Asthma is a chronic respiratory disease, affecting roughly 10% the developed world. Treatments exist to mitigate the common symptoms of coughing and airway restrictions, however a small percentage experience a severe condition with heightened symptoms and mortality rates that does not respond to common treatments. Pharmacogenomic and transcriptomic studies have found and revalidated the most common biomarkers of severe asthma, however there is push for a robust discovery-based, untargeted approach to better understand the heterogeneity of the disease.
Matrix-assisted laser desorption/ionisation mass spectrometry imaging is an untargeted discovery-based technique seeing use in identifying potential biomarkers in cancer and shows potential in illuminating the diverse pathophysiology of asthma. In this work we aimed to optimise the matrix application part of sample preparation by characterising matrix properties and determining optimal deposition for commonly used matrices.. We then aimed to use this optimised MALDI-IMS workflow to characterise the spatial distribution of peptides in steroid-resistance using severe steroid-resistant allergic airway murine models. We then cross-reference our peptide data with LC-MS/MS data to validate peptide identities and parent proteins.
Three different deposition profiles were successfully created, detailing the ideal time and temperature required for optimal matrix deposition on glass laboratory slides. As well as a sublimation rate, and temperature at which sublimation begun to occur. The fully optimised workflow was successfully used to generate a differential display of peptides on fresh-frozen and formalin-fixed paraffin-embedded murine lung models. This data was then successfully cross-referenced with a COPD LC-MS/MS database to identify key proteins that play a role in severe asthma .
We were able to successfully create a reliable and working matrix-assisted laser desorption/ionisation peptide mass spectrometry imaging workflow, using optimised matrix sublimation profiles. We generated three different global spectra from section, along with at least 4 differential display images for each. We were also able to successfully identify key cytoskeletal and immune proteins using LC-MS/MS cross-referencing. Future directions will involve further perfecting the sublimation protocol with more matrices. The optimised workflow will then be used further