Cross-modality supervised image restoration enables nanoscale tracking of synaptic plasticity in living mice.
Cross-modality supervised image restoration enables nanoscale tracking of synaptic plasticity in living mice.
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DOI：
10.1038/s41592-023-01871-6
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发表时间：
2023-06
期刊：
影响因子：
48
通讯作者：
Sulam, Jeremias
中科院分区：
文献类型：
--
作者：
Xu, Yu Kang T.;Graves, Austin R.;Coste, Gabrielle I.;Huganir, Richard L.;Bergles, Dwight E.;Charles, Adam S.;Sulam, Jeremias
Learning is thought to involve changes in glutamate receptors at synapses, submicron structures that mediate communication between neurons in the central nervous system. Due to their small size and high density, synapses are difficult to resolve in vivo, limiting our ability to directly relate receptor dynamics to animal behavior. Here we developed a combination of computational and biological methods to overcome these challenges. First, we trained a deep-learning image-restoration algorithm that combines the advantages of ex vivo super-resolution and in vivo imaging modalities to overcome limitations specific to each optical system. When applied to in vivo images from transgenic mice expressing fluorescently labeled glutamate receptors, this restoration algorithm super-resolved synapses, enabling the tracking of behavior-associated synaptic plasticity with high spatial resolution. This method demonstrates the capabilities of image enhancement to learn from ex vivo data and imaging techniques to improve in vivo imaging resolution. XTC is a supervised deep-learning-based image-restoration approach that is trained with images from different modalities and applied to an in vivo modality with no ground truth. XTC’s capabilities are demonstrated in synapse tracking in the mouse brain.
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影响因子：
64.8
作者：
Bock, Davi D.;Lee, Wei-Chung Allen;Kerlin, Aaron M.;Andermann, Mark L.;Hood, Greg;Wetzel, Arthur W.;Yurgenson, Sergey;Soucy, Edward R.;Kim, Hyon Suk;Reid, R. Clay
通讯作者：
Reid, R. Clay
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L
影响因子：
25
作者：
Dombeck, Daniel A.;Harvey, Christopher D.;Tian, Lin;Looger, Loren L.;Tank, David W.
通讯作者：
Tank, David W.
影响因子：
4.6
作者：
Hirabayashi Y;Tapia JC;Polleux F
通讯作者：
Polleux F
影响因子：
4.7
作者：
Cai W;Wu S;Pan Z;Xiao J;Li F;Cao J;Zang W;Tao YX
通讯作者：
Tao YX