Dr Xiao Fu, Dr Dirk Sieger, Prof Steven Pollard
Project Description
Despite our improved understanding of how genomic alterations fuel the development of glioblastomas (GBM), patients rarely benefit from existing treatments. There remains an unmet clinical need for the discovery of novel therapeutic targets. Recent studies demonstrated that the microtube network formed by glioma cells can mediate efficient long-distance communications between cells and enhance their collective resilience to therapeutic challenges. Nevertheless, how traits and behaviours of glioma cells shape the dynamic formation of microtube networks remains incompletely delineated. Crucially, clarification of key cellular and molecular mechanisms has the potential to reveal vulnerabilities of glioma tumorigenesis and lead to the discovery of novel therapeutic targets.
Theoretically, a range of biological processes spanning varying length and time scales collectively sculpt the glioma microtube networks. Glioma cells’ states and behaviours, such as proliferation and migration, influence the density and spatial distribution of cells. Processes of cellular protrusion, such as extension, retraction, and connection impact the frequency of microtube connections between cells. These biological processes, when integrated in space and time, inevitably results in a complex system that is challenging to study based solely on experimental and clinical analyses. Mathematical and computational modelling is playing an increasingly important role in unpicking mechanisms of complex biological systems, as exemplified by previous studies by us and others in the context of tumour evolution [Fu, et al. Nat Ecol Evol (2022)].
This project seeks to dissect the mechanisms driving the formation of glioma microtube networks and identify key vulnerabilities to target, by integrating our multi-disciplinary expertise in computational modelling (XF), pre-clinical experimental modelling (DS), neural stem cells and lineage reprogramming (SP).
Primarily, the student will develop a computational model to simulate the formation of glioma microtube networks. The model will encode glioma cell behaviours and processes of cellular protrusion. Extensive parameter exploration will be performed to characterise and categorise network phenotypes that result from different combinations of model parameters. Subsequently, simulated phenotypes in two dimensions (2D) will be compared with those in 2D colony formation assays using different glioma stem cell lines, in collaboration with DS lab. Next, the model will be used to predict the consequences of in silico perturbations of biological processes, or an “in silico drug screen”. Those shown to disrupt the integrity of the microtube network will be tested experimentally. Furthermore, simulations realised in three dimensions (3D) will predict the (dis)similarity between 2D and 3D, which can be experimentally validated by comparing 2D cell cultures and spheroid or organoid assays of glioma tumorigenesis.
In addition, the student will adapt the computational model to investigate the effect of a gene therapy involving an enzyme prodrug payload, in which the prodrug is transferred via microtubes as part of a bystander effect, in collaboration with SP lab. These simulations will generate quantitative insights into the distribution of the gene therapy within the microtube network, as a starting step toward more detailed collaborative investigation in the future.
Overall, the project will improve our understanding of the vulnerabilities of glioma microtube networks and accelerate the discovery of therapeutic targets.
Training offered
In this project, the student will develop mechanistic computational modelling methods, combined with quantitative bioimage analysis of experimental data, to investigate formation of glioma microtube networks and explore the potential vulnerabilities.
Customised computer codebase (e.g., in C++) will be developed to implement the computational model. The model will adapt XF’s previous models of tumour evolution [Fu, et al. Nat Ecol Evol (2022)], with the implementation of glioma cell behaviours and processes of cellular protrusion. Extensive parameter study with a large amount of replicate computer simulations will be performed on the CRUK SI HPC. BASH scripts will be developed to schedule and manage simulation jobs on the HPC.
In connecting the computational model with experimental data, a common set of quantitative descriptors of the microtube networks will be defined and extracted, using customised scripts (e.g., in Python). Bioimage analysis of time-lapse imaging data will be performed in FIJI.
The student will receive training in computational modelling (XF) and bioimage analysis (XF, DS, SP) within supervisors’ research groups, as well as support to attend relevant courses onsite and online for further skill development. They will also attend CRUK SI’s training courses including research integrity, equality and diversity, and career development.
For further information on the project or informal enquiries, please contact Dr Xiao Fu, This email address is being protected from spambots. You need JavaScript enabled to view it.
To place an application, please visit this site at the University of Glasgow.
When submitting your application please upload the completed recruitment form.
Lab Websites
Dr Xiao Fu
Dr Dirk Sieger
Prof Steven Pollard
Papers of interest
1. Fu, X., Zhao, Y., Lopez, J. I., Rowan, A., Au, L., Fendler, A., Hazell, S., Xu, H., Horswell, S., Shepherd, S. T. C., Spencer, C. E., Spain, L., Byrne, F., Stamp, G., O'Brien, T., Nicol, D., Augustine, M., Chandra, A., Rudman, S., Toncheva, A., … Bates, P. A. (2022). Spatial patterns of tumour growth impact clonal diversification in a computational model and the TRACERx Renal study. Nature ecology & evolution, 6(1), 88–102. https://doi.org/10.1038/s41559-021-01586-x
2. Mazzolini, J., Le Clerc, S., Morisse, G., Coulonges, C., Zagury, J. F., & Sieger, D. (2022). Wasl is crucial to maintain microglial core activities during glioblastoma initiation stages. Glia, 70(6), 1027–1051. https://doi.org/10.1002/glia.24154
3. Gangoso, E., Southgate, B., Bradley, L., Rus, S., Galvez-Cancino, F., McGivern, N., Güç, E., Kapourani, C. A., Byron, A., Ferguson, K. M., Alfazema, N., Morrison, G., Grant, V., Blin, C., Sou, I., Marques-Torrejon, M. A., Conde, L., Parrinello, S., Herrero, J., Beck, S., … Pollard, S. M. (2021). Glioblastomas acquire myeloid-affiliated transcriptional programs via epigenetic immunoediting to elicit immune evasion. Cell, 184(9), 2454–2470.e26. https://doi.org/10.1016/j.cell.2021.03.023