Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Video-based pooled screening yields improved far-red genetically encoded voltage indicators

An Author Correction to this article was published on 10 February 2023

This article has been updated

Abstract

Video-based screening of pooled libraries is a powerful approach for directed evolution of biosensors because it enables selection along multiple dimensions simultaneously from large libraries. Here we develop a screening platform, Photopick, which achieves precise phenotype-activated photoselection over a large field of view (2.3 × 2.3 mm, containing >103 cells, per shot). We used the Photopick platform to evolve archaerhodopsin-derived genetically encoded voltage indicators (GEVIs) with improved signal-to-noise ratio (QuasAr6a) and kinetics (QuasAr6b). These GEVIs gave improved signals in cultured neurons and in live mouse brains. By combining targeted in vivo optogenetic stimulation with high-precision voltage imaging, we characterized inhibitory synaptic coupling between individual cortical NDNF (neuron-derived neurotrophic factor) interneurons, and excitatory electrical synapses between individual hippocampal parvalbumin neurons. The QuasAr6 GEVIs are powerful tools for all-optical electrophysiology and the Photopick approach could be adapted to evolve a broad range of biosensors.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Photopick enables video-based pooled screening in mammalian cells.
Fig. 2: Directed evolution of an archaerhodopsin-derived genetically encoded voltage indicator.
Fig. 3: Characterization of QuasAr6a and QuasAr6b in neurons in culture and slice.
Fig. 4: Characterization of somQuasAr6a- and somQuasAr6b-based Optopatch in vivo.
Fig. 5: Optical dissection of inhibitory connections between NDNF interneurons in visual cortex.
Fig. 6: Detection of electric coupling between hippocampal parvalbumin cells.

Similar content being viewed by others

Data availability

Data used in the study are available upon reasonable request to A.E.C.

Change history

References

  1. Lin, M. Z. & Schnitzer, M. J. Genetically encoded indicators of neuronal activity. Nat. Neurosci. 19, 1142–1153 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Yang, W. & Yuste, R. In vivo imaging of neural activity. Nat. Methods 14, 349–359 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Kim, T. H. & Schnitzer, M. J. Fluorescence imaging of large-scale neural ensemble dynamics. Cell 185, 9–41 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Tian, L. et al. Imaging neural activity in worms, flies and mice with improved GCaMP calcium indicators. Nat. Methods 6, 875–881 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Chen, T. W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Hasle, N. et al. High-throughput, microscope-based sorting to dissect cellular heterogeneity. Mol. Syst. Biol. 16, e9442 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Lee, J. et al. Versatile phenotype-activated cell sorting. Sci. Adv. 6, eabb7438 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chien, M. P. et al. Photoactivated voltage imaging in tissue with an archaerhodopsin-derived reporter. Sci. Adv. 7, eabe3216 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kanfer, G. et al. Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes. J. Cell Biol. 220, e202006180 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Yan, X. et al. High-content imaging-based pooled CRISPR screens in mammalian cells. J. Cell Biol. 220, e202008158 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lawson, M. & Elf, J. Imaging-based screens of pool-synthesized cell libraries. Nat. Methods 18, 358–365 (2021).

    Article  CAS  PubMed  Google Scholar 

  12. Akemann, W., Mutoh, H., Perron, A., Rossier, J. & Knopfel, T. Imaging brain electric signals with genetically targeted voltage-sensitive fluorescent proteins. Nat. Methods 7, 643–649 (2010).

    Article  CAS  PubMed  Google Scholar 

  13. Knopfel, T. Genetically encoded optical indicators for the analysis of neuronal circuits. Nat. Rev. Neurosci. 13, 687–700 (2012).

    Article  PubMed  Google Scholar 

  14. Gong, Y. et al. High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor. Science 350, 1361–1366 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Marshall, J. D. et al. Cell-type-specific optical recording of membrane voltage dynamics in freely moving mice. Cell 167, 1650–1662 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Adam, Y. et al. Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics. Nature 569, 413–417 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Abdelfattah, A. S. et al. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science 365, 699–704 (2019).

