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深度学习使快速和密集的单分子定位具有高精确度
作者:小柯机器人 发布时间:2021/9/8 16:38:18

美国霍华德休斯医学研究所Srinivas C. Turaga发现,深度学习使快速和密集的单分子定位具有高精确度。2021-09-22,国际知名学术期刊《自然—方法学》在线发表了这一成果。

研究人员表示,单分子定位显微镜(SMLM)在以纳米分辨率对细胞结构进行成像方面取得了显著的成功,但标准分析算法需要稀疏的发射器,这限制了成像速度和标记密度。

研究人员利用深度学习克服了这一主要限制。研究人员开发了DECODE(deep context dependent),这是一个计算工具,可以在大范围的成像模式和条件下,以最高的精度在三维空间高密度定位单个发射体。在一个公开的软件基准竞赛中,当比较检测精度和定位误差时,它在所有12个数据集上的表现超过了所有其他适配者,而且往往有很大的差距。DECODE使研究人员能够在减少光照的情况下获得快速的动态活细胞SMLM数据,并以超高的标记密度对微管进行成像。DECODE包装简单,安装和使用方便,将使许多实验室减少成像时间,提高SMLM的定位密度。

附:英文原文

Title: Deep learning enables fast and dense single-molecule localization with high accuracy

Author: Speiser, Artur, Mller, Lucas-Raphael, Hoess, Philipp, Matti, Ulf, Obara, Christopher J., Legant, Wesley R., Kreshuk, Anna, Macke, Jakob H., Ries, Jonas, Turaga, Srinivas C.

Issue&Volume: 2021-09-22

Abstract: Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but standard analysis algorithms require sparse emitters, which limits imaging speed and labeling density. Here, we overcome this major limitation using deep learning. We developed DECODE (deep context dependent), a computational tool that can localize single emitters at high density in three dimensions with highest accuracy for a large range of imaging modalities and conditions. In a public software benchmark competition, it outperformed all other fitters on 12 out of 12 datasets when comparing both detection accuracy and localization error, often by a substantial margin. DECODE allowed us to acquire fast dynamic live-cell SMLM data with reduced light exposure and to image microtubules at ultra-high labeling density. Packaged for simple installation and use, DECODE will enable many laboratories to reduce imaging times and increase localization density in SMLM. DECODE uses deep learning for localizing single emitters in high-density two-dimensional and three-dimensional single-molecule localization microscopy data. DECODE outperforms available methods and enables fast live-cell SMLM of dynamic processes.

DOI: 10.1038/s41592-021-01236-x

Source: https://www.nature.com/articles/s41592-021-01236-x

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:28.467
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex

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