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跨模态监督图像恢复实现了对活体小鼠突触可塑性的纳米级跟踪
作者:小柯机器人 发布时间:2023/5/18 10:31:57


美国约翰霍普金斯大学Jeremias Sulam和Adam S. Charles共同合作,近期取得重要工作进展。他们通过跨模态监督图像恢复实现了对活体小鼠突触可塑性的纳米级跟踪。相关研究成果2023年5月11日在线发表于《自然—方法学》杂志上。

据介绍,学习被认为涉及突触处谷氨酸受体的变化,突触是介导中枢神经系统神经元之间交流的亚微米结构。由于突触体积小、密度高,很难在体内解析,这限制了人们将受体动力学与动物行为直接联系起来的能力。

研究人员开发了一种计算和生物学相结合的方法来克服这些挑战。首先,研究人员训练了一种深度学习图像恢复算法,该算法结合了离体超分辨率和体内成像模式的优势,以克服每个光学系统特有的局限性。当应用于表达荧光标记谷氨酸受体的转基因小鼠的体内图像时,这种恢复算法能够超分辨突触,从而能够以高空间分辨率跟踪与行为相关的突触可塑性。

总之,该方法展示了从离体数据和成像技术中学习图像增强的能力,以提高体内成像分辨率率。

附:英文原文

Title: Cross-modality supervised image restoration enables nanoscale tracking of synaptic plasticity in living mice

Author: Xu, Yu Kang T., Graves, Austin R., Coste, Gabrielle I., Huganir, Richard L., Bergles, Dwight E., Charles, Adam S., Sulam, Jeremias

Issue&Volume: 2023-05-11

Abstract: 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.

DOI: 10.1038/s41592-023-01871-6

Source: https://www.nature.com/articles/s41592-023-01871-6

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