Mechanical constraints to cell-cycle progression in a pseudostratified epithelium

Patterned apoptosis has an instructive role for local growth and tissue shape regulation in a fast-growing epithelium

What regulates organ size and shape remains one fundamental mystery of modern biology. Research in this area has primarily focused on deciphering the regulation in time and space of growth and cell division, while the contribution of cell death has been overall neglected. This includes studies of the Drosophila wing, one of the best-characterized systems for the study of growth and patterning, undergoing massive growth during larval stage and important morphogenetic remodeling during pupal stage. So far, it has been assumed that cell death was relatively neglectable in this tissue both during larval stage and pupal stage, and as a result, the pattern of growth was usually attributed to the distribution of cell division. Here, using systematic mapping and registration combined with quantitative assessment of clone size and disappearance as well as live imaging, we outline a persistent pattern of cell death and clone elimination emerging in the larval wing disc and persisting during pupal wing morphogenesis. Local variation of cell death is associated with local variation of clone size, pointing to an impact of cell death on local growth that is not fully compensated by proliferation. Using morphometric analyses of adult wing shape and genetic perturbations, we provide evidence that patterned death locally and globally affects adult wing shape and size. This study describes a roadmap for precise assessment of the contribution of cell death to tissue shape and outlines an important instructive role of cell death in modulating quantitatively local growth and morphogenesis of a fast-growing tissue.

The Drosophila ecdysone receptor promotes or suppresses proliferation according to ligand level

The steroid hormone 20-hydroxy-ecdysone (20E) promotes proliferation in Drosophila wing precursors at low titer but triggers proliferation arrest at high doses. Remarkably, wing precursors proliferate normally in the complete absence of the 20E receptor, suggesting that low-level 20E promotes proliferation by overriding the default anti-proliferative activity of the receptor. By contrast, 20E needs its receptor to arrest proliferation. Dose-response RNA sequencing (RNA-seq) analysis of ex vivo cultured wing precursors identifies genes that are quantitatively activated by 20E across the physiological range, likely comprising positive modulators of proliferation and other genes that are only activated at high doses. We suggest that some of these "high-threshold" genes dominantly suppress the activity of the pro-proliferation genes. We then show mathematically and with synthetic reporters that combinations of basic regulatory elements can recapitulate the behavior of both types of target genes. Thus, a relatively simple genetic circuit can account for the bimodal activity of this hormone.

Active mesh and neural network pipeline for cell aggregate segmentation

Segmenting cells within cellular aggregates in 3D is a growing challenge in cell biology due to improvements in capacity and accuracy of microscopy techniques. Here, we describe a pipeline to segment images of cell aggregates in 3D. The pipeline combines neural network segmentations with active meshes. We apply our segmentation method to cultured mouse mammary gland organoids imaged over 24 h with oblique plane microscopy, a high-throughput light-sheet fluorescence microscopy technique. We show that our method can also be applied to images of mouse embryonic stem cells imaged with a spinning disc microscope. We segment individual cells based on nuclei and cell membrane fluorescent markers, and track cells over time. We describe metrics to quantify the quality of the automated segmentation. Our segmentation pipeline involves a Fiji plugin that implements active mesh deformation and allows a user to create training data, automatically obtain segmentation meshes from original image data or neural network prediction, and manually curate segmentation data to identify and correct mistakes. Our active meshes-based approach facilitates segmentation postprocessing, correction, and integration with neural network prediction.