Diagnosing the Masses - Molecular pathology through multimodal mass spectrometry imaging - summary to follow

Lead Research Organisation: University of Manchester
Department Name: Chemistry


Recent developments in ambient ionisation methods in mass spectrometry have resulted in new applications in real-time diagnostics. Arguably the area of highest potential impact is in the clinical arena, where mass spectrometry has been shown to be capable of distinguishing health/diseased tissue in-situ in operating theatres. The aim is to provide real-time chemical analysis of excised tissue and through chemometric pathological classification guide the surgeon with regard to the tumour margins, which is critical in determining patient outcome. The' intelligent-knife' (iknife) technology developed at Imperial College [1] has attracted considerable attention including the national media and is now undergoing clinical trials. The technology behind the iknife, rapid evaporative ionisation mass spectrometry (REIMS) has been acquired wholly by Waters Corp.
Early development of the REIMS molecular pathology technique has focused on the lipid signatures from biological tissue. These signals are amenable to other imaging mass spectrometry techniques, both ambient and in-vacuum, including secondary ion mass spectrometry (SIMS) where Manchester has world-leading expertise and infrastructure. This project aims to establish a robust chemical basis for the classification of biological material using the REIMS and complementary mass spectrometry imaging approaches.
The student will develop biological models of increasing complexity ranging from cell cultures to spheroids (tumour-mimics) and xenografts to systematically determine the role of cellular phenotype, hypoxic-status and metabolic changes induced by the host organism [2]. MS analysis will be performed by high-resolution SIMS, ambient desorption DESI-MS, and next-generation REIMS tools developed by Waters and donated to the University for the purpose of this project. This research will extend the chemical landscape on which the classification is based to include a range of molecular weight compounds including new low mass metabolite biomarkers and proteins. Access to primary tissue archives and standard histological procedures together with the re-sampling MS imaging approaches will inform classification models and improve their accuracy and specificity within the heterogeneous tumour microenvironment.
[1] J. Balog et al. (2013) 'Intraoperative tissue identification using rapid evaporative ionization mass spectrometry.' Sci. Transl. Med. 5, 194ra93.
[2] Kotze, HL et al. (2013). 'ToF-SIMS as a tool for metabolic profiling small biomolecules in cancer systems.' Surface Interface Analysis, 45, 277-281.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509565/1 01/10/2016 30/09/2021
1792601 Studentship EP/N509565/1 15/09/2016 31/03/2020 Danielle McDougall