Bridging the gap between qualitative and quantitative colocalization results in fluorescence microscopy studies.

Researchers doing fluorescence microscopy and performing quantitative colocalization analysis know how difficult it is to describe obtained quantitative data using natural language with subjective qualifiers, such as "Weak", "Moderate", "Strong", etc. Depending on what they expect, many tend to misreport their results: "Strong" colocalization in the case of overlap coefficient (standard values are from 0 to 1.0) may mean 0.99 to one researcher and 0.51 to another. This discrepancy can cause significant confusions and create errors.

For the last several years we tried to find a solution to solving this problem. Assistance came from using the fuzzy system model. With its help, we developed a tool to characterize colocalization data in a way so that they are understandable and comparable both qualitatively and quantitatively. We produced a set of just five linguistic variables tied to the values of popular colocalization coefficients: "Very Weak", "Weak", "Moderate", "Strong", and "Very Strong". The use of these variables ensures that the results of colocalization studies are properly reported, easily shared, and universally understood by all researchers working in the field. Importantly, when new coefficients are introduced, their values can be readily fitted into the set.

To make this information accessible to as many researchers as possible, we decided to publish it in Scientific Reports, an open access journal from Nature Publishing Group (NPG), so that all interested can download a free copy of the paper. Get your copy of the paper here and don't hesitate to ask if you have any questions.