Bayesian nonparametrics allow for super resolution microscopy without photo-switching

Super resolution microscopy permits direct observation of biomolecules. However, due to noise, distinguish single molecules demands specialized analysis models that facilitate the interpretation of the acquired images. In such models, the positions of the molecules are represented by random variables and conventionally the total number of random variables in a model is kept fixed and finite. However, prevailing uncertainty in the number of individual molecules contributing photons to the images, makes existing approaches inappropriate as they rely on pre-specification of the number of unknown parameters. Switching of the fluorophores between bright and dark states, induced by experimental means (STORM, PALM, PAINT), ensures that each time at most one fluorophore is visible leading to a convenient solution. Nevertheless, photo-switching requires specialized fluorophores and long experiments necessary to collect photons from multiple switching events. In the talk, I will walk through recent modeling advances and highlight how Bayesian nonparametrics can be used to achieve super resolved localization of single molecules without photo-switching and so allowing for super resolution microscopy with less specialized fluorophores and significantly shorter experiments.