Marker-independent imaging reveals a correlation of fibrotic and epigenetic alterations in endometriosis

T. L. Beyer, Lucas Becker, Cansun Demir, Sema Demir, Simone Liebscher, Daniel Carvajal Berrio, Hans Bösmüller, Katharina Rall, Bernhard K. Krämer, Sara Y. Brucker, Katja Schenke‐Layland, Martin Weiß, Julia Marzi
In: bioRxiv · 2025 · doi:10.1101/2025.07.10.664232 · W4412486867
preprint OA: green CC0
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Raman microspectroscopy identified distinct collagen and nuclear differences, indicative of fibrosis and increased methylation, between normal endometrium and endometriosis, enabling accurate tissue classification.

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Abstract

Abstract Introduction Endometriosis describes the presence of endometrial glands outside of the uterus and can cause various symptoms such as chronic pelvic pain, hypermenorrhea and infertility. These complications pose an extreme burden on the patients, especially as up to date, the average time until diagnosis can consume several years and requires invasive laparoscopy. Objectives The aim of this study is to molecularly characterize endometrium and endometriosis using marker-independent Raman microspectroscopy to identify potential biomarkers and validate its diagnostic potential. Methods After histopathological characterization of tissue sections of human endometrium and endometriosis, Raman microspectroscopy was performed on the gland region. Multivariate analysis of the hyperspectral maps was used to localize major subcellular structures and further decipher their molecular composition. Samples from different anatomical regions and throughout all menstrual cycle phases were analyzed. Results Raman imaging enabled label-free visualization of tissue morphology and submolecular tissue characterization. Distinct differences between endometrium and endometriosis were found for collagen type I and nuclear signatures. Spectral deconvolution allowed identification of a Raman biomarker indicative of fibrotic changes in endometriosis samples. Additionally, a significant increase in epigenetic 5mC foci and an increased signal intensity relevant for methylations was detected in nuclei of endometriosis. Furthermore, a neural network-based classification of Raman data resulted in high accuracies in discriminating endometrial and peritoneal tissue from endometriosis. Conclusion The non-destructive approach by hyperspectral Raman imaging enabled for molecular sensitive characterization of endometriotic lesions which could not only be an asset in complementing histopathological tissue evaluation but combined with data-driven classification models support in situ tissue diagnosis.

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Condition tags

endometriosischronic_pelvic_paininfertility

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References (58)

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