Claire Dunham: Characterising the obese osteoarthritic phenotype: Developing a novel protocol for spatial lipidomics analysis of cartilage
Background: Obesity is a major risk factor for osteoarthritis (OA) due to joint loading and systemic inflammation. A characteristic of obesity is lipid accumulation although the role of lipids related to obesity and inflammation in OA remains poorly understood. This study aims to use un-targeted MALDI-IMS (matrix-assisted laser desorption/ionisation-imaging mass spectroscopy) to spatially and quantitatively profile lipids in human knee OA cartilage. Objectives: 1) Develop a protocol for sectioning non-decalcified human OA cartilage suitable for MALDI-IMS and histology 2) Optimise section preparation (matrix sublimation testing) and imaging parameters that yield the greatest lipid ionisation in the range of 100–2000Da. Methods: Human tibial plateau were obtained with consent from patients undergoing knee replacement. Tape-stabilised tissue cryosectioning was optimised for temperature and tissue thickness and applied to two types of slides compatible with MALDI-IMS and histology. Ammonium formate (AmF) pre-treatment, three matrices, and ion-polarity modes were optimised and compared for lipid ionisation using MALDI-IMS. Qualitative data analysis with five criteria and bioinformatics were used to determine which combination provided the greatest lipid ionisation with the lowest signal-to-noise ratio. Results: A combination of cryofilm-stabilised sections on copper slides yielded flat sections stable for MALDI-IMS. Normharmane matrix/AmF improved lipid ionisation in negative-ion mode. Matrices - 1,5-diaminonapthalene (DAN), 2,5-dihydroxybenzoic acid (DHB) without Amf improved ionisation in both ionisation modes. DAN was more favourable for lipid ionisation and downstream lipidomics analysis. Discussion: A protocol was developed that allows native cartilage-on-bone to be sectioned to allow optimal ionisation for spatial lipidomic imaging and analysis using MALDI-IMS.
This poster was uploaded for the SGS Research Showcase 2023.
