Reproductive Biology and Endocrinology (Jul 2017)

Differentiating mouse embryonic stem cells express markers of human endometrium

  • P. Parasar,
  • C. R. Sacha,
  • N. Ng,
  • E. R. McGuirk,
  • S. Chinthala,
  • P. Ozcan,
  • J. Lindsey,
  • S. Salas,
  • M. R. Laufer,
  • S. A. Missmer,
  • R. M. Anchan

DOI
https://doi.org/10.1186/s12958-017-0273-2
Journal volume & issue
Vol. 15, no. 1
pp. 1 – 11

Abstract

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Abstract Background Modeling early endometrial differentiation is a crucial step towards understanding the divergent pathways between normal and ectopic endometrial development as seen in endometriosis. Methods To investigate these pathways, mouse embryonic stem cells (mESCs) and embryoid bodies (EBs) were differentiated in standard EB medium (EBM). Immunofluorescence (IF) staining and reverse-transcription polymerase chain reaction (RT-PCR) were used to detect expression of human endometrial cell markers on differentiating cells, which were sorted into distinct populations using fluorescence-activated cell sorting (FACS). Results A subpopulation (50%) of early differentiating mESCs expressed both glandular (CD9) and stromal (CD13) markers of human endometrium, suggestive of a novel endometrial precursor cell population. We further isolated a small population of endometrial mesenchymal stem cells, CD45−/CD146+/PDGFR-β+, from differentiating EBs, representing 0.7% of total cells. Finally, quantitative PCR demonstrated significantly amplified expression of transcription factors Hoxa10 and Foxa2 in CD13+ EBs isolated by FACS (p = 0.03). Conclusions These findings demonstrate that mESCs have the capacity to express human endometrial cell markers and demonstrate potential differentiation pathways of endometrial precursor and mesenchymal stem cells, providing an in vitro system to model early endometrial tissue development. This model represents a key step in elucidating the mechanisms of ectopic endometrial tissue growth. Such a system could enable the development of strategies to prevent endometriosis and identify approaches for non-invasive monitoring of disease progression.

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