About the Project
Identification of cellular signatures may help us to identify mechanisms of disease/treatment response. Previous studies have focused on analysing cells in bulk where differences between individual disease associated cells may be masked. Single cell genomics (such as, single cell RNA-Sequencing, CITE-Seq, CYTOF and flow cytometry) are powerful methods offering the ability to explore disease mechanisms and treatment response without the confounding of cellular heterogeneity; thus they potentially play a key role in implementing personalised medicine initiatives. Towards this end, we propose to use single cell methodologies to identify cell subsets and biomarkers indicative of treatment response in JIA.
1. Generate single cell data on PBMCs taken prior to treatment using paired blood and synovial fluid samples to allow for direct comparisons between sites of inflammation and the patients’ blood compartment.
2. Apply statistical analysis to identify cell sub-types/phenotypes indicative of non-response.
3. Replicate findings using alternative platforms.
4. Use the data to identify novel mechanisms of disease to facilitate treatment stratification using pre-existing therapies or identify novel therapeutic targets.
This research has the potential to bring about change in the delivery of healthcare and transform the lives of children with JIA as well as an economic benefit. For example, by understanding the disease at a molecular level we can develop more targeted treatment strategies to make better use of existing therapies thereby fast-tracking patients onto potentially more effective/aggressive therapies.
Training/techniques to be provided:
This project offers opportunities to develop interdisciplinary and quantitative skills by the analysis a very large single cell datasets using and developing cutting-edge biostatistics and bioinformatics analysis skills. These key skills will be essential for the development of biomarkers/mechanism involved in treatment response in JIA; a field where such data is sorely needed. Within the department for Versus Arthritis Centre for Genetic and Genomics, the student will be offered formal training in handling large scale data sets and in the use of statistical programmes such as R, STATA and PLINK.
In particular Prof. Wendy Thomson will offer expertise and guidance in genetic epidemiology and in her role as co-PI for the CLUSTER consortium, will act as a crucial link with clinical nurses, paediatricians and bioinformaticians around the UK. Dr Samantha Smith and Dr Paul Martin will offer support and expertise in wet-lab methodologies and bioinformatics analysis. Prof Andrew Morris will provide training in biostatistics and statistical genetics.
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.
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. For more information please visit www.internationalphd.manchester.ac.uk
As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.
Wallace CA, Ringold S, Bohnsack J, Spalding SJ, Brunner HI, Milojevic D, Schanberg LE, Higgins GC, O'Neil KM, Gottlieb BS et al.: Extension study of participants from the trial of early aggressive therapy in juvenile idiopathic arthritis. J Rheumatol 2014, 41:2459-2465.
Wallace CA, Giannini EH, Spalding SJ, Hashkes PJ, O'Neil KM, Zeft AS, Szer IS, Ringold S, Brunner HI, Schanberg LE et al.: Trial of early aggressive therapy in polyarticular juvenile idiopathic arthritis. Arthritis Rheum 2012, 64:2012-2021.
Hinks A, Cobb J, Marion MC, Prahalad S, Sudman M, Bowes J, Martin P, Comeau ME, Sajuthi S, Andrews R, Brown M, Chen WM, Concannon P, Deloukas P, Edkins S, Eyre S, Gaffney PM, Guthery SL, Guthridge JM, Hunt SE, James JA, Keddache M, Moser KL, Nigrovic PA, Onengut-Gumuscu S, Onslow ML, Rose CD, Rich SS, Steel KJ, Wakeland EK, Wallace CA, Wedderburn LR, Woo P, Bohnsack JF, Haas JP, Glass DN, Langefeld CD, Thomson W, Thompson SD. Dense genotyping of immune-related disease regions identifies 14 new susceptibility loci for juvenile idiopathic arthritis. Nature Genetics 2013; 45(6): 664-669.
Cobb J, Cule E, Moncrieffe H, Hinks A, Ursu S, Patrick F, Kassoumeri L, Flynn E, Bulatovic M, Wulffraat N, van Zelst B, de Jonge R, Bohm M, Dolezalova P, Hirani S, Newman S, Whitworth P, Southwood TR, De lorio M, CHARMS, CAPS, BSPAR Study Group, Wedderburn LR, Thomson W. Genome-wide data reveal novel genes for methotrexate response in a large cohort of juvenile idiopathic arthritis cases. Pharmacogenomics Journal. 2014; 14:356-64.
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