Objective: The present study aimed at establishing a method for production of a three-dimensional (3D) human neural tissue derived from induced pluripotent stem cells (iPSCs) and analyzing the outcome by a combination of tissue ultrastructure and expression of neural markers.

Methods: A two-step cell culture procedure was implemented by subjecting human iPSCs to a 3D scaffoldbased neural differentiation protocol. First, neural fate-inducing small molecules were used to create a neuroepithelial monolayer. Second, the monolayer was trypsinized into single cells and seeded into a porous polystyrene scaffold and further cultured to produce a 3D neural tissue. The neural tissue was characterized by a combination of immunohistochemistry and transmission electron microscopy (TEM).

Results: iPSCs developed into a 3D neural tissue expressing markers for neural progenitor cells, early neural differentiation and maturation, radial glial cells and cellular proliferation including SOX2, Nestin, β-III Tubulin, MAP2, Tau, BLBP and Ki67. We found an abundance of rosette structures resembling the morphology of the developing neural tube. These neural tube-like structures (NTLS) were shown to contain areas of neural progenitor cell maintenance and proliferation. The resemblance to the embryonic neural tube was further supported by TEM analyses demonstrating luminal tight junctions and primary cilia. Moreover, the NTLS consisted of radial glial-like cells, radiating from the lumen, and neural progenitor cells presenting elongated nuclei, signaling nucleus transposition seen at interkinetic nuclear migration during early neurogenesis, and mitoses.

Conclusion: Our findings revealed that this relatively simple 3D scaffold-based neural differentiation protocol for human iPSCs was able to recapitulate several key events in early neural development. The organization into NTLS and the presence of mature neural cells in the tissue surrounding these structures showed that this protocol has potential for in vitro studies of neural development and disease modeling.