Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking

Area of Science:

• Cell Biology
• Biophysics
• Microscopy

Background:

• Single-molecule detection in fluorescence nanoscopy is crucial for cell biology.
• Current image analysis methods face challenges like low signal, background noise, and false positives, limiting detection efficiency.

Purpose of the Study:

• To develop a robust, threshold-free framework for single-molecule detection in fluorescence nanoscopy.
• To replace empirical parameter tuning with a probability-based hypothesis test for improved accuracy and error estimation.

Main Methods:

• A novel probability-based hypothesis testing framework was developed.
• The method was validated using simulations and experimental data, including tracking low-signal mRNAs in yeast cells.

Main Results:

• The new method achieves a detection efficiency of over 70% with a false-positive rate below 5%.
• It demonstrates superior performance in photon-limited conditions (down to 17 photons/pixel background, 180 photons/molecule signal).
• The probability value enables advanced downstream data analysis and metric definition.

Conclusions:

• This probability-based approach offers a significant advancement over existing methods for single-molecule detection in fluorescence nanoscopy.
• It enhances the detection of weak signals and provides a defined error estimate, crucial for photon-limited applications.
• The framework is broadly applicable to various challenges in live-cell imaging and nanoscopy.