    Article  CAS  PubMed  Google Scholar 

  18. Piatkevich, K. D. et al. Population imaging of neural activity in awake behaving mice. Nature 574, 413–417 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Villette, V. et al. Ultrafast two-photon imaging of a high-gain voltage indicator in awake behaving mice. Cell 179, 1590–1608 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Fan, L. Z. et al. All-optical electrophysiology reveals the role of lateral inhibition in sensory processing in cortical layer 1. Cell 180, 521–535 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Böhm, U. L. et al. Voltage imaging identifies spinal circuits that modulate locomotor adaptation in zebrafish. Neuron 110, 1211–1222 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kannan, M. et al. Dual-polarity voltage imaging of the concurrent dynamics of multiple neuron types. Science 378, eabm8797 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Evans, S. W. et al. A positively tuned voltage indicator reveals electrical correlates of calcium activity in the brain. Preprint at bioRxiv https://doi.org/10.1101/2021.10.21.465345 (2021).

  24. Abdelfattah, A. S. et al. Sensitivity optimization of a rhodopsin-based fluorescent voltage indicator. Preprint at bioRxiv https://doi.org/10.1101/2021.11.09.467909 (2021).

  25. Herwig, L. et al. Directed evolution of a bright near-infrared fluorescent rhodopsin using a synthetic chromophore. Cell Chem. Biol. 24, 415–425 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hochbaum, D. R. et al. All-optical electrophysiology in mammalian neurons using engineered microbial rhodopsins. Nat. Methods 11, 825–833 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Landau, A. T. et al. Dendritic branch structure compartmentalizes voltage-dependent calcium influx in cortical layer 2/3 pyramidal cells. eLife 11, e76993 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Werley, C. A., Chien, M. P. & Cohen, A. E. Ultrawidefield microscope for high-speed fluorescence imaging and targeted optogenetic stimulation. Biomed. Opt. Express 8, 5794–5813 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Paez-Segala, M. G. et al. Fixation-resistant photoactivatable fluorescent proteins for CLEM. Nat. Methods 12, 215–218 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kannan, M. et al. Fast, in vivo voltage imaging using a red fluorescent indicator. Nat. Methods 15, 1108–1116 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Park, J. et al. Screening fluorescent voltage indicators with spontaneously spiking HEK cells. PLoS ONE 8, e85221 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Zhang, H., Reichert, E. & Cohen, A. E. Optical electrophysiology for probing function and pharmacology of voltage-gated ion channels. eLife 5, e15202 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Huang, Y. L., Walker, A. S. & Miller, E. W. A photostable silicon rhodamine platform for optical voltage sensing. J. Am. Chem. Soc. 137, 10767–10776 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. McNamara, H. M. et al. Geometry-dependent arrhythmias in electrically excitable tissues. Cell Syst. 7, 359–370 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Piatkevich, K. D. et al. A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters. Nat. Chem. Biol. 14, 352–360 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Maclaurin, D., Venkatachalam, V., Lee, H. & Cohen, A. E. Mechanism of voltage-sensitive fluorescence in a microbial rhodopsin. Proc. Natl Acad. Sci. USA 110, 5939–5944 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Penzkofer, A., Silapetere, A. & Hegemann, P. Photocycle dynamics of the archaerhodopsin 3 based fluorescent voltage sensor QuasAr1. Int. J. Mol. Sci. 21, 160 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Penzkofer, A., Silapetere, A. & Hegemann, P. Photocycle dynamics of the Archaerhodopsin 3 based fluorescent voltage sensor Archon2. J. Photochem. Photobiol. B 225, 112331 (2021).

    Article  CAS  PubMed  Google Scholar 

  39. Werley, C. A. et al. All-optical electrophysiology for disease modeling and pharmacological characterization of neurons. Curr. Protoc. Pharmacol. 78, 11.20.1–11.20.24 (2017).

    CAS  PubMed  Google Scholar 

  40. Buchanan, E. K. et al. Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data. Preprint at bioRxiv https://doi.org/10.1101/334706 (2019).

  41. Lim, S. T., Antonucci, D. E., Scannevin, R. H. & Trimmer, J. S. A novel targeting signal for proximal clustering of the Kv2.1 K+ channel in hippocampal neurons. Neuron 25, 385–397 (2000).

    Article  CAS  PubMed  Google Scholar 

  42. Baker, C. A., Elyada, Y. M., Parra, A. & Bolton, M. M. Cellular resolution circuit mapping with temporal-focused excitation of soma-targeted channelrhodopsin. eLife 5, e14193 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Abs, E. et al. Learning-related plasticity in dendrite-targeting layer 1 interneurons. Neuron 100, 684–699 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Schuman, B. et al. Four unique interneuron populations reside in neocortical layer 1. J. Neurosci. 39, 125–139 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Anastasiades, P. G., Collins, D. P. & Carter, A. G. Mediodorsal and ventromedial thalamus engage distinct L1 circuits in the prefrontal cortex. Neuron 109, 314–330 (2021).

