DLITE Uses Cell-Cell Interface Movement to Better Infer Cell-Cell Tensions

Abstract

Cell shapes and connectivities evolve over time as the colony changes shape or embryos develop. Shapes of intercellular interfaces are closely coupled with the forces resulting from actomyosin interactions, membrane tension, or cell-cell adhesions. Although it is possible to computationally infer cell-cell forces from a mechanical model of collective cell behavior, doing so for temporally evolving forces in a manner robust to digitization difficulties is challenging. Here, we introduce a method for dynamic local intercellular tension estimation (DLITE) that infers such evolution in temporal force with less sensitivity to digitization ambiguities or errors. This method builds upon previous work on single time points (cellular force-inference toolkit). We validate our method using synthetic geometries. DLITE’s inferred cell colony tension evolutions correlate better with ground truth for these synthetic geometries as compared to tension values inferred from methods that consider each time point in isolation. We introduce cell connectivity errors, angle estimate errors, connection mislocalization, and connection topological changes to synthetic data and show that DLITE has reduced sensitivity to these conditions. Finally, we apply DLITE to time series of human-induced pluripotent stem cell colonies with endogenously expressed GFP-tagged zonulae occludentes-1. We show that DLITE offers improved stability in the inference of cell-cell tensions and supports a correlation between the dynamics of cell-cell forces and colony rearrangement.

Inferring cell junction tension and pressure from cell geometry

“…As expected for slow developmental processes, in a time-lapse movie the inferred relative tensions display small fluctuations with time, of order of minutes . This justifies the method of Vasan et al (2019) discussed previously.…”

“…Third, all variants discussed so far consider individual static images; where a time-lapse movie is being analysed, each image is treated independently. Astutely, Vasan et al (2019) use the values of tension at each edge and pressure in each cell from the previous time point as an initial guess for the current time point (see below, in the 'Validations' section, a justification for this method). This informs the inversion method and slightly improves its robustness against noise.…”

Non-uniform distribution of myosin-mediated forces governs red blood cell membrane curvature through tension modulation

“…Future efforts focusing on the shape transformations of RBCs from discocytes to echinocytes or stomatocytes will be important to connect RBC morphology to physiological function and molecular mechanisms. There is also an opportunity to extend the current models for non biconcave RBCs shape including experimental manipulation of the membrane tension or inhibition of the myosin activity in RBCs [106,123,124]. These require the adaptation of newly emerging technologies for RBC biology and will provide new insight into the molecular mechanisms of RBC shape generation and maintenance.…”

The complex three-dimensional organization of epithelial tissues

“…only relative values of tensions and pressure differences can be estimated), it also has many advantages (e.g. it is non-invasive) and has been instrumental in understanding a number of morphogenetic events (Gómez-González et al, 2020;Noll et al, 2020;Sugimura et al, 2016;Vasan et al, 2019;Veldhuis et al, 2017). Notably, GFI has traditionally focused on the 2D cellular organization of tissues and, to our knowledge, only two recent studies have proposed a 3D extension (Veldhuis et al, 2017;Xu et al, 2018): Veldhuis and colleagues have introduced CellFIT-3D, a tool based on the inference of 3D properties using 2D slides (e.g.…”