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The ability of eukaryotic cells to squeeze through constrictions is limitedby the stiffness of their large and rigid nucleus. However, migrating cells areoften able to overcome this limitation and pass through constrictions muchsmaller than their nucleus, a mechanism that is not yet understood. This iswhat we address here through a data-driven approach using microfluidic deviceswhere cells migrate through controlled narrow spaces of sizes comparable to theones encountered in physiological situations. Stochastic Force Inference isapplied to experimental nuclear trajectories and nuclear shape descriptors,resulting in equations that effectively describe this phenomenon of nucleartranslocation. By employing a model where the channel geometry is an explicitparameter and by training it over experimental data with different sizes ofconstrictions, we ensure that the resulting equations are predictive to othergeometries. Altogether, the approach developed here paves the way for amechanistic and quantitative description of dynamical cell complexity duringits motility.

Inferring geometrical dynamics of cell nucleus translocation