Development of a Hybrid-Type Fluorescent GABA Sensor through Structure-Guided Efficient Screening

γ-Aminobutyric acid (GABA) is a major inhibitory neurotransmitter in the brain, and visualizing its fast synaptic dynamics is crucial for understanding neural function. However, detecting these transient signals requires sensors with both high spatiotemporal resolution and robust performance under physiological conditions. Here, we report the development of a novel hybrid-type fluorescent GABA sensor generated by site-specifically labeling a fluorescent dye onto a GABA-binding protein. For efficiently identifying the optimal labeling site, we devised a structure-guided screening strategy based on a principle derived from our comprehensive analysis of a homologous glutamate-binding protein: that optimal labeling sites are primarily concentrated at the edge of the cleft regions. Applying this principle to an AI-predicted structure of the GABA-binding protein led to the identification of our lead sensor, Pf622 G183C-JF585, which exhibits a large dynamic range (>300%), high selectivity for GABA with little response to other neurotransmitters, fast kinetics, and excellent stability within the physiological pH range. Importantly, Pf622 G183C-JF585 successfully detected endogenous GABA released from cultured neurons upon electrical stimulation, demonstrating its utility for detecting the transient release of GABA. This new high-performance sensor, therefore, represents a powerful tool for the spatiotemporal analysis of GABAergic neurotransmission.

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Publication Details

Journal
ACS Sensors
Published
2025-09-25
DOI
https://doi.org/10.1021/acssensors.5c00372
Primary Topic
Neuroscience and Neuropharmacology Research
Type
article
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article

Development of a Hybrid-Type Fluorescent GABA Sensor through Structure-Guided Efficient Screening

Hiroshi Nishimune, Kenji Takikawa, Daisuke Asanuma, Shigeyuki Namiki et al.
ACS Sensors
Neuroscience and Neuropharmacology Research
article

Development of a Hybrid-Type Fluorescent GABA Sensor through Structure-Guided Efficient Screening

Hiroshi Nishimune, Kenji Takikawa, Daisuke Asanuma, Shigeyuki Namiki, Kenzo Hirose, Keitaro Umezawa
article en

Abstract

γ-Aminobutyric acid (GABA) is a major inhibitory neurotransmitter in the brain, and visualizing its fast synaptic dynamics is crucial for understanding neural function. However, detecting these transient signals requires sensors with both high spatiotemporal resolution and robust performance under physiological conditions. Here, we report the development of a novel hybrid-type fluorescent GABA sensor generated by site-specifically labeling a fluorescent dye onto a GABA-binding protein. For efficiently identifying the optimal labeling site, we devised a structure-guided screening strategy based on a principle derived from our comprehensive analysis of a homologous glutamate-binding protein: that optimal labeling sites are primarily concentrated at the edge of the cleft regions. Applying this principle to an AI-predicted structure of the GABA-binding protein led to the identification of our lead sensor, Pf622 G183C-JF585, which exhibits a large dynamic range (>300%), high selectivity for GABA with little response to other neurotransmitters, fast kinetics, and excellent stability within the physiological pH range. Importantly, Pf622 G183C-JF585 successfully detected endogenous GABA released from cultured neurons upon electrical stimulation, demonstrating its utility for detecting the transient release of GABA. This new high-performance sensor, therefore, represents a powerful tool for the spatiotemporal analysis of GABAergic neurotransmission.

ACS SensorsVol. 10(10)
Jichi Medical University (JP), Tokyo Metropolitan Institute of Gerontology (JP), The University of Tokyo (JP), Tokyo University of Agriculture and Technology (JP)
Openalex Percentile: Top 71%
Neuroscience and Neuropharmacology Research
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Development of a Hybrid-Type Fluorescent GABA Sensor through Structure-Guided Efficient Screening — Hiroshi Nishimune, Kenji Takikawa, et al. · ACS Sensors (2025) | TGRS Research Map | TGRS