Available
Project number:
2025_48
Start date:
October 2025
Project themes:
Main supervisor:
Lecturer in Bioinformatics
Co-supervisor:
Prof. Catherine Shanahan, School of Cardiovascular and Metabolic Medicine & Sciences
Additional Information:
Interrogating smooth muscle cell heterogeneity in vascular ageing and disease using an integrated workflow for scRNAseq
Background: Substantial progress has been made in atherosclerosis, but the major ambiguities remain regarding the mechanisms that induce atherosclerotic plaque development. Single-cell RNA-sequencing (scRNA-seq) can provide novel insights into molecular mechanisms of atherosclerosis. We have recently integrated scRNA-seq datasets of atherosclerotic plaques from coronary and carotid plaques identifying markers of modulated SMCs and verifying culprit SMC populations that express large aggregating proteoglycans.
Novelty & Importance: Existing methods for processing scRNAseq data are not optimized and their parameters are set by manual fine-tuning, while network techniques to identify cell-cell communication patterns and reconstruct correlation networks are limited by biased literature networks and known transcription factors. Many of the identified atherosclerosis pathways have not been validated in mechanistic studies or resolved at the spatial level and these methods of analysis introduce bias into how the data is interpreted restricting novelty.
Aims & Objectives: This project aims to implement novel computational methods and tools that will allow the harnessing of information within single-cell RNA-sequencing data and integrate them with spatial-RNA-seq data and proteomics measurements. The following objectives will be addressed:
• Optimize the algorithm and parameter selection for scRNA-seq data analysis
• Develop a network reconstruction and analysis workflow for the identification of causal regulatory genes in cell clusters.
• Analyze existing scRNA-seq human data to identify novel therapeutic targets.
• Verify findings on a longitudinal mechanistic mouse scRNAseq dataset
• Integrate methods, tools and datasets, together with methods to integrate data with spatial-RNAseq and proteomics data into a web application.
We are now accepting applications for 1 October 2025
How to apply
Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.
Important information for International Students:
It is the responsibility of the student to apply for their Student Visa. Please note that the EPSRC DRIVE-Health studentship does not cover the visa application fees or the Immigration Health Surcharge (IHS) required for access to the National Health Service. The IHS is mandatory for anyone entering the UK on a Student Visa and is currently £776 per year for each year of study. Further detail can be found under the International Students tab below.
Next Steps
- Applications submitted by the closing date of Thursday 30 January 2025 will be considered by the CDT. We will contact shortlisted applicants with information about this part of the recruitment process.
- Candidates will be invited to attend an interview. Interviews are projected to take place in March 2025.
- Project selection will be through a panel interview chaired by either Professor Richard Dobson and Professor Vasa Curcin (CDT Directors) followed by informal discussion with prospective supervisors.
- If you have any questions related to the specific project you are applying for, please contact the main supervisor of the project directly.
For any other questions about the recruitment process, please email us at
