HOME  | PRODUCTS  | COMPANY  | TECHNICAL DOCS  | CONTACT US 
USA/Canada: +1 (248) 436-8085

Reference data

TitleImaging Voltage in Genetically Defined Neuronal Subpopulations with a Cre Recombinase-Targeted Hybrid Voltage Sensor
AuthorPeter O. Bayguinov, Yihe Ma, Yu Gao, Xinyu Zhao and Meyer B. Jackson
Affiliation(s)1Department of Neuroscience, 3Waisman Center, University of Wisconsin, Madison, Wisconsin 53705
PublishedJournal of Neuroscience 20 September 2017, 37 (38) 9305-9319; DOI: https://doi.org/10.1523/JNEUROSCI.1363-17.2017
Keyword 
Snippet 
AbstractGenetically encoded voltage indicators create an opportunity to monitor electrical activity in defined sets of neurons as they participate in the complex patterns of coordinated electrical activity that underlie nervous system function. Taking full advantage of genetically encoded voltage indicators requires a generalized strategy for targeting the probe to genetically defined populations of cells. To this end, we have generated a mouse line with an optimized hybrid voltage sensor (hVOS) probe within a locus designed for efficient Cre recombinase-dependent expression. Crossing this mouse with Cre drivers generated double transgenics expressing hVOS probe in GABAergic, parvalbumin, and calretinin interneurons, as well as hilar mossy cells, new adult-born neurons, and recently active neurons. In each case, imaging in brain slices from male or female animals revealed electrically evoked optical signals from multiple individual neurons in single trials. These imaging experiments revealed action potentials, dynamic aspects of dendritic integration, and trial-to-trial fluctuations in response latency. The rapid time response of hVOS imaging revealed action potentials with high temporal fidelity, and enabled accurate measurements of spike half-widths characteristic of each cell type. Simultaneous recording of rapid voltage changes in multiple neurons with a common genetic signature offers a powerful approach to the study of neural circuit function and the investigation of how neural networks encode, process, and store information.

Back

© Prizmatix,  Israel
Consent Preferences