Development of in vitro and in silico models to predict impact of intermittent flow on hypoxic gene expression

Project Outline

The presence of regions of hypoxia is well established as a key factor of tumour progression and therapy resistance, in particular radiotherapy (RT). Although regions of chronic hypoxia are present in tumours, it is the more dynamic cycles of hypoxia-reoxygenation, arising from both changes in tumour biology and structure as well after therapy, that are thought to drive selection of the more aggressive clones which ultimately may lead to tumour spread. Microfluidic (or lab-on-chip) models allow precise temporospatial control of tumour microenvironmental variables, such as flow. We have recently developed a ‘spheroid-on-chip’ device which allows for media to perfuse spheroids (Collins et al 2021) and evaluated flow-dependent changes in gene expression, whose patterns can vary dependent on the level of oxygenation the model is exposed to (Pyne et al 2024). A comprehensive analysis of the biological changes underpinned directly by hypoxia/reperfusion cycles and their direct role in RT response and resistance remain a gap in the field.

This PhD project will therefore aim at developing develop in vitro (using our spheroid on chip device) and computational models of changes in gene expression linked with intermittent flow/perfusion in hypoxia, and their impact on RT responses.

In order to do this, the student optimise the use of our spheroid-on-chip model (which uses perfusion at physiological levels to model interstitial flow) for use with intermittent flow conditions in hypoxia and generate gene expression datasets using RNA-seq to compare continuous with intermittent flow in tumour relevant physoxic and hypoxic conditions. Datasets will also be generated for irradiated spheroids in radiobiological hypoxia conditions.

The student will use these datasets as well as other datasets already available in the lab for continuous flow conditions (Pyne et al, 2024) to develop computational model(s). These models allow a much wider range of combinations between the various conditions (flow rates, oxygen availability, etc), and this flexibility will allow us to model a wider range of tissues. Promising models will in turn validated using the spheroid-on-chip system in our laboratory. A model for oxygen diffusion in presence of a flow will also be built.

Finally, using both the generated datasets and the model outcomes the student will identify and validate signalling pathways associated with intermittent flow models will be validated for their impact on RT response.

This PhD project, which is funded by the CRUK RadNet Manchester Centre, will be suitable for ambitious and driven candidates with either a background in computational biology and modelling looking at developing skills in wet lab research to apply their findings, or candidates with experience in cancer biology that wishes to develop computational modelling skills. Interest in multidisciplinary research is essential.

Applications for this project are now open. Please complete your application on The University of Manchester website.