Extracellular vesicles (EVs) are nanoscale particles produced and released by almost all cells to carry a cargo of proteins, metabolites and nucleic acids (Figure). They are thus central to cell-cell communication and progression of diseases such as cancer. The potential of utilising EVs as biomarkers and therapeutic vectors within a cellular population has been attracting great interest, as these natural nanoparticles offer unsurpassed efficiency in terms of cell entry and biocompatibility.
The aim of this project is to monitor, study and mathematically model EV kinetics and characterise functional changes associated with disease progression. EV movement will be visualised by fluorescent tagging and high-resolution microscopy to identify functional and phenotypical changes associated with the development of disease. To study the dynamics of EV mobility in detail, we will also use artificial nanoparticles whose size and function are commensurate with EVs, and can be loaded with proteins to closely mimic EV signalling. An acoustic nano-manipulation platform will be used to extract/sort EVs and artificial nanoparticles by size, density, compressibility and other physicochemical properties to deliver optimal resolution and targeted cell to cell signalling (Figure).
Overall, we aim to evaluate the efficacy of a novel, potentially ground-breaking therapeutic modality. We are therefore seeking a talented candidate with mathematics or engineering background, to contribute to this exciting field of applied research.
This project uses a set of well-established tools that enable dynamic tracking of EV movement, diffusion and uptake in a bone-tissue model. High resolution time lapse imaging of these dynamic processes will be obtained. Cells exposed to EV loaded with fluorescent DNA-damaging agent will be monitored for the rapid generation of DNA double strand breaks. The relationships between EV-numbers, EV-drug load per vesicles, the colloidal dispersion parameters and the surface-profile of sorted EV subsets will all be relevant factors in delivering the DNA damage-perturbation
Imaging pipelines will be developed to identify and track EVs and artificial nanoparticles. The candidate will develop robust image processing and classification protocols. Software will be developed to detect and interpret the dynamics of the particles to provide an understanding of the movement and dispersion of subcellular entities within a variety of cellular systems. Machine learning techniques for object tracking and parameter/feature classification will be employed. The main challenge is to ascertain which parameters have the greatest influence in altering intra- and inter-cellular signalling under induced states of pathology.
The parameters, once identified, will facilitate optimisation of EV signalling and provide a rational basis for their implementation for pharmacokinetic uptake. EV’s have the capacity to be vessels for targeted therapeutic in vivo drug delivery, allowing for controlled cellular system perturbations. This capability alongside advanced imaging, image analysis and mathematical modelling provide an ideal system to identify biological parameters that control EV signalling.
Dr Dimitris Parthimos: Data analytics/mathematical modelling.
Prof. Rachel Errington: Microscopy/EVs in cancer.
Dr Xin Yang: Acoustic manipulation of nanoparticles.
Dr Rowan Brown: Artificial nanoparticles.
We are seeking enthusiastic and motivated students with an interest in Cancer or genomic research. Applicants should possess a minimum of an upper second-class Honours degree, master's degree, or equivalent in a relevant subject.
Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS)
The total duration of this PhD programme is 3.5 years. Following discussion with proposed supervisors and to be considered you must submit a formal application via Cardiff University’s online application service. Medicine - Study - Cardiff University
There is a box at the top right of the page labelled ‘Apply’, please ensure you select the correct ‘Qualification’ (Doctor of Philosophy), the correct ‘Mode of Study’ (Full Time) and the correct ‘Start Date’ (i.e. October 2022). This will take you to the application portal.
In the ‘Research Proposal’ section of the application enter the name of the project you are applying to.
Candidates must submit the following information:
- Supporting statement
- Qualification certificates
- Proof of Funding i.e. a letter of intent from your sponsor or confirmation of self-funded status.
- References x 2
- Proof of English language (if applicable)
Closing date for applicants is 31st March 2022.