2025-26 Project (Kim & Reyes-Aldasoro & Barrick)

Harnessing Artificial Intelligence to Identify Novel Biomarkers for Ageing

SUPERVISORY TEAM

Supervisor

Dr Soo-Hyun Kim at City St George’s
Email: skim@sgul.ac.uk

Co-Supervisor

Dr Constantino Carlos Reyes-Aldasoro at City St George’s
Email: Constantino-Carlos.Reyes-Aldasoro@city.ac.uk

Co-Supervisor

Dr Tom Barrick at City St George’s
Email: tbarrick@sgul.ac.uk

PROJECT SUMMARY

Project Summary

Discover Cellular Mechanisms of Ageing with AI-Driven Imaging.  

Join a groundbreaking project to unravel how cells age using advanced microscopy technology. With the growing ageing population, there’s an urgent need to study ageing mechanisms beyond disease. This research will focus on primary cilia – a tiny cellular structure – using advanced 2D and 3D imaging combined with deep learning framework to identify age-associated changes. You’ll receive training in cutting-edge microscopy techniques and collaborate with leading experts in the UK and Korea. This is a unique opportunity to work at the intersection of AI, cell biology, and ageing research, contributing to the development of novel biomarkers and therapeutic strategies that could transform the future of healthy ageing. You will be part of a team pushing the boundaries of science and technology to combat the challenges of ageing.

Project Key Words

AI, microscopy, live imaging, cilia, ageing

MRC LID Themes

  1. Health Data Science
  2. Translational and Implementation Research
  3. Global Health

Skills

MRC Core Skills

  1. Interdisciplinary skills
  2. Quantitative skills
  3. Whole organism physiology

Skills we expect a student to develop/acquire whilst pursuing this project

Advanced microscopy (time-lapse confocal live imaging, super resolution confocal, electronic);
Development of algorithms and automated imaging data analysis platform;
Understanding of cilia biology and cellular ageing.

Routes

Which route/s are available with this project?

  • 1+4 = Yes
  • +4 = Yes

Possible Master’s programme options identified by supervisory team for 1+4 applicants:

  • City St George’s – MSc Data Science
  • City St George’s – MRes Biomedical Science – Molecular Mechanisms of Cancer
  • City St George’s – MRes Biomedical Science – Reproduction and Development

Full-time/Part-time Study

Is this project available for full-time study? Yes
Is this project available for part-time study? Yes

Location & Travel

Students funded through MRC LID are expected to work on site at their primary institution, meeting – at the minimum – the institutional research degree regulations and expectations. Students may also be required to travel for conferences (up to 3 over the duration of the studentship), and for any required training (for research degree study). Other travel expectations and opportunities highlighted by the supervisory team are noted below.

Primary location for duration of this research degree: City St George’s, London

Travel requirements for this project: 2 months lab placement with colleagues in Seoul, Korea, to learn more about cilia biology and animal models of ageing-associated ciliopathy.

Eligibility/Requirements

Particular prior educational requirements for a student undertaking this project

  • Minimum City St George’s institutional eligibility criteria for doctoral study.
  • Minimum 2:1 honours BSc in a cell biology discipline.
  • Some background in computer science, mathematics, engineering, AI or machine learning would be advantageous.

Other useful information

  • Potential Industrial CASE (iCASE) conversion? = No

PROJECT IN MORE DETAIL

Scientific description of this research project

1. Objectives   

Primary cilia are small, antenna-like structures that act as crucial signalling hubs, playing key roles in cellular development, metabolism, and homeostasis throughout life. Dysfunction of these micro-organelles has been widely linked to ageing-related conditions, including neurological disorders, kidney and liver diseases, obesity, and diabetes. Emerging evidence suggests that primary cilia influence fundamental ageing mechanisms, such as inflammation, genomic instability, and protein homeostasis. Maintaining proper ciliary function is therefore critical to preventing age-related cellular decline. This project aims to investigate how ciliary structure and function are altered during normal ageing process in the absence of diseases and whether these changes can be used as biomarkers to detect early signs of cellular ageing. Using AI-driven imaging analyses, we will determine the dynamic changes over the life cycle of cilia in real-time to uncover the ageing-induced alterations potentially underlying the ageing-related pathologies.   

2. Techniques   

This project combines advanced microscopy with AI analysis to produce 3D and 4D live-cell images of primary cilia. We will utilise advanced confocal and super-resolution microscopy, Correlative Light and Electron Microscopy (CLEM), and SEM Array Tomography, in collaboration with UCL’s renowned Bloomsbury Consortium for Light and Electron Microscopy Unit. By developing deep-learning models, we aim to automate the analysis of complex 2D, 3D, and 4D data sets, allowing us to track cilia dynamics in real time. This integration of AI and high-resolution imaging will help elucidate key aspects of cilia’s life cycle, including formation, maintenance, vesicle shedding, and disassembly, providing a comprehensive understanding of how cilia change with ageing.   

3. Availability of Required Materials   

Human diploid fibroblast cells will be used, which are commercially available and can be cultured until they reach senescence. Cells will be stored at different intervals to capture multiple stages of the ageing process. No additional specialised materials are needed.   

4. Potential Risks and Mitigation Strategies   

Initial setup and optimisation of 3D and 4D live imaging may pose a challenge due to the complexity of the data. To mitigate this, we will begin with simpler 2D data sets before transitioning to more advanced models. We recognise that integrating AI, microscopy, and cell biology can be demanding. Thus, we will focus on developing a core set of algorithms before scaling up to more comprehensive analyses. The research team’s combined expertise-Dr. Kim in cilia biology and ageing, Dr. Reyes-Aldasoro in AI-based image analysis, and Dr. Barrick in large-scale data management-ensures a robust support system for overcoming these challenges. Furthermore, a two-month placement in Seoul, Korea, will provide additional training and insights from leading cilia experts. This collaboration will offer a comprehensive training environment, exposing the student to diverse methodologies and international research perspectives. The project is ambitious but feasible, focusing on generating impactful data that could redefine our understanding of ageing and its associated diseases, paving the way for future therapeutic interventions.

Further reading

Relevant preprints and/or open access articles:
(DOI = Digital Object Identifier)

Additional information from the supervisory team

The supervisory team has provided a recording for prospective applicants who are interested in their project. This recording should be watched before any discussions begin with the supervisory team.

Kim-ReyesAldasoro-Barrick recording

MRC LID LINKS

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