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      The effect of BMP-mimetic peptide tethering bioinks on the differentiation of dental pulp stem cells (DPSCs) in 3D bioprinted dental constructs

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      Biofabrication
      IOP Publishing

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          Abstract

          The goal of this study was to use 3D bioprinting technology to create a bioengineered dental construct containing human dental pulp stem cells (hDPSCs). To accomplish this, we first developed a novel bone morphogenetic protein (BMP) peptide-tethering bioink formulation and examined its rheological properties, its printability, and the structural stability of the bioprinted construct. Second, we evaluated the survival and differentiation of hDPSCs in the bioprinted dental construct by measuring cell viability, proliferation, and gene expression, as well as histological and immunofluorescent analyses. Our results showed that the peptide conjugation into the gelatin methacrylate (GelMA)-based bioink formulation was successfully performed. We determined that greater than 50% of the peptides remained in the bioprinted construct after 3 weeks in vitro cell culture. Human DPSC viability was >90% in the bioprinted constructs immediately after the printing process. Alizarin Red staining showed that the BMP peptide construct group exhibited the highest calcification as compared to the growth medium, osteogenic medium, and non-BMP peptide construct groups. In addition, immunofluorescent and quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analyses showed robust expression of dentin sialophosphoprotein (DSPP) and osteocalcin (OCN) in the BMP peptide dental constructs. Together, these results strongly suggested that BMP peptide-tethering bioink could accelerate the differentiation of hDPSCs in 3D bioprinted dental constructs.

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          Contributors
          (View ORCID Profile)
          (View ORCID Profile)
          Journal
          Biofabrication
          Biofabrication
          IOP Publishing
          1758-5090
          July 01 2020
          July 01 2020
          : 12
          : 3
          : 035029
          Article
          10.1088/1758-5090/ab9492
          7641314
          32428889
          a399d3f8-ca9b-400a-a5bd-b566a2c59554
          © 2020

          http://iopscience.iop.org/info/page/text-and-data-mining

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