Abstract
Single-Molecule Localization Microscopy remains limited in its ability for robust and simple multi-color imaging. Whereas the fluorophore Alexa647 is widely used due to its brightness and excellent blinking dynamics, other excellent blinking fluorophores, such as CF660 and CF680, spectrally overlap. Here we present Probability-based Fluorophore Classification, a method to perform multi-color SMLM with Alexa647, CF660 and CF680 that uses statistical decision theory for optimal classification. The emission is split in a short and long wavelength channel to enable classification and localization without a major loss in localization precision. Each emitter is classified using a Generalized Maximum Likelihood Ratio Test using the photon statistics of both channels. This easy-to-adopt approach does not require nanometer channel registration, is able to classify fluorophores with tunable low false positive rates (<0.5%) and optimal true positive rates and outperforms traditional ratiometric spectral de-mixing and Salvaged Fluorescence. We demonstrate its applicability on a variety of samples and targets.
Competing Interest Statement
The authors have declared no competing interest.
