Matrix scaffolds for endometrium-derived organoid models

Front Endocrinol (Lausanne). 2023 Aug 10:14:1240064. doi: 10.3389/fendo.2023.1240064. eCollection 2023.

Abstract

The uterus-lining endometrium is essential to mammalian reproduction, receiving and accommodating the embryo for proper development. Despite its key role, mechanisms underlying endometrial biology (menstrual cycling, embryo interaction) and disease are not well understood. Its hidden location in the womb, and thereby-associated lack of suitable research models, contribute to this knowledge gap. Recently, 3D organoid models have been developed from both healthy and diseased endometrium. These organoids closely recapitulate the tissue's epithelium phenotype and (patho)biology, including in vitro reproduction of the menstrual cycle. Typically, organoids are grown in a scaffold made of surrogate tissue extracellular matrix (ECM), with mouse tumor basement membrane extracts being the most commonly used. However, important limitations apply including their lack of standardization and xeno-derivation which strongly hinder clinical translation. Therefore, researchers are actively seeking better alternatives including fully defined matrices for faithful and efficient growth of organoids. Here, we summarize the state-of-the-art regarding matrix scaffolds to grow endometrium-derived organoids as well as more advanced organoid-based 3D models. We discuss remaining shortcomings and challenges to advance endometrial organoids toward defined and standardized tools for applications in basic research and translational/clinical fields.

Keywords: endometrium; extracellular matrix; hydrogel; matrix scaffold; organoid.

Publication types

  • Review
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Basement Membrane
  • Endometrium*
  • Female
  • Mammals
  • Menstrual Cycle
  • Mice
  • Organoids
  • Uterus*

Grants and funding

The authors are supported by grants from the KU Leuven Research Fund and from the Fund for Scientific Research-Flanders (FWO). SDV is supported by a PhD Fellowship from the FWO (1S00823N).