Kathryn Lilley
Subcellular proteome and transcriptome.
The Lilley Group focuses on the development and application of technologies which enable the measurement of the dynamic proteome in space and time in cells in a high-throughput manner. More specifically, we are interested in looking at the changes in abundance, location, interacting partners, post-translational modification status and structure of proteins during dynamic cellular processes such as differentiation. We also develop methods to determine the subcellular distribution of the transcriptome and how this changes upon perturbation, and the complement of proteins that form dynamic ribonuclear complexes. Finally, we aim to determine the spatial and temporal links between the trafficking of the proteome and its corresponding transcriptome.
Our technologies involve innovative experimental pipelines coupled with quantitative mass spectrometry, RNA-seq, and computational tools. We use these methods to dissect cellular mechanisms related to stress, drug treatment, differentiation and disease in many collaborative projects with academic labs and the pharmaceutical industry.
Research objectives
What is the impact of post-transcriptional and post-translational processing of RNA and proteins on their interactions and location?
What are the changes in interactions/location upon perturbation, for example during drug treatment?
Do proteins adopt different structures/functions in different locations (moonlighting)?
What role does RNA play in moonlighting (for example RNA-protein interactions and/or localised translation)?
What is the spatial relationship between a protein and its corresponding mRNA?
Key publications
Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M (2020). DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat. Methods, 17(1):41-44. doi: 10.1038/s41592-019-0638-x
Minde DP, Ramakrishna M, Lilley KS (2020). Biotin proximity tagging favours unfolded proteins and enables the study of intrinsically disordered regions. Commun. Biol., 3(1):38. doi: 10.1038/s42003-020-0758-y
Queiroz RML, Smith T, Villanueva E, Marti-Solano M, Monti M, Pizzinga M, Mirea DM, Ramakrishna M, Harvey RF, Dezi V, Thomas GH, Willis AE, Lilley KS (2019). Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS). Nat. Biotechnol., 37(2):169-178. doi: 10.1038/s41587-018-0001-2
Geladaki A, Kočevar Britovšek N, Breckels LM, Smith TS, Vennard OL, Mulvey CM, Crook OM, Gatto L, Lilley KS (2019). Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics. Nat. Commun., 10(1):331. doi: 10.1038/s41467-018-08191-w
Crook OM, Mulvey CM, Kirk PDW, Lilley KS, Gatto L (2018). A Bayesian mixture modelling approach for spatial proteomics. PLoS Comput. Biol., 14(11):e1006516. doi: 10.1371/journal.pcbi.1006516