Soft-lithography based surface micro-patterning approaches for controlling the topology of in vitro neural networks

Abstract

Cultured neurons, like the neurons that constitute the in vivo brain, are capable of expressing ion channels capable of generating action potentials, observing the growth of axons and dendrites, and forming neurons and synapses. A neural network consisting of cultured neural networks is used as a bottom-up approach to study the brain. The cultured neural network is reduced as compared to the neural network of the in vivo brain, and it is easy to use as a model because it has the basic electrophysiological characteristics of the brain neural network. Since it is easy to regulate the growth of cultured neurons externally, studies are underway to control the topology of the networks formed from cultured neurons. Surface patterning techniques can be used to control the neural adhesion and neurite outgrowth of in vitro culture substrates at cellular scale. This approach basically defines cell adhesive region and cell repellent region on the culture substrate by using surface patterning and realizes cell adhesion and neurite outgrowth only in relatively cell adhesive region to make neural network of desired design. This dissertation deals with the Soft-lithography based surface micro-patterning approaches for controlling the topology of in vitro neural networks. One of the soft lithography technologies, microcontact printing technology, was used to pattern the biomolecule with a microscale, and micromolding was used to pattern the cell repellent materials agarose hydrogel and alginate hydrogel to design the neural network. The physiological characteristics of the neural network formed by the micropatterning technique were examined by using immunohistochemistry, and the electrical signals were measured from the designed neural network using the microelectrode chip system, and the electrophysiological characteristics and connectivity were analyzed. This dissertation is expected to contribute to in vitro neuroscience by suggesting designing principal of neural network as a reduced model of the brain.