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Image-based parameter inference for epithelial mechanics

Measuring mechanical parameters in tissues, such as the elastic modulus of cell-cell junctions, is essential to decipher the mechanical control of morphogenesis. However, their in vivo measurement is technically challenging. Here, we formulated an image-based statistical approach to estimate the mec...

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Detalles Bibliográficos
Autores principales: Ogita, Goshi, Kondo, Takefumi, Ikawa, Keisuke, Uemura, Tadashi, Ishihara, Shuji, Sugimura, Kaoru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223404/
https://www.ncbi.nlm.nih.gov/pubmed/35737656
http://dx.doi.org/10.1371/journal.pcbi.1010209
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author Ogita, Goshi
Kondo, Takefumi
Ikawa, Keisuke
Uemura, Tadashi
Ishihara, Shuji
Sugimura, Kaoru
author_facet Ogita, Goshi
Kondo, Takefumi
Ikawa, Keisuke
Uemura, Tadashi
Ishihara, Shuji
Sugimura, Kaoru
author_sort Ogita, Goshi
collection PubMed
description Measuring mechanical parameters in tissues, such as the elastic modulus of cell-cell junctions, is essential to decipher the mechanical control of morphogenesis. However, their in vivo measurement is technically challenging. Here, we formulated an image-based statistical approach to estimate the mechanical parameters of epithelial cells. Candidate mechanical models are constructed based on force-cell shape correlations obtained from image data. Substitution of the model functions into force-balance equations at the cell vertex leads to an equation with respect to the parameters of the model, by which one can estimate the parameter values using a least-squares method. A test using synthetic data confirmed the accuracy of parameter estimation and model selection. By applying this method to Drosophila epithelial tissues, we found that the magnitude and orientation of feedback between the junction tension and shrinkage, which are determined by the spring constant of the junction, were correlated with the elevation of tension and myosin-II on shrinking junctions during cell rearrangement. Further, this method clarified how alterations in tissue polarity and stretching affect the anisotropy in tension parameters. Thus, our method provides a novel approach to uncovering the mechanisms governing epithelial morphogenesis.
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spelling pubmed-92234042022-06-24 Image-based parameter inference for epithelial mechanics Ogita, Goshi Kondo, Takefumi Ikawa, Keisuke Uemura, Tadashi Ishihara, Shuji Sugimura, Kaoru PLoS Comput Biol Research Article Measuring mechanical parameters in tissues, such as the elastic modulus of cell-cell junctions, is essential to decipher the mechanical control of morphogenesis. However, their in vivo measurement is technically challenging. Here, we formulated an image-based statistical approach to estimate the mechanical parameters of epithelial cells. Candidate mechanical models are constructed based on force-cell shape correlations obtained from image data. Substitution of the model functions into force-balance equations at the cell vertex leads to an equation with respect to the parameters of the model, by which one can estimate the parameter values using a least-squares method. A test using synthetic data confirmed the accuracy of parameter estimation and model selection. By applying this method to Drosophila epithelial tissues, we found that the magnitude and orientation of feedback between the junction tension and shrinkage, which are determined by the spring constant of the junction, were correlated with the elevation of tension and myosin-II on shrinking junctions during cell rearrangement. Further, this method clarified how alterations in tissue polarity and stretching affect the anisotropy in tension parameters. Thus, our method provides a novel approach to uncovering the mechanisms governing epithelial morphogenesis. Public Library of Science 2022-06-23 /pmc/articles/PMC9223404/ /pubmed/35737656 http://dx.doi.org/10.1371/journal.pcbi.1010209 Text en © 2022 Ogita et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Ogita, Goshi
Kondo, Takefumi
Ikawa, Keisuke
Uemura, Tadashi
Ishihara, Shuji
Sugimura, Kaoru
Image-based parameter inference for epithelial mechanics
title Image-based parameter inference for epithelial mechanics
title_full Image-based parameter inference for epithelial mechanics
title_fullStr Image-based parameter inference for epithelial mechanics
title_full_unstemmed Image-based parameter inference for epithelial mechanics
title_short Image-based parameter inference for epithelial mechanics
title_sort image-based parameter inference for epithelial mechanics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9223404/
https://www.ncbi.nlm.nih.gov/pubmed/35737656
http://dx.doi.org/10.1371/journal.pcbi.1010209
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