Dear all,
I performed an in vitro luminescence bioassay to determine the effect which an arthropod toxin has on the cell viability (CellTiter Glo) of mouse myoblasts and neuroblasts. After blank reduction, I prepared a calibration curve from the control data (Fig. 1). A statistical evaluation of my control data suggests that a polynomial regression of 2nd order fits best (based on highest R2 adjusted and lowest standard error).
The problem I am facing is that unexpectedly the toxins tested display HIGHER luminescence values than the positive control (direct correlation of cell survival and luminescence units). This leads to a negative root, when trying to resolve the polynom equation by pq-formula, and thus, I get no cell survival results for my toxins.
Do you have any suggestions how to solve this problem? Did I miss a "normalization step" of my dataset which could overcome this problem? Or is it possible that it has simply no solution because we are trying to extrapolate from an "interpolation" approach?
Fig. 1. Regression. Shows the data for my calibration curve, and the polynomial curve itself.
Fig. 2. Calculations. Shows how I calculated the cell survival % for my samples and also recalculated for the controls based on their induced luminescence signal.
Thank you for your time in advance!
I am not entirely sure what you're asking but might a Kaplan-Meier curve help. There's some nice R code here: https://b-ok.cc/book/5825760/ed313f
Best wishes, David Booth.
David Eugene Booth thank you for your recommendation! Kaplan-Meier seems to be an good option! I will try with it!
Jochen Wilhelm you are right, this result also surprised me. An possible explanation is that the unknown toxin that we tested may support proliferation or ATP production (which is the metabolite reaction with luciferin and giving the luminescence signal recorded).
David Eugene Booth if you allow another question, could it be that Kaplan-Meier estimation does just work with several time points? Because my luminescence assay is an end-point assay and I just measured once after 24h.
Jochen Wilhelm sorry if I did not explain myself properly. It is a cell viability assay which allows a linear correlation between the number of viable cells per well with luminescence record, as the luciferin in the assay reacts with the ATP produced by viable cells. Thus, as more viable cells per well more ATP is produce and the luminescence record is higher. So, we measure luminescence which is directly related to ATP produced and indirectly with proliferation. Hope this makes it clearer.
So this is not a very direct assessment of viability, and any treatment affecting the energy metabolism will affect your read-out. This does not seem to be a good method if you want to compare treated to untreated (or differently treated) cells, as almost any treatment will impact the energy metabolism.
You should seek for a way to measure viability that is more direct and is not affected by the treatment (other than by viability itself, for sure).
Kim N. Kirchhoff Apologies but you have cut off the variable names on your graphs. Now I used to do a lot of chemical mutagen studies in Eukaryotic fungi. Since most of these mutagens are toxic we did routinely survival curves so that we could control for toxicity of the treatment. You didn't do that as i understand you. What did you control for and how do you know that endpoint survival is not determined by other things going on in your cells? Could your calibration curve have been different with more survival survival measurements across time. n other words aren't you tacitly assuming your survival curve is linear without measuring it . I guess what I'm really asking is did you adequately control your experiment and how do you know that? Apologies again but I believe this is related to Prof. Wilhelm's last question. In order to be clear I think I'm asking how you know the relationship between ATP produced and proliferation is linear without measuring it?. Best wishes, D. Booth
Dear David Eugene Booth and Jochen Wilhelm I perfectly understand your concerns regarding this assay. In fact, the ATP content in the cells can vary based on environmental factors (e.g. temperature), cell type, oxygen depletion, etc. But Promega (company producing this assay) shows several experiments indicating the direct relationship of luminescence and cell number over three orders of magnitude (100-20,000 cells per well). Of course I tested this also for my cell lines (N2a, C2C12) by seeding different cell numbers, with different incubation times, treatments, and luminescence recording parameters. The calibration on each testing plate comprises a serial dilution of 104 to 103 cells per well with a total of seven calibration points including a blank control. The linearity of luminescence and this calibration series is shown by a Pearson coefficient of >0.9 in all my experiments.
It is not the point that the assay gives a signal that is nicely correlated to the cell number. The point is that these cells are all comparable (allsimilar cells under similar conditions), but your cells are not (different treatments, different environments).
Dear Jochen Wilhelm, sorry for the late reply. If I understand you right, you are criticizing that, in the case of energy metabolism based cell viability measurements, we cannot compared control cells (under normal growth medium) with cells under treatment as the treatment could induce an unknown disruption of the energy metabolism and we may “loose” the linear correlation between luminescence and number of viable cells. Correct? Do you have a recommendation how we could test for “assay linearity” under the effect of the treatment? Testing a dilution series, for example?
Anyway thank you for the good critics! I will perform some more intra- and inter-assay comparisons, and surely find a solution for this question!
Given your treatment impacts the (energy-)metabolism, I think you must find a way to measure viability with a different assay. Otherwise, your treatment effect on ATP production will always be confounded with the effect on the viability, and you have no chance to separate one from the other.
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