Correcting Artifacts in Single Molecule Localization Microscopy Analysis Arising from Pixel Quantum Efficiency...

Area of Science:

• Microscopy and Imaging Technologies
• Biophysics and Computational Biology

Background:

• Accurate analysis of single molecule localization microscopy (SMLM) data is essential for biological research.
• Scientific Complementary Metal-Oxide-Semiconductor (sCMOS) cameras are widely used for SMLM data acquisition.
• Existing SMLM analysis algorithms account for pixel-dependent gain, offset, and readout noise.

Purpose of the Study:

• To investigate the impact of pixel-to-pixel relative quantum efficiency (RQE) variations in sCMOS cameras on SMLM data analysis.
• To develop and present modifications to SMLM analysis algorithms to correct for RQE differences.

Main Methods:

• Characterization of RQE variations across pixels in tested sCMOS sensors.
• Simulation of SMLM data to assess the effect of RQE differences on analysis results.
• Modification of the Poisson maximum likelihood estimation (MLE) algorithm to incorporate RQE correction.

Main Results:

• Identified RQE differences of up to 4% in sCMOS sensors.
• Demonstrated that these RQE variations significantly affect SMLM analysis outcomes in both simulations and biological data.
• Validated the effectiveness of the modified MLE algorithm in correcting for RQE-induced artifacts.

Conclusions:

• Pixel-to-pixel RQE variations in sCMOS cameras are a critical factor impacting SMLM data analysis accuracy.
• The proposed modifications to the Poisson MLE algorithm effectively compensate for RQE differences.
• Implementing RQE correction is necessary for reliable and precise SMLM data interpretation.