    Article  CAS  PubMed  Google Scholar 

  46. Ferguson, B. R. & Gao, W. J. PV interneurons: critical regulators of E/I balance for prefrontal cortex-dependent behavior and psychiatric disorders. Front. Neural Circuits 12, 37 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Jouhanneau, J. S., Kremkow, J. & Poulet, J. F. A. Single synaptic inputs drive high-precision action potentials in parvalbumin expressing GABA-ergic cortical neurons in vivo. Nat. Commun. 9, 1540 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Antonoudiou, P., Tan, Y. L., Kontou, G., Upton, A. L. & Mann, E. O. Parvalbumin and somatostatin interneurons contribute to the generation of hippocampal gamma oscillations. J. Neurosci. 40, 7668–7687 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Dombeck, D. A., Harvey, C. D., Tian, L., Looger, L. L. & Tank, D. W. Functional imaging of hippocampal place cells at cellular resolution during virtual navigation. Nat. Neurosci. 13, 1433–1440 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ahmadi, N., Constandinou, T. G. & Bouganis, C. S. Estimation of neuronal firing rate using Bayesian adaptive kernel smoother (BAKS). Plos ONE 13, e0206794 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  51. van Welie, I., Roth, A., Ho, S. S., Komai, S. & Hausser, M. Conditional spike transmission mediated by electrical coupling ensures millisecond precision-correlated activity among interneurons in vivo. Neuron 90, 810–823 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Feldman, D. et al. Optical pooled screens in human cells. Cell 179, 787–799 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Mansoury, M., Hamed, M., Karmustaji, R., Al Hannan, F. & Safrany, S. T. The edge effect: a global problem. The trouble with culturing cells in 96-well plates. Biochem. Biophys. Rep. 26, 100987 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Chien, M. P., Werley, C. A., Farhi, S. L. & Cohen, A. E. Photostick: a method for selective isolation of target cells from culture. Chem. Sci. 6, 1701–1705 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Binan, L. et al. Opto-magnetic capture of individual cells based on visual phenotypes. eLife 8, e45239 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Lee, D., Kume, M. & Holy, T. E. Sensory coding mechanisms revealed by optical tagging of physiologically defined neuronal types. Science 366, 1384–1389 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zimanyi, L., Cao, Y., Needleman, R., Ottolenghi, M. & Lanyi, J. K. Pathway of proton uptake in the bacteriorhodopsin photocycle. Biochemistry 32, 7669–7678 (1993).

    Article  CAS  PubMed  Google Scholar 

  58. Brown, L. S. et al. The proton transfers in the cytoplasmic domain of bacteriorhodopsin are facilitated by a cluster of interacting residues. J. Mol. Biol. 239, 401–414 (1994).

    Article  CAS  PubMed  Google Scholar 

  59. Ferrarese, L. et al. Dendrite-specific amplification of weak synaptic input during network activity in vivo. Cell Rep. 24, 3455–3465 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Jouhanneau, J. S. & Poulet, J. F. A. Multiple two-photon targeted whole-cell patch-clamp recordings from monosynaptically connected neurons in vivo. Front. Synaptic Neurosci. 11, 15 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Lambert, T. J. FPbase: a community-editable fluorescent protein database. Nat. Methods 16, 277–278 (2019).

    Article  CAS  PubMed  Google Scholar 

  62. Hofherr, A., Fakler, B. & Klocker, N. Selective Golgi export of Kir2.1 controls the stoichiometry of functional Kir2.x channel heteromers. J. Cell Sci. 118, 1935–1943 (2005).

    Article  CAS  PubMed  Google Scholar 

  63. Stockklausner, C., Ludwig, J., Ruppersberg, J. P. & Klocker, N. A sequence motif responsible for ER export and surface expression of Kir2.0 inward rectifier K(+) channels. FEBS Lett. 493, 129–133 (2001).

