some outliers expressions.
=> mean expression is correct if your gene is quite stable among all patients
Hope it helps
regards
Hi Francesco,
thank you for your help!!!
I think you solved my probs. As I don't have a plate at each timepoint of one patient it's not possible to take day 1 (of Patient A) as a control for day 3 (of Patient A). But I will definitely use the mean of day 1 for each patient and timepoint, as you described.
Thank you again.
Best,
Konstantin
- Badges
- Science method