Molecular Profiling and Optimisation for the Analyses of Endometriosis FFPE Tissues using MALDI MSI

Jayden J Heath

Endometriosis is a complex, chronic disease characterised by appearance and growth of benign endometrial-like glands or stromal lesions outside the uterine cavity. The disease affects predominantly women and those assigned female at birth, with research suggesting 11.4% of reproductive age Australian women diagnosed or clinically suspected of having endometriosis by 40-44 years. We still do not fully understand the origins, pathogenesis and mechanisms of endometriosis hence the current lack of clinically useful non-invasive diagnostic biomarkers. Mass Spectrometry Imaging (MSI) is a key analytical tool allowing for the identification of biomolecules (i.e. metabolites, proteins and glycans) on histology slides and analysis of molecular spatial distribution. MSI enables specific biomolecules to be linked with diseased tissue permitting discovery of aberrant molecular pathways and highlighting disease mechanisms. We therefore investigated spatial ‘omic profiles (metabolomic, lipidomic, proteomic and glycomic) of endometriotic lesions with MSI using FFPE tissues sourced from a tissue biobank to identify biomarkers, assess disease heterogeneity and better understand disease workings. Firstly, we developed and optimised different ‘omic preparation methods for formalin-fixed paraffin-embedded (FFPE) tissues for analysis by MSI. We then assessed ‘omic MSI data obtained from human endometriosis tissues paired with corresponding histological images against healthy endometrium from the endometrial cycle to gain insight into and characterise the molecular workings of the disease. This data found notable molecules between lesion and non-lesion tissue regions, linking biomolecules with disease. We were able to differentiate phases of the endometrial cycle using detected peptides, finding a much higher number of peptides within the secretory phase compared to the proliferative phase. Of these differential peptides, we were able to produce receiver operating characteristic area under the curve (ROC AUC) values of up to 0.117 to discriminate between phases. Eight peptides were identified to be co-localised to the endometriotic lesions in ≥25% of lesion sites; six of these peptides were found exclusively in the lesions and were present in up to 66.67% of total lesions and with future validation may act as potential biomarkers. However, no one peptide signal was able to identify the endometriotic lesion site highlighting the disease’s heterogeneity. We were able to create a linear discriminant analysis (LDA) classification model using the eight identified peptides within a small sample subset had no false positives or negatives and with validation with a larger cohort may have diagnostic use. In our research into the N-glycome, we were able to find 15 top differential N-glycans between the phases of the endometrial cycle (with ROC AUC values of up to 0.236) of which almost half (7/15) the N-glycans found were increased during the mid-secretory phase when implantation may occur. The increased glycosylation during the midsecretory phase was also mimicked in the endometriotic lesions. Those endometriosis patients within the secretory phase had an average of ~150 (ranging from 103-165) co-localised peaks per lesion compared to an average of ~90 peaks (ranging from 80-100) in the proliferative phase. This is suggestive of a link between endometriosis and the effect of glycosylation, particularly between phases of the endometrial cycle. Localised to the endometriotic lesion sites we found 16 N-glycan suggestive peaks present in ≥45% of lesions investigated. Twelve of these peaks were found in the normal endometrial tissue microarray (TMA); however, there appeared to be higher expression within the lesion sites; four peaks were found exclusively at the lesion and not in the TMA. One N-glycan which was found exclusively the endometriotic lesion site was present in 66.67% of samples, whereas one also seen in the controls but with increased expression was present in 93.33% of samples. We were also able to create a LDA classification model using the sixteen identified N-glycans within a small sample subset had no false positives or negatives and with validation with a larger cohort may have diagnostic use. Our research has produced optimised methods for metabolomic, lipidomic, peptide proteomic and N-glycan analysis of gynaecological FFPE tissues that will provide important resources for future researchers in further assessment of endometriosis (and other) FFPE tissues. This research suggests a potential link with endometriosis and the window of implantation during the mid-secretory phase which warrants further analysis. The differential peptides and N-glycans we discovered will require further investigation, annotation and validation using orthogonal tandem MS, but hold some promise of becoming tools in the understanding of endometriosis.

Molecular Profiling and Optimisation for the Analyses of Endometriosis FFPE Tissues using MALDI MSI

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