    Article  CAS  PubMed  Google Scholar 

  64. Gradinaru, V. et al. Molecular and cellular approaches for diversifying and extending optogenetics. Cell 141, 154–165 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Nguyen, C. et al. Simultaneous voltage and calcium imaging and optogenetic stimulation with high sensitivity and a wide field of view. Biomed. Opt. Express 10, 789–806 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Goldey, G. J. et al. Removable cranial windows for long-term imaging in awake mice. Nat. Protoc. 9, 2515–2538 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank B.L. Sabatini and O. Yizhar for advice and discussion; C. Dulac for the PV-Cre mouse line; M. Andermann and J. Fernando for advice on cranial window surgery; T.D. Green, K. Williams, Urs Böhm, A. Preecha, H. Dahche, Y. Adam and G. Testa-Silva for technical assistance and advice; E.M. Moult for advice on statistics; M.P. Chien for advice on pooled screening; E. Miller for the BeRST1 dye; B. Arnold from Harvard FAS Informatics for assistance with Illumina sequencing data analysis; the Bauer Core Facility at Harvard University for FACS service; the Biopolymers Facility at Harvard Medical School for next-generation sequencing; and Harvard Center for Biological Imaging (RRID:SCR_018673) for infrastructure and support on confocal imaging. This work was supported by the Howard Hughes Medical Institute (A.E.C. and K.D.), NIH grants 1RF1MH117042 and 1R01NS126043 (A.E.C.), a Vannevar Bush Faculty Fellowship N00014-18-1-2859 (A.E.C.), a National Science Foundation QuBBE QLCI grant OMA-2121044 (A.E.C.), a Helen Hay Whitney Fellowship (L.Z.F.) and a Merck fellowship from the Life Science Research Foundation (J.D.W.-C.).

Author information

Authors and Affiliations

Authors

Contributions

H.T. and A.E.C. conceived and designed the study. H.T. designed all of the experiments and conducted the experiments except for the high-throughput Optopatch assay in cultured neurons and electrophysiology in acute slice. B.G. and V.P. assisted with the optics on the ultra-widefield microscope for pooled screening. H.C.D, H.T. and J.D.W.-C. improved the structured illumination microscope for in vivo imaging based on an earlier version built by L.Z.F. H.C.D. developed the Matlab control software for the structured illumination microscope. H.T., C.A.W. and G.B.B. designed the high-throughput Optopatch experiment in cultured neurons for characterizing GEVIs. H.U, H.S. and J.J. performed the high-throughput Optopatch assay. P.P. performed electrophysiological experiments in acute brain slice. Y.Q. assisted with the in vivo imaging experiments. S.B. prepared the cultured neurons for GEVI characterization and performed the mouse husbandry. L.Z.F. and K.D. contributed to the in vivo validation of the GEVIs in the early stage. H.T. and A.E.C. analyzed the data and wrote the manuscript. A.E.C. supervised the research.

Corresponding author

Correspondence to Adam E. Cohen.

Ethics declarations

Competing interests

A.E.C. is a founder of Q-State Biosciences. A.E.C. and H.T. filed a patent on the genetically encoded voltage indicators described in this study. All other authors have no competing interests.

Peer review

Peer review information

Nature Methods thanks Srdjan Antic, Xue Han, and the other, anonymous, reviewer for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Calibration of Photopick, an imaging-based method for isolating mammalian cells from pooled culture (Related to Fig. 1).

a. Procedure for registering the DMD and camera pixels. An 11 × 11 grid of spots was projected onto a homogeneous exposure target. The observed locations in the camera were used to develop a piecewise-linear transformation to map DMD pixels onto camera pixels. In this example, the registration reduced the average projection error from 11.6 pixels to 0.22 pixels. b. Fluorescence excitation and emission spectra of three phototaggable FPs, PA-GFP, PA-mCherry, and mEos4a. For mEos4a, the spectra are given in the pre-activation state (green) and post-activation state (red). For the other FPs, the activated spectra are shown. c. Phototransformation efficiency vs. optical dose of 405-nm LED light. The decreased signal under prolonged illumination is due to photobleaching. d. Selective phototagging of mEos4a+ cells embedded in PA-mCherry+ cells (mEos4a+:PA-mCherry+ = 1:20; n = 1 trial). Based on the green channel image (i), a mEOS4a mask was created for targeted photoconversion of mEos4a with violet (ii). The red channel image shows that the phototagging was highly specific (iii). The monolayer of cells was then broadly illuminated with violet light (iv) to drive the photoactivation of PA-mCherry+ cells (v). Targeted violet illumination of the mEos4+ cells resulted in selective phototagging of mEos4a+ cells but not surrounding PA-mCherry+ cells.

