How do you calibrate between runs? - (Mar/05/2009 )

Hi,

I wondered if any of you out there had any advice on how to standardise between real-time runs?

A little information about my experiment...

Run on LC480 (96well plates)
SYBR green assay looking at 2 genes of interest and housekeeping gene (HRPT)
Samples: -ve cell population (reference sample), 2 treated populations (test samples), a calibrator sample (nothing to do with experiment, just a kind donor's whole blood RNA sample)
Using the 2- delta delta Ct method

So far I have two plates of information and when I compare the calibrator sample (identical on both plates) the average Ct values are slightly different (run1=26.07 run2=25.15). How will this error affect the expression calculations of my actual samples? Does anyone know how to correct for this?

Also, my Ct values are between 28-35. Is this too high? My negative control samples are truly negative and with some the Ct value is >35 so undetectable and negative.

Thanks in advance,
Bunsen

-Bunsen Honeydew-

Bunsen Honeydew on Mar 5 2009, 06:46 AM said:

Not sure about the Calibration as we do not have this problem, but your Ct values are too high as you said. You should increase the amount of template or dilute less, whichever works for you. Aim for Ct values around 20 as this allows for large changes with continued assurance of accurate readings. Also, I would not say that the change in average ct values from 26.07 to 25.15 is "slight" unless all readings are fluctuating by a similar amount in the same direction. A one cycle change amounts to a 2-fold increase/decrease in relative expression if your control value stays the same.

-Dr Teeth-

can you go into more detail how you have spread your samples on the two plates? from one sample, you have the reference gene and all your target genes on one plate but you have so many samples that they are spreaded on two different plates? Moreover, you have the same calibrator sample one every plate?

Concerning your question about the Ct range: this is not simply to answer and I disagree that the Cts have to be around 20. This is very low and I achieve this only for very high expressed genes like ACTB. If you want to do it correctly, you have to do a standard curve to determine the linear dynamic range of your assay. Then you can analyze all your samples which Cts are located within that linear range.
This is just a short explanation of a detail within a complex method, but if you are interested and want to know more you can download the literature in the pinned thread. I think many of your questions will be answered there and if something is unclear do not hesitate and ask here again. :huh:

just one more comment: if possible, always try to avoid inter-run calibration. always try to pack as much samples as possible on one plate. let's say for a 96well plate with duplicates it would be 47 samples and one water control for one target gene (eg. gene "A"). you would do a second plate with your reference gene (eg. 18SrRNA). then you don't have to do a calibration between the plates because you only compare your samples for gene A expression which are all together on one plate. although you normalize with your reference samples which are on a different plate the technical variation between the two runs (one plate gene A and one plate 18S) doesn't concern you because the relative 18S expression differences within your samples are not affected by this.
this approach also saves expensive reagents.
If you have more than 47 samples which you have to split between plates then try to use more than one inter-run calibrator (IRC) because one failed IRC on a plate can ruin all the other data.

-tea-test-

Hi tea-test and Dr Teeth,

Thanks for getting back to me. So far I've been running my samples in triplicate, but if I run them in duplicate I could fit everything on the same plate = make my life much easier I hadn't appreciated I could run my housekeeping gene samples on a separate plate. Thanks for the suggestion, I'll definitely use this approach in the future if I have too many samples!

My samples are sorted cell populations, so the RNA content is very low in some of them. I suspect this is why I have such high Ct values, plus one of my genes does not have great expression (my standard curves corroborated this). With the limited RNA I have left I'm repeating the whole experiment with 3x the amount of starting material. Hopefully this will shift my Ct values, even just a little.

Thanks,
Bunsen

-Bunsen Honeydew-