Comparison of Immunosuppressive and Angiogenic Properties of Human Amnion-Derived Mesenchymal Stem Cells between 2D and 3D Culture Systems.
Vitale MiceliMariangela PampaloneSerena VellaAnna Paola CarrecaGiandomenico AmicoPier Giulio ConaldiPublished in: Stem cells international (2019)
The secretion of potential therapeutic factors by mesenchymal stem cells (MSCs) has aroused much interest given the benefits that it can bring in the field of regenerative medicine. Indeed, the in vitro multipotency of these cells and the secretive capacity of both angiogenic and immunomodulatory factors suggest a role in tissue repair and regeneration. However, during culture, MSCs rapidly lose the expression of key transcription factors associated with multipotency and self-renewal, as well as the ability to produce functional paracrine factors. In our study, we show that a three-dimensional (3D) culture method is effective to induce MSC spheroid formation, to maintain the multipotency and to improve the paracrine activity of a specific population of human amnion-derived MSCs (hAMSCs). The regenerative potential of both 3D culture-derived conditioned medium (3D CM) and their exosomes (EXO) was assessed against 2D culture products. In particular, tubulogenesis assays revealed increased capillary maturation in the presence of 3D CM compared with both 2D CM and 2D EXO. Furthermore, 3D CM had a greater effect on inhibition of PBMC proliferation than both 2D CM and 2D EXO. To support this data, hAMSC spheroids kept in our 3D culture system remained viable and multipotent and secreted considerable amounts of both angiogenic and immunosuppressive factors, which were detected at lower levels in 2D cultures. This work reveals the placenta as an important source of MSCs that can be used for eventual clinical applications as cell-free therapies.
Keyphrases
- mesenchymal stem cells
- umbilical cord
- stem cells
- cell free
- endothelial cells
- bone marrow
- cell therapy
- poor prognosis
- induced apoptosis
- signaling pathway
- high throughput
- electronic health record
- cell death
- pluripotent stem cells
- machine learning
- artificial intelligence
- deep learning
- cell cycle arrest
- single cell
- climate change