Memory encoding of confinement-adaptive dynamics in immune cells

Objective

The fate and function of migratory cells is intrinsically linked to their dynamics, which determines for instance their sampling efficiency or their invasive capacity. Previously neglected, the interplay between cells and their environment is emerging as a critical determinant of this dynamics. Interstitial tissues constitute packed environments that are rarely homogeneous or regular, subjecting cells to large and changing deformations as they move through them. Recent findings suggest that cells adapt to confinement and display different migratory phenotypes depending on their environment. However, how confinement is encoded in the cell and the mechanisms by which it affects its dynamics are still poorly understood. To address this, I propose a novel approach that explicitly links molecular mechanisms of confinement adaptation in cells to tissue-scale migratory dynamics using theoretical tools. Leveraging coarse-grained bottom-up models, data-driven inference techniques and non-Markovian active particle models, this strategy establishes precise and tractable relations between confinement and cell dynamics through the relevant internal degrees of freedom, essential for understanding cell-environment interplay and its functional implications. Focusing on immune cells, I will apply this framework to investigate how actomyosin cortex organisation and contractility under confinement affects their migratory phenotype. Offering unprecedented insights into how cells encode and adapt to confinement, this project could revolutionise our understanding of migratory cell dynamics and its determinants in physiologically relevant environments. Deciphering the mechanisms governing immune cell migration could lead to new therapeutic targets and strategies for immune system disorders. Beyond its biological relevance, this project also paves the way for future research into adaptive synthetic active matter with targeted behaviours in heterogeneous environments.