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Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics

Nature Methods volume 16, pages 1176–1184 (2019)Cite this article

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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.

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Fig. 1: Machine learning-guided optimization of ChRs.
Fig. 2: The model-predicted ChRs exhibit a large range of functional properties often far exceeding the parents.
Fig. 3: ChRger variants in cultured neurons and in acute brain slices outperform ChR2(H134R) and CoChR.
Fig. 4: Validation of ChRger2 for minimally invasive optogenetic behavioral modulation.

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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 Supplementary Data 1 and 2. Source data for regression model training are provided in Supplementary Data 2. DNA constructs for the ChRger variants are deposited for distribution at Addgene (http://www.addgene.org, plasmid numbers 127237-44).

Code availability

Code used to train classification and regression models can be found at https://github.com/fhalab/channels.

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Acknowledgements

We thank Twist Bioscience for synthesizing and cloning ChR sequences, D. Wagenaar (California Institute of Technology) and the Caltech Neurotechnology Center for building the mouse treadmill, J. Brake (California Institute of Technology) for performing spectrometer measurements, J. Bedbrook for critical reading of the manuscript and the Gradinaru and Arnold laboratories for helpful discussions. This work was funded by the Institute for Collaborative Biotechnologies grant no. W911NF-09-0001 from the US Army Research Office (F.H.A) and the National Institutes of Health (NIH) (V.G.): NIH BRAIN grant no. RF1MH117069, NIH Director’s Pioneer Award grant no. DP1NS111369, NIH Director’s New Innovator Award grant no. DP2NS087949 and SPARC grant no. OT2OD023848. Additional funding includes the NSF NeuroNex Technology Hub grant no. 1707316 (V.G.), the CZI Neurodegeneration Challenge Network (V.G.), the Vallee Foundation (V.G.), the Heritage Medical Research Institute (V.G.) and the Beckman Institute for CLARITY, Optogenetics and Vector Engineering Research for technology development and broad dissemination: clover.caltech.edu (V.G.). 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 grant no. F31MH102913. J.E.R. is supported by the Children’s Tumor Foundation (Young Investigator Award grant no. 2016-01-006).

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Author notes

  1. These authors contributed equally: Kevin K. Yang, J. Elliott Robinson.

Authors and Affiliations

  1. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Claire N. Bedbrook, J. Elliott Robinson, Elisha D. Mackey, Viviana Gradinaru & Frances H. Arnold

  2. Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA, USA

    Kevin K. Yang & Frances H. Arnold

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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 and cloning. C.N.B. and E.D.M. performed AAV production. E.D.M. prepared cultured neurons. 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, and F.H.A. supervised the protein engineering.

Corresponding authors

Correspondence to Viviana Gradinaru or Frances H. Arnold.

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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.

Additional information

Peer review information Nina Vogt was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–11 and Tables 1–5.

Reporting Summary

Supplemental Table 6

Supplementary Table 6

Data 1

Supplementary Data 1

Data 2

Supplementary Data 2

Data 3

Supplementary Data 3

Data 4

Supplementary Data 4

Supplementary Video 1

ChRger2-expressing mouse running on a treadmill while receiving minimally invasive optogenetic stimulation exhibits clear left-turning behavior.

Supplementary Video 2

ChR2(H134R)-expressing mouse running on a treadmill while receiving minimally invasive optogenetic stimulation does not exhibit left-turning behavior.

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Bedbrook, C.N., Yang, K.K., Robinson, J.E. et al. Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics. Nat Methods 16, 1176–1184 (2019). https://doi.org/10.1038/s41592-019-0583-8

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