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Quantitative colocalization studies suffer from the lack of unified approach to interpret obtained results. We developed a tool to characterize the results of colocalization experiments in a way so that they are understandable and comparable both qualitatively and quantitatively. Employing a fuzzy system model and computer simulation, 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 the variables ensures that the results of colocalization studies are properly reported, easily shared, and universally understood by all researchers working in the field. When new coefficients are introduced, their values can be readily fitted into the set.

Interactions of proteins are examined by detecting their overlap using fluorescent markers. The observed overlap is then quantified to serve as a measure of spatial correlation. A major drawback of this approach is that it can produce false values because of the properties of the image background. To remedy this, we provide a protocol to reduce the contribution of image background and then apply a protein proximity index (PPI) and correlation coefficient to estimate colocalization. Background heterogeneity is reduced by the median filtering procedure, comprising two steps, to reduce random noise and background, respectively. Alternatively, background can be reduced by advanced thresholding. PPI provides separate values for each channel to characterize the contribution of each protein, whereas correlation coefficient determines the overall colocalization. The protocol is demonstrated using computer-simulated and real biological images. It minimizes human bias and can be universally applied to various cell types in which there is a need to understand protein-protein interactions. Background reductions require 3-5 min per image. Quantifications take <1 min. The entire procedure takes approximately 15-30 min.Published in " " which should be cited to refer to this work.

Colocalization is an important finding in many cell biological studies. This unit describes a protocol for quantitative evaluation of fluorescence microscopy images with colocalization based on calculation of a number of specialized coefficients. Images of double-stained sections are first subjected to background correction, and then various coefficients are calculated. Meanings of the coefficients and a guide to interpretation of the results of calculations based on the use of linguistic variables are given. Success in colocalization studies depends on the quality of images analyzed, proper preparation of the images for coefficients calculations, and correct interpretation of results obtained. This protocol helps ensure reliability of colocalization coefficient calculations. Curr.Keywords: quantitative colocalization r fluorescence microscopy r image analysis r data interpretation

The explosive growth of machine learning has provided scientists with insights into data in ways unattainable using prior research techniques. It has allowed the detection of biological features that were previously unrecognized and overlooked. However, because machine‐learning methodology originates from informatics, many cell biology labs have experienced difficulties in implementing this approach. In this article, we target the rapidly expanding audience of cell and molecular biologists interested in exploiting machine learning for analysis of their research. We discuss the advantages of employing machine learning with microscopy approaches and describe the machine‐learning pipeline. We also give practical guidelines for building models of cell behavior using machine learning. We conclude with an overview of the tools required for model creation, and share advice on their use. © 2020 by John Wiley & Sons, Inc.