Automatic differentiation of human induced pluripotent stem cells toward synchronous neural networks on an arrayed monolayer of nanofiber membrane

Abstract

Automatic differentiation of human-induced pluripotent stem cells (hiPSCs) facilitates the generation of cortical neural networks and studies of brain functions. Here, we present a method of directed differentiation of hiPSCs with a substrate made of a honeycomb microframe and a monolayer of crosslinked gelatin nanofibers in the form of an array of nanofiber membranes. Neural precursor cells (NPCs) were firstly derived from hiPSCs and then placed on the nanofiber membranes for automatically controlled neural differentiation over a long period. Due to the strong modulation of the substrate stiffness and permeability, most cells were found in the center area of the honeycomb compartments, giving rise to regular and inter-connected cortical neural clusters. More importantly, the neural activities of the clusters were synchronized proving the reliability of the method. Our results showed that the self-organization, as well as the neural activities of differentiating neural cells, were more efficient in the nanofiber membrane compared to the types of the substrate such as glass and nanofiber-covered glass. In addition to the inherent advantages such as manpower saving and fewer risks of contamination and human error, automatic differentiation avoided undesired shaking which might have critical effects on the formation of synchronous neural clusters. Statement of significanceSynchronization of cortical neural activities is essential for information processing and human cognition. By automated differentiation of human induced pluripotent stem cells on arrayed monolayer of nanofiber membrane, synchronous neural clusters could be formed. Such an approach would allow creating a variety of neural networks with regular and interconnected clusters for systematic studies of human cortical functions.