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Junction Mapper is a novel computer vision tool to decipher cell-cell contact phenotypes.
- Source :
-
ELife [Elife] 2019 Dec 03; Vol. 8. Date of Electronic Publication: 2019 Dec 03. - Publication Year :
- 2019
-
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.<br />Competing Interests: HB, CT, NM, PS, JE, SH, JG, SB, VB No competing interests declared<br /> (© 2019, Brezovjakova et al.)
- Subjects :
- Animals
Cadherins metabolism
Cell Adhesion physiology
Cells, Cultured
Endothelial Cells metabolism
Humans
Intercellular Junctions metabolism
Keratinocytes metabolism
Microscopy, Confocal
Myocytes, Cardiac metabolism
Phenotype
Rats, Sprague-Dawley
Software
Cell Communication physiology
Computational Biology methods
Endothelial Cells physiology
Intercellular Junctions physiology
Keratinocytes physiology
Myocytes, Cardiac physiology
Subjects
Details
- Language :
- English
- ISSN :
- 2050-084X
- Volume :
- 8
- Database :
- MEDLINE
- Journal :
- ELife
- Publication Type :
- Academic Journal
- Accession number :
- 31793877
- Full Text :
- https://doi.org/10.7554/eLife.45413