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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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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. |
format | Online Article Text |
id | pubmed-9223404 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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