Kemere, Caleb T.St-Pierre, Francois2020-09-012021-08-012020-082020-08-31August 202Liu, Zhuohe. "Automating multi-parameter engineering of protein-based sensors of neural electrical activity." (2020) Master’s Thesis, Rice University. <a href="https://hdl.handle.net/1911/109302">https://hdl.handle.net/1911/109302</a>.https://hdl.handle.net/1911/109302Engineered voltage-sensitive fluorescent proteins, termed genetically encoded voltage indicators (GEVIs), are emerging sensors for noninvasive microscopy of neural activity. However, the sensitivity and kinetics of existing GEVIs are often not sufficient for accurately reporting fast voltage dynamics in vivo, and they are not optimal for long-term recording due to low brightness and lack of photostability. A system for rapidly evaluating new variants across all performance characteristics is critically needed to accelerate GEVI development progress. This work reports an automated platform that screens libraries of GEVIs in a high-throughput 96-well plate format. This platform quantifies sensitivity, kinetics, brightness, and photostability to identify promising GEVI candidates. Using this platform, a faster and more sensitive indicator, JEDI-1P, was identified and validated in vitro and in behaving zebrafish. The platform is anticipated to optimize versatile biosensors that excel in deep-tissue imaging and promote understanding of neural computation and neurological diseases.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.MicroscopyScreening PlatformGEVIHigh-throughputThesis2020-09-01