MVLS/EPSRC STUDENTSHIPS

Below you will find exciting and diverse MVLS/EPSRC studentship projects.

The projects listed below are based at the University of Glasgow.

There are four positions available. The sucessful candidates/projects will commence in October 2022. 

A Chemical Biology Approach to Evaluate the Reversible Oxidation of Methionine in the Regulation of Protein Function

Supervisors:

Neil Bulleid, Institute of Molecular Cell & Systems Biology

Richard Hartley, School of Chemistry

PhD Project Summary

Oxidation of proteins leads to their inactivation and results in diseases such as Alzheimer’s and Parkinson’s.  Mammalian cells contain poorly characterised enzymes called methionine sulfoxide reductases that can reverse the oxidation of methionine and protect proteins from oxidative damage.  As the overexpression of these enzymes increases cellular resistance to oxidative stress, it is highly likely to act as an antioxidant during normal physiology and during specific diseases and ageing.  Most recently, we have shown that these enzymes can act as oxidases as well as reductases.  This discovery opens up the possibility that the enzyme can reversibly modify substrates indicating a role in regulation of protein function. 

This project will further our understanding of the role of methionine oxidation in cellular function.  We will design novel chemical probes that can be used to monitor the extent of methionine oxidation in different cellular compartment with both spatial and temporal resolution.  The project will require probe design and synthesis, live cell imaging and molecular cell biology techniques to determine the role of methionine sulfoxide reductases in cell physiology.   The project is a collaboration between a cell biology and chemistry laboratory enabling the student to obtain training in the burgeoning field of chemical biology. 

Deconvolution of intra-tumoural heterogeneity using whole section images in pancreatic cancer

Supervisors:

Fieke Froeling, Institute of Cancer Sciences

Ke Yuan, School of Computing Science

David Chang, Institute of Cancer Sciences

PhD Project Summary:

Molecular subtyping is a methodology to subclassify cancer that looks otherwise very similar under the microscope and is essential to better define molecular taxonomy of prognosis and treatment response. Subtyping using mRNA expression (transcriptome) is a popular method, as it not only gives a global overview of gene expression to allow (un)supervised clustering, it also provides runways to discovery of carcinogenesis pathways that underpins each subtype. In general, RNA is extracted from a small piece of tissue of a few millimetres size, prior to transcriptome sequencing. Therefore, it does not take the whole tumour or intra-tumoural heterogeneity (ITH) into consideration.

While ITH has been shown to be pervasive across cancer types, characterising them at the whole tumour scale remains technically and financially challenging. Hence, a novel solution to better understand ITH and transcriptomic subtyping is urgently needed to personalise and improve treatments for pancreatic cancer patients. This project will develop a novel strategy that leverages advanced artificial intelligence (AI) models to construct spatially resolved transcriptomic profiles of large pancreatic cancer patient cohorts in silico. It will use cutting edge deep learning techniques in computer vision and medical image analysis, bioinformatics and transcriptome analysis, and will leverage the availability of well-characterised pancreatic cancer cohorts (ICGC, TCGA and the national Precision-Panc trial).

Magnetic resonance imaging physics for the study of small vessel disease in humans at ultra-high field

Supervisors:

David Porter, Institute of Neuroscience & Psychology

Keith Muir, Institute of Neuroscience & Psychology

Natasha Fullerton, Institute of Neurological Sciences (NHS Greater Glasgow and Clyde)

Shajan Gunamony, Institute of Neuroscience & Psychology

Project Summary:

We are looking for a physicist or engineer to participate in a major initiative to develop the Living Laboratory at the Queen Elizabeth University Hospital in Glasgow. This will integrate medical research across a range of disciplines, including technology development for clinical MRI, based in the Imaging Centre of Excellence (ICE), which houses one of the first 7-tesla (7T) MRI scanners in a clinical setting. The PhD project will involve a close collaboration with partners in the NHS, Siemens Healthcare, and MR CoilTech, a local company with expertise in developing radiofrequency (RF) coils for MRI.

The project will use 7T MRI to develop an advanced imaging capability for the diagnosis and characterisation of small-vessel disease in the brain. Compared to standard MRI at 1.5T or 3T, the higher signal-to-noise ratio (SNR) at this field strength improves the visualisation of smaller vessels using magnetic resonance angiography (MRA) and the measurement of tissue perfusion using arterial spin labelling (ASL). The successful candidate will be involved in the design and implementation of new imaging methods using parallel-transmit techniques and dedicated RF hardware that is currently in development at ICE. The candidate will also contribute to a clinical study to assess the benefits of the new technology.