Extended Data Fig. 2 Video-based pooled screen for mutations that enhance the performance of Arch-derived GEVIs (Related to Fig. 2).

a. Current clamp measurement of membrane potential in spiking HEK cells reveals ‘all-or-none’ spiking in response to increasing optogenetic stimulation (n = 2 trials; exc. 488 nm). Left: membrane potential in response to optical stimuli of increasing strength (0–22 mW/mm2). Right: enlarged view showing the threshold transition. b. Fluorescence image (exc. 635 nm) of spiking HEK cell monolayer stained with BeRST1 (left) or expressing Archon1-Citrine (right). In the Archon1-Citrine image, the presence of the spacer cells (spiking HEK cells that did not express Archon1-Citrine) enabled individual cells to be resolved. c. Distribution of membrane potential changes in a spiking HEK cell monolayer, reported via imaging of a voltage-sensitive dye BeRST1, plotted for each pixel. Left: heatmap of ΔF vs. F0 for all pixels in a 2.3 × 2.3 mm FOV (500 × 500 pixels). Right: histogram of ΔF/F0. The distribution had a fractional width (S.D./mean) of 8% (mean 0.25, S.D. 0.02; 99th percentile: 0.29). d. Distribution of Archon1 baseline brightness (F0) and voltage sensitivity (ΔF/F0) in a monoclonal Archon1-expressing spiking HEK cell monolayer, plotted for each cell (n = 20900 cells). Left: heatmap of ΔF vs. F0 for all cells in a 2.3 × 2.3 mm FOV (500 × 500-pixels). Right: histogram of ΔF/F0. The distribution had a fractional width (S.D./mean) of 43% (mean 0.23, S.D. 0.10; 99th percentile: 0.54), substantially broader than the distribution for BeRST1. e. Workflow for the generation of the library cells. f. Optical system for video-based pooled screening. g. Image analysis for a representative FOV (the same as shown in Fig. 2e, f). The example was, from left to right: 1) ROIs generated by ‘Watershed’ image segmentation in the mEos4a channel (exc: 490 nm; EGFP emission filter). 2) Baseline fluorescence (F0) image in the Arch channel (exc: 635 nm; Arch emission filter). 3) Heatmap of \({{{\mathrm{{\Delta}}}}}F/\surd F_0\) for individual ROIs. Here \({{{\mathrm{{\Delta}}}}}F/\surd F_0\) is used as a proxy for shot noise limited for SNR. 4) Overlay of the patterned violet light (pseudo-color red; exc. 405 nm; CFP emission filter) and mEos4a image (exc: 490 nm; EGFP emission filter).

Extended Data Fig. 3 Engineering QuasAr6a and QuasAr6b (Related to Fig. 2).

a. Pipeline for engineering improved GEVIs. b. Comparison of the previously reported mutations (orange),26,34 and newly identified mutations in this study (lime-green, pale cyan and blue assigned in accordance with Fig. 3a). c. Violin plot for the per-molecule brightness (FArch/FCitrine) of single mutants expressed in HEK cells. The per-molecule brightness was normalized by the average per-molecule brightness of Archon1-Citrine in HEK cells. The residues selected for engineering QuasAr6a/b are shown in bold. d. Violin plot for the expression level (FCitrine) of single mutants expressed in in HEK cells. The values were normalized to the average expression level of Archon1-Citrine in HEK cells.

Extended Data Fig. 4 Characterization of QuasAr6a-Citrine and QuasAr6b-Citrine in HEK293T cells (Related to Fig. 3).

