A high-density microelectrode-tissue-microelectrode sandwich platform for application of retinal circuit study. Issue 1 (December 2015) - Yang, Frank; Yang, Chung-Hua; Wang, Fu-Min; Cheng, Ya-Ting; Teng, Chih-Ciao; Lee, Li-Jen; Yang, Chang-Hao; Fan, Long-Sheng
Abstract Background Microelectrode array (MEA) devices are frequently used in neural circuit studies, especially in retinal prosthesis. For a high throughput stimulation and recording paradigm, it is desirable to obtain the responses of multiple surface RGCs initiated from the electrical signals delivered to multiple photoreceptor cells. This can be achieved by an high density MEA-tissue-MEA (MTM) sandwich configuration. However, the retina is one of the most metabolically active tissues, consumes oxygen as rapidly as the brain. The major concern of the MTM configuration is the supply of oxygen. Methods We aimed to develop a high density MTM sandwich platform which consists of stacks of a stimulation MEA, retinal tissue and a recording MEA. Retina is a metabolically active tissue and the firing rate is very sensitive to oxygen level. We designed, simulated and microfabricated porous high density MEAs and an adjustable perfusion system that electrical signals can be delivered to and recorded from the clipped retinal tissue. Results The porous high-density MEAs linked with stimulation or recording devices within a perfusion system were manufactured and the MTM platform was assembled with a retina slice inside. The firing rate remained constant between 25 and 55 min before dramatically declined, indicating that within certain period of time (e.g. 30 min after habituation), the retina condition was kept by sufficient oxygen supply via the perfusion holes in the MEAs provided by the double perfusion system. Conclusions MTM sandwich structure is an efficient platform to study the retinal neural circuit. The material and arrangement of high density microelectrodes with porous design make this MEA appropriate for sub-retina prosthesis. Finding ways to prolong the recording time and reduce the signal-to-noise ratio are important to improve our MTM prototype.