Emma Sisley

Doctoral Researcher
Physical Sciences for Health CDT

Thesis project - "Development of liquid extraction surface analysis mass spectrometry for the investigation of biomarkers and molecular mechanisms of renal fibrosis"

Supervisors:
Professor Helen Cooper, School of Biosciences
Dr Peter Hall, UCB
DR Tim Johnson, UCB
Dr Iain Styles, School of Computer Science

This project will develop new analytical techniques to improve our understanding of the pathogenesis of fibrosis. The overall aim is to develop and apply liquid extraction surface analysis (LESA) mass spectrometry (MS) to kidney tissue samples to study the pathogenesis of renal fibrosis. This will include new physical science techniques such as a LESA MS imaging protocol for bottom-up analysis of tissue. The project contributes to the UK healthcare challenge ‘Rebuilding the ageing and diseased body’, and will be carried out in collaboration with the multinational biopharmaceutical company UCB.

LESA is emerging as a powerful tool for simultaneous spatial profiling of multiple analyte classes (proteins, peptides, lipids, drugs, metabolites) in tissue. It has been demonstrated that LESA offers improved sensitivity over other mass spectrometry imaging techniques, can accommodate both top-down (intact proteins) and bottom-up (proteolytic peptides) approaches for protein analysis, identification of protein modifications, and the ability to probe non-covalent protein interactions. Nevertheless, there are a number of challenges still to be met including quantitation of proteins by LESA, improved spatial resolution, and approaches for sampling of archival material. Further challenges include incorporation of ion mobility spectrometry and liquid chromatography (LC) approaches to maximise molecular information. This project will develop the techniques to address these challenges and apply them in order to understand the molecular mechanism of renal fibrosis.

Fibrosis is defined as the excessive accumulation of extracellular matrix (such as collagen) and is the final common pathological outcome of many chronic inflammatory diseases. Reversible fibrosis progresses to irreversible fibrosis over time, and chronic diseases such as chronic kidney disease (CKD) eventually result in permanent tissue scarring, organ failure and death. Renal fibrosis is the principal process underlying the progression of CKD, a disease which is particularly prevalent in the aging population, and is therefore a very interesting target. Recent advances in the field of mass spectrometry have allowed the discovery of novel biomarkers in many diseases. Nevertheless, there have been few studies investigating proteins and their modifications in fibrosis and even fewer that have been performed on tissue samples.

The proposed top-down and bottom-up LESA mass spectrometry imaging experiments will result in highly complex multidimensional datasets containing spatial, ion mobility, LC and mass spectral information. From these, we will derive quantitative information about the spatial distribution of proteins, identify protein biomarkers, and explore protein cross-linking in fibrosis. This will involve the development of a computational workflow to correlate spatial coordinates with m/z of proteins and peptides, ion mobility parameters (dispersion field, compensation field, and/or arrival times) and retention times (in the case of LC). In addition a computational model for fusing LESA mass spectrometry images with optical histology images will be developed, with the aim of improving the spatial resolution of the MS image.

In summary we will develop an analytical tool (LESA MS) for identification and imaging of protein biomarkers of fibrosis, together with computational tools for data analysis. These will allow us to map and compare molecular markers in normal versus pre-lesional and lesional areas of the kidney and discover signatures that predict the anatomic appearance of fibrosis and potentially act as biomarkers of disease development and progression. Discovery of biomarkers and drivers of fibrosis will help to more accurately stage renal fibrosis in humans, and quantify their response to anti-fibrotic therapy.