Automated Cyclic Super-Resolution Microscopy for Nanoscale Protein Mapping

Area of Science:

• Cellular and Molecular Biology
• Biophysics
• Microscopy and Imaging Technology

Background:

• Nanoscale mapping of molecular targets is crucial for understanding cellular architecture.
• Existing methods for super-resolution microscopy suffer from low throughput, manual labor, and signal variability.

Purpose of the Study:

• To introduce CycSTORM, an automated platform for cyclic direct stochastic optical reconstruction microscopy (dSTORM).
• To enable multiplexed nanoscale protein mapping in single cells with high precision and throughput.

Main Methods:

• Automated fluidic exchange and an oxygen-excluded environment to stabilize fluorophore blinking.
• Active 3D drift correction for sub-5 nm registration over extended imaging periods.
• Rapid inactivation of residual fluorescence using meta-chloroperoxybenzoic acid (mCPBA) to minimize crosstalk.

Main Results:

• CycSTORM achieves stable, day-long imaging with sub-5 nm registration accuracy.
• Over 99.9% of residual fluorescence is eliminated within 10 minutes, preserving sample integrity.
• Standardized Alexa Fluor 647 labeling ensures consistent localization precision across imaging cycles.

Conclusions:

• CycSTORM transforms cyclic super-resolution imaging into a scalable and robust method.
• Enables quantitative nanoscale mapping of molecular organization within single cells.
• Facilitates simultaneous mapping of multiple protein targets with nanometer precision.