Breakthrough in rapid super-resolution imaging of multiple organelles in live cells

Peking University, April  2, 2025: A team from Peking University’s College of Future Technology, led by Professor Xi Peng, in collaboration with Professor Jin Dayong at the Eastern Institute of Technology, has achieved a major breakthrough in imaging 15 cellular structures simultaneously by using lipid membrane probes to label all membrane-bound organelles, combined with dual-color spinning-disk confocal microscopy and deep learning. Their study, titled "Fast Segmentation and Multiplexing Imaging of Organelles in Live Cells", published in Nature Communications (DOI:10.1038/s41467-025-57877-5), utilizes Nile Red dye, spinning-disk confocal microscopy, and deep learning to overcome limitations in traditional fluorescent imaging.

Why it matters:
Cells contain numerous organelles that interact dynamically, but real-time imaging of multiple organelles has been a challenge due to:

• Limited fluorescence labeling: Traditional methods can only label 3-4 organelles due to spectral crosstalk.

• Low efficiency: Increasing the number of labels often fails due to labeling efficiency.

• Short observation windows: Multicolor excitation and multichannel detection cause phototoxicity, limiting long-term imaging.

These limitations have hindered the ability to simultaneously observe organelle interactions, crucial for understanding cellular functions.

Research Methodology
To overcome these challenges, the researchers developed a non-specific labeling, high-throughput imaging strategy:

1. Universal Lipid Staining: Instead of using multiple specific fluorescent labels, they employed Nile Red, a lipid dye that stains all membrane-bound organelles. The dye changes color based on the organelle’s membrane environment, allowing differentiation.

2. Dual-Color Super-Resolution Microscopy: Using spinning-disk confocal microscopy, researchers captured high-speed, low-phototoxicity images of 15 organelles simultaneously (Figure 1).

3. Deep Learning Segmentation: A deep convolutional neural network (DCNN) was trained to recognize and segment organelles automatically based on their optical fingerprints (Figure 2). 

This method is:
• Faster and more accurate than traditional segmentation.
• Highly reproducible, allowing large-scale imaging of cellular structures.

This breakthrough technique enables real-time, long-term organelle tracking, significantly improving imaging efficiency while reducing phototoxicity.

Key Findings
• The technique captured 15 organelles simultaneously such as mitochondria, endoplasmic reticulum, Golgi apparatus, and nucleus (Figure 1).

• Deep learning algorithms were used to automatically segment and predict the location and shape of these organelles, allowing rapid and accurate imaging.

• The method allows long-term observation of organelle dynamics with reduced phototoxicity, making it ideal for studying organellar interactions.

Implications
• This method offers a powerful tool for real-time imaging of organelle interactions in live cells, overcoming the limitations of traditional methods. 

• It is also scalable and has been successfully applied to various cell lines and tissues, including fruit fly testis tissue, demonstrating its broad applicability.

*This article is featured in PKU News' "Why It Matters" series. More from this series.

Click “here” to read the paper.

Written by: Akaash Babar
Edited by: Zhang Jiang
Source: Department of Biomedical Engineering, College of Future Technology, Peking University