Spatial transcriptomics investigation of the cellular basis of tissue injury and regeneration in human disease

PROJECT OVERVIEW 

Researchers in the Sansom and Nanchahal group are studying tissue damage and repair in the context of inflammatory bowel disease, inflammatory arthritis, Dupuytren's, liver disease and in response to acute injury.

Spatial transcriptomics approaches hold great promise for investigation of tissue damage and repair because they enable phenotypic profiling of the involved cells together with elucidation of the cellular structure of the affected tissue niches. Spatial approaches are particularly important for study of conditions such as fibrosis for which it is often impossible to fully dissociate tissues for single-cell analysis.

In this computational project you will investigate datasets generated from human tissue biopsies using the latest single cell and spatial transcriptomics technologies (including the 10x Visium, nanoString GeoMx and nanoString CosMx platforms). To do so, you will learn and apply cutting-edge computational and machine-learning approaches, developing new analysis pipelines and methods as needed.

The aims of this project are to advance understanding of cellular mechanisms of tissue damage and regeneration in human disease and to prioritise candidate cells, pathways and genes for much needed therapeutic interventions. This research will be performed in close collaboration with experimental and clinical colleagues. 

KEYWORDS 

Spatial transcriptomics, single-cell, inflammatory disease, inflammation, fibrosis, tissue damage, tissue repair and tissue regeneration. 

TRAINING OPPORTUNITIES 

The Kennedy Institute is a world-renowned research centre, housed in a brand new, state-of-the-art facility at the University of Oxford. Students will become fluent in computational genomics and acquire an expert understanding of chronic inflammatory and fibrotic diseases.

Training will be provided in data science techniques including the writing of computational pipelines with Python, the use of Linux high-performance compute clusters, and statistical data analysis and visualisation with R. Students will have the opportunity to learn and utilise machine learning approaches, to work closely with world-leading research scientists and clinicians, and to perform integrated analyses with community datasets from sources such as the Human Cell Atlas (https://www.humancellatlas.org/).

You will develop a close understanding of experimental research through regular attendance of wet-lab group meetings. You will have the opportunity to be closely involved in the design, planning and execution of spatial and single cell genomics experiments and to develop a deep understanding of the various immunological techniques that are up and running in the Nanchahal lab.

For more information on our work please visit our websites: https://www.kennedy.ox.ac.uk/research/computational-genomics (Sansom group), https://www.kennedy.ox.ac.uk/research/tissue-fibrosis-and-regeneration-old (Nanchahal group).
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 Nanchahal group meetings and to attend external conferences to present their research globally.

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.

KEY PUBLICATIONS 

1. Aschenbrenner D, Quaranta M, Banerjee S, et al Deconvolution of monocyte responses in inflammatory bowel disease reveals an IL-1 cytokine network that regulates IL-23 in genetic and acquired IL-10 resistance Gut 2021;70:1023-1036. http://dx.doi.org/10.1136/gutjnl-2020-321731
2. Croft, A.P., Campos, J., Jansen, K. et al. Distinct fibroblast subsets drive inflammation and damage in arthritis. Nature 570, 246–251 (2019). https://doi.org/10.1038/s41586-019-1263-7
3. Layton, T.B., Williams, L., McCann, F. et al. Cellular census of human fibrosis defines functionally distinct stromal cell types and states. Nat Commun 11, 2768 (2020). https://doi.org/10.1038/s41467-020-16264-y
4. Lee, G., Santo, A.I.E., Zwingenberger, S. Fully reduced HMGB1 accelerates the regeneration of multiple tissues by transitioning stem cells to G Alert. PNAS (2018). https://doi.org/10.1073/pnas.1802893115
5. Rao, A., Barkley, D., França, G.S. et al. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021). https://doi.org/10.1038/s41586-021-03634-9

KEY THEMES 

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