Multimodal High-Content Optimization of Neural Activity Biosensors
| dc.contributor.advisor | Veeraraghavan, Ashok | en_US |
| dc.contributor.advisor | St-Pierre, François | en_US |
| dc.creator | Liu, Zhuohe | en_US |
| dc.date.accessioned | 2023-01-05T16:37:43Z | en_US |
| dc.date.created | 2022-12 | en_US |
| dc.date.issued | 2022-12-01 | en_US |
| dc.date.submitted | December 2022 | en_US |
| dc.date.updated | 2023-01-05T16:37:43Z | en_US |
| dc.description.abstract | Protein-based biosensors enable optical monitoring of neural activities with cell-type specificity. Genetically encoded voltage indicator (GEVI) is an emerging fluorescent biosensor that reports voltage dynamics. However, the performance of engineered biosensors cannot suffice the applications that require prolonged and faithful recording of rapid activity in deep-tissue targets. The underlying cause is the slow and challenging protein engineering to optimize holistically multiple spatial and temporal properties. To expand the biosensor toolbox, innovations in software, hardware, and wetware are needed in multimodal and automated protein optimization. We first aim to optimize GEVIs by developing a high-throughput platform to screen biosensor variants under two-photon illumination. Electric field stimulation was used to induce transient voltage responses in cells layered onto 96-well plates. Using this platform, we identified an indicator, JEDI-2P, which is faster, brighter, and more sensitive and photostable than its predecessors. JEDI-2P can report voltage response to visual stimuli in the dendrites and somata of amacrine cells of isolated mouse retina and in axonal termini of fruit fly interneurons. In awake behaving mice, JEDI-2P enables optical voltage recording of individual cortical neurons for more than 30 min using both resonant-scanning and ULoVE random-access two-photon microscopy. Especially with ULoVE, JEDI-2P can robustly detect spikes at depths exceeding 400 μm and report voltage correlations in pairs of neurons. To further boost throughput, we developed SPOTlight, a versatile single-cell screening platform. Using a microscope, SPOTlight captures visual phenotypes of individual cells that reflect both spatial and temporal properties. Single cells of interest can be precisely tagged using light because they contain phototransformable proteins or dyes. Tagged cells are retrieved by fluorescence-activated cell sorting to recover genotypes. To demonstrate the platform, we opted to optimize photostability of a yellow fluorescent protein (YFP), a common building block for biosensors, while monitoring brightness. We identified mGold, the most photostable YFP to date after screening ~3 million cells. We anticipate that accelerated and automated screening platforms will empower protein engineering for robust and well-performing biosensors that are crucial for neural activity monitoring, unlocking the secrets of our brains. | en_US |
| dc.embargo.lift | 2023-12-01 | en_US |
| dc.embargo.terms | 2023-12-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Liu, Zhuohe. "Multimodal High-Content Optimization of Neural Activity Biosensors." (2022) Diss., Rice University. <a href="https://hdl.handle.net/1911/114226">https://hdl.handle.net/1911/114226</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/114226 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright 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.subject | directed evolution | en_US |
| dc.subject | protein engineering | en_US |
| dc.subject | biosensor | en_US |
| dc.subject | GEVI | en_US |
| dc.subject | fluorescent protein | en_US |
| dc.subject | microscopy | en_US |
| dc.title | Multimodal High-Content Optimization of Neural Activity Biosensors | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Electrical and Computer Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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