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) towards 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 9 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
- cardiovascular disease
- induced apoptosis
- drug discovery
- extracellular matrix
- cell cycle arrest
- cell therapy
- glycemic control
- rna seq
- stem cells
- signaling pathway
- wastewater treatment
- adipose tissue
- metabolic syndrome
- cell death
- high throughput
- bone marrow
- machine learning
- current status
- weight loss
- insulin resistance
- replacement therapy
- deep learning
- climate change