The coordination of cell proliferation and migration in growing tissues is crucial in development and regeneration but remains poorly understood. Here, we find that, while expanding with an edge speed independent of initial conditions, millimeter-scale epithelial monolayers exhibit internal patterns of proliferation and migration that depend not on the current but on the initial tissue size, indicating memory effects. Specifically, the core of large tissues becomes very dense, almost quiescent, and ceases cell-cycle progression. In contrast, initially-smaller tissues develop a local minimum of cell density and a tissue-spanning vortex. To explain vortex formation, we propose an active polar fluid model with a feedback between cell polarization and tissue flow. Taken together, our findings suggest that expanding epithelia decouple their internal and edge regions, which enables robust expansion dynamics despite the presence of size and history-dependent patterns in the tissue interior.

Fluorescence reconstruction microscopy (FRM) describes a class of techniques where transmitted light images are passed into a convolutional neural network that then outputs predicted epifluorescence images. This approach enables many benefits including reduced phototoxicity, freeing up of fluorescence channels, simplified sample preparation, and the ability to re-process legacy data for new insights. However, FRM can be complex to implement, and current FRM benchmarks are abstractions that are difficult to relate to how valuable or trustworthy a reconstruction is. Here, we relate the conventional benchmarks and demonstrations to practical and familiar cell biology analyses to demonstrate that FRM should be judged in context. We further demonstrate that it performs remarkably well even with lower-magnification microscopy data, as are often collected in screening and high content imaging. Specifically, we present promising results for nuclei, cell-cell junctions, and fine feature reconstruction; provide data-driven experimental design guidelines; and provide researcher-friendly code, complete sample data, and a researcher manual to enable more widespread adoption of FRM.

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In the last decade, key advances in our understanding of collective cell migration and tissue growth have been made by studying the expansion of epithelial monolayers in vitro. However, most studies have focused on monolayers of sub-millimetric sizes, and how cell proliferation and migration are coordinated on larger scales remains poorly known. To fill this gap, we measured cell velocity, cell density, and cell-cycle state over 2 days in millimeter-scale freely-expanding monolayers. We find that tissues of different initial sizes exhibit very different spatiotemporal patterns of cell proliferation and collective cell migration in their internal regions. Specifically, within several cell cycles, the core of large tissues becomes very dense, almost quiescent, and ceases cell-cycle progression. In contrast, the core of smaller tissues develops a local minimum of cell density as well as a tissue-spanning vortex. These different dynamics are determined not by the current but by the initial tissue size, indicating that the state of the tissue depends on its history. Despite these marked differences at the internal regions, the edge zone of both large and small tissues displays rapid cell-cycle progression and radially-oriented migration with a steady velocity independent of tissue size. As a result, the overall area expansion rate is dictated by the perimeter-to-area ratio of the tissue. Our findings suggest that cell proliferation and migration are regulated in a collective manner that decouples the internal and edge regions of the tissue, which leads to size-and history-dependent internal patterns in expanding epithelia. tissue growth | cell cycle | collective migration | epithelia

We consider the problem of controlling an invasive mechanical ventilator for pressure-controlled ventilation: a controller must let air in and out of a sedated patient’s lungs according to a trajectory of airway pressures specified by a clinician.Hand-tuned PID controllers and similar variants have comprised the industry standard for decades, yet can behave poorly by over- or under-shooting their target or oscillating rapidly.We consider a data-driven machine learning approach: First, we train a simulator based on data we collect from an artificial lung. Then, we train deep neural network controllers on these simulators. We show that our controllers are able to track target pressure waveforms significantly better than PID controllers.We further show that a learned controller generalizes across lungs with varying characteristics much more readily than PID controllers do.