Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics

Abstract

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 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 substantial literature of ChR variants to train statistical models for the design of high-performance ChRs. With Gaussian process models trained on a limited experimental set of 102 functionally characterized ChRs, we designed high-photocurrent ChRs with high light sensitivity. Three of these, ChRger1–3, enable optogenetic activation of the nervous system via systemic transgene delivery. ChRger2 enables light-induced neuronal excitation without fiber-optic implantation; that is, this opsin enables transcranial optogenetics.

ICB Affiliated Authors

Authors
Claire N. Bedbrook, Kevin K. Yang, J. Elliott Robinson, Elisha D. Mackey, Viviana Gradinaru, and Frances H. Arnold
Date
Type
Peer-Reviewed Article
Journal
Nature Methods
Volume
16
Pages
1176–1184
Emblems