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Super-resolution Imaging of the Cytokinetic Z Ring in Live Bacteria Using Fast 3D-Structured Illumination Microscopy f3D-SIM
Published on: September 29, 2014
Three-dimensional structured illumination microscopy with enhanced axial resolution
Xuesong Li1,2, Yicong Wu3,4, Yijun Su5,6,7,8,9
1Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA. lix3@janelia.hhmi.org.
Two new methods enhance three-dimensional structured illumination microscopy (3D SIM) resolution. A mirror setup and deep learning achieve near-isotropic imaging, improving nanoscale visualization of cellular structures and dynamics.
Area of Science:
- Biophysics
- Optical Microscopy
- Cell Biology
Background:
- Three-dimensional structured illumination microscopy (3D SIM) offers enhanced resolution over conventional microscopy.
- However, its axial resolution is typically limited to approximately 300 nm, restricting nanoscale investigations in the z-axis.
Purpose of the Study:
- To develop and present novel methods for improving axial resolution in 3D SIM.
- To achieve near-isotropic imaging with enhanced resolution for detailed cellular analysis.
Main Methods:
- Implementation of a four-beam interference technique using a mirror opposite the sample.
- Development of a deep learning-based approach for image reconstruction and resolution enhancement.
- Combination of deep learning with denoising for time-lapse volumetric imaging.
Main Results:
- Achieved near-isotropic imaging with approximately 120-nm lateral and 160-nm axial resolution using the mirror method.
- Attained approximately 120-nm isotropic resolution with the deep learning method.
- Demonstrated successful application in imaging vimentin, microtubules, caveolae, lysosomes, and T cell immune synapses.
Conclusions:
- The presented methods significantly improve axial resolution in 3D SIM with minimal optical system modifications.
- These advancements enable high-resolution, near-isotropic imaging of cellular structures and dynamics.
- The deep learning approach facilitates efficient volumetric and time-lapse imaging for advanced cell biology research.
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