Deep learning permits imaging of multiple structures with the same fluorophores.

Journal: Biophysical journal
Published Date: Sep 3, 2024

Abstract

Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.

Authors

  • Luhong Jin
    Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China; Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 310058, China.
  • Jingfang Liu
    Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
  • Heng Zhang
    Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yunqi Zhu
  • Haixu Yang
    Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027, China. yingkexu@zju.edu.cn.
  • Jianhang Wang
    Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
  • Luhao Zhang
    Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China; Binjiang Institute of Zhejiang University, Hangzhou, China.
  • Cuifang Kuang
    State Key Laboratory of Extreme Photonics and Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, 310027, China; State Key Laboratory of Extreme Photonics and Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 315100, China; Collaborative Innovation Center of Extreme Optics, Shanxi University, Taiyuan, 030006, China. Electronic address: cfkuang@zju.edu.cn.
  • Baohua Ji
    Shandong Provincial Key Laboratory of Biophysics, Institute of Biophysics, Dezhou University, Dezhou 253023, China.
  • Ju Zhang
    Department of Mathematics and Numerical Simulation and High-Performance Computing Laboratory, School of Sciences, Nanchang University, Nanchang 330031, China.
  • Xu Liu
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore. liuxu16@bjut.edu.cn.
  • Yingke Xu
    Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou 310027, China. Electronic address: yingkexu@zju.edu.cn.

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