Hi, I conducted qPCR using probe included FAM and BHQ-1.
At two annealing temperatures(60 and 65 C), the Ct value, R^2 and PCR efficiency in the amplification plot were also within the normal range.
When the PCR product was confirmed on an agarose gel, a normal band was confirmed.
However, only a slightly higher level of fluorescence than background was detected in the multicomponent plot(square box in figure).
But why is the Ct value calculated? Can I continue to use this probe?
In the same experiment, graphs for other probes containing FAM were normally generated.
Hi,
I have exactly the same problem with a reverse transcriptase real time PCR cancer test which shows low amplification in negative samples.(attached pic)(VIC dye)(I made dilution of primer probe and nothing changed)(I used another primer probe and nothing changed)(My standard curve is as you and linear)(My test on commercial cell lines shows no problem)
Maybe it's for impurity of extracted product?
In another experiment with Monkeypox test setup I changed master mix to a new one (without reverse transcriptase) and it resolved but Monkeypox was a DNA based test and I didn't need reverse transcription but for cancer test I need reverse transcription.
Did you resolved your problem?
What if I change master mix to a new one with reverse transcriptase?
Anyone has the answer?
Is it probe degradation? If the probes degrade, and the quencher probe separates from the reporter, it will cause phosphorescence that increases linearly instead of exponentially. Remember that automatically these graphs are shown on a log scale. I do not believe there is a true asymptote in your left hand plots (directed to OP). You can linearize the graph (change axis so it is not a log scale) and see if the growth is exponential or linear. If it is not exponential, it is not caused by amplification but rather by probe degradation.
To OP: It's hard to read your axes, but it seams your c(t) is very different for your plot on left and plot on right. If you made them the same, and scaled them the same, the two graphs would not look the same (I think, if I'm deciphering the axes correctly, but hard to read). If you placed your C(t) at 0.1, the plot on left would look very unreliable, the plateau being far too low. C(t) set at 0.1 is useful for identifying noise that arises before (beneath) that value.
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