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Junction Mapper is a novel computer vision tool to decipher cell–cell contact phenotypes
Stable cell–cell contacts underpin tissue architecture and organization. Quantification of junctions of mammalian epithelia requires laborious manual measurements that are a major roadblock for mechanistic studies. We designed Junction Mapper as an open access, semi-automated software that defines t...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034980/ https://www.ncbi.nlm.nih.gov/pubmed/31793877 http://dx.doi.org/10.7554/eLife.45413 |
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author | Brezovjakova, Helena Tomlinson, Chris Mohd Naim, Noor Swiatlowska, Pamela Erasmus, Jennifer C Huveneers, Stephan Gorelik, Julia Bruche, Susann Braga, Vania MM |
author_facet | Brezovjakova, Helena Tomlinson, Chris Mohd Naim, Noor Swiatlowska, Pamela Erasmus, Jennifer C Huveneers, Stephan Gorelik, Julia Bruche, Susann Braga, Vania MM |
author_sort | Brezovjakova, Helena |
collection | PubMed |
description | Stable cell–cell contacts underpin tissue architecture and organization. Quantification of junctions of mammalian epithelia requires laborious manual measurements that are a major roadblock for mechanistic studies. We designed Junction Mapper as an open access, semi-automated software that defines the status of adhesiveness via the simultaneous measurement of pre-defined parameters at cell–cell contacts. It identifies contacting interfaces and corners with minimal user input and quantifies length, area and intensity of junction markers. Its ability to measure fragmented junctions is unique. Importantly, junctions that considerably deviate from the contiguous staining and straight contact phenotype seen in epithelia are also successfully quantified (i.e. cardiomyocytes or endothelia). Distinct phenotypes of junction disruption can be clearly differentiated among various oncogenes, depletion of actin regulators or stimulation with other agents. Junction Mapper is thus a powerful, unbiased and highly applicable software for profiling cell–cell adhesion phenotypes and facilitate studies on junction dynamics in health and disease. |
format | Online Article Text |
id | pubmed-7034980 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-70349802020-02-24 Junction Mapper is a novel computer vision tool to decipher cell–cell contact phenotypes Brezovjakova, Helena Tomlinson, Chris Mohd Naim, Noor Swiatlowska, Pamela Erasmus, Jennifer C Huveneers, Stephan Gorelik, Julia Bruche, Susann Braga, Vania MM eLife Cancer Biology Stable cell–cell contacts underpin tissue architecture and organization. Quantification of junctions of mammalian epithelia requires laborious manual measurements that are a major roadblock for mechanistic studies. We designed Junction Mapper as an open access, semi-automated software that defines the status of adhesiveness via the simultaneous measurement of pre-defined parameters at cell–cell contacts. It identifies contacting interfaces and corners with minimal user input and quantifies length, area and intensity of junction markers. Its ability to measure fragmented junctions is unique. Importantly, junctions that considerably deviate from the contiguous staining and straight contact phenotype seen in epithelia are also successfully quantified (i.e. cardiomyocytes or endothelia). Distinct phenotypes of junction disruption can be clearly differentiated among various oncogenes, depletion of actin regulators or stimulation with other agents. Junction Mapper is thus a powerful, unbiased and highly applicable software for profiling cell–cell adhesion phenotypes and facilitate studies on junction dynamics in health and disease. eLife Sciences Publications, Ltd 2019-12-03 /pmc/articles/PMC7034980/ /pubmed/31793877 http://dx.doi.org/10.7554/eLife.45413 Text en © 2019, Brezovjakova et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Cancer Biology Brezovjakova, Helena Tomlinson, Chris Mohd Naim, Noor Swiatlowska, Pamela Erasmus, Jennifer C Huveneers, Stephan Gorelik, Julia Bruche, Susann Braga, Vania MM Junction Mapper is a novel computer vision tool to decipher cell–cell contact phenotypes |
title | Junction Mapper is a novel computer vision tool to decipher cell–cell contact phenotypes |
title_full | Junction Mapper is a novel computer vision tool to decipher cell–cell contact phenotypes |
title_fullStr | Junction Mapper is a novel computer vision tool to decipher cell–cell contact phenotypes |
title_full_unstemmed | Junction Mapper is a novel computer vision tool to decipher cell–cell contact phenotypes |
title_short | Junction Mapper is a novel computer vision tool to decipher cell–cell contact phenotypes |
title_sort | junction mapper is a novel computer vision tool to decipher cell–cell contact phenotypes |
topic | Cancer Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7034980/ https://www.ncbi.nlm.nih.gov/pubmed/31793877 http://dx.doi.org/10.7554/eLife.45413 |
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