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Widefield imaging of rapid pan-cortical voltage dynamics with an indicator evolved for one-photon microscopy

Author

Listed:
  • Xiaoyu Lu

    (Systems, Synthetic, and Physical Biology Program, Rice University
    Department of Neuroscience, Baylor College of Medicine)

  • Yunmiao Wang

    (Neuroscience Graduate Program, Emory University
    Biology Department, Emory University)

  • Zhuohe Liu

    (Department of Electrical and Computer Engineering, Rice University
    The University of Texas MD Anderson Cancer Center)

  • Yueyang Gou

    (Department of Neuroscience, Baylor College of Medicine)

  • Dieter Jaeger

    (Biology Department, Emory University)

  • François St-Pierre

    (Systems, Synthetic, and Physical Biology Program, Rice University
    Department of Electrical and Computer Engineering, Rice University
    Department of Neuroscience, Baylor College of Medicine
    Department of Biochemistry and Molecular Biology, Baylor College of Medicine)

Abstract

Widefield imaging with genetically encoded voltage indicators (GEVIs) is a promising approach for understanding the role of large cortical networks in the neural coding of behavior. However, the limited performance of current GEVIs restricts their deployment for single-trial imaging of rapid neuronal voltage dynamics. Here, we developed a high-throughput platform to screen for GEVIs that combine fast kinetics with high brightness, sensitivity, and photostability under widefield one-photon illumination. Rounds of directed evolution produced JEDI-1P, a green-emitting fluorescent indicator with enhanced performance across all metrics. Next, we optimized a neonatal intracerebroventricular delivery method to achieve cost-effective and wide-spread JEDI-1P expression in mice. We also developed an approach to correct optical measurements from hemodynamic and motion artifacts effectively. Finally, we achieved stable brain-wide voltage imaging and successfully tracked gamma-frequency whisker and visual stimulations in awake mice in single trials, opening the door to investigating the role of high-frequency signals in brain computations.

Suggested Citation

  • Xiaoyu Lu & Yunmiao Wang & Zhuohe Liu & Yueyang Gou & Dieter Jaeger & François St-Pierre, 2023. "Widefield imaging of rapid pan-cortical voltage dynamics with an indicator evolved for one-photon microscopy," Nature Communications, Nature, vol. 14(1), pages 1-22, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41975-3
    DOI: 10.1038/s41467-023-41975-3
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    References listed on IDEAS

    as
    1. Nicholas A. Steinmetz & Peter Zatka-Haas & Matteo Carandini & Kenneth D. Harris, 2019. "Distributed coding of choice, action and engagement across the mouse brain," Nature, Nature, vol. 576(7786), pages 266-273, December.
    2. Changjia Cai & Johannes Friedrich & Amrita Singh & M Hossein Eybposh & Eftychios A Pnevmatikakis & Kaspar Podgorski & Andrea Giovannucci, 2021. "VolPy: Automated and scalable analysis pipelines for voltage imaging datasets," PLOS Computational Biology, Public Library of Science, vol. 17(4), pages 1-28, April.
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