2026-27 Project (Kim & Reyes-Aldasoro & Barrick)

AI-driven multidimensional analysis of cilia as a biomarker of ageing and health

SUPERVISORY TEAM

Supervisor

Dr Soo-Hyun Kim at City St George’s
School of Health & Medical Sciences, Department of Molecular and Biomedical Sciences
Email: skim@citystgeorges.ac.uk

Co-Supervisor

Dr Constantino Carlos Reyes-Aldasoro at City St George’s
School of Science & Technology, Department of Computer Science
Email: Constantino-Carlos.Reyes-Aldasoro@citystgeorges.ac.uk

Co-Supervisor

Dr Tom Barrick at City St George’s
School of Health & Medical Sciences, Department of Psychology and Neuroscience
Email: tbarrick@citystgeorges.ac.uk

PROJECT SUMMARY

Project Summary

Discover Cellular Mechanisms of Ageing with AI-Driven Multi-Dimensional Imaging Analysis. Join a ground breaking 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, 3D and 4D 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 South 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

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

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. At a minimum, all students must meet the institutional research degree regulations and expectations about onsite working and under this scheme they may be expected to work onsite (in-person) more frequently. 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 and training. Other travel expectations and opportunities highlighted by the supervisory team are noted below.

Day-to-day work (primary location) for the duration of this research degree project will be at: City St George’s – Tooting campus, London

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

Eligibility/Requirements

Particular prior educational requirements for a student undertaking this project

  • Minimum standard institutional eligibility criteria for doctoral study at City St George’s
  • Minimum 2:1 honours BSc in any Biomedical Science discipline
  • 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

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 neuro-degenerative 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.

Techniques
This project combines advanced microscopy with AI analysis to produce 3D, 4D and 5D 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 multi-dimensional analysis of complex image 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.

Availability of Required Materials
Normal human diploid fibroblast cells will be used, which are commercially available and can be cultured until they reach senescence. Cells will be stored at regular intervals (3 population doublings) to capture different stages of the ageing process. No additional specialised materials are needed.

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 working with 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 a new regime for early diagnosis and future therapeutic interventions.

Further reading

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

Other pre-application materials:

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 & Reyes-Aldasoro & Barrick Recording

MRC LID LINKS

To apply for a studentship: MRC LID How to Apply
Full list of available projects: MRC LID Projects
For more information about the DTP: MRC LID About Us

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