In a previous post on this topic, I discussed ways of preventing edge effects in plate-based potency assays. But how do you know you have them in the first place?   Sometimes it can take a long time and many runs to figure out that these problems exist. At that point you may have generated results for several samples and by tampering with the assay you lose connection to historical data or at the very least cause the previous results to be looked at with suspicion.

Instead of waiting for bias to appear in your data, I advocate doing edge effect studies early in assay development. The results you find can be invaluable in determining your plate layout, assay testing strategy and assay analysis.

There are several different ways you can characterize your assay for edge effects during development. A simple and useful experiment is to run a “heatmap” on your plate. This type of experiment is as simple as picking a single dose response, usually from the reference standard, and applying it to every well on the plate. The data is then graphed using a color code to visually pick up patterns in the results and/or statistical tests on the row and column data can be performed.

An important part of this experiment is to pick a suitable dose response. Ideally this response should come from the part of the dose response curve with the steepest slope (see the red circle in the image below). This is to ensure that you will have the optimum sensitivity to small changes in signal.

The resulting data from the experiment can be visualized using a graph such as this one:

As you can clearly see in this "heatmap" the center wells of the plate are lower in signal than the outside of the plate.

Most plate based assays will have some sort of edge effect due to the asymmetrical nature of 96 well plates. It isn’t always clear if the effect will actually have a significant impact on the results of the assay.  If the differences in signal between rows and columns are much smaller than the variability of the signal itself, then the effect may not be noticeable in the final results. One way to determine if this is the case is to do a two-way  ANOVA for statistical significance.  In this case, the ANOVA clearly shows that the effect is significant across both rows and columns (p value is less than 0.05):

Two-way ANOVA: Response versus Row, Column

Source  DF        SS       MS     F      P
Row      7   9088182  1298312  7.87  0.000
Column  10   4776165   477616  2.90  0.004
Error   70  11540595   164866
Total   87  25404942

S = 406.0   R-Sq = 54.57%   R-Sq(adj) = 43.54%

Heat map experiments generate a lot of information about your assay, but it only deals with detecting an edge effect in a single dose. Since the ultimate readout of these assays, the potency, relies on the entire dose response range, you may want to do some further characterization of the edge effect by running the reference standard in multiple sample positions on several plates.  The graph below shows the results of such a study where the reference standard was placed in every row of five plates (positions 2-8) and a potency was generated relative to the reference in position 1. The expected potency is 1.0 since every position contains the reference standard. It is clear that in this assay, the potency is severely biased by the position effect.  It is much higher in the middle of the plate.  This type of analysis can also indicate if there is a bias in the variance of the response. In this case, positions 4 and 5 clearly have much less variability than the others.

As you can see, there is much information about your assay that can be gained from just a few experiments early in the development process. Once you have this information, the assay protocol can be designed to prevent a bias in your results. But that’s a topic for another post.

Thanks for reading,

Dan

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