Screening of Vibrational Spectroscopic Voltage Indicator by Stimulated Raman Scattering Microscopy.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Jingyuan Li, Ninghui Shao, Yongqing Zhang, Xingxin Liu, Hanbin Zhang, Liangfei Tian, Kiryl D Piatkevich, Delong Zhang, Hyeon Jeong Lee
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引用次数: 0

Abstract

Genetically encoded voltage indicators (GEVIs) have significantly advanced voltage imaging, offering spatial details at cellular and subcellular levels not easily accessible with electrophysiology. In addition to fluorescence imaging, certain chemical bond vibrations are sensitive to membrane potential changes, presenting an alternative imaging strategy; however, challenges in signal sensitivity and membrane specificity highlight the need to develop vibrational spectroscopic GEVIs (vGEVIs) in mammalian cells. To address this need, a vGEVI screening approach is developed that employs hyperspectral stimulated Raman scattering (hSRS) imaging synchronized with an induced transmembrane voltage (ITV) stimulation, revealing unique spectroscopic signatures of sensors expressed on membranes. Specifically, by screening various rhodopsin-based voltage sensors in live mammalian cells, a characteristic peak associated with retinal bound to the sensor is identified in one of the GEVIs, Archon, which exhibited a 70 cm-1 red shift relative to the membrane-bound retinal. Notably, this peak is responsive to changes in membrane potential. Overall, hSRS-ITV presents a promising platform for screening vGEVIs, paving the way for advancements in vibrational spectroscopic voltage imaging.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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