Revolutionizing Spatial Dermatology: Investigating Sunfilter Efficacy on Reconstructed Human Epidermis with AP-MALDI MSI Metabolomics and Dedicated Data Analysis Software

Applications of this unique analytical and data analysis workflow to RHE sections enabled lipids species accumulation within sections to be visualised and identified, providing insight into the metabolomics hallmarks of different sunfilters.

Leveraging the segmentation, based on clustering algorithms, and co-localization capabilities of the Pyxis software, we delineated regions of interest (ROIs) on the RHE sections to perform supervised statistical analyses, i.e. partial least squares discriminant analysis (PLS-DA). The comparison between RHE sections exposed and unexposed to UV light revealed distinctive changes in m/z values across specific cell layers. It allowed us to pinpoint inflammation biomarkers associated with UV exposure. Using these biomarkers, we directly visualised the efficacy of a test sunfilter, annotating the biomarkers, directly within Pyxis, with the integrated LIPID MAPS and HMDB databases. Annotations were ranked using a scoring system, enabling the precise identification of signal and spatial alterations in particular lipid classes, notably sterols. For instance, the oxysterol , 25-hydroxy-cholesterol 3-sulfate, recognized for its involvement in anti-inflammatory response, emerged as a key indicator. While this oxysterol is significantly expressed in UV-exposed and unprotected RHE sections, the opposite was true in the RHE sections not exposed to UV or protected by the sunscreen. Hence, the effectiveness of the sunfilter in mitigating this pathway was discerned by the observed modulation of this lipid. Our findings highlight the utility of the Pyxis platform, providing a user-friendly interface and a full exploitation of comprehensive AP-MALDI MSI data processing workflow, offering insights into metabolomic signatures relevant to dermatological research.