Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.

Single-molecule localization microscopy in a typical wide-field setup has been widely used for investigating subcellular structures with super resolution; however, field-dependent aberrations restrict the field of view (FOV) to only tens of micrometers. Here, we present a deep-learning method for precise localization of spatially variant point emitters (FD-DeepLoc) over a large FOV covering the full chip of a modern sCMOS camera. Using a graphic processing unit-based vectorial point spread function (PSF) fitter, we can fast and accurately model the spatially variant PSF of a high numerical aperture objective in the entire FOV. Combined with deformable mirror-based optimal PSF engineering, we demonstrate high-accuracy three-dimensional single-molecule localization microscopy over a volume of ~180 × 180 × 5 µm3, allowing us to image mitochondria and nuclear pore complexes in entire cells in a single imaging cycle without hardware scanning; a 100-fold increase in throughput compared to the state of the art.

Ratiometric 4Pi single-molecule localization with optimal resolution and color assignment.

4Pi single-molecule localization microscopy (4Pi-SMLM) with two opposing objectives achieves sub-10 nm isotropic 3D resolution when as few as 250 photons are collected by each objective. Here, we develop a new ratiometric multi-color imaging strategy for 4Pi-SMLM that employs the intrinsic multi-phase interference intensity without increasing the complexity of the system and achieves both optimal 3D resolution and color separation. By partially linking the photon parameters between channels with an interference difference of π during global fitting of the multi-channel 4Pi single-molecule data, we show via simulated data that the loss of localization precision is minimal compared with the theoretical minimum uncertainty, the Cramer-Rao lower bound.