Th2 responses are primed by skin dendritic cells with distinct transcriptional profiles

David A. Eccles, Olivier Lamiable, Melanie J. McConnell, Franca Ronchese, Lisa M. Connor, Shiau-Choot Tang, Emmanuelle Cognard, Sotaro Ochiai, Kerry L. Hilligan, Samuel I. Old, Christophe Pellefigues, Ruby F. White, Deepa Patel, Adam Alexander T. Smith
  • The Journal of Experimental Medicine, December 2016, Rockefeller University Press
  • DOI: 10.1084/jem.20160470

Two different treatments produce two distinct Th2-type responses

What is it about?

The immune system has a number of different and observably distinct responses to foreign invaders. One of these responses is called Th2, which is typically brought into play when the body becomes infected by parasites and bacteria. In an attempt to better understand this Th2-type response of the immune system, we applied two different Th2-inducing treatments to mouse ears (a parasite, and a chemical paste), then looked at how genes were influenced by these treatments. While these two treatments appear to produce the same response on the outside (i.e. Th2), we looked under the hood and saw almost completely different cellular machinery to deal with the different treatments.

Why is it important?

The Th2 response is known to be important in many types of allergy. Any treatments for allergy need to be targeted to deal with the correct underlying biological system. Our research suggests that a single treatment will not work for Th2 allergies, and there needs to be a classification of the type of allergy before treatment.


Dr David A Eccles
Malaghan Institute of Medical Research

This was my first big bioinformatics project at the Malaghan Institute, and the bioinformaticians were able to provide input throughout most of this project, including on the experimental design prior to sequencing. One of AATS' initial contributions was to run simulations to work out how many mice we needed to pool in order to get sufficient biological variation (and smoothing of that variation) from three replicates per sample. I used my experience with RNASeq and Illumina sequencing to decide on what sequencing parameters were needed to make sure our sequence data would be of sufficient quality and coverage to get good results. We understood that we wouldn't be able to pick up changes in low-expression genes, but hoped that there would be enough of a sniff of differential expression in the high-expression genes that our biological questions could be answered. I also developed a Shiny app (based on graphs that AATS had made) that allowed me to manage the data, and allowed the biologists to manage the results. This application provided other authors with many of the tools they needed to quickly explore the data themselves, without having to ask me for a specific view of the data set. Most of the RNASeq results figures in the paper have been taken from this application, with only minimal modification for publication. The shiny app includes the following views of the data: * Heatmap of expression (via VSTPk) and differential expression (DESeq2 results) * Volcano plot * MA plot * Venn diagram * Scatter plot * PCA Biologists are able to select their preferred statistical model (data set), subset on a particular set of genes, and choose which treatments to display in graphs. Many of the graphs (including the venn diagram) allow the user to select a region of the graph and display the names of the selected genes. There is also an ability to download the displayed data for loading into other analysis programs (e.g. IPA). The Shiny app is supplemented by a genome browser, allowing the biologists to look at the raw data to see if/when there were any issues with the results. This lead to comments like "gene X looks like it's expressed on JBrowse, but it's not showing up as significant in the Shiny app", which were immensely helpful for correcting bugs in the scripts used to analyse the data.

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The following have contributed to this page: Dr David A Eccles