Feathers are arranged in a precise pattern in avian skin. They first arise during development in a row along the dorsal midline, with rows of new feather buds added sequentially in a spreading wave. We show that the patterning of feathers relies on coupled fibroblast growth factor (FGF) and bone morphogenetic protein (BMP) signalling together with mesenchymal cell movement, acting in a coordinated reaction-diffusion-taxis system. This periodic patterning system is partly mechanochemical, with mechanical-chemical integration occurring through a positive feedback loop centred on FGF20, which induces cell aggregation, mechanically compressing the epidermis to rapidly intensify FGF20 expression. The travelling wave of feather formation is imposed by expanding expression of Ectodysplasin A (EDA), which initiates the expression of FGF20 . The EDA wave spreads across a mesenchymal cell density gradient, triggering pattern formation by lowering the threshold of mesenchymal cells required to begin to form a feather bud. These waves, and the precise arrangement of feather primordia, are lost in the flightless emu and ostrich, though via different developmental routes. The ostrich retains the tract arrangement characteristic of birds in general but lays down feather primordia without a wave, akin to the process of hair follicle formation in mammalian embryos. The embryonic emu skin lacks sufficient cells to enact feather formation, causing failure of tract formation, and instead the entire skin gains feather primordia through a later process. This work shows that a reaction-diffusion-taxis system, integrated with mechanical processes, generates the feather array. In flighted birds, the key role of the EDA/Ectodysplasin A receptor (EDAR) pathway in vertebrate skin patterning has been recast to activate this process in a quasi-1-dimensional manner, imposing highly ordered pattern formation.

BackgroundThe domestic chicken (Gallus gallus) is widely used as a model in developmental biology and is also an important livestock species. We describe a novel approach to data integration to generate an mRNA expression atlas for the chicken spanning major tissue types and developmental stages, using a diverse range of publicly-archived RNA-seq datasets and new data derived from immune cells and tissues.ResultsRandomly down-sampling RNA-seq datasets to a common depth and quantifying expression against a reference transcriptome using the mRNA quantitation tool Kallisto ensured that disparate datasets explored comparable transcriptomic space. The network analysis tool Graphia was used to extract clusters of co-expressed genes from the resulting expression atlas, many of which were tissue or cell-type restricted, contained transcription factors that have previously been implicated in their regulation, or were otherwise associated with biological processes, such as the cell cycle. The atlas provides a resource for the functional annotation of genes that currently have only a locus ID. We cross-referenced the RNA-seq atlas to a publicly available embryonic Cap Analysis of Gene Expression (CAGE) dataset to infer the developmental time course of organ systems, and to identify a signature of the expansion of tissue macrophage populations during development.ConclusionExpression profiles obtained from public RNA-seq datasets – despite being generated by different laboratories using different methodologies – can be made comparable to each other. This meta-analytic approach to RNA-seq can be extended with new datasets from novel tissues, and is applicable to any species.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4972-7) contains supplementary material, which is available to authorized users.

The phosphatidylserine receptor, TIM4, encoded by TIMD4, mediates the phagocytic uptake of apoptotic cells. We applied anti-chicken TIM4 monoclonal antibodies, in combination with CSF1R reporter transgenes to dissect the function of TIM4 in the chick (Gallus gallus). During development in ovo, TIM4 was present on the large majority of macrophages but expression became more heterogeneous post-hatch. Blood monocytes expressed KUL01, class II MHC and CSF1R-mApple uniformly. Around 50% of monocytes were positive for surface TIM4. They also expressed many other monocyte-specific transcripts at a higher level than TIM4- monocytes. In liver, highly-phagocytic TIM4hi cells shared many transcripts with mammalian Kupffer cells and were associated with uptake of apoptotic cells. Although they expressed CSF1R mRNA, Kupffer cells did not express the CSF1R-mApple transgene, suggesting that additional CSF1R transcriptional regulatory elements are required by these cells. By contrast, CSF1R-mApple was detected in liver TIM4lo and TIM4- cells which were not phagocytic and were more abundant than Kupffer cells. These cells expressed CSF1R, alongside high levels of FLT3, MHCII, XCR1 and other markers associated with conventional dendritic cells (cDC) in mice. In bursa, TIM4 was present on the cell surface of two populations. Like Kupffer cells, bursal TIM4hi phagocytes co-expressed many receptors involved in apoptotic cell recognition. TIM4lo cells appear to be a sub-population of bursal B cells. In overview, TIM4 is associated with phagocytes that eliminate apoptotic cells in the chick. In the liver, TIM4 and CSF1R reporters distinguished Kupffer cells from an abundant population of DC-like cells.