Cell migration in confining physiological environments relies on the concerted dynamics of several cellular components, including protrusions, adhesions with the environment, and the cell nucleus. However, it remains poorly understood how the dynamic interplay of these components and the cell polarity determine the emergent migration behavior at the cellular scale. Here, we combine data-driven inference with a mechanistic bottom-up approach to develop a model for protrusion and polarity dynamics in confined cell migration, revealing how the cellular dynamics adapt to confining geometries. Specifically, we use experimental data of joint protrusion-nucleus migration trajectories of cells on confining micropatterns to systematically determine a mechanistic model linking the stochastic dynamics of cell polarity, protrusions, and nucleus. This model indicates that the cellular dynamics adapt to confining constrictions through a switch in the polarity dynamics from a negative to a positive self-reinforcing feedback loop. Our model further reveals how this feedback loop leads to stereotypical cycles of protrusion-nucleus dynamics that drive the migration of the cell through constrictions. These cycles are disrupted upon perturbation of cytoskeletal components, indicating that the positive feedback is controlled by cellular migration mechanisms. Our data-driven theoretical approach therefore identifies polarity feedback adaptation as a key mechanism in confined cell migration.

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受限细胞迁移中突出和极性动力学的几何适应

受限生理环境中的细胞迁移依赖于几种细胞成分的协同动力学，包括突起、与环境的粘附和细胞核。然而，人们仍然知之甚少，这些成分的动态相互作用和细胞极性如何决定细胞尺度上的紧急迁移行为。在这里，我们将数据驱动的推理与自下而上的机械方法相结合，开发了受限细胞迁移中的突出和极性动力学模型，揭示了细胞动力学如何适应受限几何形状。具体来说，我们使用限制微图案上细胞的联合突起-细胞核迁移轨迹的实验数据，系统地确定了一个连接细胞极性、突起和细胞核的随机动力学的机制模型。该模型表明，细胞动力学通过极性动力学从负到正的自增强反馈回路的切换来适应限制收缩。我们的模型进一步揭示了这种反馈回路如何导致突出-核动力学的刻板循环，从而通过收缩驱动细胞迁移。这些循环在细胞骨架成分的扰动时被打乱，表明正反馈受细胞迁移机制控制。因此，我们的数据驱动的理论方法将极性反馈适应确定为受限细胞迁移的关键机制。我们的模型进一步揭示了这种反馈回路如何导致突出-核动力学的刻板循环，从而通过收缩驱动细胞迁移。这些循环在细胞骨架成分的扰动时被打乱，表明正反馈受细胞迁移机制控制。因此，我们的数据驱动的理论方法将极性反馈适应确定为受限细胞迁移的关键机制。我们的模型进一步揭示了这种反馈回路如何导致突出-核动力学的刻板循环，从而通过收缩驱动细胞迁移。这些循环在细胞骨架成分的扰动时被打乱，表明正反馈受细胞迁移机制控制。因此，我们的数据驱动的理论方法将极性反馈适应确定为受限细胞迁移的关键机制。