Cells under high confinement migrate efficiently in low-adhesion environments by forming stable, polarized, hydrostatic pressure-driven leader blebs. Here we investigated the basis of polarized bleb morphology in metastatic melanoma cells migrating under low-adhesive and highly confined microenvironments. Using high-resolution live imaging, molecular perturbations and biosensors, we show that EGF signalling through PI3K stabilizes and maintains polarized leader blebs. EGFR and PI3K activities form a gradient within leader blebs that decreases from rear to front, promoting phosphatidylinositol 3,4,5-trisphosphate and Rac1-GTP accumulation at the bleb rear, whereas phosphatidylinositol 4,5-bisphosphate and RhoA-GTP concentrate at the bleb tip, the inverse of the organization observed in integrin-dependent mesenchymal migration. Optogenetic disruption of this gradient triggers bleb retraction, underscoring its functional importance. Mathematical modelling and experiments identified a mech

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Author summary Cell migration velocity varies across biological processes–from immune responses to cancer metastasis. Yet accurate measurement remains challenging due to inherent cell-to-cell variabil...

Motivation Accurate quantification of live cell migration, including velocity, is essential for understanding biological processes such as development, immune function, and cancer metastasis. High-throughput migration assays generate complex, hierarchically structured datasets with technical noise, batch effects, and biological variability, introducing significant uncertainty into velocity estimates. Current statistical approaches often fail to rigorously quantify this uncertainty, undermining reproducibility and comparability across independent experimental datasets. To address this challenge, we present cellmig , a computational tool that employs Bayesian hierarchical modeling to separate biological signals from technical variation while explicitly quantifying uncertainty in migration velocity. Results cellmig provides a robust framework for analyzing cell migration assays, including dose-response studies and large-scale screens with multiple biological and technical replicates. By modeling biological variability (e.g., compound-dependent effects) and technical confounders (e.g., batch variability) within a unified Bayesian framework, cellmig estimates condition-specific effects on cell velocity with probabilistic uncertainty intervals, avoiding common pitfalls associated with null-hypothesis testing. Additionally, its generative models enable simulation of migration velocities under various assumptions, aiding experimental planning. In summary, cellmig enhances reproducibility, reliability, and biological insight in high-throughput migration studies, facilitating inter-dataset comparisons. Availability and Implementation cellmig is an open-source R package, freely available at: <https://github.com/snaketron/cellmig> Contact barbara.gruener{at}uk-essen.de, daniel.hoffmann{at}uni-due.de, simo.kitanovski{at}uni-due.de ### Competing Interest Statement The authors have declared no competing interest.

The evolution of multicellular organisms hinges on self-organization mechanisms that generate tissues with diverse functions. A central process is the breaking of symmetry to form spatial patterns fro...