There is a long-standing association between wound healing and cancer, with cancer often described as a “wound that does not heal”. However, little is known about how wounding, such as following surgery, biopsy collection or ulceration, might impact on cancer progression. Here, we use a translucent zebrafish larval model of RasG12V-driven neoplasia to image the interactions between inflammatory cells drawn to a wound, and to adjacent pre-neoplastic cells. We show that neutrophils are rapidly diverted from a wound to pre-neoplastic cells and these interactions lead to increased proliferation of the pre-neoplastic cells. One of the wound-inflammation-induced trophic signals is prostaglandin E2 (PGE2). In an adult model of chronic wounding in zebrafish, we show that repeated wounding with subsequent inflammation leads to a greater incidence of local melanoma formation. Our zebrafish studies led us to investigate the innate immune cell associations in ulcerated melanomas in human patients. We find a strong correlation between neutrophil presence at sites of melanoma ulceration and cell proliferation at these sites, which is associated with poor prognostic outcome.

By analysing the cellular and subcellular events that occur in the centre of the developing zebrafish neural rod, we have uncovered a novel mechanism of cell polarisation during lumen formation. Cells from each side of the neural rod interdigitate across the tissue midline. This is necessary for localisation of apical junctional proteins to the region where cells intersect the tissue midline. Cells assemble a mirror-symmetric microtubule cytoskeleton around the tissue midline, which is necessary for the trafficking of proteins required for normal lumen formation, such as partitioning defective 3 and Rab11a to this point. This occurs in advance and is independent of the midline cell division that has been shown to have a powerful role in lumen organisation. To our knowledge, this is the first example of the initiation of apical polarisation part way along the length of a cell, rather than at a cell extremity. Although the midline division is not necessary for apical polarisation, it confers a morphogenetic advantage by efficiently eliminating cellular processes that would otherwise bridge the developing lumen.

The development of a vertebrate neural epithelium with well-organized apico-basal polarity and a central lumen is essential for its proper function. However, how this polarity is established during embryonic development and the potential influence of surrounding signals and tissues on such organization has remained less understood. In recent years the combined superior transparency and genetics of the zebrafish embryo has allowed for in vivo visualization and quantification of the cellular and molecular dynamics that govern neural tube structure. Here, we discuss recent studies revealing how co-ordinated cell-cell interactions coupled with adjacent tissue dynamics are critical to regulate final neural tissue architecture. Furthermore, new findings show how the spatial regulation and timing of orientated cell division is key in defining precise lumen formation at the tissue midline. In addition, we compare zebrafish neurulation with that of amniotes and amphibians in an attempt to understand the conserved cellular mechanisms driving neurulation and resolve the apparent differences among animals. Zebrafish neurulation not only offers fundamental insights into early vertebrate brain development but also the opportunity to explore in vivo cell and tissue dynamics during complex three-dimensional animal morphogenesis. Developmental Dynamics 245:197-208, 2016. V C 2015 Wiley Periodicals, Inc.

SummarySpatial structures often constrain the 3D movement of cells or particles in vivo, yet this information is obscured when microscopy data are analyzed using standard approaches. Here, we present methods, called unwrapping and Riemannian manifold learning, for mapping particle-tracking data along unseen and irregularly curved surfaces onto appropriate 2D representations. This is conceptually similar to the problem of reconstructing accurate geography from conventional Mercator maps, but our methods do not require prior knowledge of the environments’ physical structure. Unwrapping and Riemannian manifold learning accurately recover the underlying 2D geometry from 3D imaging data without the need for fiducial marks. They outperform standard x-y projections, and unlike standard dimensionality reduction techniques, they also successfully detect both bias and persistence in cell migration modes. We demonstrate these features on simulated data and zebrafish and Drosophila in vivo immune cell trajectory datasets. Software packages that implement unwrapping and Riemannian manifold learning are provided.

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