Nanovibrational control of chondrogenic differentiation

Supervisors:

Matthew Dalby, Institute of Molecular Cell & Systems Biology

Manuel Salmeron-Sanchez, Centre for the Cellular Microenvironment / School of Engineering

PhD project summary: 

Technologies to help support the quality of life of the ageing population are a major research focus. Osteoarthritis (OA) is the most prevalent chronic joint problem in the developed world – the annual cost of to the UK alone is £13B. OA is hard to treat as cartilage has no blood supply and so damaged cartilage cannot regrow. Surgeons have a strong desire to find regenerative interventions, such as cell therapies. However, lab grown chondrocytes, the cells that produce cartilage, develop the wrong phenotype when cultured in the lab ahead of being provided to the patient and this means that cell therapy approaches do not work well.

We have developed a new bioreactor that delivers tiny, nanoscale vibrations to isolated mesenchymal stem cells (MSCs), the stem cells from the bone marrow that can form chondrocytes. These 30 nm, 1000 Hz vibrations can be used to tell the MSCs to form chondrocytes, and, interestingly, the chondrocytes produced in the bioreactor have the correct phenotype (collagen II high, collagen type I and X low) to form the smooth, articulating, cartilage of the joint.

This project will use approaches such as super-resolution confocal microscopy, qPCR, western analysis, RNA seq and metabolomics to investigate MSC response to nanovibration in cell culture. The project will also use novel hydrogels to culture the nanovibrated cells in 3D to provide predictive studies of the chondrocyte behaviour in 3D as a way of exploring new methods to replace animal experimentation.

Neural Networks models to predict individual behaviour and stroke impairment from Multimodal MRI Data

Supervisors:

Cassandra Sampaio Baptista, Institute of Neuroscience & Psychology

Tanaya Guha, School of Computing Science

PhD Project Summary: 

Understanding and predicting individual complex behaviour in healthy and pathological conditions is a key goal in neuroscience and psychology. Multimodal Magnetic Resonance Imaging (MRI) allows us to image functional and structural human brain properties in vivo and to relate them to behavioural performance and impairment. Machine Learning (ML) methods have seen major breakthroughs in the last decade in the domain of natural image understanding, making its way to medical image analysis. Most ML-based MRI analysis use large data sets with hundreds or thousands of individuals, making it less than ideal for MRI-based studies that, with some exceptions, rely on small sample sizes. Recent advances in ML focus on learning from smaller datasets through approaches like self-supervised/unsupervised learning and data augmentation.

In this PhD project the student will leverage multimodal MRI using ML methods to perform data augmentation and to discover participant-specific data features that relate to the performance in different cognitive-motor tasks in healthy individuals and develop models to predict impairment in stroke survivors in small sample studies.

New chemical biology tools to untangle damage from signalling of lipid peroxidation in human cells

Supervisors:

Kostas Tokatlidis, Institute of Molecular Cell & Systems Biology

Richard Hartley, School of Chemistry

Leandro Lemgruber, Institute of Infection, Immunity & Inflammation

Project summary:

Every living organism must separate the inside from the outside of their cells. They do this using lipid membranes. The cells of higher organisms also have specialised internal spaces for different functions. These organelles, which include the mitochondria and nucleus, are enclosed by membranes. Unfortunately, the lipids that make up these membranes are easily damaged by reactive oxygen species (ROS) in a process called lipid peroxidation. This can cause the cells to malfunction and fail under oxidative stress, a process that is implicated in cardiovascular diseases and in ageing itself.

However, ROS are not just damaging species, they are involved in signalling. Their concentrations and locations within the cell are important to the balance between communication and malfunction. Lipid peroxidation is mostly on the malfunction side of this balance, so we aim to locate the exact regions of the cell where the concentration lipid-peroxidising species are highest. We will develop, validate and use small molecule probes to identify the sites of these hot spots, even within the structure of organelles. This will allow us to understand how communication and oxidative damage is occurring within the cells, and to propose interventions to limit disease and ageing without interfering with signalling.

How to Apply

Click here for details on how to APPLY.