With steady improvement of the reliability of immunofluorescence techniques and quality of confocal microscopes, quantitative colocalization analysis of confocal images emerged as a powerful tool capable of closing the gap between anecdotal evidence and the actual proof of existence of colocalization.

Colocalization is defined as the presence of two or more types of fluorescence molecules at the same location. Physically, this means that the colors emitted by them occupy the same pixel in the image. Biologically, this means that two or more different molecules attach to the same structure in the cell. In the context of digital imaging this is described as the spatial overlap of two or more dyes in a multichannel image. Although colocalization is relative, the information on the appearance of distinct molecules at the same location can be of particular significance to researchers. If the question is “Does protein A overlap/colocalize with protein B in a given image, and if so to what degree?”, quantitative colocalization provides the answer. The degree of colocalization in medico-biological specimens can not be judged by naked eye even when seems obvious. Since colocalization is characterized by the change of the color of dyes, reliable estimation of the degree of this change should be performed exclusively by analyzing colocalization coefficients.

It is important to follow several guidelines to have your colocalization images suitable for quantitation. Ignoring them can undermine reliability of quantitative colocalization analysis or even make your confocal images fully unusable for it.

CoLocalizer Express software estimates colocalization by calculating a number of values representing the proportion of colocalized pixels in dual-color images. These values are called colocalization coefficients.

Coefficients calculated by CoLocalizer Express and their significance:

Pearson's correlation coefficient is one of the standard measures in pattern recognition. It is used for describing the correlation of the intensity distributions between channels. It takes into consideration only similarity between shapes, while ignoring the intensities of signals:

Overlap coefficient according to Manders indicates an overlap of the signals and thus represents the true degree of colocalization. This coefficient is not sensitive to the limitations of typical fluorescence imaging, such as efficiency of hybridization, sample photobleaching, and camera quantum efficiency:

Overlap coefficients k1 and k2 split the value of colocalization into the two separate parameters. k1 and k2 coefficients depend on the sum of the products of the intensities of two channels. Thus, they are sensitive to the differences in the intensities of signals:

Colocalization coefficients m1 and m2 describe the contribution of each channel to the image ROI. They are not sensitive to the intensities of signals and can be used when the numbers of objects are not equal:

Colocalization coefficients M1 and M2 describe the contribution of each channel to the scatter gram ROI. They are not sensitive to the intensities of signals and can be used when the numbers of objects are not equal:

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