美国威斯康辛大学麦迪逊分校Jan Huisken团队取得一项新突破。他们的研究以近红外成像技术为媒介，对损坏的延时显微镜数据进行图像修复。相关论文于2024年1月4日发表在《自然-方法学》杂志上。

研究人员利用卷积神经网络来增强在固定样本上拍摄的深层组织图像的活体成像数据。研究证明了卷积神经网络可用于恢复基于GFP延时成像中的深层组织对比度，是使用近红外染料获取的成对最终状态数据集，这种方法被称为红外介导的图像复原（IR2）。

此外，该网络在很长的发育时间范围内都非常稳定。研究利用IR2增强了斑马鱼和果蝇胚胎/幼虫发育的绿色荧光蛋白延时图像信息含量，并证明了其在提高发育过程中梭形体细胞追踪/线粒体保真度方面的定量潜力。因此，IR2有望将活体成像扩展到原本无法触及的深度。

研究人员表示，延时荧光显微镜是揭示生物发育和功能的关键工具；然而，由于活体系统的性质只能对其进行有限的检测，其中包含的未知信息只能通过更具侵入性的方法来捕捉。由于活细胞成像探针/荧光蛋白的光谱范围有限，只能对散射组织提供适度的光学穿透，因此对深层组织的活体成像具有一定挑战。

附：英文原文

Title: Image restoration of degraded time-lapse microscopy data mediated by near-infrared imaging

Author: Gritti, Nicola, Power, Rory M., Graves, Alyssa, Huisken, Jan

Issue&Volume: 2024-01-04

Abstract: Time-lapse fluorescence microscopy is key to unraveling biological development and function; however, living systems, by their nature, permit only limited interrogation and contain untapped information that can only be captured by more invasive methods. Deep-tissue live imaging presents a particular challenge owing to the spectral range of live-cell imaging probes/fluorescent proteins, which offer only modest optical penetration into scattering tissues. Herein, we employ convolutional neural networks to augment live-imaging data with deep-tissue images taken on fixed samples. We demonstrate that convolutional neural networks may be used to restore deep-tissue contrast in GFP-based time-lapse imaging using paired final-state datasets acquired using near-infrared dyes, an approach termed InfraRed-mediated Image Restoration (IR2). Notably, the networks are remarkably robust over a wide range of developmental times. We employ IR2 to enhance the information content of green fluorescent protein time-lapse images of zebrafish and Drosophila embryo/larval development and demonstrate its quantitative potential in increasing the fidelity of cell tracking/lineaging in developing pescoids. Thus, IR2 is poised to extend live imaging to depths otherwise inaccessible.

DOI: 10.1038/s41592-023-02127-z

Source: https://www.nature.com/articles/s41592-023-02127-z