High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation

Area of Science:

• Biophysics
• Cell Biology
• Microscopy

Background:

• Live-cell super-resolution microscopy is crucial for observing biological dynamics below the diffraction limit.
• Existing super-resolution radial fluctuations (SRRF) methods have limitations in image fidelity and resolution.
• Need for accessible, artifact-minimized super-resolution techniques with optimized parameters.

Purpose of the Study:

• To present enhanced super-resolution radial fluctuations (eSRRF) for improved live-cell super-resolution imaging.
• To develop automated parameter optimization for maximizing resolution and fidelity.
• To extend eSRRF to three-dimensional (3D) live-cell imaging.

Main Methods:

• Development of the enhanced super-resolution radial fluctuations (eSRRF) algorithm.
• Implementation of automated, data-driven parameter optimization within eSRRF.
• Integration of eSRRF with multifocus microscopy for 3D super-resolution imaging.

Main Results:

• eSRRF significantly enhances image fidelity and resolution compared to the original SRRF method.
• Automated parameter optimization provides insights into resolution-fidelity trade-offs.
• Demonstrated eSRRF across diverse imaging modalities and biological systems, including 3D live-cell imaging at ~1 volume/second.

Conclusions:

• eSRRF offers an accessible super-resolution approach, maximizing information extraction while minimizing artifacts.
• The method's optimal parameter prediction is generalizable for unbiased super-resolution microscopy analysis.
• eSRRF advances live-cell volumetric super-resolution imaging capabilities.