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.
Subscribe to Journal
Get full journal access for 1 year
only $21.58 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
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 used to train classification and regression models can be found at https://github.com/fhalab/channels.
Deisseroth, K. & Hegemann, P. The form and function of channelrhodopsin. Science 357, eaan5544 (2017).
Yizhar, O., Fenno, L. E., Davidson, T. J., Mogri, M. & Deisseroth, K. Optogenetics in neural systems. Neuron 71, 9–34 (2011).
Lin, J. Y. A user’s guide to channelrhodopsin variants: features, limitations and future developments. Exp. Physiol. 96, 19–25 (2011).
Zhang, F. et al. Optogenetic interrogation of neural circuits: technology for probing mammalian brain structures. Nat. Protoc. 5, 439–456 (2010).
Gradinaru, V. et al. Molecular and cellular approaches for diversifying and extending optogenetics. Cell 141, 154–165 (2010).
Mattis, J. et al. Principles for applying optogenetic tools derived from direct comparative analysis of microbial opsins. Nat. Methods 9, 159–172 (2011).
Chuong, A. S. et al. Noninvasive optical inhibition with a red-shifted microbial rhodopsin. Nat. Neurosci. 17, 1123–1129 (2014).
Bedbrook, C. N., Yang, K. K., Rice, A. J., Gradinaru, V. & Arnold, F. H. Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization. PLoS Comput. Biol. 13, e1005786 (2017).
Bedbrook, C. N. et al. Structure-guided SCHEMA recombination generates diverse chimeric channelrhodopsins. Proc. Natl Acad. Sci. USA 114, E2624–E2633 (2017).
Romero, P. A. & Arnold, F. H. Exploring protein fitness landscapes by directed evolution. Nat. Rev. Mol. Cell Biol. 10, 866–876 (2009).
Klapoetke, N. C. et al. Independent optical excitation of distinct neural populations. Nat. Methods 11, 338–346 (2014).
Govorunova, E. G., Sineshchekov, O. A., Janz, R., Liu, X. & Spudich, J. L. Natural light-gated anion channels: a family of microbial rhodopsins for advanced optogenetics. Science 349, 647–650 (2015).
Lin, J. Y., Knutsen, P. M., Muller, A., Kleinfeld, D. & Tsien, R. Y. ReaChR: a red-shifted variant of channelrhodopsin enables deep transcranial optogenetic excitation. Nat. Neurosci. 16, 1499–1508 (2013).
Berndt, A., Yizhar, O., Gunaydin, L. A., Hegemann, P. & Deisseroth, K. Bi-stable neural state switches. Nat. Neurosci. 12, 229–234 (2009).
Lin, J. Y., Lin, M. Z., Steinbach, P. & Tsien, R. Y. Characterization of engineered channelrhodopsin variants with improved properties and kinetics. Biophysical J. 96, 1803–1814 (2009).
Berndt, A. et al. Structural foundations of optogenetics: determinants of channelrhodopsin ion selectivity. Proc. Natl Acad. Sci. USA 113, 822–829 (2016).
Kato, H. E. et al. Crystal structure of the channelrhodopsin light-gated cation channel. Nature 482, 369–374 (2012).
Wietek, J. et al. Conversion of channelrhodopsin into a light-gated chloride channel. Science 344, 409–412 (2014).
Chan, K. Y. et al. Engineered AAVs for efficient noninvasive gene delivery to the central and peripheral nervous systems. Nat. Neurosci. 20, 1172–1179 (2017).
Smith, M. A., Romero, P. A., Wu, T., Brustad, E. M. & Arnold, F. H. Chimeragenesis of distantly-related proteins by noncontiguous recombination. Protein Sci. 22, 231–238 (2013).
Voigt, C. A., Martinez, C., Wang, Z. G., Mayo, S. L. & Arnold, F. H. Protein building blocks preserved by recombination. Nat. Struct. Biol. 9, 553–558 (2002).
Hochbaum, D. R. et al. All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins. Nat. Methods 11, 825–833 (2014).
Gunaydin, L. A. et al. Ultrafast optogenetic control. Nat. Neurosci. 13, 387–392 (2010).
Romero, P. A., Krause, A. & Arnold, F. H. Navigating the protein fitness landscape with Gaussian processes. Proc. Natl Acad. Sci. USA 110, E193–E201 (2013).
