超分辨率
计算机科学
人工智能
深度学习
超分辨显微术
显微镜
生物物理学
纳米技术
化学
材料科学
生物
光学
图像(数学)
物理
扫描共焦电子显微镜
作者
Alon Saguy,Onit Alalouf,Nadav Opatovski,Soohyun Jang,Mike Heilemann,Yoav Shechtman
出处
期刊:Nature Methods
[Springer Nature]
日期:2023-07-27
卷期号:20 (12): 1939-1948
被引量:4
标识
DOI:10.1038/s41592-023-01966-0
摘要
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.
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