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

Simple item page
dc.contributor.advisorKemere, Caleb T.
dc.contributor.advisorSt-Pierre, Francois
dc.creatorLiu, Zhuohe
dc.date.accessioned2020-09-01T19:51:54Z
dc.date.available2021-08-01T05:01:14Z
dc.date.created2020-08
dc.date.issued2020-08-31
dc.date.submittedAugust 2020
dc.date.updated2020-09-01T19:51:54Z
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.
dc.embargo.terms2021-08-01
dc.format.mimetypeapplication/pdf
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>.
dc.identifier.urihttps://hdl.handle.net/1911/109302
dc.language.isoeng
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.
dc.subjectMicroscopy
dc.subjectScreening Platform
dc.subjectGEVI
dc.subjectHigh-throughput
dc.titleAutomating multi-parameter engineering of protein-based sensors of neural electrical activity
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.majorNeuroengineering
thesis.degree.nameMaster of Science
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
LIU-DOCUMENT-2020.pdf
Size:
4.39 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
5.84 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
LICENSE.txt
Size:
2.6 KB
Format:
Plain Text
Description:
Download
Collections
Rice University Electronic Theses and Dissertations