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HUMAN DENTAL PULP STEM CELL BIOPRINTING AS A PLATFORM FOR PERSONALIZED 3D NEUROGENIC TISSUE MODELS
Jon Luzuriagaand 13 co-authors
University of the Basque Country (EHU)
FENS Forum 2026 (2026)
Barcelona, Spain
Board PS04-08PM-183
Presentation
Date TBA
Board: PS04-08PM-183
Event Information
Poster Board
PS04-08PM-183
Poster
View posterAbstract
The development of accurate, patient-specific in vitro models is essential for advancing neural tissue engineering. Three-dimensional (3D) bioprinting enables the generation of complex stem cell–based constructs for disease modeling, drug screening, and regenerative applications.
Current bioprinting strategies frequently rely on established cell lines or stem cell sources such as embryonic stem cells (ESCs) and induced pluripotent stem cells (iPSCs). However, personalized neural models require patient-derived cells capable of capturing individual biological variability. Despite their potential, neural stem cells and iPSCs present limitations related to accessibility, genetic instability, and prolonged differentiation timelines.
Human dental pulp stem cells (hDPSCs), neural crest–derived cells, represent an attractive alternative for personalized neurogenic applications. Their ability to generate neuronal action potentials and their resistance to hypoxic environments make them particularly suitable for 3D bioprinting approaches. In this study, hDPSCs were bioprinted using collagen–fibronectin bioinks and cultured under neurogenic differentiation conditions or maintained as non-neurogenic constructs. Cell viability was assessed using live/dead assays, and phenotypic characterization was performed by analyzing mesenchymal (vimentin), neuronal (DCX, TUJ1, NeuN, MAP2), and glial (GFAP, S100β) marker expression over a 14-day differentiation protocol.
Comparative analysis revealed a clear shift from mesenchymal to neurogenic and glial marker expression in neurogenic constructs. These results demonstrate that collagen–fibronectin bioinks support hDPSC viability and controlled neurogenic differentiation, providing strong evidence for their use in patient-specific 3D neural modeling and future autologous applications.
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