Shape matters: inferring the motility of confluent cells from static images

Abstract

Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the heterogeneity in cell motility, but identifying the motility of individual cells is challenging. Previous work has established the importance of the average cell shape in predicting cell dynamics. Here, we aim to identify the importance of individual cell shape features, rather than collective features, to distinguish between high-motility and low-motility (or zero-motility) cells in heterogeneous cell layers. Employing the cellular Potts model, we generate simulation snapshots and extract static features as inputs for a simple machine-learning model. Our results show that when cells are either motile or non-motile, this machine-learning model can accurately predict a cell's phenotype using only single-cell shape features. Furthermore, we explore scenarios where both cell types exhibit some degree of motility, characterized by high or low motility. In such cases, our findings indicate that a neural network trained on shape features can accurately classify cell motility, particularly when the number of highly motile cells is low, and high-motility cells are significantly more motile compared to low-motility cells. This work offers potential for physics-inspired predictions of single-cell properties with implications for inferring cell dynamics from static histological images.

Graphical abstract: Shape matters: inferring the motility of confluent cells from static images

Supplementary files

Article information

Article type
Paper
Submitted
03 Mar 2025
Accepted
17 Jun 2025
First published
24 Jun 2025
This article is Open Access
Creative Commons BY license

Soft Matter, 2025, Advance Article

Shape matters: inferring the motility of confluent cells from static images

Q. J. S. Braat, G. Janzen, B. C. Jansen, V. E. Debets, S. Ciarella and L. M. C. Janssen, Soft Matter, 2025, Advance Article , DOI: 10.1039/D5SM00222B

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements