Designs and sensing mechanisms of genetically encoded fluorescent voltage indicators

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Highlights

  • Genetically encoded voltage indicators show great promise as tools for probing neuronal activity with millisecond-timescale resolution.

  • To develop voltage indicators, protein engineers have utilized proteins whose conformational or photophysical states are sensitive to voltage.

  • Further improvement in the kinetics, dynamic range and/or brightness are needed to facilitate broader deployment of voltage sensors in vivo.

Neurons tightly regulate the electrical potential difference across the plasma membrane with millivolt accuracy and millisecond resolution. Membrane voltage dynamics underlie the generation of an impulse, the transduction of impulses from one end of the neuron to the other, and the release of neurotransmitters. Imaging these voltage dynamics in multiple neurons simultaneously is therefore crucial for understanding how neurons function together within circuits in intact brains. Genetically encoded fluorescent voltage sensors have long been desired to report voltage in defined subsets of neurons with optical readout. In this review, we discuss the diverse strategies used to design and optimize protein-based voltage sensors, and highlight the chemical mechanisms by which different classes of reporters sense voltage. To guide neuroscientists in choosing an appropriate sensor for their applications, we also describe operating trade-offs of each class of voltage indicators.

Introduction

Neurons can encode and transmit information by regulating the electrical field (voltage) across their plasma membrane. Voltage dynamics track both neural inputs and outputs: voltage can be modulated by neurotransmitters released by upstream neurons; in turn, voltage controls whether neurotransmitters will be discharged onto downstream neurons. The central role of voltage as a carrier of neural information thus motivates the development of powerful tools to image voltage transients within individual cells and across large populations. Although voltage is most commonly measured with electrodes, recent engineering efforts have substantially improved the ability of protein-based fluorescent sensors to image fast electrical activity in neural tissue. Optical detection of voltage signals with protein-based sensors presents unique opportunities over monitoring voltage with electrodes. First, voltage sensors can image subcellular regions such as dendritic spines or axonal termini that are typically too small to be accessible by standard electrodes. Second, they could enable monitoring of voltage dynamics over thousands or millions of cells. By contrast, electrode arrays have lower spatial resolution given their limited number and density of electrodes. Third, protein-based voltage sensors can restrict visualization to genetically defined cell types of interest, rather than selectively monitoring electrical activity in neurons that happen to be near the recording electrode.

Yet, imaging voltage dynamics with protein sensors also poses several challenges. First, to report on membrane voltage transients, the indicator must be in the plasma membrane, or be tightly coupled to a sensor element in the membrane. As a result, the sensor must hijack the cellular plasma membrane trafficking machinery and avoid accumulating in intermediate organelles such as the endoplasmic reticulum or the Golgi apparatus. Second, voltage transients are often rapid; for example, action potentials last less than a few milliseconds, while neurotransmitter-induced depolarizations typically have time courses of less than tens of milliseconds. Sensors must therefore have sufficiently rapid kinetics and be very sensitive to detect these voltage transients. Finally, voltage indicators must be sufficiently bright and photostable to report voltage dynamics with the required spatiotemporal resolution over the course of an entire experiment. We review different strategies for developing protein-based probes that begin to address these challenges. We focus on voltage indicators that are fully genetically encoded; voltage-sensitive dyes, and hybrid sensors combining a protein component and a synthetic dye, are reviewed elsewhere [1, 2].

Section snippets

Sensors exploiting voltage-induced conformational changes in natural voltage sensing domains

In one family of genetically encoded voltage indicators, integral membrane voltage sensing domains (VSDs) are fused to fluorescent proteins from jellyfish or coral. In their native proteins, VSDs either control the opening and closing of ion channels, or the activity of a phosphatase. In all cases, VSDs are composed of four transmembrane helices, with the fourth (S4) containing several positively-charged residues  arginines, or a mixture of arginines and lysines. These residues are sensitive to

Sensors exploiting voltage-sensitive photophysical properties of microbial rhodopsins

A surprising development came in 2011 with the demonstration that microbial rhodopsins can be repurposed as voltage indicators (Figure 1, right column). Rhodopsins are light-sensitive membrane proteins composed of 7 transmembrane α-helices; they normally function as light-driven ion pumps, channels or light sensors [38]. They have met broad adoption over the last decade for optical control of neural activity, a technology called optogenetics [39]. The light sensitivity of these proteins comes

Future directions

Given the role of voltage for encoding, transforming and propagating information in animal brains, high performance genetically encoded voltage indicators are on the wish lists of many neuroscientists [50]. Interestingly, protein engineers have leveraged not one, but two distinct voltage sensing mechanisms for developing protein-based voltage sensors: voltage-sensitive conformational states, and voltage-sensitive photophysical states. These sensors are beginning to be used to capture neural

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

This work was supported by National Science Foundation grant 1134416 (F.S.-P., M.Z.L.), DARPA grant W911NF-14-1-0013 (M.Z.L.) and NIH Brain Initiative Grant 1U01NS090600-01 (M.Z.L.). M.Z.L. receives funding from the Rita Allen Foundation and the Burroughs Wellcome Fund.

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