Genetically Encoded Voltage Indicators: Opportunities and Challenges

Abstract

A longstanding goal in neuroscience is to understand how spatiotemporal patterns of neuronal electrical activity underlie brain function, from sensory representations to decision making. An emerging technology for monitoring electrical dynamics, voltage imaging using genetically encoded voltage indicators (GEVIs), couples the power of genetics with the advantages of light. Here, we review the properties that determine indicator performance and applicability, discussing both recent progress and technical limitations. We then consider GEVI applications, highlighting studies that have already deployed GEVIs for biological discovery. We also examine which classes of biological questions GEVIs are primed to address and which ones are beyond their current capabilities. As GEVIs are further developed, we anticipate that they will become more broadly used by the neuroscience community to eavesdrop on brain activity with unprecedented spatiotemporal resolution.

Significance statement: Genetically encoded voltage indicators are engineered light-emitting protein sensors that typically report neuronal voltage dynamics as changes in brightness. In this review, we systematically discuss the current state of this emerging method, considering both its advantages and limitations for imaging neural activity. We also present recent applications of this technology and discuss what is feasible now and what we anticipate will become possible with future indicator development. This review will inform neuroscientists of recent progress in the field and help potential users critically evaluate the suitability of genetically encoded voltage indicator imaging to answer their specific biological questions.

Keywords: biosensors; fluorescence imaging; genetically encoded voltage indicators (GEVI); voltage imaging.

Figure 1.

GEVI performance metrics and challenges. Each table row presents an important criterion for evaluating sensor performance. For each criterion, one or more possible challenges are listed, along with potential solutions that could address the corresponding challenge. The selected examples cite articles that illustrate the solutions, regardless of effects on other performance metrics. *This solution was demonstrated with a GFP-based GECI but may be extended to GFP-based GEVIs.

Figure 1.

GEVI performance metrics and challenges. Each table row presents an important criterion for evaluating sensor performance. For each criterion, one or more possible challenges are listed, along with potential solutions that could address the corresponding challenge. The selected examples cite articles that illustrate the solutions, regardless of effects on other performance metrics. *This solution was demonstrated with a GFP-based GECI but may be extended to GFP-based GEVIs.

Figure 1.

GEVI performance metrics and challenges. Each table row presents an important criterion for evaluating sensor performance. For each criterion, one or more possible challenges are listed, along with potential solutions that could address the corresponding challenge. The selected examples cite articles that illustrate the solutions, regardless of effects on other performance metrics. *This solution was demonstrated with a GFP-based GECI but may be extended to GFP-based GEVIs.

Figure 2.

Applications of GEVIs across experimental systems. A, Simultaneous single-trial recordings of action potentials from two neurons in cortical area V1 of an awake mouse. Cells were illuminated with light-emitting diodes (LEDs) at a power density of 25 mW/mm² and imaged at 1 kHz using a scientific complementary metal-oxide semiconductor camera. Scale bar, 40 μm. Adapted with permission from Gong et al. (2015). B, Retinotopic maps from wide-field imaging of cortical layer 2/3 neurons in an awake mouse in response to 2 Hz flickering visual stimuli. Top, Stimulus azimuth response map. Bottom, Stimulus elevation response map. Responses to each stimulus were averaged over 20 trials. Scale bar, 1 mm. The brain was illuminated with an LED and photons were collected at 50 Hz using a scientific complementary metal oxide semiconductor camera. Contour lines indicate the boundaries of the visual cortex (blue), barrel cortex (green), and auditory cortex (pink). Adapted with permission from Madisen et al. (2015). C, Action potential backpropagation in a cultured rat hippocampal neuron. Illumination was performed with a laser at a power density of 3000 mW/mm². Fluorescence was collected at 1 kHz using an electron multiplying charge-coupled device. Subframe interpolation was used to infer submillisecond timing from image sequences averaged over 203 action potentials. Scale bar, 50 μm. Adapted with permission from Hochbaum et al. (2014). D, Action potentials recorded from a presynaptic bouton and the soma of the same cultured hippocampal neuron. Action potential amplitude was calibrated by comparing the fluorescence change elicited by an action potential with the fluorescence change elicited by application of gramicidin, which brought the resting membrane potential to 0 mV. Traces were averaged over 9 cells, 400 trials per cell. Neurons were illuminated with a laser at a power density of ∼22,500 mW/mm², with fluorescence captured at 2 kHz using an electron multiplying charge-coupled device. Adapted with permission from Hoppa et al. (2014). E, In vivo voltage (left) and calcium (right) signals recorded across subcellular regions of the Drosophila visual interneuron Tm3 in response to 25 ms flashes of light off of a gray background. Cells were imaged with two-photon laser scanning microscopy at a frame rate of 38.9 Hz that was upsampled during analysis to 120 Hz. Power was ∼10 mW at the sample plane. Response traces for each region are mean ± 1 SEM averaged over 13–158 cells, 100 trials per cell for voltage, 50 trials per cell for calcium. Adapted with permission from Yang et al. (2016). F, Single-trial spontaneous electrical activity recorded in the dendrites of sleep-promoting γ2α′1 cells in acute brain explants from sleep-deprived and nondeprived Drosophila. Cells were illuminated with an LED and fluorescence captured at a frame rate of 125 Hz using a charge-coupled device. Adapted with permission from Sitaraman et al. (2015).