Volkov, O. et al. Structural insights into ion conduction by channelrhodopsin 2. Science 358, eaan8862 (2017).
Oda, K. et al. Crystal structure of the red light-activated channelrhodopsin Chrimson. Nat. Commun. 9, 3949 (2018).
Bamann, C., Gueta, R., Kleinlogel, S., Nagel, G. & Bamberg, E. Structural guidance of the photocycle of channelrhodopsin-2 by an interhelical hydrogen bond. Biochemistry 49, 267–278 (2010).
Nagel, G. et al. Light activation of channelrhodopsin-2 in excitable cells of Caenorhabditis elegans triggers rapid behavioral responses. Curr. Biol. 15, 2279–2284 (2005).
Chen, S. et al. Near-infrared deep brain stimulation via upconversion nanoparticle-mediated optogenetics. Science 359, 679–684 (2018).
Bedbrook, C. N., Deverman, B. E. & Gradinaru, V. Viral strategies for targeting the central and peripheral nervous systems. Annu. Rev. Neurosci. 41, 323–348 (2018).
Challis, R. C. et al. Systemic AAV vectors for widespread and targeted gene delivery in rodents. Nat. Protoc. 14, 379–414 (2019).
Pascoli, V., Terrier, J., Hiver, A. & Luscher, C. Sufficiency of mesolimbic dopamine neuron stimulation for the progression to addiction. Neuron 88, 1054–1066 (2015).
Gradinaru, V. et al. Targeting and readout strategies for fast optical neural control in vitro and in vivo. J. Neurosci. 27, 14231–14238 (2007).
Yang, K. K., Wu, Z. & Arnold, F. H. Machine-learning-guided directed evolution for protein engineering. Nat. Methods 16, 687–694 (2019).
Flytzanis, N. C. et al. Archaerhodopsin variants with enhanced voltage-sensitive fluorescence in mammalian and Caenorhabditis elegans neurons. Nat. Commun. 5, 4894 (2014).
Bedbrook, C. N. et al. Genetically encoded spy peptide fusion system to detect plasma membrane-localized proteins in vivo. Chem. Biol. 22, 1108–1121 (2015).
Robert, X. & Gouet, P. Deciphering key features in protein structures with the new ENDscript server. Nucleic Acids Res. 42, W320–W324 (2014).
Fan, J. et al. Reduced hyperpolarization-activated current contributes to enhanced intrinsic excitability in cultured hippocampal neurons from PrP−/− mice. Front. Cell. Neurosci. 10, 74 (2016).
Slomowitz, E. et al. Interplay between population firing stability and single neuron dynamics in hippocampal networks. eLife 4, e04378 (2015).
Kroon, T., van Hugte, E., van Linge, L., Mansvelder, H. D. & Meredith, R. M. Early postnatal development of pyramidal neurons across layers of the mouse medial prefrontal cortex. Sci. Rep. 9, 5037 (2019).
van Aerde, K. I. & Feldmeyer, D. Morphological and physiological characterization of pyramidal neuron subtypes in rat medial prefrontal cortex. Cereb. Cortex 25, 788–805 (2015).
Deverman, B. E. et al. Cre-dependent selection yields AAV variants for widespread gene transfer to the adult brain. Nat. Biotechnol. 34, 204–209 (2016).
Ben-Shaul, Y. OptiMouse: a comprehensive open source program for reliable detection and analysis of mouse body and nose positions. BMC Biol. 15, 41 (2017).
Walt, S., Colbert, S. C. & Varoquaux, G. The NumPy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13, 22–30 (2011).
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Oliphant, T. E. Python for scientific computing. Comput. Sci. Eng. 9, 10–20 (2007).
Pedregosa, F. et al. Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
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).
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.
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–11 and Tables 1–5.
Supplementary Table 6
Supplementary Data 1
Supplementary Data 2
Supplementary Data 3
Supplementary Data 4
ChRger2-expressing mouse running on a treadmill while receiving minimally invasive optogenetic stimulation exhibits clear left-turning behavior.
ChR2(H134R)-expressing mouse running on a treadmill while receiving minimally invasive optogenetic stimulation does not exhibit left-turning behavior.
About this article
Cite this article
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
Molecular Therapy - Methods & Clinical Development (2021)
Trends in Biotechnology (2020)
Nature Communications (2020)
Frontiers in Neural Circuits (2020)