Fast and robust drift correction for single-molecule localization microscopy

Area of Science:

• Biophysics
• Optical Microscopy
• Nanotechnology

Background:

• Single-molecule localization microscopy (SMLM) requires precise correction of sample drifts during imaging.
• Existing drift-correction methods for SMLM are often slow and unreliable, hindering resolution and throughput.

Purpose of the Study:

• To introduce NP-Cloud, a novel, rapid, and robust method for correcting sample drifts in SMLM.
• To demonstrate the effectiveness of NP-Cloud in improving SMLM data fidelity and speed.

Main Methods:

• Developed NP-Cloud by pairing nearest molecules within SMLM data segments.
• Calculated molecular displacements within a small search radius to reduce computational load.
• Utilized continuously valued positions of super-localized molecules for accurate drift estimation.

Main Results:

• NP-Cloud demonstrated superior robustness and fidelity in 3D drift correction using simulated and experimental SMLM data.
• Achieved speed increases over 100-fold compared to single-referenced methods and over 10^4-fold over redundant cross-referenced approaches.
• Provided excellent drift correction for diverse samples in seconds.

Conclusions:

• NP-Cloud offers a significant advancement in SMLM drift correction.
• The method provides a fast, robust, and practical solution, enhancing SMLM's resolution and throughput.
• Facilitates high-performance SMLM imaging across various biological samples.