Kennedy Trust Prize Studentships
Cross-tissue and cross-disease immunometabolic landscape characterisation at single-cell resolution*
- Project No: MRC-2
- Intake: 2024 KIR Non Clinical
Immune cells play a fundamental role in health and disease, with many of their functions relying on the careful orchestration of metabolic and energetic pathways. As part of the Oxford-Janssen CARTOGRAPHY consortium we are applying the latest molecular and cellular multi-omics analysis platforms (single-cell and spatial omics) and computational methods to deliver insights into immune cell biology across a range of human tissues (including the gut, joint, skin, liver, kidney, bladder and blood) and diseases (including immune-mediated diseases such as inflammatory bowel disease, coeliac disease, rheumatoid arthritis, psoriatic arthritis, psoriasis and atopic dermatitis; malignancies such as kidney and bladder cancer; and infectious diseases such as HBV). The insights obtained through these omics analyses will help to facilitate and rationalise target selection, drug development, positioning and repurposing strategies, and hence precision medicine across diseases.
The successful applicant will make use of available and emerging single-cell and spatially-resolved multi-omic data to computationally characterise metabolic and energetic fluxes and investigate differences that may be associated with different cell states, tissues types and diseases. In addition to performing analyses with available tools (e.g. COMPASS, scFEA) the student will have the opportunity to further refine or develop computational/machine learning tools and/or to validate findings and putative targets experimentally depending on their interests. The student will also be involved in developing and contributing to a platform for the user-friendly visualization of high-resolution metabolic data, including the incorporation of AI-powered language learning tools.
KEYWORDS (5 WORDS)
Artificial intelligence, Bioinformatics, Immunology, Machine Learning, Pathology
Software developing, machine learning, natural language models
CONTACT INFORMATION OF SUPERVISORS
Calli Dendrou firstname.lastname@example.org
Holm Uhlig email@example.com
Stephen Taylor: firstname.lastname@example.org
*Stipend for this studentship is set at MRC rates