Designing well-defined photopolymerized synthetic matrices for three-dimensional culture and differentiation of induced pluripotent stem cells

设计明确的光聚合合成基质用于诱导多能干细胞的三维培养和分化

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作者:Elisa M Ovadia, David W Colby, April M Kloxin

Abstract

Induced pluripotent stem cells (iPSCs) are of interest for the study of disease, where these cells can be derived from patients and have the potential to be differentiated into any cell type; however, three-dimensional (3D) culture and differentiation of iPSCs within well-defined synthetic matrices for these applications remains limited. Here, we aimed to establish synthetic cell-degradable hydrogels that allow precise presentation of specific biochemical cues for 3D culture of iPSCs with relevance for hypothesis testing and lineage-specific differentiation. We synthesized poly(ethylene glycol)-(PEG)-peptide-based hydrogels by photoinitiated step growth polymerization and used them to test the hypothesis that the viability of iPSCs within these matrices could be rescued with appropriate biochemical cues inspired by proteins and integrins important for iPSC culture on Matrigel. Specifically, we selected a range of motifs inspired by iPSC binding to Matrigel, including laminin-derived IKVAV and YIGSR, α5β1-binding PHSRNG10RGDS, αvβ5-binding KKQRFRHRNRKG, and RGDS that is known to bind a variety of integrins for generally promoting cell adhesion. YIGSR and PHSRNG10RGDS resulted in the highest iPSC viability, where binding of β1 integrin was key, and these permissive compositions also allowed iPSC differentiation into neural progenitor cells (NPCs) (decreased oct4 expression and increased pax6 expression) in response to soluble factors. The resulting NPCs formed clusters of different sizes in response to each peptide, suggesting that matrix biochemical cues affect iPSC proliferation and clustering in 3D culture. In summary, we have established photopolymerizable synthetic matrices for the encapsulation, culture, and differentiation of iPSCs for studies of cell-matrix interactions and deployment in disease models.

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