3D drift correction for super-resolution imaging with a single laser light.

Single-molecule localization microscopy (SMLM) enables three-dimensional (3D) super-resolution imaging of nanoscale structures within biological samples. However, prolonged acquisition introduces a drift between the sample and the imaging system, resulting in artifacts in the reconstructed super-resolution image. Here, we present a novel, to our knowledge, 3D drift correction method that utilizes both the reflected and scattered light from the sample. Our method employs the reflected light of a near-infrared (NIR) laser for focus stabilization while synchronously capturing speckle images to estimate the lateral drift. This approach combines high-precision active compensation in the axial direction with lateral post-processing compensation, achieving the abilities of 3D drift correction with a single laser light. Compared to the popular localization events-based cross correlation method, our approach is much more robust, especially for datasets with sparse localization points.

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.

Deformable mirror based optimal PSF engineering for 3D super-resolution imaging.

Point spread function (PSF) engineering is an important technique to encode the properties (e.g., 3D positions, color, and orientation) of a single molecule in the shape of the PSF, often with the help of a programmable phase modulator. A deformable mirror (DM) is currently the most widely used phase modulator for fluorescence detection as it shows negligible photon loss. However, it relies on careful calibration for precise wavefront control. Therefore, design of an optimal PSF not only relies on the theoretical calculation of the maximum information content, but also the physical behavior of the phase modulator, which is often ignored during the optimization process. Here, we develop a framework for PSF engineering which could generate a device specific optimal PSF for 3D super-resolution imaging using a DM. We use our method to generate two types of PSFs with depths of field comparable to the widely used astigmatism and tetrapod PSFs, respectively. We demonstrate the superior performance of the DM specific optimal PSF over the conventional astigmatism and tetrapod PSF both theoretically and experimentally.