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  • Project No: Clinical-11
  • Intake: 2024 KIR Clinical


Ankylosing spondylitis (AS) is a common and highly heritable form of inflammatory arthritis. Inflammation in AS, which characteristically affects the sacroiliac joints and spine, is known to involve the IL-23/IL-17 immune pathway1, but the cellular and molecular origins of the disease remain mysterious. The strong genetic association of AS with the human leukocyte antigen (HLA) class I molecule HLA-B*27 suggested that an ‘arthritogenic’ peptide triggers inflammation. This idea is strongly supported by the recent identification of a specific T cell receptor (TCR) motif in the blood, knees and eyes of AS patients2. There is also compelling evidence for the involvement of myeloid and innate lymphoid cells (ILCs)1. While AS primarily affects the axial skeleton, the presence of sub-clinical intestinal inflammation and microbial dysbiosis in many AS patients suggests that pathogenic immune cells may arise in the gut and circulate via the blood to target tissues1.

In this project, you will use the latest in-situ based spatial transcriptomics methods (including Nanostring CosMx) to characterise the cellular microarchitecture of gut tissue from AS patients. This ground-breaking approach allows sensitive quantitation of thousands of genes at sub-cellular resolution, enabling comprehensive cellular maps of intact tissue to be generated. You will use machine-learning approaches to compare the cellular niches present in gut tissue biopsies from AS patients with those from patients with other forms of intestinal inflammation (including Crohn’s disease) to identify AS-specific cellular phenotypes and disease processes.

To investigate whether pathogenic cells might circulate from the gut to affected tissues, you will perform cross-tissue analysis to search for immune cell signatures common to AS patient guts and peripheral tissues. Recently we have generated multimodal single-cell atlases from several AS target tissues (including the gut and axial joints) that contain both gene expression and TCR repertoire information. You will integrate these atlases with the spatial transcriptomics data to compare the cellular phenotypes present in AS patient joints and intestines.

Working closely with clinical and experimental colleagues, you will also have the opportunity to design follow-up experiments and test hypotheses using functional genomics approaches such as CRISPR-based gene editing and mouse models as appropriate. Ultimately, the results of this research will provide a rational basis for the development of more effective therapeutics that target the causes, rather than the symptoms, of AS. This work is supported by funding from Versus Arthritis.



spatial transcriptomics; single-cell genomics; bioinformatics; arthritis; immunology



The project is supported by a strong supervisory team at the University of Oxford that has complementary computational, experimental and clinical expertise. Based at the Kennedy institute, the Sansom lab is using genomic approaches to study immune mediated diseases3,4 and are expert with the use of computational genomics and data science approaches for the analysis and integration of single-cell and spatial transcriptomics data ( The group of Dame Professor Fiona Powrie has world-leading expertise with the experimental and cellular investigation of inflammatory bowel disease4, including with microbiome analysis ( Also at the Kennedy Institute (from October 2023), Dr Adrien Hallou and his team are developing novel interdisciplinary approaches for machine learning-based analysis of spatial transcriptomics data5. Dr Davide Simone is a post-doc in the Sansom group: he is a clinically trained Rheumatologist who is expert with the analysis of AS associated T cells. During your studies, you will work closely with researchers across the labs of the supervisory team to gain the skills needed to undertake your studies.

Whilst undertaking this DPhil, you will develop strong data science skills, learning how to program in Python and to perform statistical data analysis and visualisation with R. You will become expert with the use of machine learning approaches to analyse and integrate single-cell and spatial transcriptomics data. Working closely with clinical colleagues, you will gain an expert understanding of chronic inflammatory disease. You will develop a close understanding of experimental research, including the generation of single-cell and spatial genomics data, through regular attendance of wet-lab group meetings.

A core curriculum of lectures will be taken in the first term to provide a solid foundation in a broad range of subjects including musculoskeletal biology, inflammation, epigenetics, translational immunology, data analysis and the microbiome. Students will attend regular seminars within the department and those relevant in the wider University. Students will be expected to present data regularly in the departmental PGR seminars, Sansom and Powrie group meetings. You will have the opportunity to present your research in local, national and international meetings and conferences.

Students will have access to various courses run by the Medical Sciences Division Skills Training Team and other departments. All students are required to attend a 2 - day Statistical and Experimental Design course at NDORMS.



(1) Mauro D et al. Ankylosing spondylitis: an autoimmune or autoinflammatory disease? Nature Reviews Rheumatology 2021 (

(2) Yang, X. et al. Autoimmunity-associated T cell receptors recognize HLA-B*27-bound peptides. Nature 2022 (

(3) Croft AP et al. Distinct fibroblast subsets drive inflammation and damage in arthritis. Nature 2019.

(4) Friedrich M, Pohin M, Jackson MA et al. IL-1-driven stromal-neutrophil interactions define a subset of patients with inflammatory bowel disease that does not respond to therapies. Nature Medicine. 2021 (

(5) Hallou A, He R,  Simons BD,  Dumitrascu B. A computational pipeline for spatial mechano-transcriptomics. (


THEMES (4 key themes)

Bioinformatics, Statistics and Computational Biology; Genes, Genetics, Epigenetics and Genomics; Immunology



Please contact Associate Professor Stephen Sansom (, Professor Fiona Powrie (, Dr Adrien Hallou ( or Dr Davide Simone ( for more information.