(BRC) Identification and characterization of disease-specific subsets of antigen-presenting cells (APC) in inflammatory arthritis (Non-Clinical)

   Faculty of Biology, Medicine and Health

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  Dr S Viatte, Dr Maria Christofi, Mr Paul Martin  No more applications being accepted  Competition Funded PhD Project (UK Students Only)

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 (inc. psoriatic arthritis). The presentation of autoantigens by APCs to T cells is likely to be involved in the aetiology of autoimmune diseases [1]. For examples, a strong genetic association between HLA-DRB1 alleles and RA susceptibility suggests a role for the presentation of an arthritogenic peptide by APC to CD4+ T cells 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.

Research question

The aim of this study is to characterize HLA-DR+ cell subsets (therefore potentially antigen-presenting) with a large mass cytometry (CyTOF) panel (> 40 markers) across 3 compartments (blood, synovial fluid and disaggregated synovial biopsies) across 5 different arthritic diseases (> 60 patients). 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).

Methods and objectives

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:

Quality control procedures will be applied to remove bad markers, samples and batches (using custom R scripts [4]); batch detection and correction will be performed with R packages (like CytoNorm).

Objective 2, identification of stromal and immune cell subsets:

Automated clustering algorithms (FlowSOM) will be used to agnostically define cell populations (subsets of monocytes, macrophages, dendritic cells, fibroblasts and epithelial cells) based on lineage markers (f.e. CD45 for immune cells; podoplanin for stromal cells like fibroblasts; etc…).

Objective 3, association testing:

The association of cell clusters with compartment and disease will be performed with linear mixed models (f.e. MASC [3]) or CNA [5].

Objective 4, functional insight:

Differential expression of co-stimulatory molecules, activation markers and inflammatory cytokines across various HLA-DR+ cell clusters will be tested.


a) An atlas of APCs across compartments and various types of IA; b) the identification of compartment- and disease-specific APC subsets; c) functional insight to inform further experiments and suggest new therapeutic targets for precision medicine in rheumatology.




Applicants must have obtained or be about to obtain a First or Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, Msc degree in Bioinformatics or Immunology or related disciplines and a strong interest and background in command line programming and bioinformatics is desirable, although analysis with FlowJo (windows interface, no command line) will also represent a substantial part of the project.

Before you Apply 

Applicants must make direct contact with the primary supervisor before applying to discuss their interest in the project. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.  

How to Apply 

To be considered for this project you MUST submit a formal online application form - full details on how to apply can be found on the BRC website https://www.bmh.manchester.ac.uk/study/research/funded-programmes/manchester-brc-phd-studentships/ 

Your application form must be accompanied by a number of supporting documents by the advertised deadlines. Without all the required documents submitted at the time of application, your application will not be processed and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered. If you have any queries regarding making an application please contact our admissions team.

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/  

Biological Sciences (4) Computer Science (8) Mathematics (25) Medicine (26)

Funding Notes

This studentship covers tuition fees and stipend for home candidates only.


[1] Viatte S, Plant D, Raychaudhuri S. Genetics and epigenetics of rheumatoid arthritis. Nat Rev Rheumatol. 2013 Mar;9(3):141-53.
[2] Viatte S, Plant D, Han B, Fu B, Yarwood A, Thomson W, Symmons DP, Worthington J, Young A, Hyrich KL, Morgan AW, Wilson AG, Isaacs JD, Raychaudhuri S, Barton A. Association of HLA-DRB1 haplotypes with rheumatoid arthritis severity, mortality, and treatment response. JAMA. 2015 Apr 28;313(16):1645-56.
[3] Fonseka CY, Rao DA, Teslovich NC, Korsunsky I, Hannes SK, Slowikowski K, Gurish MF, Donlin LT, Lederer JA, Weinblatt ME, Massarotti EM, Coblyn JS, Helfgott SM, Todd DJ, Bykerk VP, Karlson EW, Ermann J, Lee YC, Brenner MB, Raychaudhuri S. Mixed-effects association of single cells identifies an expanded effector CD4+ T cell subset in rheumatoid arthritis. Sci Transl Med. 2018 Oct 17;10(463)
[4] Mulhearn B, Marshall L, Sutcliffe M, Hannes SK, Fonseka C, Hussell T, Raychaudhuri S, Barton A, Viatte S. Automated clustering reveals CD4+ T cell subset imbalances in rheumatoid arthritis. Front Immunol. 2023
[5] Reshef YA, Rumker L, Kang JB, Nathan A, Korsunsky I, Asgari S, Murray MB, Moody DB, Raychaudhuri S. Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics. Nat Biotechnol. 2022 Mar;40(3):355-363.