Science Advances· 2026Q1
Deep learning–enhanced single-shot triorganelle STED-FLIM imaging of lipid dynamics in living cells
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- Q1SCImago
- 2026year
Short summary
A new single-shot STED-FLIM imaging technique combined with deep learning demultiplexing allows for simultaneous visualization and quantification of lipid dynamics across the ER, lipid droplets, and mitochondria in living cells.
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Key points
- Introduces a single-shot STED-FLIM workflow for live-cell imaging of lipid dynamics across ER, lipid droplets, and mitochondria.
- Combines STED-resolved nanoscale ultrastructure with lifetime-encoded microenvironmental contrast.
- Utilizes deep learning (VGG16-UNet) for automated segmentation and tricompartment quantification.
- Captures coordinated remodeling across the ER-LD-mitochondria axis during lipid stress, including ferroptosis and apoptosis transitions.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Science Advances, 2026 · DOI ↗
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Field: Biochemistry (Biochemistry, Genetics and Molecular Biology)
BiochemistryBiochemistry, Genetics and Molecular Biology