qrt pcr - quantitative analysis (Aug/01/2007 )
Hi
I am trying to see whether the expression of my gene is more in subconfluent or superconflent cells i have.
i have used beta actin as refernce gene.
from the ct values i got with my target genes, i have normalised by substracting from the ct values of the reference genes.
now how to make out whether i have more expression in high density or low density cells i used.
thanks
If you wish, I can give you a small excel-based program that can calculate the fold change in sample as compared to control. 
yes please
Hi
I am trying to see whether the expression of my gene is more in subconfluent or superconflent cells i have.
i have used beta actin as refernce gene.
from the ct values i got with my target genes, i have normalised by substracting from the ct values of the reference genes.
now how to make out whether i have more expression in high density or low density cells i used.
thanks
I am trying to see whether the expression of my gene is more in subconfluent or superconflent cells i have.
i have used beta actin as refernce gene.
from the ct values i got with my target genes, i have normalised by substracting from the ct values of the reference genes.
now how to make out whether i have more expression in high density or low density cells i used.
thanks
Julee,
First off, don't assume that your B-actin mRNA levels are remaining constant, especially at first. If this is your first run at qPCR, then I would recommend using at least two loading control genes. There have been at least a hlaf a dozen papers published in the last few years simply about _which_ gene is the "best" loading control becuase they all seem to vary at times. Of course, the perfect control gene would be one that never changes over the course of an experiment, but in reality its not a good idea to assume your gene fits the bill just becuase "its what everyone else uses". My recommendations would be to choose two or three genes from the following list: b-actin, 18S rRNA, GADPH, RPL32, or 5S rRNA. If you normalize to each of them, you can see if your results generally agree regardless of which loading control you choose; and then for future experiments you can just use the one loading control that shows the least variability between treatments. I hope that all makes sense.
As for the math of qPCR; check out this page http://pathmicro.med.sc.edu/pcr/realtime-home.htm It has an excellent walk through of nearly all the issues people should be aware of (and often are not) in regards to qPCR. And, unless your fold-change differences are huge, you have to pay close attention as to how you work out your propagation of error. Most qPCR fold-change data are basically ratio of ratios, so for the uninitiated, the propagation of error bars can be a little tricky.
thanks a lot
I have actually used beta cateni and beta actin as housekeeping gene. the results are similar. i will try to search whether they change with cell density or not.
thanks once again
I am trying to see whether the expression of my gene is more in subconfluent or superconflent cells i have.
i have used beta actin as refernce gene.
from the ct values i got with my target genes, i have normalised by substracting from the ct values of the reference genes.
now how to make out whether i have more expression in high density or low density cells i used.
thanks
Julee,
First off, don't assume that your B-actin mRNA levels are remaining constant, especially at first. If this is your first run at qPCR, then I would recommend using at least two loading control genes. There have been at least a hlaf a dozen papers published in the last few years simply about _which_ gene is the "best" loading control becuase they all seem to vary at times. Of course, the perfect control gene would be one that never changes over the course of an experiment, but in reality its not a good idea to assume your gene fits the bill just becuase "its what everyone else uses". My recommendations would be to choose two or three genes from the following list: b-actin, 18S rRNA, GADPH, RPL32, or 5S rRNA. If you normalize to each of them, you can see if your results generally agree regardless of which loading control you choose; and then for future experiments you can just use the one loading control that shows the least variability between treatments. I hope that all makes sense.
As for the math of qPCR; check out this page http://pathmicro.med.sc.edu/pcr/realtime-home.htm It has an excellent walk through of nearly all the issues people should be aware of (and often are not) in regards to qPCR. And, unless your fold-change differences are huge, you have to pay close attention as to how you work out your propagation of error. Most qPCR fold-change data are basically ratio of ratios, so for the uninitiated, the propagation of error bars can be a little tricky.
Hi! Julee,
I can send you that program but I don't know how to attach a file in Bio-forum.
ok
dont worry
thanks
I can send you that program but I don't know how to attach a file in Bio-forum.
Hi
control is to which all the other values are compared in rt pcr, rite?
how do you do that control.
for example, I am using sub and superconfluent cancer cell line, checking e-cadherin levels in both. for the control, what to be done?
I can send you that program but I don't know how to attach a file in Bio-forum.
You have to consider at least one condition out of these, or some other, as control and the other one as your test condition. Only then you can analyse your results.