Good colocalization will give a scatterplot which is best fit by a linear curve, where the slope of this curve is representative of the ratio of immunostained colors. Ideally, the slope should be equal to 1 (y=x), however, it is more likely that one of the immunostained colors will be darker than the other causing the slope to tend more towards that axis. Slope of the line represents the "red to green" ratio, as a measure of both image intensity and colocalization. However, the number of objects in both channel of the image has to be more or less equal. I subtracted background and tried Coloc2 with below settings: (I calculated PSF using 1.22/NA formula to get the value of approx. It ranges between 1 and zero with 1 being high-colocalisation, zero being low. In theory, these clustered proteins should co-localize well, resulting in high Pearson’s and Mander’s coefficients. This is easier than the Pearson’s coefficient to comprehend. However, a value close to 1 does indicate reliable colocalisation. Low (close to zero) and negative values for Pearson’s correlation coefficient for fluorescent images can be difficult to interpret. A Fiji/ ImageJ macro (as specified in the Methods section) was used for object recognition, segmentation and calculation of a combination, as well as a colocalization mask. While perfect correlation gives a value of 1, perfect exclusion does not give a value of -1. However, this is not the case for images. A value of 1 represents perfect correlation -1 represents perfect exclusion and zero represents random localisation. In many forms of correlation analysis the values for Pearson’s will range from 1 to -1. This is a popular method of quantifying correlation in many fields of research from psychology to economics. Zero-zero pixels are not included in this calculation. This is the Pearson’s correlation coefficient. Select max intensity from the pulldown list. Flatten z-stacks by going Image->Z-Project and selecting the range of images (typically the first x images are one color and then the last x images are the next color).Open image and change to 8-bit (Image->Type->8 bit).Assess colocalisation by Plugins->Analyze->Colocalisation Finder.Select each image and change to 8-bit greyscale by Image->type->8bit.Select File and split by Image->color->Split.Select File and create composite by Image->color>create composite.Open an experiment file in ImageJ by File->Open->Select File.Prior to performing colocalisation analysis save cell images as experiment files.
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