Using 2^-(del del ct) formula whatever result we get is fold difference in the sample as compared to control right? The fold difference values for my miRNAs of interest are very much variable .. from 2F to 2000F both upregulation and downregulation in different patients.. Is these results right? If yes, how do i represent these values? If i have use log to get my values in a specific range what base should use 2/10 or ln? and why?
Shruthi, I have attached a spreadsheet that I have created to calculate relative transcript abundance using the delta Ct method. In the spreadsheet I include some example data from a recent experiment so that you can work through the calculations. I have also included two articles that I utilized heavily in preparing the spreadsheet. Although I was doing qPCR for protein-coding genes, I assume the spreadsheet and information in the articles would apply to any qPCR targets (e.g., micro RNAs). Let me know if you have any questions.
Best.
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Ricardo... Thank u for the response... I went through the papers and excel file... Those did help me... However I have got few doubts....
1. For internal control selection.. the nature prot. paper describes.. there should not be >2F difference in treated and untreated.. i mean if there is such difference it is not a good internal control.. I think my problem somewhat lies here.. as in my case mostly my internal control varies +/- 4ct values in different samples... Do u think this is right?
2. even though if the internal does not vary much.. yet in some of the samples the gene of interest has a lot of ct variation which gives rise to 2F to 100F difference in expression
3. In the excel file which u had attached .. for the genes.. 1:1 and 1:8 dilutions have almost similar value.. is that possible?Shouldnt it be higher for 1:8? I am asking this just out of curiosity..
Ricardo and Prabu.. Thank you soo much for your reply.. I am attaching a sample excel file here... If you have time please go through it and let me know where am I going wrong...
Shruthi:
1. You are correct, if the gene you are using as your reference ( I used GAPDH and you used U6) varies by a lot then your choice of reference is not good. I have attached another paper below that I used in a recent study when GAPDH proved not to be a good reference gene, I ended up using RAB7, but you will have to determine experimentally which gene is best for your situation. If you work with biopsy samples then there may be many factors that will impact the levels of your reference gene. So, before performing any additional qPCRs for genes of interest, it is best to systematically test various reference genes for the one that shows the least variation.
2. Without knowing the exact details of how or from where you are sourcing your samples, then it is difficult to say what the reason is for the large variation between samples with respect to the expression of your gene of interest. Depending on the nature of the tissue/cells you are isolating RNA from, the variation in target gene expression that you have observed may be easily explained and may not be all that worrisome. In the end, it may mean you will have to be more selective in the way you obtain your tissue/cells. By the way, I would not determine fold difference in the way you have show in your spreadsheet. I would only perform statistical and fold change analysis on the mean values you obtain for the 2^-deltaCT (column S). The 2^-deltaCT is one value that you can graph and the papers I attached previously indicate this as an option.
3. Yes, the Ct value is higher for 1:8 and in most instances it is higher by bout 3Ct (see column E and H), which reflects the 1:8 dilution. The deltaCt calculated in column Q has been independently determined from the raw data for each of the two dilutions (1:1 amd 1:8) and if done correctly the values in column Q should be similar. By the way, your data appears to only shown data from a single, presumably undiluted, sample. I recommend that you perform qPCR on at least two dilutions (1:1 and 1:8) as this will help with troubleshooting and will confirm the integrity of your qPCR reactions. I also recommend three technical replicates. The spreadsheet I provided you earlier assumes you have done both of these.
I have attached a spreadsheet in which I have analyzed your data. Of note, your Ct values are pretty high suggesting low target gene transcript levels, so you might consider increasing the amount of your input or consider the possibility that your gene of interest has very low level expression for which the biological impact may be small or insignificant, but that is a conclusion you will have to reach for yourself. I also performed a t-test of the results and the mean values between your two groups are not significantly different by this statistical test(I've attached a graph of your results), the reason being because of your huge intra-sample variation. Nevertheless, I think there is a fair amount of work you must do with respect to optimizing your qPCR experimental conditions and design before you can present these results as biologically relevant. For one, I think you need a new reference gene.
Hope this sets you in the right direction.
Best.
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Ricardo... Thanks a lot...
We are looking for serum miRNAs in healthy and cancer patients.. U6 is considered as an internal control in many papers... However there are several papers pointing out one can not take U6 as control.. The reference is not yet completely standardized...I had checked for other miRNAs mentioned in literature which can be taken as control... which showed higher variation than U6.. Hence I sticked for U6. MOreover, there are contrary reports for those miRNAs as well...
Additionally,lower Ct values for our miRNAs is because their expression is very low.. since we are considering serum miRNAs.
I do perform 1 and 1:5 dilutions...1st one in triplicates ... diluted in duplicates..However I used to do it only for my satisfaction... I did not know how to use it in my calculations...as there was 2 to 3 ct difference... ..Well... now I know... Thank you....
And yes.. I will work on the reference gene now... Thank you...:)
Thank you Prabhu...I will work on my internal control...And my sample size is 70... I will calculate the statistical parameters keeping this sample size... let me check what i get...Thank you anyways...:)
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