Fast, multicolour optical sectioning over extended fields of view with patterned illumination and machine learning.

Structured illumination can reject out-of-focus signal from a sample, enabling high-speed and high-contrast imaging over large areas with widefield detection optics. However, this optical sectioning technique is currently limited by image reconstruction artefacts and poor performance at low signal-to-noise ratios. We combine multicolour interferometric pattern generation with machine learning to achieve high-contrast, real-time reconstruction of image data that is robust to background noise and sample motion. We validate the method in silico and demonstrate imaging of diverse specimens, from fixed and live biological samples to synthetic biosystems, reconstructing data live at 11 Hz across a 44 × 44µm2 field of view, and demonstrate image acquisition speeds exceeding 154 Hz.

Open-source software package for on-the-fly deskewing and live viewing of volumetric lightsheet microscopy data.

Oblique plane microscopy, OPM, is a form of lightsheet microscopy that permits volumetric imaging of biological samples at high temporal and spatial resolution. However, the imaging geometry of OPM, and related variants of light sheet microscopy, distorts the coordinate frame of the presented image sections with respect to the real space coordinate frame in which the sample is moved. This makes live viewing and practical operation of such microscopes difficult. We present an open-source software package that utilises GPU acceleration and multiprocessing to transform the display of OPM imaging data in real time to produce a live extended depth of field projection. Image stacks can be acquired, processed and plotted at rates of several Hz, making live operation of OPMs, and similar microscopes, more user friendly and intuitive.

Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging.

Structured Illumination Microscopy, SIM, is one of the most powerful optical imaging methods available to visualize biological environments at subcellular resolution. Its limitations stem from a difficulty of imaging in multiple color channels at once, which reduces imaging speed. Furthermore, there is substantial experimental complexity in setting up SIM systems, preventing a widespread adoption. Here, we present Machine-learning Assisted, Interferometric Structured Illumination Microscopy, MAI-SIM, as an easy-to-implement method for live cell super-resolution imaging at high speed and in multiple colors. The instrument is based on an interferometer design in which illumination patterns are generated, rotated, and stepped in phase through movement of a single galvanometric mirror element. The design is robust, flexible, and works for all wavelengths. We complement the unique properties of the microscope with an open source machine-learning toolbox that permits real-time reconstructions to be performed, providing instant visualization of super-resolved images from live biological samples.

SARS-CoV-2 nucleocapsid protein adheres to replication organelles before viral assembly at the Golgi/ERGIC and lysosome-mediated egress.

Despite being the target of extensive research efforts due to the COVID-19 (coronavirus disease 2019) pandemic, relatively little is known about the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication within cells. We investigate and characterize the tightly orchestrated virus assembly by visualizing the spatiotemporal dynamics of the four structural SARS-CoV-2 proteins at high resolution. The nucleoprotein is expressed first and accumulates around folded endoplasmic reticulum (ER) membranes in convoluted layers that contain viral RNA replication foci. We find that, of the three transmembrane proteins, the membrane protein appears at the Golgi apparatus/ER-to-Golgi intermediate compartment before the spike and envelope proteins. Relocation of a lysosome marker toward the assembly compartment and its detection in transport vesicles of viral proteins confirm an important role of lysosomes in SARS-CoV-2 egress. These data provide insights into the spatiotemporal regulation of SARS-CoV-2 assembly and refine the current understanding of SARS-CoV-2 replication.