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Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm

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 formati...

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Autores principales: Gómez, Ana I., Cruz, Marcos, López-Giménez, Juan F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373910/
https://www.ncbi.nlm.nih.gov/pubmed/30759168
http://dx.doi.org/10.1371/journal.pone.0211330
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author Gómez, Ana I.
Cruz, Marcos
López-Giménez, Juan F.
author_facet Gómez, Ana I.
Cruz, Marcos
López-Giménez, Juan F.
author_sort Gómez, Ana I.
collection PubMed
description 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 Δm 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 Δm sigmoid time-response curve and statistically significant differences were observed among drugs in terms of efficacy and kinetic parameters.
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spelling pubmed-63739102019-03-01 Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm Gómez, Ana I. Cruz, Marcos López-Giménez, Juan F. PLoS One Research Article 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 Δm 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 Δm sigmoid time-response curve and statistically significant differences were observed among drugs in terms of efficacy and kinetic parameters. Public Library of Science 2019-02-13 /pmc/articles/PMC6373910/ /pubmed/30759168 http://dx.doi.org/10.1371/journal.pone.0211330 Text en © 2019 Gómez et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Gómez, Ana I.
Cruz, Marcos
López-Giménez, Juan F.
Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm
title Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm
title_full Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm
title_fullStr Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm
title_full_unstemmed Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm
title_short Evaluating the pharmacological response in fluorescence microscopy images: The Δm algorithm
title_sort evaluating the pharmacological response in fluorescence microscopy images: the δm algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373910/
https://www.ncbi.nlm.nih.gov/pubmed/30759168
http://dx.doi.org/10.1371/journal.pone.0211330
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