Saguy Alon, Alalouf Onit, Opatovski Nadav, Jang Soohyen, Heilemann Mike, Shechtman Yoav
Department of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, Israel.
Russell Berrie Nanotechnology Institute, Technion-Israel Institute of Technology, Haifa, Israel.
Nat Methods. 2023 Dec;20(12):1939-1948. doi: 10.1038/s41592-023-01966-0. Epub 2023 Jul 27.
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.
单分子定位显微镜（SMLM）彻底改变了生物成像，将传统显微镜的空间分辨率提高了一个数量级。然而，SMLM技术需要较长的采集时间，通常为几分钟，才能生成单个超分辨图像，因为它们依赖于在数千个记录帧上积累许多定位点。因此，SMLM在高时间分辨率下观察动态过程的能力一直受到限制。在这项工作中，我们提出了DBlink，一种基于深度学习的方法，用于从SMLM数据中进行超时空分辨率重建。DBlink的输入是SMLM数据的记录视频，输出是超时空分辨率视频重建。我们使用卷积神经网络与双向长短期记忆网络架构相结合，该架构旨在捕捉不同输入帧之间的长期依赖性。我们在模拟细丝和线粒体样结构上、在受控运动条件下的实验SMLM数据上以及在活细胞动态SMLM上展示了DBlink的性能。DBlink的时空插值在活细胞动态过程的超分辨率成像方面构成了一项重要进展。
