RT Journal Article
T1 Shape matters: inferring the motility of confluent cells from static images
A1 Braat, Quirine J. S.
A1 Janzen, Giulia
A1 Jansen, Bas C.
A1 Debets, Vincent E.
A1 Ciarella, Simone
A1 Janssen, Liesbeth M. C.
AB 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.
PB Royal Society of Chemistry
SN 1744-683X
YR 2025
FD 2025
LK https://hdl.handle.net/20.500.14352/124081
UL https://hdl.handle.net/20.500.14352/124081
LA eng
NO Braat, Quirine JS, et al. "Shape matters: inferring the motility of confluent cells from static images." Soft Matter 21.33 (2025): 6504-6515.
NO OCENW.GROOT.2019.0222022-T1/TIC-24007
NO Dutch Research Council (NWO)
NO Comunidad de Madrid
NO Universidad Complutense de Madrid
DS Docta Complutense
RD 31 dic 2025