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Recommended maximum OD values in ELISA optimization. What's the reason?

competitive ELISA OD values optimization

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#1 RILC

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Posted 23 April 2012 - 02:58 PM

Greetings to all, ye Lore Keepers! This is my first post. I'm a postgraduate student in Argentina.
I'm optimizing an indirect competitive ELISA to quantify a small molecule in plant tissue. Reading Crowther's ELISA Guidebook (awfully edited) i've found that when optimizing a competitive ELISA, the recommended antigen concentration to coat plates with is that which results in a maximum OD (plateau) of 1-1.5 for antibody excess. And an antibody dilution which results in 70% of that maximum OD (since competitive ELISAs are reagent limited).
My question is: why not greater OD plateau values e.g. 2.0 (provided we are having the same background OD)?
I'm using a Tecan Sunrise plate reader.
Any ideas? Thanks in advance!

#2 Ben Lomond

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Posted 23 April 2012 - 03:41 PM

The advice that you have received is a good starting point. The OD issue is really arbitrary, and more dependent on the performance of the plate reader. The spectramax plate readers that I am familiar with tend to gain some randomness above 3 OD units with acidified TMB at 450-650 nm. What is important is to know your target assay range ahead of time and to tailor the conditions to yield acceptable precison and accuracy over that concentration range. With everything else optimized, 20-80% of the dose response is often where acceptable P+A can be found.

I suspect that your assay optimized at 1 OD unit will have only slightly less statistical power than one optimized at 2 units.

#3 RILC

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Posted 24 April 2012 - 06:52 PM

Thanks a lot for the quick reply! I suspected that it could have something to do with instrumental limitations but was not sure. Our reader, according to its specs, if I remember well, is also more precise and accurate til 2 OD than in the 2-3 OD range.
In one of the optimization checkerboard assays I've done (figure below), when I cover plates with more antigen (green line, primary Ab dilution in x axis) I get a higher plateau and a steeper slope with more or less the same background, so I thought that would be preferable. Though, graphed values are just means of duplicate samples, so my suppositions might be called into question. But, as a first approach I thought it could be of use. If you want to point out something else, be my guest!
Thanks a lot again! It´s really enthusing to find people willing to share their knowledge.
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

#4 RILC

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Posted 24 April 2012 - 06:59 PM

Oops! Thought I could paste images. Sorry! Hope my explanation was clear enough.





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