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Evaluating the pharmacological response in fluorescence microscopy images: The ?m algorithm

Abstract: Current drug discovery procedures require fast and effective quantification of the pharmacological response evoked in living cells by agonist compounds. In the case of G-protein coupled receptors (GPCRs), the efficacy of a particular drug to initiate the endocytosis process is related to the formation of endocytic vesicles or endosomes and their subsequent internalisation within intracellular compartments that can be observed with high spatial and temporal resolution by fluorescence microscopy techniques. Recently, an algorithm has been proposed to evaluate the pharmacological response by estimating the number of endosomes per cell on time series of images. However, the algorithm was limited by the dependence on some manually set parameters and in some cases the quality of the image does not allow a reliable detection of the endosomes. Here we propose a simple, fast and automated image analysis method?the ?m algorithm- to quantify a pharmacological response with data obtained from fluorescence microscopy experiments. This algorithm does not require individual object detection and computes the relative increment of the third order moment in fluorescence microscopy images after filtering with the Laplacian of Gaussian function. It was tested on simulations demonstrating its ability to discriminate different experimental situations according to the number and the fluorescence signal intensity of the simulated endosomes. Finally and in order to validate this methodology with real data, the algorithm was applied to several time-course experiments based on the endocytosis of the mu opioid receptor (MOP) initiated by different agonist compounds. Each drug displayed a different ?m sigmoid time-response curve and statistically significant differences were observed among drugs in terms of efficacy and kinetic parameters.

 Autoría: Gómez A., Cruz M., López-Giménez J.,

 Fuente: PLoS ONE 14(2): e0211330.

 Editorial: Public Library of Science

 Fecha de publicación: 01/02/2019

 Nº de páginas: 13

 Tipo de publicación: Artículo de Revista

 DOI: 10.1371/journal.pone.0211330

 ISSN: 1932-6203

 Url de la publicación: https://doi.org/10.1371/journal.pone.0211330