Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics

We engineered light-gated channelrhodopsins (ChRs) whose current strength and light sensitivity enable minimally-invasive neuronal circuit interrogation. Current ChR tools applied to the mammalian brain require intracranial surgery for transgene delivery and implantation of invasive fiber-optic cables to produce light-dependent activation of a small volume of tissue. To facilitate expansive optogenetics without the need for invasive implants, our engineering approach leverages the significant literature of ChR variants to train statistical models for the design of new, high-performance ChRs. With Gaussian Process models trained on a limited experimental set of 102 functionally characterized ChRs, we designed high-photocurrent ChRs with unprecedented light sensitivity; three of these, ChRger1–3, enable optogenetic activation of the nervous system via minimally-invasive systemic transgene delivery, not possible previously due to low per-cell transgene copy produced by systemic delivery. ChRger2 enables light-induced neuronal excitation without invasive intracranial surgery for virus delivery or fiber optic implantation, i.e. enables minimally-invasive optogenetics.