Divide and conquer: real-time maximum likelihood fitting of multiple emitters for super-resolution localization...

Area of Science:

• Biophysics
• Microscopy
• Computational Biology

Background:

• Super-resolution localization microscopy achieves high resolution by localizing individual emitters.
• Current multi-emitter localization algorithms are often too slow for real-time processing of large datasets.
• This limits the application of advanced microscopy techniques in high-throughput biological studies.

Purpose of the Study:

• To develop a fast and efficient multi-emitter localization algorithm for super-resolution microscopy.
• To enable real-time image analysis for large fields of view in localization microscopy.
• To enhance the practical application of super-resolution microscopy in high-content and high-throughput imaging.

Main Methods:

• Implemented a fitting-based method, QC-STORM, inspired by the 'divide and conquer' computer science strategy.
• Utilized simulated and experimental data to validate the algorithm's performance.
• Compared QC-STORM's speed and spatial resolution against established methods like ThunderSTORM and WindSTORM.

Main Results:

• QC-STORM achieves real-time processing of large fields of view (100 µm × 100 µm) with short exposure times (10 ms).
• The method demonstrates comparable spatial resolution to existing state-of-the-art algorithms (ThunderSTORM, WindSTORM).
• QC-STORM significantly accelerates image analysis in super-resolution localization microscopy.

Conclusions:

• QC-STORM offers a breakthrough in speeding up multi-emitter localization for super-resolution microscopy.
• This advancement facilitates high-throughput and high-content imaging applications, such as analyzing cell populations or identifying rare biological events.
• The developed algorithm broadens the practical utility of super-resolution microscopy in biological research.