a. Arch-channel (exc: 635 nm, em: 670–746 nm) fluorescence images of QuasAr6a-Citrine and QuasAr6b-Citrine expressed in HEK cells (n > 20 cells for each construct). b. Relative brightness per molecule of Archon1-Citrine (n = 10 cells), QuasAr6a-Citrine (n = 7 cells), and QuasAr6b-Citrine (n = 10 cells) measured as a ratio of whole-cell FArch to FCitrine. n.s. not significant, p > 0.05; ***p : 0.0001~ 0.001 (two-sided Wilcoxon rank-sum test). The brightness per molecule was calculated as the ratio of Arch-channel fluorescence (exc. 635 nm; 420 W/cm2) to Citrine-channel fluorescence (exc. 488 nm; 0.1 W/cm2). c. Voltage sensitivity measured by concurrent voltage clamp and fluorescence in HEK cells. Left: Fractional fluorescence change vs. membrane voltage; shading: S.D. Right: Voltage sensitivity (ΔF/F per 100 mV: Archon1-Citrine, n = 4 cells; QuasAr6a-Citrine, n = 5 cells; QuasAr6b-Citrine, n = 6 cells). n.s. not significant, p > 0.05; **p: 0.01 ~ 0.05 (two-sided Wilcoxon rank-sum test). Error bars mean ± S.D. d. Voltage step-response kinetics measured by recording the average fluorescence change during a 100-ms voltage step from −70 mV to +30 mV (Archon1-Citrine, n = 6 cells; QuasAr6a-Citrine, n = 7 cells; QuasAr6b-Citrine, n = 7 cells); shading: SEM. Measurements were performed at 30 °C and a frame rate of 2,443 Hz. e. Summary of the step-response kinetic data at 30 °C, fitted with a biexponential model. Compared with Archon1, QuasAr6b showed significant improvement in both activation and deactivation kinetics. **p: 0.01 ~ 0.05 (two-sided Wilcoxon rank-sum test). f. Photobleaching by 635 nm laser (420 W/cm2) over 10 min (n = 2 cells for each construct). All constructs showed < 40% photobleaching over 10 min. S.D. g. Voltage clamp measurement of HEK cells expressing QuasAr6a or QuasAr6b showed negligible photocurrents under either 488 nm, 635 nm or combined illumination at either −70 mV or 0 mV holding potentials (488 nm: 124 W/cm2; 635 nm: 1500 W/cm2). All photocurrents were less than the variability in baseline holding current and were < 10 pA (in most cases < 2 pA). The onsets of red or blue illumination are indicated with dashed lines and numbered sequentially. h. Summary of the photocurrent measurement in g. All values are mean ± S.D. Transient changes in the holding current were calculated as the differences of the mean holding currents during the 20-ms epochs before and after the light was turned on. Red-on: average of 1′ and 3′; Blue-on: average of 4′ and 6′; Red after blue: 5′; Blue after red: 2’.

Extended Data Fig. 5 Metrics of GEVI performance in high-throughput Optopatch assay in cultured neurons (Related to Fig. 3).

a. SNR: spike height divided by the root mean square (RMS) baseline noise. b. Optical spike width: full width measured at 80% below the action potential peak. Note offset vertical axis. c. ΔFArch/F0, Arch: voltage sensitivity as a ratio of the increase in fluorescence during a spike to the baseline fluorescence. d. FArch/Fex488: per-molecule brightness as a ratio of baseline fluorescence in the Arch channel to the baseline fluorescence in the Citrine channel. The data for Archon1-EGFP were omitted because EGFP and Citrine fluorescence are not directly comparable. e. F0, Arch: baseline fluorescence in the Arch channel (exc: 635 nm). f. Fex488: baseline fluorescence in the Citrine channel (exc: 488 nm). In all measurements, the relative titers (from low to high) were: 1.19, 1.78, 2.67, 4, 6, 9. Each data point represents the average from 4 wells. The intensive properties (b, c, d) are largely insensitive to virus titer while the extensive properties (a, e, f) scale with virus titer. Error bars: SEM. g-j. Distribution of spike widths for neurons with low (0–33 percentile), medium (33–67 percentile) and high (67–100 percentile) expression level (Fex488). The distributions were similar across expression levels, for all GEVIs. k. Cell counts in high-throughput Optopatch assay. The total and well-average (mean ± S.D.) number of optically detected spiking cells, for each combination of GEVI construct and virus titer. At the higher titers, the well-to-well variations in detected cells within a given condition were ~10%, much smaller than the 200–300% differences between GEVI variants.

Extended Data Fig. 6 Expression of somQuasAr6a- and somQuasAr6b-based Optopatch in mouse brain (Related to Fig. 3).

a, b. Confocal images showing bicistronic expression of soma-targeted QuasAr6a-EGFP (somQuasAr6a) in L5 somatosensory cortex or soma-targeted QuasAr6b-EGFP (somQuasAr6b) with somCheRiff-HA in L5 cingulate cortex. The expression of GEVIs was visualized in the EGFP channel and the expression of CheRiff in the Cy5 channel (anti-HA immunostaining). c. Confocal images showing bicistronic expression of soma-targeted QuasAr6b-EGFP (somQuasAr6b) with somCheRiff-HA in hippocampal PV cells in a PV-Cre+ mouse.

