Junction Mapper is a novel computer vision tool to decipher cell-cell contact phenotypes

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.

Data availability

The Junction Mapper code is licensed in github as GNU GENERAL PUBLIC LICENSE. The address is:https://github.com/ImperialCollegeLondon/Junction_MapperThe software is downloadable from as an executable jar file from;https://dataman.bioinformatics.ic.ac.uk/junction_mapper/The image data used in this study has been previously published elsewhere (Erasmus et al., 2016; Huveneer et al., 2012) or are in preparation in separate mechanistic studies (Bruche et al., in preparation).Excel files of the output of parameters and calculations has been provided as source data files online.

Article and author information

Author details

  1. Helena Brezovjakova

    National Heart and Lung Institute, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3554-6084
  2. Chris Tomlinson

    Bioinformatics Data Science Group, Faculty of Medicine, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  3. Noor Mohd Naim

    National Heart and Lung Institute, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  4. Pamela Swiatlowska

    National Heart and Lung Institute, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3705-7495
  5. Jennifer E Erasmus

    National Heart and Lung Institute, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
  6. Stephan Huveneers

    Department Medical Biochemistry, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, Netherlands
    Competing interests
    The authors declare that no competing interests exist.
  7. Julia Gorelik

    National Heart and Lung Institute, Imperial College London, London, United Kingdom
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1148-9158
  8. Susann Bruche

    National Heart and Lung Institute, Imperial College London, London, United Kingdom
    For correspondence
    susann.bruche@dpag.ox.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5814-7166
  9. Vania MM Braga

    National Heart and Lung Institute, Imperial College London, London, United Kingdom
    For correspondence
    v.braga@imperial.ac.uk
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0546-7163

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

  1. Valerie Horsley, Yale University, United States

Version history

  1. Received: January 23, 2019
  2. Accepted: December 2, 2019
  3. Accepted Manuscript published: December 3, 2019 (version 1)
  4. Version of Record published: February 21, 2020 (version 2)

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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  1. Helena Brezovjakova
  2. Chris Tomlinson
  3. Noor Mohd Naim
  4. Pamela Swiatlowska
  5. Jennifer E Erasmus
  6. Stephan Huveneers
  7. Julia Gorelik
  8. Susann Bruche
  9. Vania MM Braga
(2019)
Junction Mapper is a novel computer vision tool to decipher cell-cell contact phenotypes
eLife 8:e45413.
https://doi.org/10.7554/eLife.45413

Share this article

https://doi.org/10.7554/eLife.45413

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