Analysis of the bystander effect in cone photoreceptors via a guided neural network platform.

The mammalian retina system consists of a complicated photoreceptor structure, which exhibits extensive random synaptic connections. To study retinal development and degeneration, various experimental models have been used previously, but these models are often uncontrollable, are difficult to manipulate, and do not provide sufficient similarity or precision. Therefore, the mechanisms in many retinal diseases remain unclear because of the limited capability in observing the progression and molecular driving forces. For example, photoreceptor degeneration can spread to surrounding healthy photoreceptors via a phenomenon known as the bystander effect; however, no in-depth observations can be made to decipher the molecular mechanisms or the pathways that contribute to the spreading. It is then necessary to build dissociated neural networks to investigate the communications with controllability of cells and their treatment. We developed a neural network chip (NN-Chip) to load single neurons into highly ordered microwells connected by microchannels for synapse formation to build the neural network. By observing the distribution of apoptosis spreading from light-induced apoptotic cones to the surrounding cones, we demonstrated convincing evidence of the existence of a cone-to-cone bystander killing effect. Combining the NN-Chip with microinjection technology, we also found that the gap junction protein connexin 36 (Cx36) is critical for apoptosis spreading and the bystander effect in cones. In addition, our unique NN-Chip platform provides a quantitative, high-throughput tool for investigating signaling mechanisms and behaviors in neurons and opens a new avenue for screening potential drug targets to cure retinal diseases.