Identification and characterization of disease-specific subsets of antigen-presenting cells (APC) in inflammatory arthritis
About the Project
Inflammatory arthritis (IA) can result from non-autoimmune diseases (like crystal arthritis) and from autoimmune diseases like rheumatoid arthritis (RA) and spondyloarthropathies. The presentation of an arthritogenic autoantigen by HLA-DR on APCs to CD4+ T cells is likely to be involved in the aetiology of RA [1-4]. However, the exact antigens, APC subsets (stromal cells versus immune cells) and compartment (secondary or tertiary lymphoid structures) of antigen presentation remains poorly explored across autoimmune diseases.
The aim of this study is to characterize HLA-DR+ cell subsets with a large mass cytometry (CyTOF) panel (> 40 markers) across 3 compartments (blood, synovial fluid and disaggregated synovial biopsies) across 5 different arthritic diseases (> 100 samples). Insight into the function of APC subsets will be gained at the single cell level by analysing the expression of co-stimulatory molecules (like f.e. CD80) and polarising cytokines (for example, IL6 and IL23, to induce the inflammatory Th17 CD4+ T cell subset).
The data has already been generated and this project is a data analysis / bioinformatics project – it will not include any wet lab experiments.
Objective 1: quality control and batch correction with custom R scripts [4]); batch detection and correction with R packages (like CytoNorm).
Objective 2: identification of stromal and immune cell subsets using automated clustering algorithms (FlowSOM).
Objective 3: testing the association of cell clusters with compartment and disease with linear mixed models (f.e. MASC [3]) or CNA [5].
Objective 4: functional insight will be gained by studying the differential expression of co-stimulatory molecules, activation markers and inflammatory cytokines across various HLA-DR+ cell clusters.
Outcome: a) An atlas of APCs across various types of IA; b) the identification of compartment- and disease-specific APC subsets; c) functional insight to suggest new therapeutic targets for precision medicine in rheumatology.
Training
Training opportunities: This project represents a fantastic training opportunity for a candidate with a strong interest and background in command line programming and bioinformatics. Training will be provided from a multidisciplinary in rheumatology (Anne Barton and Sebastien Viatte), basic and human immunology (Maria Christofi), statistical data analysis (Sebastien Viatte) and bioinformatics (Paul Martin - including Linux, R and other languages and packages)
Entry Requirements
Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in bioinformatics / data analysis/ systems biology / genomics / information technology or in immunology with a strong focus / experience in data analysis (command line programming). Candidates with experience and a strong interest in command line programming in Linux (R, Python) 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 PhD Musculoskeletal and Dermatological Sciences.
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.
Equality, Diversity and Inclusion
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/”
Funding Notes
References
Sharma S, Plant D, Bowes J, Macgregor A, Verstappen S, Barton A, Viatte S. HLA-DRB1 haplotypes predict cardiovascular mortality in inflammatory polyarthritis independent of CRP and anti-CCP status. Arthritis Res Ther. 2022 Apr 25;24(1):90. doi: 10.1186/s13075-022-02775-0. PMID: 35468805; PMCID: PMC9036773.
Ding J, Smith SL, Orozco G, Barton A, Eyre S, Martin P. Characterisation of CD4+ T-cell subtypes using single cell RNA sequencing and the impact of cell number and sequencing depth. Sci Rep. 2020 Nov 13;10(1):19825. doi: 10.1038/s41598-020-76972-9. PMID: 33188258; PMCID: PMC7666207.
Smith SL, Alexander S, Nair N, Viatte S, Eyre S, Hyrich KL, Morgan AW, Wilson AG, Isaacs JD, Plant D, Barton A. Pre-treatment calprotectin (MRP8/14) provides no added value to testing CRP alone in terms of predicting response to TNF inhibitors in rheumatoid arthritis in a post hoc analysis. Ann Rheum Dis. 2023 May;82(5):611-620. doi: 10.1136/ard-2022-222519. Epub 2023 Feb 21. PMID: 36810200; PMCID: PMC10176427.
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