Inference of Cell Mechanics in Heterogeneous Epithelial Tissue Based on Multivariate Clone Shape Quantification

Abstract

Cell populations in multicellular organisms show genetic and non-genetic heterogeneity, even in undifferentiated tissues of multipotent cells during development and tumorigenesis. The heterogeneity causes difference of mechanical properties, such as, cell bond tension or adhesion, at the cell–cell interface, which determine the shape of clonal population boundaries via cell sorting or mixing. The boundary shape could alter the degree of cell–cell contacts and thus influence the physiological consequences of sorting or mixing at the boundary (e.g., tumor suppression or progression), suggesting that the cell mechanics could help clarify the physiology of heterogeneous tissues. While precise inference of mechanical tension loaded at each cell–cell contacts has been extensively developed, there has been little progress on how to distinguish the population-boundary geometry and identify the cause of geometry in heterogeneous tissues. We developed a pipeline by combining multivariate analysis of clone shape with tissue mechanical simulations. We examined clones with four different genotypes within Drosophila wing imaginal discs: wild-type, tartan (trn) overexpression, hibris (hbs) overexpression, and Eph RNAi. Although the clones were previously known to exhibit smoothed or convoluted morphologies, their mechanical properties were unknown. By applying a multivariate analysis to multiple criteria used to quantify the clone shapes based on individual cell shapes, we found the optimal criteria to distinguish not only among the four genotypes, but also non-genetic heterogeneity from genetic one. The efficient segregation of clone shape enabled us to quantitatively compare experimental data with tissue mechanical simulations. As a result, we identified the mechanical basis contributed to clone shape of distinct genotypes. The present pipeline will promote the understanding of the functions of mechanical interactions in heterogeneous tissue in a non-invasive manner.

Two types of critical cell density for mechanical elimination of abnormal cell clusters from epithelial tissue

“…In high-density cell populations, individual cells are sensitive to fluctuations in size due to the T1 process and/or division of surrounding cells, and the probability of them losing their stable equilibrium and disappearing via saddle-node bifurcation increases [31]. In a related study of the number of cell sides, Tsuboi et al addressed the issue of which cell population fills the gap when cell death occurs at the interface between two proliferating cell populations [52]. Thus, for abnormal cells, rapid growth is not always an advantage.…”

Bayesian parameter inference for epithelial mechanics

“…With some recent exceptions [17][18][19][20], many previous studies using the CVM have relied on standard model functions that qualitatively represent epithelial mechanics [15,16]. Parameter values are determined by iteratively running numerical simulations with different parameter sets and fitting the simulation results to experimental observations [7,[21][22][23][24][25]. Summary statistics, such as the polygonal distribution of cells, are used for this fitting process, making the comparison between simulation and experimental data indirect.…”

Continuum description of confluent tissues with spatial heterogeneous activity

“…8 Non-genetic heterogeneity, also known as phenotypic heterogeneity, can be induced by extrinsic and intrinsic variables in epithelial tissues, 8 and has been associated with changes in cell-level mechanical properties. 6,9 Variations in cellular adhesion and contractility specifically govern cell sorting or mixing. 9 In the context of cancer, heterogeneity is of special importance since intratumor areas exhibit high morphological variations linked with adaptation and resistance to therapy, hence promoting disease progression.…”

“…6,9 Variations in cellular adhesion and contractility specifically govern cell sorting or mixing. 9 In the context of cancer, heterogeneity is of special importance since intratumor areas exhibit high morphological variations linked with adaptation and resistance to therapy, hence promoting disease progression. 4,5 Some recent cell-level computational and continuum models account for tissue heterogeneities.…”

“…4,5 Some recent cell-level computational and continuum models account for tissue heterogeneities. 7,9,10 Specifically, in ref. 7, the authors analyzed population heterogeneity in relation to mechanical heterogeneity using a cell-level model, showing that variations in the cellular shape index (defined as the ratio of the preferred perimeter to the square root of the preferred area) increase tissue-level rigidity.…”