A Method for Quantifying Molecular Interactions Using Stochastic Modelling and Super-Resolution Microscopy

Keria Bermudez-Hernandez, Sarah Keegan, Donna R. Whelan, Dylan A. Reid, Jennifer Zagelbaum, Yandong Yin, Sisi Ma, Eli Rothenberg, David Fenyö

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13 Scopus citations

Abstract

We introduce the Interaction Factor (IF), a measure for quantifying the interaction of molecular clusters in super-resolution microscopy images. The IF is robust in the sense that it is independent of cluster density, and it only depends on the extent of the pair-wise interaction between different types of molecular clusters in the image. The IF for a single or a collection of images is estimated by first using stochastic modelling where the locations of clusters in the images are repeatedly randomized to estimate the distribution of the overlaps between the clusters in the absence of interaction (IF = 0). Second, an analytical form of the relationship between IF and the overlap (which has the random overlap as its only parameter) is used to estimate the IF for the experimentally observed overlap. The advantage of IF compared to conventional methods to quantify interaction in microscopy images is that it is insensitive to changing cluster density and is an absolute measure of interaction, making the interpretation of experiments easier. We validate the IF method by using both simulated and experimental data and provide an ImageJ plugin for determining the IF of an image.

Original languageEnglish (US)
Article number14882
JournalScientific reports
Volume7
Issue number1
DOIs
StatePublished - Dec 1 2017

Bibliographical note

Funding Information:
K.B. was supported by the Molecular Oncology and Immunology training grant T32 CA009161. Work in E.R.’s laboratory is supported by National Institutes of Health Grants CA187612, GM108119, and the American Cancer Society RSG DMC-16-241-01-DMC. Work in D.F.’s laboratory is supported by National Institutes of Health Grant U24 CA210972. We would like to thank Mario Delmar and Esperanza Agullo-Pascual for the opportunity to collaborate and for comments on the ImageJ plugin. We would also like to thank Bart Aromando for his comments and work on the user guide for the plugin.

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