小柯机器人

以色列理工学院Yoav Shechtman研究组开发出通过深度学习实现超时空分辨率的动态定位显微技术。2023年7月27日，《自然—方法学》杂志在线发表了这项成果。

研究人员提出了 DBlink，这是一种基于深度学习的方法，用于从单分子定位显微镜（SMLM）数据重建超时空分辨率。DBlink的输入是录制的SMLM数据视频，输出是超时空分辨率视频重建。研究人员使用的卷积神经网络与双向长短期记忆网络架构相结合，用于捕捉不同输入帧之间的长期依赖关系。研究人员在模拟的细丝和线粒体类结构、受控运动条件下的实验SMLM数据以及活细胞动态SMLM上演示了DBlink的性能。DBlink的时空插值技术是活细胞动态过程超分辨率成像的重要进步。

据介绍，SMLM彻底改变了生物成像技术，将传统显微镜的空间分辨率提高了一个数量级。然而，单分子定位显微镜技术需要较长的采集时间（通常为几分钟）才能获得单个超分辨图像，因为它们依赖于数千个记录帧中许多定位的累积。因此，SMLM观察高时间分辨率动态的能力一直受到限制。

附：英文原文

Title: DBlink: dynamic localization microscopy in super spatiotemporal resolution via deep learning

Author: Saguy, Alon, Alalouf, Onit, Opatovski, Nadav, Jang, Soohyen, Heilemann, Mike, Shechtman, Yoav

Issue&Volume: 2023-07-27

Abstract: Single-molecule localization microscopy (SMLM) has revolutionized biological imaging, improving the spatial resolution of traditional microscopes by an order of magnitude. However, SMLM techniques require long acquisition times, typically a few minutes, to yield a single super-resolved image, because they depend on accumulation of many localizations over thousands of recorded frames. Hence, the capability of SMLM to observe dynamics at high temporal resolution has always been limited. In this work, we present DBlink, a deep-learning-based method for super spatiotemporal resolution reconstruction from SMLM data. The input to DBlink is a recorded video of SMLM data and the output is a super spatiotemporal resolution video reconstruction. We use a convolutional neural network combined with a bidirectional long short-term memory network architecture, designed for capturing long-term dependencies between different input frames. We demonstrate DBlink performance on simulated filaments and mitochondria-like structures, on experimental SMLM data under controlled motion conditions and on live-cell dynamic SMLM. DBlink’s spatiotemporal interpolation constitutes an important advance in super-resolution imaging of dynamic processes in live cells.

DOI: 10.1038/s41592-023-01966-0

Source: https://www.nature.com/articles/s41592-023-01966-0

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