Spatial omics using histology, mass spectrometry imaging and spatial transcriptomics to investigate metabolites across tissue regions
Supervisors:
Ke Chen, Dept of Mathematics and Stats, University of Strathclyde
Ian Styles, School of EEE and Computer Science, Queen’s University of Belfast
Industry Partner - Astra-Zeneca
Summary:
This multidisciplinary project aims to address a key step in designing new drugs and testing their efficacy is a comprehensive understanding of disease mechanisms at the single-cell and tissue context level. Working with teams from Astra-Zeneca, we investigate a range of biomedical imaging modalities and study cellular heterogeneity and the intricate interactions in disease microenvironments to chart cellular heterogeneity, complex tissue structures, and dynamic changes during diseases progression.
In terms of research and development, it addresses a set of outstanding challenges in both mathematical and AI fields. Working with applied mathematicians from Strathclyde and computing scientists from Queen’s University of Belfast, along with AI and data analysis teams from AZ, the successful student will get a thorough training in Mathematical Imaging (e.g variational models and diffeomorphic maps), Artificial Intelligence (neural networks and transfer learning), Biomedical Research (Multimodal imaging, transcriptomics and H&E) and gain practical problems solving skills that are highly values in both Academia and Industries. Through the supervisors’ contacts and networks, the student will also benefit from and apply his or her skills to discussions and potential collaborators with other Universities including Universities of Birmingham and Liverpool and NHS hospitals such Clatterbridge Cancer Centre.