2. Academic Validation
  3. Fast segmentation and multiplexing imaging of organelles in live cells

Fast segmentation and multiplexing imaging of organelles in live cells

  • Nat Commun. 2025 Mar 21;16(1):2769. doi: 10.1038/s41467-025-57877-5.
Karl Zhanghao # 1 2,  Meiqi Li # 3 4,  Xingye Chen 5,  Wenhui Liu 5,  Tianling Li 6,  Yiming Wang 7,  Fei Su 8,  Zihan Wu 9,  Chunyan Shan 7,  Jiamin Wu 5,  Yan Zhang 7,  Jingyan Fu 6,  Peng Xi 10 11 12,  Dayong Jin 13 14 15
Affiliations

Affiliations

  • 1 Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China.
  • 2 Zhejiang Provincial Engineering Research Center for Organelles Diagnostics and Therapy, Eastern Institute of Technology, Ningbo, Zhejiang, China.
  • 3 Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China. limeiqi@pku.edu.cn.
  • 4 School of Life Sciences, Peking University, Beijing, China. limeiqi@pku.edu.cn.
  • 5 Department of Automation, Tsinghua University, Beijing, China.
  • 6 State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing, China.
  • 7 School of Life Sciences, Peking University, Beijing, China.
  • 8 Institute for Biomedical Materials & Devices (IBMD), University of Technology Sydney, Sydney, NSW, Australia.
  • 9 UTS-SUStech Joint Research Centre for Biomedical Materials & Devices, College of Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China.
  • 10 Department of Biomedical Engineering, College of Future Technology, Peking University, Beijing, China. xipeng@pku.edu.cn.
  • 11 UTS-SUStech Joint Research Centre for Biomedical Materials & Devices, College of Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China. xipeng@pku.edu.cn.
  • 12 National Biomedical Imaging Center, Peking University, Beijing, China. xipeng@pku.edu.cn.
  • 13 Zhejiang Provincial Engineering Research Center for Organelles Diagnostics and Therapy, Eastern Institute of Technology, Ningbo, Zhejiang, China. dayong.jin@uts.edu.au.
  • 14 Institute for Biomedical Materials & Devices (IBMD), University of Technology Sydney, Sydney, NSW, Australia. dayong.jin@uts.edu.au.
  • 15 UTS-SUStech Joint Research Centre for Biomedical Materials & Devices, College of Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China. dayong.jin@uts.edu.au.
  • # Contributed equally.
PMID: 40118840 DOI: 10.1038/s41467-025-57877-5
Abstract

Studying organelles' interactome at system level requires simultaneous observation of subcellular compartments and tracking their dynamics. Conventional multicolor approaches rely on specific fluorescence labeling, where the number of resolvable colors is far less than the types of organelles. Here, we use a lipid-specific dye to stain all the membrane-associated organelles and spinning-disk microscopes with an extended resolution of ~143 nm for high spatiotemporal acquisition. Due to the chromatic polarity sensitivity, high-resolution ratiometric images well reflect the heterogeneity of organelles. With deep convolutional neuronal networks, we successfully segmented up to 15 subcellular structures using one laser excitation. We further show that transfer learning can predict both 3D and 2D datasets from different microscopes, different cell types, and even complex systems of living tissues. We succeeded in resolving the 3D anatomic structure of live cells at different mitotic phases and tracking the fast dynamic interactions among six intracellular compartments with high robustness.

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