Using synthetic biology and quantitative analysis to understand differences in tolerance to tumorogeneic mutations

Background

Healthy tissues often harbour tumorogeneic mutations. It is not entirely clear why such mutations are sometimes tolerated but at other times lead to disease.  There is an increasing appreciation of that surrounding healthy cells (the tumourogenic ‘niche’) play a critical role, but it is not clear how these cells sense and response to mutant cells.  If we are able to uncover the molecular and cellular basis of this ‘sense and response’ mechanisms, we can then determine how genetic differences between individuals could explain different “niche responses” and therefore different susceptibilities to disease and to therapy.

In this project we combine two novel synthetic biology technologies to address this question. The Lowell lab have developed a novel ‘neighbour labelling’ system that will enable multi-omic analysis and quantitative image analysis in order to characterise the behavoir of cells within the niche surrounding early cancer cells.  The Pollard Lab has expertise in developing highly specific synthetic enhancers that can be used to deliver ‘neighbour labelling” machinery to particular cell states, including pre-neoplastic or more advanced tumorogeneic states.  The Wilson lab have expertise in grafting and electroporation methods to deliver these systems to tissues in vivo. The Schumacher lab have expertise in mathematical modelling of cell-cell interactions, including spatial analysis of the cancer niche. 

Aims

1) To combine two existing technologies to establish a cancer-niche-labelling system

2) To apply this system to models of preneoplasia already established in the supervisor labs, including glioblastoma (Pollard) and skin cancer (Lowell)

3) To extract and model quantitative information about ‘sense and response’ mechanisms

This will pave the way towards understanding when and how healthy cells are able to suppress tumour formation, and how this process differs between individuals.

Training Outcomes

• Synthetic biology approaches for cell engineering
• In-vivo and organoid based models of early tumorogenesis
• Bioinformatic analysis of ‘omics data
• Quantitative image analysis of neighbour relationships
• Mathematical modelling of quantitative data to establish rules underlying neighbour relationships.

References

Malaguti…Lowell. SyNPL: Synthetic Notch pluripotent cell lines to monitor and manipulate cell interactions in vitro and in vivo. Development. 2022 doi: 10.1242/dev.200226

Lebek… Lowell PUFFFIN: A novel, ultra-bright, customisable, single-plasmid system for labelling cell neighbourhoods bioRxiv 2023 doi: https://doi.org/10.1101/2023.09.06.556381

Gangoso … Pollard. Glioblastomas acquire myeloid-affiliated transcriptional programs via epigenetic immunoediting to elicit immune evasion. Cell. 2021 doi: 10.1016/j.cell.2021.03.023

Martin, Schumacher, Chandra. Modelling the Dynamics of Senescence Spread Ageing Cell 2023 doi: 10.1111/acel.13892