Optimized molecule detection in localization microscopy with selected false positive probability

Area of Science:

• Biophysics
• Microscopy
• Computational Biology

Background:

• Single-molecule localization microscopy (SMLM) enables super-resolution imaging.
• Accurate molecule detection is critical for SMLM data analysis.
• Current detection methods suffer from uncontrolled false positives and lack standardization, leading to artifacts and hindering reproducibility.

Purpose of the Study:

• To develop an optimized molecule detection method for SMLM.
• To provide quantitative control over false positive detections.
• To enhance the robustness and reproducibility of SMLM data analysis.

Main Methods:

• Combined probabilistic thresholding with theoretically optimal filtering.
• Utilized a theoretically optimal Poisson matched filter as a benchmark.
• Evaluated existing filtering methods against the optimal filter.

Main Results:

• The optimized method enables quantitative control over false positive detections.
• Optimal filtering minimizes false negative detections.
• The approach provides robust, single-parameter, and user-unbiased molecule detection.

Conclusions:

• The developed method minimizes artifacts in SMLM analysis.
• This approach significantly improves the reproducibility of SMLM data.
• This advancement is crucial for reliable super-resolution microscopy.