Central nervous system (CNS) diseases and chronic pain affect a large percentage of the population and current medical interventions fail to provide adequate relief from symptoms or to cure underlying disease mechanisms. While animal models have been the standard within preclinical drug screening, their track-record for identifying potential drug compounds that go on to achieve clinical success is limited. In vitro models have been proposed as a practical alternative but have yet to achieve the necessary level of biological mimicry and engineering control. As such, there is a need for innovative approaches that progress neural microphysiological systems (MPS) towards more closely recapitulating in vivo complexity by using techniques that are robust and supply desired metrics.

In this work, we address this need by expanding upon a previously-described 3D dual hydrogel-based neural microphysiological system (MPS) to instantiate new models of the CNS and pain. Limitations of our previous digital projection lithography (DPL) process were overcome by implementation of a new “optical engine” and corresponding photoinitator system that allowed for the creation of micropatterned hydrogels with enhanced throughput and shape fidelity. This new DPL system formed the basis of our CNS and pain model work. A CNS cell source was developed by generating spheroids from magnetized spinal cord cells. These spheroids were viable, contained cell types relevant to the CNS and its disease states, and could be retained in 3D culture for several weeks. A model of the ascending afferent synapse, relevant in pain transmission and processing, was constructed by co-culturing dorsal root ganglia and dorsal horn spheroids. Unidirectional neurite outgrowth relevant to the in vivo structural architecture of this circuitry was established by exploiting the differences in substrate stiffness preferences between the two cell populations. The presence and functionality of synapses was confirmed. Taken together, this work takes significant steps towards creating neural MPS with increased biological complexity while maintaining engineering control and provides a technological foundation for future work within the MPS field.