Integration of multi-omic data into subcellular spatial proteomics cell models
Supervision
Kathryn Lilley
UCAM, 1st Supervisor
Isabell Bludau
UKHD, 2nd Supervisor
Objectives
Enhancement of our existing Bayesian approaches, which calculates the likelihood of protein localization alterations upon cellular disturbances, ensuring uncertainty quantification. Broaden the orthogonal data types integrated into this computational workflow, capturing the cell’s functional landscape. Incorporate dynamic post-translational modification data, transcriptome insights, and immune-fluorescent images to develop comprehensive cell-wide models in response to stimuli.
Methodology
Re-engineering of the Bayesian framework to incorporate multiple data types in the pipeline. Creation and contribution of open-source tools within the open-access open-development R Bioconductor framework and submission of an F1000 workflow to the Bioconductor channel. Transfer learning from other data sources to enhance probability calculations and expand training data. Creation of freely available open-access R Shiny on-line interactive visualisation tools.
Required skills
Background in R, statistics and development of computation tools. Knowledge of cell biology is also desirable.
Expected Results
Creation of new AI tools for subcellular spatial proteomics, that can combine data from multiple sources to form functional models of the proteome and how it relocalizes upon perturbation. The DC will be trained in Bayesian approaches and also how to create open-source R packages that allows dissemination of the tools to the wider public. Openly available data sets of cellular responses to stress in time and space.
Planned Secondments
Host: UKHD (I. Bludau), Duration: 2 Months; When: Year 1, Goal: Determine distribution of proteoforms in subcellular locations and their spatial structures.
Host: TUM (M. Wilhelm), Duration: 1 Month; When: Year 2, Goal: Use prediction tools to search for isoform specific peptides in proteome-wide data sets.
Host: ASTRAZENECA , Duration: 1 Month; When: Year 3, Goal: High content imaging approaches to complement subcellular proteomics data.
Enrolment in doctoral programs
THE UNIVERSITY OF CAMBRIDGE
References
1 Christopher JA, Breckels LM, Crook OM, Vazquez-Chantada M, Barratt D, Lilley KS. Global Proteomics Indicates Subcellular-Specific Anti-Ferroptotic Responses to Ionizing Radiation. Mol Cell Proteomics. 2025 Jan;24(1):100888. doi:10.1016/j.mcpro.2024.100888. Epub 2024 Nov 29.
2 Breckels LM, Hutchings C, Ingole KD, Kim S, Lilley KS, Makwana MV, McCaskie KJA, Villanueva E. Advances in spatial proteomics: Mapping proteome architecture from protein complexes to subcellular localizations. Cell Chem Biol. 2024 Sep 19;31(9):1665-1687. doi: 10.1016/j.chembiol.2024.08.008.
3 Monti M, Herman R, Mancini L, Capitanchik C, Davey K, Dawson CS, Ule J, Thomas GH, Willis AE, Lilley KS, Villanueva E. Interrogation of RNA-protein interaction dynamics in bacterial growth. Mol Syst Biol. 2024 May;20(5):573-589. doi: 10.1038/s44320-024-00031-y. Epub 2024 Mar 26.
4 Villanueva E, Smith T, Pizzinga M, Elzek M, Queiroz RML, Harvey RF, Breckels LM, Crook OM, Monti M, Dezi V, Willis AE, Lilley KS. System-wide analysis of RNA and protein subcellular localization dynamics. Nat Methods. 2024 Jan;21(1):60-71. doi: 10.1038/s41592-023-02101-9. Epub 2023 Nov 30.
5 Hutchings C, Dawson CS, Krueger T, Lilley KS, Breckels LM. A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data. F1000Res. 2023 Oct 24;12:1402. doi: 10.12688/f1000research.139116.1.