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Orthogonal DNA Self-Assembly-Based Expansion Microscopy Platform for Amplified, Multiplexed Biomarker Imaging
Xiaochen Tang1,2, Wenxing Li3, Tianshu Chen1,2
1Department of Clinical Laboratory Medicine, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China.
A new orthogonal DNA self-assembly-based expansion microscopy (o-DAExM) method enhances signal amplification and signal-to-noise ratio for nanoscale imaging. This technique offers improved resolution for cellular structures and exosomes.
Area of Science:
- Biotechnology
- Microscopy
- Molecular Biology
Background:
- Expansion microscopy (ExM) enables nanoscale imaging but suffers from signal loss and low signal-to-noise ratio (SNR).
- Existing ExM methods have limitations in signal amplification and multiplexing capabilities.
Purpose of the Study:
- To develop a novel orthogonal DNA self-assembly-based ExM (o-DAExM) platform.
- To overcome the limitations of conventional ExM, including fluorescence loss and low SNR.
- To enhance signal amplification and multiplexing for super-resolution imaging.
Main Methods:
- Developed an o-DAExM platform utilizing hybridization chain reaction for signal amplification.
- Compared o-DAExM with immunofluorescence-based ExM for cellular cytoskeleton imaging.
- Applied o-DAExM for exosome analysis and multiplexed protein target detection in single cells.
Main Results:
- o-DAExM demonstrated significant signal amplification and enhanced SNR compared to conventional methods.
- Achieved high-resolution imaging of cellular cytoskeleton with outstanding performance and reliability.
- Successfully revealed heterogeneous exosome information and enabled multiplexed protein analysis.
Conclusions:
- o-DAExM provides a straightforward and effective method for nanoscale imaging with improved signal and resolution.
- The platform offers expandable multiplexing capabilities for diverse biological samples.
- o-DAExM presents significant potential for studying nanoscale structures and biological networks.
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