Simulating Multicolor Super-Resolution Imaging Using an RGB Camera

High-order multiplexing in super-resolution microscopy is limited by trade-offs between spectral discrimination, imaging speed, and experimental complexity. Here, we show that red-green-blue (RGB) complementary metal oxide semiconductor (CMOS) cameras provide a simple and scalable solution for multicolor DNA Point Accumulation in Nanoscale Tomography by exploiting their intrinsic spectral sensitivity for statistical fluorophore discrimination. Using a realistic simulation framework incorporating experimentally derived photon budgets, optical response functions, and camera noise, we achieve simultaneous classification of up to 6 fluorophores with a mean precision of ~99%, including perfect discrimination of spectrally overlapping dye pairs, while maintaining an average localization precision of ~3.6 nm and an average localization accuracy of ~32 nm. Performance remains robust to variations in classification thresholds but degrades with reduced photon budgets, and more modestly with increasing fluorophore number, due to spectral overlap and photon noise. The results presented here provide preliminary evidence that RGB detection could support multiplexed super-resolution imaging using a relatively simple and cost-effective experimental configuration. Further studies will be needed to assess its broader applicability and performance relative to conventional spectral imaging approaches.