Junction Mapper is a novel computer vision tool to decipher cell-cell contact phenotypes
- Imperial College London, United Kingdom
- University of Amsterdam, Netherlands
Abstract
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.
Article and author information
Author details
Helena Brezovjakova
Pamela Swiatlowska
Funding
Medical Research Council (MR/M026310/1)
- Vania MM Braga
Biotechnology and Biological Sciences Research Council (BB/M022617/1)
- Vania MM Braga
Cancer Research UK (C1282/A11980)
- Vania MM Braga
Netherlands Organization of Scientific Research (VIDI 016.156.327)
- Stephan Huveneers
Prime Minister's Office, Brunei Darussalam (JPLL/A/4:A[2010]/J1(703))
- Noor Mohd Naim
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Valerie Horsley, Yale University, United States
Publication history
- Received: January 23, 2019
- Accepted: December 2, 2019
- Accepted Manuscript published: December 3, 2019 (version 1)
Copyright
© 2019, Brezovjakova et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Cancer Biology
- Human Biology and Medicine
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- Cancer Biology
- Computational and Systems Biology
