Trade-offs between structural integrity and acquisition time in stochastic super-resolution microscopy techniques.

Area of Science:

• Biophysics
• Optical Microscopy
• Computational Biology

Background:

• Widefield stochastic microscopy techniques like Photoactivated Localization Microscopy (PALM) and Stochastic Optical Reconstruction Microscopy (STORM) offer super-resolution but are limited by long acquisition times.
• Image formation relies on accumulating many frames with sparse, super-resolved localizations, leading to a trade-off between spatial and temporal resolution.

Purpose of the Study:

• To derive analytical predictions for the minimum time required to achieve a reliable super-resolution image at a specified spatial resolution.
• To investigate the relationship between localization distribution, spatial resolution, and temporal acquisition limits in stochastic microscopy.

Main Methods:

• Development of a theoretical framework to predict image completion time based on localization density and image dimensions.
• Analysis of the scaling of image completion time with image size and spatial resolution, including corrections for background noise.
• Validation of theoretical predictions using experimental STORM data from labeled microtubule filaments.

Main Results:

• The time to complete a reliable super-resolution image scales logarithmically with the ratio of image size to spatial resolution volume.
• Second-order corrections to completion time arise from spurious localizations within background noise.
• Experimental validation confirmed the theoretical predictions for STORM imaging.

Conclusions:

• The developed theoretical framework provides a method to compare emitter efficiencies and optimize labeling strategies for super-resolution microscopy.
• Enables the implementation of real-time monitoring algorithms for adaptive data acquisition, defining optimal stopping criteria to reduce overall imaging time.