Fluctuations of cell geometry and their nonequilibrium thermodynamics in living epithelial tissue

Immune cells adapt to confined environments in vivo to optimise nuclear plasticity for migration

Cells navigating in complex 3D microenvironments frequently encounter narrow spaces that physically challenge migration. While in vitro studies identified nuclear stiffness as a key rate-limiting factor governing the movement of many cell types through artificial constraints, how cells migrating in vivo respond dynamically to confinement imposed by local tissue architecture, and whether these encounters trigger molecular adaptations, is unclear. Here, we establish an innovative in vivo model for mechanistic analysis of nuclear plasticity as Drosophila immune cells transition into increasingly confined microenvironments. Integrating live in vivo imaging with molecular genetic analyses, we demonstrate how rapid molecular adaptation upon environmental confinement (including fine-tuning of the nuclear lamina) primes leukocytes for enhanced nuclear deformation while curbing damage (including rupture and micronucleation), ultimately accelerating movement through complex tissues. We find nuclear dynamics in vivo are further impacted by large organelles (phagosomes) and the plasticity of neighbouring cells, which themselves deform during leukocyte passage. The biomechanics of cell migration in vivo are thus shaped both by factors intrinsic to individual immune cells and the malleability of the surrounding microenvironment.

ModularImageAnalysis (MIA): Assembly of modularised image and object analysis workflows in ImageJ

ModularImageAnalysis (MIA) is an ImageJ plugin providing a code-free graphical environment in which complex automated analysis workflows can be constructed and distributed. The broad range of included modules cover all stages of a typical analysis workflow, from image loading through image processing, object detection, extraction of measurements, measurement-based filtering, visualisation and data exporting. MIA provides out-of-the-box compatibility with many advanced image processing plugins for ImageJ including Bio-Formats, DeepImageJ, MorphoLibJ and TrackMate, allowing these tools and their outputs to be directly incorporated into analysis workflows. By default, modules support spatially calibrated 5D images, meaning measurements can be acquired in both pixel and calibrated units. A hierarchical object relationship model allows for both parent-child (one-to-many) and partner (many-to-many) relationships to be established. These relationships underpin MIA's ability to track objects through time, represent complex spatial relationships (e.g. topological skeletons) and measure object distributions (e.g. count puncta per cell). MIA features dual graphical interfaces: the 'editing view' offers access to the full list of modules and parameters in the workflow, while the simplified 'processing view' can be configured to display only a focused subset of controls. All workflows are batch-enabled by default, with image files within a specified folder being processed automatically and exported to a single spreadsheet. Beyond the included modules, functionality can be extended both internally, through integration with the ImageJ scripting interface, and externally, by developing third-party Java modules that extend the core MIA framework. Here we describe the design and functionality of MIA in the context of a series of real-world example analyses.

Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair

Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large numbers of cell divisions (including their spatio-temporal synchronicity and orientation) over extended periods of time. It would thus be advantageous to perform such analyses in an automated fashion, which can naturally be enabled using deep learning. Hence, we develop a pipeline of deep learning models that accurately identify dividing cells in time-lapse movies of epithelial tissues in vivo. Our pipeline also determines their axis of division orientation, as well as their shape changes before and after division. This strategy enables us to analyse the dynamic profile of cell divisions within the Drosophila pupal wing epithelium, both as it undergoes developmental morphogenesis and as it repairs following laser wounding. We show that the division axis is biased according to lines of tissue tension and that wounding triggers a synchronised (but not oriented) burst of cell divisions back from the leading edge.

Elasticity tunes mechanical stress localization around active topological defects

Mechanical stresses are increasingly found to be associated with various biological functionalities. At the same time, topological defects are being identified across a diverse range of biological systems and are points of localized mechanical stress. It is therefore important to ask how mechanical stress localization around topological defects is controlled. Here, we use continuum simulations of nonequilibrium, fluctuating and active nematics to explore the patterns of stress localization, as well as their extent and intensity around topological defects. We find that by increasing the orientational elasticity of the material, the isotropic stress pattern around topological defects is changed substantially, from a stress dipole characterized by symmetric compression-tension regions around the core of the defect, to a localized stress monopole at the defect position. Moreover, we show that elastic anisotropy alters the extent and intensity of the stresses, and can result in the dominance of tension or compression around defects. Finally, including both nonequilibrium fluctuations and active stress generation, we find that the elastic constant tunes the relative effect of each, leading to the flipping of tension and compression regions around topological defects. This flipping of the tension-compression regions only by changing the elastic constant presents an interesting, simple, way of switching the dynamic behavior in active matter by changing a passive material property. We expect these findings to motivate further exploration tuning stresses in active biological materials by varying material properties of the constituent units.

Mechanochemical Active Feedback Generates Convergence Extension in Epithelial Tissue

Convergence extension, the simultaneous elongation of tissue along one axis while narrowing along a perpendicular axis, occurs during embryonic development. A fundamental process that contributes to shaping the organism, it happens in many different species and tissue types. Here, we present a minimal continuum model, that can be directly linked to the controlling microscopic biochemistry, which shows spontaneous convergence extension. It is comprised of a 2D viscoelastic active material with a mechanochemical active feedback mechanism coupled to a substrate via friction. Robust convergent extension behavior emerges beyond a critical value of the activity parameter and is controlled by the boundary conditions and the coupling to the substrate. Oscillations and spatial patterns emerge in this model when internal dissipation dominates over friction, as well as in the active elastic limit.