Mapping the dipole orientation distribution within a super-resolution scale via fluorescence polarization modulation.

Conventional fluorescence polarization microscopy has been largely used to monitor the orientation and the structural information of biomolecules labeled with fluorescence dipoles but suffers from the optical diffraction limit. Here, we put forward a novel algorithm to simultaneously acquire the super-resolution image and the effective orientation distribution information of dipole clusters at corresponding super-resolution. In this paper, the orientation distribution of dipole clusters is statistically modeled by its mean orientation and orientation deviation, which are, respectively, represented by the middle direction and the opening angle of a sector shape. According to this model and microscopy imaging theory, the joint reconstruction algorithm is deduced mathematically in detail based on the conjugate gradient least-squares method. By applying this algorithm to different samples, the reconstructed results prove more than twice the resolution of wide-field images and the orientation distribution information at corresponding spatial resolution. Furthermore, the high accuracy of this algorithm in reconstructing super-resolution orientation distribution information is verified by Monte Carlo simulations.