Physical Sciences of Imaging in the Biomedical Sciences CDT
Completed in 2016 and secured position as a trainee patent attorney.
Thesis project - "Novel computer image analysis and machine learning methods for quantitative characterisation of the drug/receptor interactions in metastatic breast cancer"
Dr Elena Odintsova, School of Cancer Sciences
Professor Ela Claridge, School of Computer Science
Dr Rob Neely, School of Chemistry
This project aims to develop a novel physical methodology for performing single particle tracking on cell surface receptors in cells in 3D culture, along with improved computational analysis of this data to maximise the robustness of the parameterisation and amount of extracted information.
It will involve investigation of the optimal imaging modality and aim to improve imaging resolution and conditions for study of molecular membrane dynamics in cells grown in 3D cultures. Live cell imaging in the presence of extra-cellular matrix (ECM) brings unique challenges such as increased levels of autofluorescence and depth of field. The application of TIRFM will be unworkable under these conditions; consequently other microscopy techniques will be investigated (e.g. super-resolution methods (PALM) or a combination of light sheet optical sectioning microscopy and ultra sensitive high-speed imaging (Ritter et al., 2010)). Adjustments to existing imaging systems may be required. Use of quantum dots (QDs) for labelling allows single molecule tracking with high signal to noise ratio which is critical for data collection. We will investigate the temporal and spectral properties of QD emission and their modification in a biological environment, especially in 3D conditions. This will be possible in collaboration with nanoscale physics group at the University of Birmingham.
Obtaining a successful outcome for the project will require development of probabilistic methods for single (blinking) particle tracking, firstly in 2D, and application of these methods to the data derived from the experiments in 3D. Although initially this work will be based on probabilistic tracking software developed in a previous PSIBS project, design and implementation of probabilistic motion models in a multi-microdomain environment is novel and advanced. New state of the art probabilistic approaches for the classification of the results of particle tracking (both single and multiple QD labels) will be developed.
Using the above described novel approaches the project aims to investigate changes in the biophysical properties of the ErbB2 receptor (such as diffusion coefficient, lateral mobility, velocity, etc.) in response to genetic alterations of membrane composition and/or to exposure to the drug targeting ErbB2 (Trastuzumab). The functional significance of the receptor's localisation to the specific microdomains will be analysed. 3D growth conditions will be used to mimic physiological conditions in a human body, which allows us to account for additional factors such as interactions with ECM and actin cytoskeleton.
Analysis of the contribution of multiple factors affecting receptor behaviour and ultimate drug responses will only be possible using a combination of physical and computational approaches.