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A fast and responsive voltage indicator with enhanced sensitivity for unitary synaptic events.
Yukun A Hao1, Sungmoo Lee2, Richard H Roth3
1Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Neurobiology, Stanford University, Stanford, CA 94305, USA.
Neuron
|September 21, 2024
Summary
We developed ASAP5, a genetically encoded voltage indicator (GEVI), to reliably detect small neuronal signals like excitatory postsynaptic potentials (EPSPs). This new GEVI improves voltage imaging for studying neuronal activity and disease.
Area of Science:
- Neuroscience
- Molecular Biology
- Biophysics
Background:
- Genetically encoded voltage indicators (GEVIs) are crucial for monitoring neuronal activity.
- Detecting small signals like excitatory postsynaptic potentials (EPSPs) remains a challenge for current GEVIs.
Purpose of the Study:
- To develop a novel GEVI, ASAP5, with enhanced kinetics and responsivity for improved detection of neuronal electrical activity.
- To assess ASAP5's performance in detecting action potentials (APs) and subthreshold events in vivo and in vitro.
Main Methods:
- Development and characterization of the ASAP5 GEVI.
- In vivo imaging of action potentials in response to sensory stimuli.
- In vitro imaging of synaptic events in cultured rat and human neurons.
- Analysis of EPSP propagation and amplitude decay in dendrites.
Main Results:
- ASAP5 demonstrated superior signal-to-noise ratios for in vivo action potential detection compared to previous GEVIs.
- ASAP5 successfully detected subthreshold responses to sensory stimuli in single two-photon trials.
- ASAP5 could detect approximately 1-mV EPSPs in cultured rat and human neurons.
- EPSP amplitude was found to decay exponentially along dendrites, with initiation site amplitude increasing with distance from the soma.
Conclusions:
- ASAP5 significantly advances voltage imaging capabilities, enabling reliable detection of small synaptic events, including in human neurons.
- This technology opens new avenues for high-throughput characterization of neuronal dysfunction in disease models.

