Background: Endometriosis, which affects up to one in ten women worldwide, is defined by the ectopic growth of endometrial-like tissue outside of the uterus. Endometriosis has no cure and is associated with menstrual pain and infertility. There is a clear need for fundamental research to increase our understanding of the underlying disease mechanisms. This project has employed mass spectrometry imaging (MSI) to examine the spatial distribution of tissue metabolites in the eutopic tissues, endometrium and myometrium and disease-specific ectopic endometriotic lesions. Identification of endometriosis-associated metabolites may contribute to improving our ability to diagnose and treat the condition.
Methods: This study has used a large bank of formalin fixed paraffin embedded (FFPE) tissues that have been collected from patients as part of the Royal Women’s Hospital Endometriosis Project. Matrix Assisted Laser Desorption Ionisation Mass Spectrometry Imaging (MALDI-MSI) was employed to discriminate spatial metabolite profiles within the tissues of interest. Mass spectrometry imaging (MSI) adds another level of complexity to other metabolomic techniques by determining the spatial distribution of metabolites and biomolecules within tissues, that can be used to associate specific metabolites with tissue and cell types to create an in-depth comparative analysis of how diseases change over time.
Results: We have developed new methods to measure the spatial distribution of small chemicals and metabolites in archived FFPE tissues, measuring 5553 individual m/z (ions) showing spatial distribution within archived FFPE uterine and endometrial curettes through five stages of the menstrual cycle, of these we were able to annotate 168 metabolites using the metabolite annotation tool Metaspace (www.metaspace2020.eu).
Conclusion: This investigation has provided the first metabolomic MSI data on archived FFPE uterine tissues and endometrial curettes. This information will be compared to MSI data from eutopic and ectopic endometrial lesions and employed to improve our understanding endometriosis of disease mechanisms and classification.