SMART Seq2 Transcriptomics
CD4 T helper cells are important mediators of airway inflammation. These cells acquire distinct functions following activation and have been subdivided on the basis of particular protein markers over the past decades. It is accepted that considerable variability exists within a responding T helper cell population and that many more functionally-distinct subsets exist. However these have been difficult to resolve with conventional analysis techniques such as flow cytometry. Our objective is to define CD4 T helper cell responses at the level of single cell transcriptomes. Using single cell RNA sequencing we will analyse gene expression at the level of an individual T helper cell derived from both asthmatic mice and human patients. Application of such a ´top-down´ approach to understand immune function overcomes the restrictions of studying relatively small number of genes or molecules. Through the use of unbiased techniques such as RNA sequencing will we be able to more fully characterise the type of T helper response at the single cell level and the sub-populations that drive inflammation in conditions such as asthma. Specific Aims: - Determine the T cell subpopulations at single cell level in asthmatic mice. A more full understanding of T helper responses requires understanding of all T helper subsets present during inflammation. Induction of airway inflammation by exposure to House Dust Mite (HDM) antigen provides a robust murine model to assess T helper cell-driven responses at the level of single cells. Using such an approach, we expect to find both well-characterised T cells subpopulations as well as novel subsets not previously described in HDM responses. - Assessment of T helper cell gene expression in asthmatic patients before and after treatment. Although useful for assessment of basic immune principles, murine models do not fully represent the ongoing inflammation found in asthmatic patients. Thus, applying single cell RNA sequencing to human patient T cells will aid our understanding of airway inflammation in humans. We will address this by sequencing the transcriptome of individual T cells taken from asthmatic patient’s bronchial lavages before and after clinic interventions such as steroid therapy. These data will allow for a comparison between our murine study and human patients in clinic. Furthermore, we aim to identify the ‘gene signature’ of those patients that respond successfully to treatment. By assessment at the single cell level will we be able to fully characterise the key stages in T helper cell differentiation that occur in vivo during airway inflammation. Our data will provide important insights into the heterogeneity of T helper subpopulations that develop in both humans and mice. More specifically, these data have the potential to identify novel subsets that may be significant players in the development of airway inflammation. Dissecting the complexity of inflammatory T helper responses and how the type of response links to pathology in vivo may help identify novel pathways that can be assessed further and possibly targeted therapeutically.