Bioluminescent imaging in induced mouse models of endometriosis reveals differences in four model variations

Ashley Dorning, Priya Dhami, Kavita Panir, Chloe Hogg, Emma Park, Gregory D. Ferguson, Diane Hargrove, James Karras, Andrew W. Horne, Erin Greaves

Research output: Contribution to journalArticlepeer-review

Abstract

Our understanding of the aetiology and pathophysiology of endometriosis remains limited. Disease modelling in the field is problematic as many versions of induced mouse models of endometriosis exist. We integrated bioluminescent imaging of ‘lesions’ generated using luciferase-expressing donor mice. We compared longitudinal bioluminescence and histology of lesions, sensory behaviour of mice with induced endometriosis and the impact of the gonadotropin-releasing hormone antagonist Cetrorelix on lesion regression and sensory behaviour. Four models of endometriosis were tested. We found that the nature of the donor uterine material was a key determinant of how chronic the lesions were, as well as their cellular composition. The severity of pain-like behaviour also varied across models. Although Cetrorelix significantly reduced lesion bioluminescence in all models, it had varying impacts on pain-like behaviour. Collectively, our results demonstrate key differences in the progression of the ‘disease’ across different mouse models of endometriosis. We propose that validation and testing in multiple models, each of which may be representative of the different subtypes/heterogeneity observed in women, should become a standard approach to discovery science in the field of endometriosis.
Original languageEnglish
Article numberdmm049070
Number of pages13
JournalDisease Models and Mechanisms
Volume14
Issue number8
Early online date31 Aug 2021
DOIs
Publication statusPublished - Aug 2021

Keywords / Materials (for Non-textual outputs)

  • endometriosis
  • GnRH antagonist
  • lesion
  • pain

Fingerprint

Dive into the research topics of 'Bioluminescent imaging in induced mouse models of endometriosis reveals differences in four model variations'. Together they form a unique fingerprint.

Cite this