Predicting organoid morphology through a phase field model: Insights into cell division and lumenal pressure

Organoids are ideal systems to predict the phenotypes of organs. However, there is currently a lack of understanding regarding the generalized rules that enable use of simple cellular principles to make morphological predictions of entire organoids. Therefore, we employed a phase field model with the following basic components: the minimum conditions for the timing and volume of cell division, lumen nucleation rules, and lumenal pressure. Through our model, we could compute and generate a myriad of organoid phenotypes observed till date. We propose morphological indices necessary to characterize the shapes and construct phase diagrams and show their dependencies on proliferation time and lumen pressure. Additionally, we introduced the lumen-index parameter, which helped in examining the criteria to maintain organoids as spherical structures comprising a single layer of cells and enclosing an intact lumen. Finally, we predict a star-like organoid phenotype that did not undergo differentiation, suggesting that the volume constraint during cell division may determine the final phenotype. In summary, our approach provides researchers with guidelines to test the mechanisms of self-organization and predict the shape of organoid.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-08-18
DOI
https://doi.org/10.1371/journal.pcbi.1012090
Citations
5
Primary Topic
Cancer Cells and Metastasis
Type
article
Field-Weighted Citation Impact
5.23

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article

Predicting organoid morphology through a phase field model: Insights into cell division and lumenal pressure

Tristan Guyomar, Daniel Riveline, Tetsuya Hiraiwa, Kana Fuji et al.
5 citations
PLoS Computational Biology
Cancer Cells and Metastasis
5.23
article

Predicting organoid morphology through a phase field model: Insights into cell division and lumenal pressure

Tristan Guyomar, Daniel Riveline, Tetsuya Hiraiwa, Kana Fuji, Makiko Nonomura, Masaki Sano, Anne Grapin‐Botton, Linjie Lu, Alf Honigmann, Sakurako Tanida, Byung Ho Lee
article en
5 citations

Abstract

Organoids are ideal systems to predict the phenotypes of organs. However, there is currently a lack of understanding regarding the generalized rules that enable use of simple cellular principles to make morphological predictions of entire organoids. Therefore, we employed a phase field model with the following basic components: the minimum conditions for the timing and volume of cell division, lumen nucleation rules, and lumenal pressure. Through our model, we could compute and generate a myriad of organoid phenotypes observed till date. We propose morphological indices necessary to characterize the shapes and construct phase diagrams and show their dependencies on proliferation time and lumen pressure. Additionally, we introduced the lumen-index parameter, which helped in examining the criteria to maintain organoids as spherical structures comprising a single layer of cells and enclosing an intact lumen. Finally, we predict a star-like organoid phenotype that did not undergo differentiation, suggesting that the volume constraint during cell division may determine the final phenotype. In summary, our approach provides researchers with guidelines to test the mechanisms of self-organization and predict the shape of organoid.

PLoS Computational BiologyVol. 21(8)
Nihon University (JP), Centre National de la Recherche Scientifique (FR), Inserm (FR), National University of Singapore (SG), Shanghai Jiao Tong University (CN), Institut de génétique et de biologie moléculaire et cellulaire (FR), Institute of Physics, Academia Sinica (TW), Max Planck Institute of Molecular Cell Biology and Genetics (DE), College of Industrial Technology (JP), Université de Strasbourg (FR), The University of Tokyo (JP), Technische Universität Dresden (DE)
Human Frontier Science Program, National Natural Science Foundation of China, Mechanobiology Institute, Singapore, Japan Society for the Promotion of Science
Openalex Percentile: Top 4%
Cancer Cells and Metastasis
5.23
DOIOpen Access PDFOpenAlex
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