Investigating cross-tissue and cross-disease immune mechanisms using machine learning and AI-powered language learning tools

Project No: NC-4
Intake: 2024 KIR Non Clinical

PROJECT OVERVIEW

Immune cells play a fundamental role in health and disease. 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 (e.g. the gut, joint, skin, liver, kidney, bladder, brain and blood) and diseases (including immune-mediated diseases, malignancies and infectious diseases). 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 datasets (that capture the transcriptome, the cell-surface proteome, the T- and B-cell receptor repertoires and the epigenome) to investigate pathophysiological inflammatory pathways and mechanisms across diseases and over time (e.g. before and after drug treatment). In addition to performing analyses with available pipelines and tools (e.g. Panpipes, COMPASS), the applicant will have the opportunity to develop and apply generative machine learning (ML) approaches to better encapsulate the relationships between genes, enabling the investigation of regulatory gene network perturbations or other subtle behaviour that is not easily identifiable with standard data analyses.

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. Depending on student interests, a wet-lab component can be incorporated into the project with respect to tissue profiling by spatial transcriptomics, hyperplexed imaging and/or biochemical profiling (at Diamond Light Source), or experimental validation.

KEYWORDS (5 WORDS)

Immunology, single-cell, spatial transcriptomics, machine learning, artificial intelligence

TRAINING OPPORTUNITIES

The Kennedy Institute of Rheumatology is a world-class research centre, located in the University of Oxford’s Old Road campus, housing basic and clinical scientists and bioinformatics working on immunology and inflammation. This project will combine state-of-the-art omics, bioinformatics, and ML/AI approaches and the student will receive regular training and mentoring with respect to immunology and computational biology.

The student will join a vibrant postgraduate community at the Kennedy, and will benefit from attending seminars delivered by world-leading scientists in the department and across the University, from public engagement opportunities and from transferable skills and other training sessions. The student will present their work at group meetings and national and international conferences.

KEY PUBLICATIONS (5 maximum)

Thomas T, et al. (2023) A longitudinal single-cell therapeutic atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease. bioRxiv https://doi.org/10.1101/2023.05.05.539635

Rich-Griffin C, et al. (2023) Panpipes: Pipelines for multimodal single-cell data analysis. bioRxiv https://doi.org/10.1101/2023.03.11.532085; https://github.com/DendrouLab/panpipes; https://panpipes-pipelines.readthedocs.io/en/latest/

Grant-Peters M, et al. (2022) Biochemical and metabolic maladaptation underpins pathological niches in progressive multiple sclerosis. bioRxiv https://doi.org/10.1101/2022.09.26.509462

Cui H, et al. (2023) scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI. bioRxiv https://doi.org/10.1101/2023.04.30.538439

Weeratunga P, et al. (2023) Unbiased single cell spatial analysis localises inflammatory clusters of immature neutrophils-CD8 T cells to alveolar progenitor cells in fatal COVID-19 lungs. medRxiv https://doi.org/10.1101/2022.12.21.22283654; https://mdv.molbiol.ox.ac.uk/