This method is also known as the standard curve method for relative quantification maybe this sounds more familiar? Here you are employing a correction for the difference in efficiency, which basically means you are incorporating the efficiency of each primer set into the formula for relative quantification.
Whenever you have a new set of primers, you must test their amplification efficiency. Efficiency is calculated from the slope of the standard curve of each primer set, so you need to set up a little qPCR experiment to construct the standard curve. A detailed account on what to consider when determining qPCR efficiency is right here.
Next, plot the measured Ct values for every dilution in one gene against the log of the dilution factor if you are using a template of known concentration, then use the log of concentration. Do the same thing for the other gene. Then, after adding a regression line, take the value of the slope.
You can calculate the amplification efficiency of your primer set using the following formula. Ideally, if the amount of reference and target DNA regions are doubling each cycle, the efficiency will be 2 and the slope will be Then, each dilution will have a Ct value 3.
These formulae may look confusing if like me you forgot some math rules from high school. Enough math for now. Keep calm and quantify on. Has this helped you? Then please share with your network. Do I really need to test the efficiency of every new primer I purchase? Say if I buy primers from a company, can I assume that they have already been tested to have good efficiency by the company?
Yes with every new lot of primer you need to validate efficiency of the primer even if they have specified you need to verify their claim. However, the data obtained from the Agilent Bioanalyzer suggest that the integrity of RNAs was correct and, except for one of the seven samples tested, RNA quantity was accurate not shown. These data suggest that the beneficial role of BSA does not only summarize with a role of amplification facilitator.
Taken together, these data show that only the addition of 0. In the case of addition of 0. Actually, this effect was due to a variation of cycle threshold C t value for the cDNA measurements with no modification of the plasmid standard curve not shown. In a clinical laboratory, each patient sample set for molecular MRD analysis is very valuable, so any improvement of RNA quality even in some samples is suitable to apply in practice. Therefore, based on our data, this suggests that 0. Finally, a large prospective study is warranted to definitively assess the use of addition of 0.
Br J Haematol ; : 87— Inhibition affecting RQ-PCR-based assessment of minimal residual disease in acute lymphoblastic leukemia: reversal by addition of bovine serum albumin. Leukemia ; 17 : — Effects of amplification facilitators on diagnostic PCR in the presence of blood, feces, and meat. J Clin Microbiol ; 38 : — Appl Environ Microbiol ; 64 : — European standardization and quality control program of real time quantitative RT-PCR analysis of fusion gene transcripts for detection of minimal residual disease in leukemia patients.
Download references. You can also search for this author in PubMed Google Scholar. Correspondence to J Gabert. Reprints and Permissions. Silvy, M. Leukemia 18, — Download citation. Received : 11 August Accepted : 26 January Published : 11 March Issue Date : 01 May Anyone you share the following link with will be able to read this content:.
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