Wakelin, K. A. | bioRxiv Science

bioRxiv2021

The skin environment controls local dendritic cell differentiation and function through innate IL-13

The signals driving the adaptation of type-2 dendritic cells (DC2s) to diverse peripheral environments are not well understood. We show that the development of CD11blow migratory DC2s, a DC2 population unique to the dermis, requires STAT6- and KLF4-dependent IL-13 signaling, whereas DC2s in lung and small intestine are STAT6-independent. Dermal IL-13 is mostly derived from innate lymphoid cells expressing a resting ICOS+ KLRG1-ST2-phenotype. Analysis of public datasets indicates that human skin DC2s also express an IL-4/IL-13 gene signature compared to blood or spleen, suggesting a similar developmental pathway in mice and humans. In the absence of IL-13 signaling, dermal DC2s are stable in number but remain CD11bhi and show defective activation in response to allergen with diminished ability to support IL-4+ GATA3+ Th development, whereas anti-fungal IL-17+ ROR{gamma}t+ responses are increased. Thus, steady-state IL-13 fosters a non-inflammatory and pro-allergic environment in healthy skin via conditioning of local DC2s.

immunology↗

bioRxiv2019

A semi-automated technique for adenoma quantification in the ApcMin mouse using FeatureCounter

Colorectal cancer is a major contributor to death and disease worldwide. The ApcMin mouse is a widely used model of intestinal neoplasia, as it carries a mutation also found in human colorectal cancers. However, the method most commonly used to quantify tumour burden in these mice is manual adenoma counting, which is time consuming and poorly suited to standardization across different laboratories. We describe a method to produce suitable photographs of the small intestine, process them with an ImageJ macro, FeatureCounter, which automatically locates image features potentially corresponding to adenomas, and a machine learning pipeline to identify and quantify them. Compared to a manual method, the specificity (or True Negative Rate, TNR) and sensitivity (or True Positive Rate, TPR) of this method in detecting adenomas are similarly high at about 80% and 87%, respectively. Importantly, total adenoma area measures derived from the automatically-called tumours were just as capable of distinguishing high-burden from low-burden mice as those established manually. Overall, our strategy is quicker, helps control experimenter bias and yields a greater wealth of information about each tumour, thus providing a convenient route to getting consistent and reliable results from a study.

cancer biology↗