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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010209 |
| _version_ | 1817970483040092160 |
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| author | Goshi Ogita Takefumi Kondo Keisuke Ikawa Tadashi Uemura Shuji Ishihara Kaoru Sugimura |
| author_facet | Goshi Ogita Takefumi Kondo Keisuke Ikawa Tadashi Uemura Shuji Ishihara Kaoru Sugimura |
| author_sort | Goshi Ogita |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-13T20:34:41Z |
| format | Article |
| id | doaj.art-def01b1efa5f4614803acf8915884869 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-13T20:34:41Z |
| publishDate | 2022-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-def01b1efa5f4614803acf89158848692022-12-22T02:31:04ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-06-01186e101020910.1371/journal.pcbi.1010209Image-based parameter inference for epithelial mechanics.Goshi OgitaTakefumi KondoKeisuke IkawaTadashi UemuraShuji IshiharaKaoru SugimuraMeasuring 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.https://doi.org/10.1371/journal.pcbi.1010209 |
| spellingShingle | Goshi Ogita Takefumi Kondo Keisuke Ikawa Tadashi Uemura Shuji Ishihara Kaoru Sugimura Image-based parameter inference for epithelial mechanics. PLoS Computational Biology |
| 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 |
| url | https://doi.org/10.1371/journal.pcbi.1010209 |
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