Volumetric multi-particle tracking and dense 3D single molecule localization microscopy

In localization microscopy, the positions of individual nanoscale point emitters (e.g. fluorescent molecules) are determined at high precision from their point-spread functions (PSFs). This enalbes highly precise single/multiple-particle-tracking, as well as super-resolution microscopy, namely single molecule localization microscopy (SMLM). Localization in three-dimensions (3D) can be performed by modifying the PSF using additional optical elements, e.g. a phase mask in the back focal plane of the microscope. Then, extracting the 3D position of the emitter from such an image is achievable using various parameter esitmation techniques, that typically perform very well for isolated emitters. However, localizing multiple adjacent emitters in 3D poses a significant algorithmic challenge, due to the lateral overlap of their PSFs.

Here, we train a neural net to receive an image containing densely overapping PSFs of multiple emitters over a large axial range, and output a list of their 3D positions. Furthermore, we then use the net to design the optimal PSF for the dense multi-emitter case. We demonstrate our approach numerically as well as experimentally by volumetrically imaging dozens of fluorescently-labeled telomeres occupying a mammalian nucleus in a single snapshot, and by super-resolution microscopy over a 4um axial range with dense fluorescent emitters.