Comparative study of non-invasive force and stress inference methods in tissue

TL;DR

This study compares three non-invasive force inference methods in tissue, demonstrating that Bayesian inference provides the most accurate and robust estimates of cellular stresses and tensions during morphogenesis.

Contribution

The paper introduces and validates a Bayesian force inference method that simultaneously estimates cell-junction tensions and pressures, outperforming other approaches.

Findings

Bayesian method shows highest accuracy and robustness.

Force inference correlates well with tissue ablation results.

Method applicable to both artificial and experimental data.

Abstract

In the course of animal development, the shape of tissue emerges in part from mechanical and biochemical interactions between cells. Measuring stress in tissue is essential for studying morphogenesis and its physical constraints. Experimental measurements of stress reported thus far have been invasive, indirect, or local. One theoretical approach is force inference from cell shapes and connectivity, which is non-invasive, can provide a space-time map of stress and relies on prefactors. Here, to validate force- inference methods, we performed a comparative study of them. Three force-inference methods, which differ in their approach of treating indefiniteness in an inverse problem between cell shapes and forces, were tested by using two artificial and two experimental data sets. Our results using different datasets consistently indicate that our Bayesian force inference, by which cell-junction tensions and cell pressures are simultaneously estimated, performs best in terms of accuracy and robustness. Moreover, by measuring the stress anisotropy and relaxation, we cross-validated the force inference and the global annular ablation of tissue, each of which relies on different prefactors. A practical choice of force-inference methods in distinct systems of interest is discussed.