Sun Qiushi, Yang Xiaochun, Guo Jingtao, Zhao Yang, Liu Yi
Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China.
State Key Laboratory of Natural and Biomimetic Drugs, MOE Key Laboratory of Cell Proliferation and Differentiation, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, China.
Front Genet. 2022 Jul 4;13:913372. doi: 10.3389/fgene.2022.913372. eCollection 2022.
Long-term live-cell imaging technology has emerged in the study of cell culture and development, and it is expected to elucidate the differentiation or reprogramming morphology of cells and the dynamic process of interaction between cells. There are some advantages to this technique: it is noninvasive, high-throughput, low-cost, and it can help researchers explore phenomena that are otherwise difficult to observe. Many challenges arise in the real-time process, for example, low-quality micrographs are often obtained due to unavoidable human factors or technical factors in the long-term experimental period. Moreover, some core dynamics in the developmental process are rare and fleeting in imaging observation and difficult to recapture again. Therefore, this study proposes a deep learning method for microscope cell image enhancement to reconstruct sharp images. We combine generative adversarial nets and various loss functions to make blurry images sharp again, which is much more convenient for researchers to carry out further analysis. This technology can not only make up the blurry images of critical moments of the development process through image enhancement but also allows long-term live-cell imaging to find a balance between imaging speed and image quality. Furthermore, the scalability of this technology makes the methods perform well in fluorescence image enhancement. Finally, the method is tested in long-term live-cell imaging of human-induced pluripotent stem cell-derived cardiomyocyte differentiation experiments, and it can greatly improve the image space resolution ratio.
长期活细胞成像技术已出现在细胞培养与发育研究中，有望阐明细胞的分化或重编程形态以及细胞间相互作用的动态过程。该技术具有一些优势：它是非侵入性的、高通量的、低成本的，并且能帮助研究人员探索那些难以通过其他方式观察到的现象。在实时过程中会出现许多挑战，例如，由于长期实验期间不可避免的人为因素或技术因素，常常会获得低质量的显微图像。此外，发育过程中的一些核心动态在成像观察中很少见且转瞬即逝，难以再次捕捉。因此，本研究提出一种用于显微镜细胞图像增强的深度学习方法以重建清晰图像。我们将生成对抗网络与各种损失函数相结合，使模糊图像再次清晰，这对研究人员进行进一步分析更加方便。该技术不仅可以通过图像增强弥补发育过程关键时刻的模糊图像，还能让长期活细胞成像在成像速度和图像质量之间找到平衡。此外，该技术的可扩展性使这些方法在荧光图像增强方面表现良好。最后，该方法在人诱导多能干细胞衍生心肌细胞分化实验的长期活细胞成像中进行了测试，并且它可以大大提高图像空间分辨率。
