Automating multi-parameter engineering of protein-based sensors of neural electrical activity

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dc.contributor.advisorKemere, Caleb T.en_US
dc.contributor.advisorSt-Pierre, Francoisen_US
dc.creatorLiu, Zhuoheen_US
dc.date.accessioned2020-09-01T19:51:54Zen_US
dc.date.available2021-08-01T05:01:14Zen_US
dc.date.created2020-08en_US
dc.date.issued2020-08-31en_US
dc.date.submittedAugust 2020en_US
dc.date.updated2020-09-01T19:51:54Zen_US
dc.description.abstractEngineered 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.en_US
dc.embargo.terms2021-08-01en_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationLiu, 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>.en_US
dc.identifier.urihttps://hdl.handle.net/1911/109302en_US
dc.language.isoengen_US
dc.rightsCopyright 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.en_US
dc.subjectMicroscopyen_US
dc.subjectScreening Platformen_US
dc.subjectGEVIen_US
dc.subjectHigh-throughputen_US
dc.titleAutomating multi-parameter engineering of protein-based sensors of neural electrical activityen_US
dc.typeThesisen_US
dc.type.materialTexten_US
thesis.degree.departmentElectrical and Computer Engineeringen_US
thesis.degree.disciplineEngineeringen_US
thesis.degree.grantorRice Universityen_US
thesis.degree.levelMastersen_US
thesis.degree.majorNeuroengineeringen_US
thesis.degree.nameMaster of Scienceen_US
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