Genetic studies in rheumatoid arthritis (RA) have been successful in identifying regions of the genome which contribute to disease risk, but it is often unclear which genes they effect. They have therefore had limited impact on our understanding of disease and the development of new and effective treatments. Fibroblast-like synoviocytes (FLS) are an understudied, crucial cell type in the development and progression of inflammation in the joint. We have previously used molecular biology techniques to study the characteristics of these cells at the bulk population level, overlaying known genetic risk variants. However, FLS cells are a heterogeneous mix of subtly different sub-types. Single cell genomics allows us to study this cellular heterogeneity in disease samples, giving the potential to explore how FLS cell subtypes contribute to RA risk and provide insights into treatment development.
This project will utilise single cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data to explore gene regulation and cellular heterogeneity in FLS cells in response to stimulation. Samples from individuals with RA will be used to generate both scRNA-seq and scATAC-seq at multiple time points following TNF stimulation. From this, cell sub-types will be identified and gene regulatory networks specific to each sub-type will be used to identify which genes are regulated in response to stimulation. By combining this with known RA associations, genes potentially involved in RA pathogenesis will be identified. These sub-types and genes will be further explored using molecular biology techniques established within our group, including flow cytometry, mass cytometry by time of flight (CyTOF), CRISPR/Cas9 genome editing, chromosome conformation capture, ChIP-seq and RNA-seq. This project is an exciting opportunity to work with cutting edge single cell data and molecular biology techniques. It has the potential to gain insights into the role of FLS in the development and progression of RA, how an individuals’ genetics could contribute to their risk of developing RA and inform drug development.
Training/techniques to be provided:
SE will supervise training in RA epidemiology and literature reviewing and interpreting results. PM will supervise training in single cell and bioinformatics analysis and interpreting results. AM will provide training and support in statistical analysis and interpretation of results. CO will provide training in handling FLS samples, FLS immunology and interpreting results.
Training will be provided in the statistical approaches required to analyse the data available and the interpretation of the results. Specifically, in-house training courses in Epidemiology and statistical analysis are held annually. The successful applicant will join other students and research staff investigating the functional genomics, particularly the immunological aspects of these studies.
Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area / subject. Candidates with experience in bioinformatics or with an interest in immunology are encouraged to apply.
How To Apply
For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the online application form select the appropriate subject title.
For international students, we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences.
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