Simulating Multicolor Super-Resolution Imaging Using an RGB Camera.

Area of Science:

• Optical Microscopy
• Nanotechnology
• Biophysics

Background:

• High-order multiplexing in super-resolution microscopy faces limitations due to spectral discrimination, imaging speed, and experimental complexity.
• Existing methods often require complex setups, hindering scalability and accessibility.

Purpose of the Study:

• To investigate the potential of red-green-blue (RGB) complementary metal oxide semiconductor (CMOS) cameras for multicolor super-resolution microscopy.
• To demonstrate a simple and scalable solution for fluorophore discrimination in DNA Point Accumulation in Nanoscale Tomography (DNA-PAINT).

Main Methods:

• Utilized a realistic simulation framework incorporating photon budgets, optical response functions, and camera noise.
• Exploited the intrinsic spectral sensitivity of RGB CMOS cameras for statistical fluorophore discrimination.
• Evaluated performance based on classification precision, localization precision, and localization accuracy.

Main Results:

• Achieved simultaneous classification of up to 6 fluorophores with a mean precision of approximately 99%.
• Demonstrated perfect discrimination of spectrally overlapping dye pairs.
• Maintained an average localization precision of ~3.6 nm and an average localization accuracy of ~32 nm.

Conclusions:

• RGB CMOS cameras offer a promising, cost-effective approach for multiplexed super-resolution imaging.
• This method simplifies experimental configurations while achieving high performance in fluorophore discrimination and localization.
• Further research is needed to validate broader applicability and compare with conventional spectral imaging techniques.