Repopulation of decellularized organ scaffolds with human pluripotent stem cell-derived pancreatic progenitor cells.
Saik-Kia GohSuzanne BerteraThomas RichardsonIpsita BanerjeePublished in: Biomedical materials (Bristol, England) (2023)
Diabetes is an emerging global epidemic that affects more that 285 million people worldwide. Engineering of endocrine pancreas tissue holds great promise for the future of diabetes therapy. Here we demonstrate the feasibility of re-engineering decellularized organ scaffolds using regenerative cell source. We differentiated human pluripotent stem cells (hPSC) toward pancreatic progenitor (PP) lineage and repopulated decellularized organ scaffolds with these hPSC-PP cells. We observed that hPSCs cultured and differentiated as aggregates are more suitable for organ repopulation than isolated single cell suspension. However, recellularization with hPSC-PP aggregates require a more extensive vascular support, which was found to be superior in decellularized liver over the decellularized pancreas scaffolds. Upon continued culture for nine days with chemical induction in the bioreactor, the seeded hPSC-PP aggregates demonstrated extensive and uniform cellular repopulation and viability throughout the thickness of the liver scaffolds. Furthermore, the decellularized liver scaffolds was supportive of the endocrine cell fate of the engrafted cells. Our novel strategy to engineer endocrine pancreas construct is expected to find potential applications in preclinical testing, drug discovery and diabetes therapy.
Keyphrases
- tissue engineering
- pluripotent stem cells
- single cell
- endothelial cells
- type diabetes
- cell fate
- induced apoptosis
- cardiovascular disease
- drug discovery
- extracellular matrix
- glycemic control
- cell therapy
- cell cycle arrest
- rna seq
- signaling pathway
- stem cells
- induced pluripotent stem cells
- cell proliferation
- cell death
- endoplasmic reticulum stress
- wastewater treatment
- risk assessment
- mesenchymal stem cells
- climate change
- bone marrow
- big data
- human health
- pi k akt