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Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics

Bedbrook, Claire N. and Yang, Kevin K. and Robinson, J. Elliott and Gradinaru, Viviana and Arnold, Frances H. (2019) Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190304-085432637

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Abstract

We have 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 brain tissue [~1 mm3]. To enable optogenetics for large brain volumes and without the need for invasive implants, our ChR 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 ChR variants, we designed high-photocurrent ChRs with unprecedented light sensitivity; three of these, ChRger1, ChRger2, and ChRger3, enable optogenetic activation of the nervous system via minimally-invasive systemic transgene delivery with rAAV-PHP.eB, which was not possible previously due to low per-cell transgene copy produced by systemic delivery. These engineered ChRs enable light-induced neuronal excitation without invasive intracranial surgery for virus delivery or fiber optic implantation, i.e. they enable minimally-invasive optogenetics.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/565606DOIDiscussion Paper
ORCID:
AuthorORCID
Bedbrook, Claire N.0000-0003-3973-598X
Gradinaru, Viviana0000-0001-5868-348X
Arnold, Frances H.0000-0002-4027-364X
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. bioRxiv preprint first posted online Mar. 3, 2019. We thank Twist Bioscience for synthesizing and cloning ChR sequences. We thank the Gradinaru and Arnold labs for helpful discussions. We also thank Dr. John Bedbrook for critical reading of the manuscript. This work was funded by the Beckman Institute for CLARITY, Optogenetics and Vector Engineering Research for technology development and broad dissemination: clover.caltech.edu (V.G.). This work was also supported by the National Institutes of Health (NIH) through NIH BRAIN grant R01MH117069 (V.G.) and SPARC grant OT2OD023848 (V.G), and by the Institute for Collaborative Biotechnologies through grant W911NF-09-0001 from the U.S. Army Research Office (F.H.A). The content of the information does not necessarily reflect the position or the policy of the Government, and no official endorsement should be inferred. C.N.B. is funded by Ruth L. Kirschstein National Research Service Awards F31MH102913. J.E.R. is supported by the Children’s Tumor Foundation (Young Investigator Award 2016-01-006). Author Contributions: C.N.B., K.K.Y., V.G., and F.H.A. conceptualized the project. C.N.B. coordinated all experiments and data analysis. C.N.B. and K.K.Y. built machine-learning models. C.N.B. performed construct design, cloning, and AAV production. C.N.B and J.E.R. conducted electrophysiology. C.N.B. and J.E.R. performed injections. J.E.R. performed fiber cannula implants and behavioral experiments. C.N.B. performed all data analysis. C.N.B. wrote the manuscript with input and editing from all authors. V.G. supervised optogenetics/electrophysiology parts of the project. F.H.A. supervised the protein engineering part of the project. K.K.Y. & J.E.R. contributed equally to this work. Data availability: The authors declare that data supporting the findings of this study are available within the paper and its supplementary information files. Source data for classification model training are provided in Dataset 1 and Dataset 2. Source data for regression model training are provided in Dataset 2. Code availability: Code used to train classification and regression models can be found at: https://github.com/fhalab/channels. Competing interests: A provisional patent application (CIT File No.: CIT-8092-P) has been filed by Caltech based on these results. C.N.B., K.K.Y., V.G., and F.H.A. are inventors on this provisional patent.
Funders:
Funding AgencyGrant Number
Caltech Beckman InstituteUNSPECIFIED
NIHR01MH117069
NIHOT2OD023848
Army Research Office (ARO)W911NF-09-0001
NIH Predoctoral FellowshipF31MH102913
Record Number:CaltechAUTHORS:20190304-085432637
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190304-085432637
Official Citation:Machine learning-guided channelrhodopsin engineering enables minimally-invasive optogenetics Claire N Bedbrook, Kevin K Yang, J. Elliott Robinson, Viviana Gradinaru, Frances H Arnold bioRxiv 565606; doi: https://doi.org/10.1101/565606
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93420
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:04 Mar 2019 17:16
Last Modified:18 Jul 2019 21:41

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