bioRxivBioengineering
Combinatorial approach of binary colloidal crystals (BCCs) and CRISPR activation to improve induced pluripotent stem cell differentiation into neurons
D. Urrutia-Cabrera, R. H.-C. Liou, J. Lin, K. Liu, S. S.-C. Hung, A. W. Hewitt, P.-Y. Wang, R. C.-B. Wong
Abstract
Conventional methods of neuronal differentiation for human induced pluripotent stem cells (iPSCs) are tedious and complicated, involving multi-stage protocols with complex cocktails of growth factors and small molecules. Artificial extracellular matrix with defined surface topography and chemistry represents a promising venue to improve the neuronal differentiation in vitro. In the present study, we test the impact of a type of colloidal self-assembled patterns called binary colloidal crystals (BCCs) in neuronal differentiation. We developed a CRISPR activation (CRISPRa) iPSC platform that constitutively expresses the dCas9-VPR system, which allows robust activation of the proneural transcription factor NEUROD1 to rapidly induce neuronal differentiation within seven days. We showed that the combinatorial use of BCCs can further improve this neuronal differentiation system. In particular, our results indicate that fine tuning of silica and polystyrene size is critical to generate specific topographies to improve neuronal differentiation and branching. BCCs with 5 m silica and 100 nm carboxylated polystyrene has the most prominent effect on increasing neurite outgrowth and more complex ramification, while BCCs with 2m silica and 65nm carboxylated polystyrene is better in promoting neuronal enrichment. These results indicate that biophysical cues can support rapid differentiation and improve neuronal maturation. In summary, our combinatorial approach of CRISPRa and BCCs provides a robust and rapid pipeline for in vitro production of human neurons. Specific BCCs can be adapted to late stages of neuronal differentiation protocols to improve neuronal maturation, which have important implications in tissue engineering, in vitro biological studies and disease modeling.
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