Browsing by Author "Guan, Sihui"

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    Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy
    (Elsevier, 2022) Liu, Zhuohe; Lu, Xiaoyu; Villette, Vincent; Gou, Yueyang; Colbert, Kevin L.; Lai, Shujuan; Guan, Sihui; Land, Michelle A.; Lee, Jihwan; Assefa, Tensae; Zollinger, Daniel R.; Korympidou, Maria M.; Vlasits, Anna L.; Pang, Michelle M.; Su, Sharon; Cai, Changjia; Froudarakis, Emmanouil; Zhou, Na; Patel, Saumil S.; Smith, Cameron L.; Ayon, Annick; Bizouard, Pierre; Bradley, Jonathan; Franke, Katrin; Clandinin, Thomas R.; Giovannucci, Andrea; Tolias, Andreas S.; Reimer, Jacob; Dieudonné, Stéphane; St-Pierre, François
    Genetically encoded voltage indicators are emerging tools for monitoring voltage dynamics with cell-type specificity. However, current indicators enable a narrow range of applications due to poor performance under two-photon microscopy, a method of choice for deep-tissue recording. To improve indicators, we developed a multiparameter high-throughput platform to optimize voltage indicators for two-photon microscopy. Using this system, we identified JEDI-2P, an indicator that is faster, brighter, and more sensitive and photostable than its predecessors. We demonstrate that JEDI-2P can report light-evoked responses in axonal termini of Drosophila interneurons and the dendrites and somata of amacrine cells of isolated mouse retina. JEDI-2P can also optically record the voltage dynamics of individual cortical neurons in awake behaving mice for more than 30 min using both resonant-scanning and ULoVE random-access microscopy. Finally, ULoVE recording of JEDI-2P can robustly detect spikes at depths exceeding 400 μm and report voltage correlations in pairs of neurons.
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    Versatile phenotype-activated cell sorting
    (AAAS, 2020) Lee, Jihwan; Liu, Zhuohe; Suzuki, Peter H.; Ahrens, John F.; Lai, Shujuan; Lu, Xiaoyu; Guan, Sihui; St-Pierre, François
    Unraveling the genetic and epigenetic determinants of phenotypes is critical for understanding and re-engineering biology and would benefit from improved methods to separate cells based on phenotypes. Here, we report SPOTlight, a versatile high-throughput technique to isolate individual yeast or human cells with unique spatiotemporal profiles from heterogeneous populations. SPOTlight relies on imaging visual phenotypes by microscopy, precise optical tagging of single target cells, and retrieval of tagged cells by fluorescence-activated cell sorting. To illustrate SPOTlight’s ability to screen cells based on temporal properties, we chose to develop a photostable yellow fluorescent protein for extended imaging experiments. We screened 3 million cells expressing mutagenesis libraries and identified a bright new variant, mGold, that is the most photostable yellow fluorescent protein reported to date. We anticipate that the versatility of SPOTlight will facilitate its deployment to decipher the rules of life, understand diseases, and engineer new molecules and cells