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Peer-reviewedCC BYScience Advances

Deep learning–enhanced single-shot triorganelle STED-FLIM imaging of lipid dynamics in living cells

  • Lu Gao1
  • Beibei Gao1
  • Wei Ge1
  • Tianze Sun1
  • Wenshuang Liang1
  • Li Jiang1,2
  • Linyong Zhu1
  • Fu Wang1
  1. 1Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
  2. 2Optogenetics & Synthetic Biology Interdisciplinary Research Center, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Mei Long Road, Shanghai 200237, China.
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Abstract

Lipid homeostasis is orchestrated by rapid exchange and remodeling across the endoplasmic reticulum (ER), lipid droplets (LDs), and mitochondria. However, live-cell visualization of this triorganelle network remains limited by subdiffraction structures, multiplexed labeling burden, and the ambiguity of intensity-only readouts. Here, we introduce a single-shot stimulated emission depletion-fluorescence lifetime imaging (STED-FLIM) workflow that combines Nile Red analogs with deep learning-based demultiplexing to generate compartment-resolved maps of lipid-organelle organization and dynamics. By combining the STED-resolved nanoscale ultrastructure with lifetime-encoded microenvironmental contrast, our approach separates ER, LDs, and mitochondria from a single acquisition and enables automated tricompartment quantification using a lightweight VGG16-UNet segmentation model. This platform captures coordinated remodeling across the ER-LD-mitochondria axis during lipid stress, including ferroptosis- and apoptosis-associated transitions, while simultaneously reporting nanoscale organization and microenvironmental shifts. Together, this strategy provides a practical route to high-spatiotemporal-resolution, lifetime-encoded multiorganelle lipid imaging in living cells.

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Cite

BibTeXRIS
BibTeX
@article{gao2026deep,
  title = {{Deep learning–enhanced single-shot triorganelle STED-FLIM imaging of lipid dynamics in living cells}},
  author = {Lu Gao and Beibei Gao and Wei Ge and Tianze Sun and Wenshuang Liang and Li Jiang and Linyong Zhu and Fu Wang},
  journal = {Science Advances},
  year = {2026},
  month = oct,
  doi = {10.1126/sciadv.aeh3416},
  url = {https://www.science.org/doi/10.1126/sciadv.aeh3416},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Deep learning–enhanced single-shot triorganelle STED-FLIM imaging of lipid dynamics in living cells
AU  - Lu Gao
AU  - Beibei Gao
AU  - Wei Ge
AU  - Tianze Sun
AU  - Wenshuang Liang
AU  - Li Jiang
AU  - Linyong Zhu
AU  - Fu Wang
PY  - 2026
DA  - 2026/10/02
JO  - Science Advances
DO  - 10.1126/sciadv.aeh3416
UR  - https://www.science.org/doi/10.1126/sciadv.aeh3416
AB  - Lipid homeostasis is orchestrated by rapid exchange and remodeling across the endoplasmic reticulum (ER), lipid droplets (LDs), and mitochondria. However, live-cell visualization of this triorganelle network remains limited by subdiffraction structures, multiplexed labeling burden, and the ambiguity of intensity-only readouts. Here, we introduce a single-shot stimulated emission depletion-fluorescence lifetime imaging (STED-FLIM) workflow that combines Nile Red analogs with deep learning-based demultiplexing to generate compartment-resolved maps of lipid-organelle organization and dynamics. By combining the STED-resolved nanoscale ultrastructure with lifetime-encoded microenvironmental contrast, our approach separates ER, LDs, and mitochondria from a single acquisition and enables automated tricompartment quantification using a lightweight VGG16-UNet segmentation model. This platform captures coordinated remodeling across the ER-LD-mitochondria axis during lipid stress, including ferroptosis- and apoptosis-associated transitions, while simultaneously reporting nanoscale organization and microenvironmental shifts. Together, this strategy provides a practical route to high-spatiotemporal-resolution, lifetime-encoded multiorganelle lipid imaging in living cells.
SN  - 2375-2548
ER  -