Active Cells, Biochemistry & Physiology

European Training Program for Deconvolution of Multi-scale Cilia Function in Health and Disease by Integrating Machine Learning-AI Approaches.

Funder UKRI
Recipient organisation University of Edinburgh
Funding £268K
Period Oct 2025 — Oct 2029

In plain English

AI plain-English summary

Tiny hair-like projections on the surface of nearly every human cell—called primary cilia—coordinate the signalling pathways that govern development, hearing, smell, respiration, excretion, and reproduction, and when they malfunction they cause more than 35 severe diseases, known as ciliopathies, that affect up to 1 in 400 people. Why this matters: Despite their importance, cilia are so small and complex that their multi-level organisation and regulation remain poorly understood. Current imaging and data-analysis tools cannot handle the high-resolution datasets needed to see how cilia work in health and disease. This gap limits both fundamental understanding and the development of diagnostics or treatments for ciliopathies. What the project does: Cilia-AI trains 15 doctoral candidates to combine structural biology, omics, organoid technologies, and advanced imaging—super-resolution microscopy, cryo-electron tomography, and expansion microscopy—with machine learning approaches that can decipher the resulting high-content datasets. The training spans academic and industrial settings, equipping researchers with skills for both sectors. Potential impact: If successful, the project will produce a deeper fundamental understanding of cilia biology and a cohort of specialists who can apply AI to complex biomedical data. This is primarily curiosity-driven fundamental science; no immediate clinical application is promised. However, similar fundamental work on cellular structures has historically underpinned breakthroughs in drug development and genetic therapies.

View original technical description
Cilia-AI will train a new generation of multidisciplinary biomedical researchers and entrepreneurs, and those specializing in emerging machine learning technologies, a subset of AI. The focus is the study of primary cilia, microtubule-based projections on cell surfaces that play a pivotal role in coordinating cellular signalling pathways during development and homeostasis of cells, tissues and organs. These tiny structures are essential for various physiological functions such as hearing, smell, respiration, excretion and reproduction. Dysfunctional cilia can lead to >35 severe human diseases known as ciliopathies, exhibiting diverse and overlapping phenotypes, affecting up to 1 in 400 people. To unravel the multi-level organisation and regulation of cilia in health and disease, Cilia-AI employs a multidisciplinary approach, integrating cutting edge technologies like structural biology, omics- and organoid technologies. Advanced imaging techniques, including super-resolution microscopy, cryo-electron tomography and expansion microscopy, will be used to generate high-resolution and versatile datasets. Processing such data requires sophisticated computational methods. Cilia-AI is at the forefront of implementing and developing machine learning approaches to decipher these high-content datasets and integrate diverse multidisciplinary data. Cilia-AI offers unparalleled training opportunities for 15 Doctoral Candidates (DCs) in both academic and industrial settings. The training involves individual research projects, secondments, and network-wide sessions. This training equips DCs with skills attractive to both industrial and academic sectors, enhancing their career prospects in these domains. Overall, Cilia-AI's research and training activities contribute to advancing the understanding of cilia in health and disease while fostering a new generation of skilled professionals with broad competences.

View the original record at the funder ↗

Researchers

Pleasantine Mill (Principal Investigator)

Related Research

Grants with similar aims, by meaning.

Identification and characterisation of conserved protein families involved in ciliary function and diseases
Improving Primary Ciliary Dyskinesia diagnosis using artificial intelligence.
Molecular principles of mammalian cilia diversity
Studying Ciliary Signaling in Development and Disease
Real-time prediction of cellular states in 3D lattice light sheet microscopy

Original classification

Training Grant

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