Extended Data Fig. 7 Effect of GEVI expression on membrane electrical properties and excitabilities (Related to Fig. 3).

Mouse L2/3 cortical neurons expressing Arch-based GEVIs were measured by patch clamp in acute slices (somQuasAr6a, n = 2 animals, 12 cells; somQuasAr6b, n = 2 animals, 12 cells). Non-expressing cells from the same slices were used as the control (n = 4 animals, 15 cells). Box plots: central mark indicates median, bottom edge 25th percentile, top edge 75th percentile, whiskers most extreme data points excluding outliers, ’+’ symbol outliers. n.s., not significant, two-sided Wilcoxon rank-sum test. Error bars in f: SEM.

Extended Data Fig. 8 Optopatch in hippocampal PV cells (Related to Fig. 4).

a, b. Two ways of patterning 635-nm light to the cell with a spatial light modulator (SLM). Left: soma-targeted. Right: membrane-focal. The cell shown here was a hippocampal PV neuron (imaged with 25x, NA = 1.05 objective). Compared to whole-soma illumination, membrane-focal illumination provides improved shot noise-limited SNR but greater sensitivity to motion artifacts. c, d. Representative Optopatch traces of somQuasAr6b+ PV cells, recorded at 2 kHz (1973 Hz) and 4 kHz (3947 Hz) with a 10× objective (NA 0.6). Magnified views of the boxed regions are shown on the right. For the 2 kHz-imaging experiment, soma-targeted illumination was used. For the 4 kHz-imaging experiment, membrane-focal illumination was used. Due to this difference in the optical configuration, the SNRs from these two datasets were not compared in the analysis. e. Comparison of the in vivo SNR of QuasAr6b (n = 20 cells, 3 animals) and Archon1 in PV cells (n = 24 cells, 2 animals), two-sided Wilcoxon rank-sum test. f. Comparison of optical spike full width at half-maximum (FWHM) of optogenetically triggered spikes in PV cells, imaged with somQuasAr6b and somArchon1 at a 2 kHz frame rate, two-sided Wilcoxon rank-sum test. g. Comparison of optical spike FWHM of optogenetically triggered spikes in PV cells, imaged with somQuasAr6b a 2 kHz (n = 20 cells, 3 animals) and 4 kHz (n = 13 cells, 2 animals) frame rate, two-sided Wilcoxon rank-sum test. h. Spike-triggered average fluorescence waveform of optogenetically trigged spikes recorded with somQuasAr6b a 2 kHz (n = 20 cells, 3 animals) and 4 kHz (n = 13 cells, 2 animals) frame rate.

Extended Data Fig. 9 Photostability of QuasAr6a and QuasAr6b in vivo (Related to Fig. 4).

a. Raw Arch-channel fluorescence trace without baseline or photobleaching correction of a Layer 1 NDNF cell (visual cortex) expressing QuasAr6a-based Optopatch, imaged for 200 seconds at 1 kHz (n = 2 cells). The 635-nm power delivered to the cell was 4 mW. b. Raw Arch-channel fluorescence trace of a hippocampal PV cell expressing QuasAr6b-based Optopatch, imaged for 200 seconds at 2 kHz (n = 2 cells). The 635-nm power delivered to the cell was 8 mW. The measurement was done in anesthetized animals. The fluorescence traces were the raw traces directly extracted from cell mask and not corrected for background. The SNR and FWHM was calculated for the all the optogenetically evoked spikes in the magnified region.

Extended Data Fig. 10 Additional examples of electrical coupling between hippocampal PV cells (Related to Fig. 6).

a. An example where gap junction-induced spikelets were detected between PV pairs in both directions. The inter-soma distances were 90 μm. b. An example where no gap junction-induced spikelet was detected between the PV pair (inter-soma distance = 298 μm).

Supplementary information

Supplementary Information

Supplementary Tables 1–3, Figs. 1–3, and Methods

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tian, H., Davis, H.C., Wong-Campos, J.D. et al. Video-based pooled screening yields improved far-red genetically encoded voltage indicators. Nat Methods 20, 1082–1094 (2023). https://doi.org/10.1038/s41592-022-01743-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-022-01743-5

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing