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基于视频合并筛选产生改进的远红基因编码电压指示器
作者:小柯机器人 发布时间:2023/1/11 17:09:42

美国哈佛大学Adam E. Cohen团队近期取得重要工作进展,他们研究开发了基于视频合并筛选产生改进的远红基因编码电压指示器。相关研究成果2023年1月9日在线发表于《自然—方法学》杂志上。

据介绍,基于视频的集合库筛选是生物传感器定向进化的有力方法,因为它能够同时从大型库中沿多个维度进行选择。

研究人员开发了一个筛选平台Photopick,它可以在大视野范围内实现精确的表型激活光选择(2.3×2.3 mm,每次拍摄超过103个细胞)。研究人员使用Photopick平台进化出古菌视紫红质来源的基因编码电压指示器(GEVI),其具有改进的信噪比(QuasAr6a)和动力学(QuasAr 6b)。这些GEVI在培养的神经元和活的小鼠大脑中提供了改善的信号。通过将靶向体内光遗传学刺激与高精度电压成像相结合,研究人员表征了单个皮层NDNF(神经元源性神经营养因子)中间神经元之间的抑制性突触耦合,以及单个海马小白蛋白神经元之间的兴奋性电突触。

总之,QuasAr6 GEVI是用于全光电生理学的强大工具,Photopick方法可用于发展广泛的生物传感器。

附:英文原文

Title: Video-based pooled screening yields improved far-red genetically encoded voltage indicators

Author: Tian, He, Davis, Hunter C., Wong-Campos, J. David, Park, Pojeong, Fan, Linlin Z., Gmeiner, Benjamin, Begum, Shahinoor, Werley, Christopher A., Borja, Gabriel B., Upadhyay, Hansini, Shah, Himali, Jacques, Jane, Qi, Yitong, Parot, Vicente, Deisseroth, Karl, Cohen, Adam E.

Issue&Volume: 2023-01-09

Abstract: Video-based screening of pooled libraries is a powerful approach for directed evolution of biosensors because it enables selection along multiple dimensions simultaneously from large libraries. Here we develop a screening platform, Photopick, which achieves precise phenotype-activated photoselection over a large field of view (2.3×2.3mm, containing >103cells, per shot). We used the Photopick platform to evolve archaerhodopsin-derived genetically encoded voltage indicators (GEVIs) with improved signal-to-noise ratio (QuasAr6a) and kinetics (QuasAr6b). These GEVIs gave improved signals in cultured neurons and in live mouse brains. By combining targeted in vivo optogenetic stimulation with high-precision voltage imaging, we characterized inhibitory synaptic coupling between individual cortical NDNF (neuron-derived neurotrophic factor) interneurons, and excitatory electrical synapses between individual hippocampal parvalbumin neurons. The QuasAr6 GEVIs are powerful tools for all-optical electrophysiology and the Photopick approach could be adapted to evolve a broad range of biosensors.

DOI: 10.1038/s41592-022-01743-5

Source: https://www.nature.com/articles/s41592-022-01743-5

期刊信息

Nature Methods:《自然—方法学》,创刊于2004年。隶属于施普林格·自然出版集团,最新IF:47.99
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex