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

Hazen P Babcock¹, Fang Huang², Colenso M Speer³

¹Center for Advanced Imaging, Harvard University, Cambridge, MA, 02138, USA. hbabcock@fas.harvard.edu.

Scientific Reports

|December 4, 2019

Summary

Compensating for relative quantum efficiency (RQE) variations in scientific Complementary Metal-Oxide-Semiconductor (sCMOS) cameras is crucial for accurate single molecule localization microscopy (SMLM) data analysis. This study introduces necessary algorithm modifications for improved SMLM data processing.

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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.