Spotlight on Professor Kathryn Lilley

Affiliation:
Professor Kathryn Lilley
Department of Biochemistry, University of Cambridge

Research focus: A thorough knowledge of the location of biomolecules within a cell and how these change under different conditions is a pre-requisite to understanding health and disease.

My group develops and applies technologies sitting at the interface of cell biology and mass spectrometry. We have created approaches to determine the spatial organisation of the proteome, including LOPIT [1] a mass spectrometry based method coupled with robust machine learning algorithms [2], which enables thousands of proteins per experiment to be mapped to major subcellular niches and large protein complexes. Importantly, this detects dynamic changes in location that would not be detected by measurement of abundance alone [3].

My group has also developed OOPS as a method to interrogate which proteins bind RNA and how their interactions change in different conditions [4]. Application of this method has unearthed many novel RNA binding proteins including many metabolic enzymes and cytoskeletal proteins. Our data also demonstrate a clear overlap between RNA and drug binding sites on some leading therapeutic targets. Both OOPS and LOPIT are now routinely employed by researchers world-wide.

Recent advance from the lab: The holy grail of my research in the past few years has been to couple subcellular spatial proteomics with transcriptomics. We have created LoRNA, through which it is possible to map the subcellular location of the entire transcriptome, including lncRNA without the need for fluorescent reagents [5]. Using this approach we have been able to accurately determine which RNAs and proteins are trafficked to stress granules during a cellular insult. We can now combine LoRNA and LOPIT in a single experimental workflow, to measure how RNA and protein ‘move’ during any cellular perturbation be that stress, drug treatment or in comparison of diseased and healthy tissue.

Key challenge for the field: Our methods require millions of cells and so we only record the ‘average location of proteins’. Driving down cell numbers is vital, but requires very sensitive mass spectrometry. Some groups use microdissect single nuclei, but the contamination from other subcellular niches significantly reduces the power of this approach.

A key question remains how do RNA and protein relocalise in a given situation. Is what we are seeing the destruction of all copies of a molecule in one place and new synthesis and trafficking to the new location? Is it because of post transcriptional and post translational modification? It remains impossible to capture all this essential information in one experiment!

Most exciting basic breakthrough in the past few years: We need massively parallel single molecule methods that enable us to identify, quantify and determine post transcriptional/translational status, allowing a step change in subcellular spatial proteomics. Emerging transformative methods will overcome the reliance on mass spectrometry, which is an inherently insensitive technique.

Nanopore technology has been largely developed with nucleic acids in mind, but more and more data are emerging from approaches that allow protein analysis. In addition, other approaches such as: fluorescent sequencing by chemical modification; sequencing using fluorescently tagged N-terminal probes; or protein linear barcoding using DNA-PAINT, although not yet mainstream, offer a tantalising view into how parallel protein detection will be carried out in future.

https://www.bioc.cam.ac.uk/research/lilley

References:

  1. Geladaki, A., et al., Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics.Nat Commun, 2019. 10(1): p. 331.
  2. Crook, O.M., et al., Inferring differential subcellular localisation in comparative spatial proteomics using BANDLE. Nat Commun, 2022. 13(1): p. 5948.
  3. Mulvey, C.M., et al., Spatiotemporal proteomic profiling of the pro-inflammatory response to lipopolysaccharide in the THP-1 human leukaemia cell line. Nature Communications, 2021. 12(1): p. 5773.
  4. Queiroz, R.M.L., et al., Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS). Nat Biotechnol, 2019. 37(2): p. 169-178.
  5. Villanueva, E., et al., A system-wide quantitative map of RNA and protein subcellular localisation dynamics.bioRxiv, 2022: p. 2022.01.24.477541.

 

Published January 2023