Automated distinction of shearing and distortion artefacts in structured illumination microscopy

Extended-depth of field random illumination microscopy, EDF-RIM, provides super-resolved projective imaging

The ultimate aim of fluorescence microscopy is to achieve high-resolution imaging of increasingly larger biological samples. Extended depth of field presents a potential solution to accelerate imaging of large samples when compression of information along the optical axis is not detrimental to the interpretation of images. We have implemented an extended depth of field (EDF) approach in a random illumination microscope (RIM). RIM uses multiple speckled illuminations and variance data processing to double the resolution. It is particularly adapted to the imaging of thick samples as it does not require the knowledge of illumination patterns. We demonstrate highly-resolved projective images of biological tissues and cells. Compared to a sequential scan of the imaged volume with conventional 2D-RIM, EDF-RIM allows an order of magnitude improvement in speed and light dose reduction, with comparable resolution. As the axial information is lost in an EDF modality, we propose a method to retrieve the sample topography for samples that are organized in cell sheets.

Superresolution structured illumination microscopy reconstruction algorithms: a review

Structured illumination microscopy (SIM) has become the standard for next-generation wide-field microscopy, offering ultrahigh imaging speed, superresolution, a large field-of-view, and long-term imaging. Over the past decade, SIM hardware and software have flourished, leading to successful applications in various biological questions. However, unlocking the full potential of SIM system hardware requires the development of advanced reconstruction algorithms. Here, we introduce the basic theory of two SIM algorithms, namely, optical sectioning SIM (OS-SIM) and superresolution SIM (SR-SIM), and summarize their implementation modalities. We then provide a brief overview of existing OS-SIM processing algorithms and review the development of SR-SIM reconstruction algorithms, focusing primarily on 2D-SIM, 3D-SIM, and blind-SIM. To showcase the state-of-the-art development of SIM systems and assist users in selecting a commercial SIM system for a specific application, we compare the features of representative off-the-shelf SIM systems. Finally, we provide perspectives on the potential future developments of SIM.

Motion-resistant structured illumination microscopy based on principal component analysis

Structured illumination microscopy (SIM) has become one of the most significant super-resolution techniques in bioscience for observing live-cell dynamics, thanks to fast full-field imaging and low photodamage. However, artifact-free SIM super-resolution reconstruction requires precise knowledge about variable environment-sensitive illumination parameters. Conventional algorithms typically, under the premise of known and reliable constant phase shifts, compensate for residual parameters, which will be easily broken by motion factors such as environment and medium perturbations, and sample offsets. In this Letter, we propose a robust motion-resistant SIM algorithm based on principal component analysis (mrPCA-SIM), which can efficiently compensate for nonuniform pixel shifts and phase errors in each raw illumination image. Experiments demonstrate that mrPCA-SIM achieves more robust imaging quality in complex, unstable conditions compared with conventional methods, promising a more compatible and flexible imaging tool for live cells.

Improving axial resolution in Structured Illumination Microscopy using deep learning

Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Fast TIRF-SIM imaging of dynamic, low-fluorescent biological samples

Fluorescence microscopy is the standard imaging technique to investigate the structures and dynamics of living cells. However, increasing the spatial resolution comes at the cost of temporal resolution and vice versa. In addition, the number of images that can be taken in sufficiently high quality is limited by fluorescence bleaching. Hence, super-resolved imaging at several Hertz of low fluorescent biological samples is still a big challenge and, especially in structured illumination microscopy (SIM), is often visible as imaging artifacts. In this paper, we present a TIRF-SIM system based on scan-mirrors and a Michelson interferometer, which generates images at 110 nm spatial resolution and up to 8 Hz temporal resolution. High resolution becomes possible by optimizing the illumination interference contrast, even for low fluorescent, moving samples. We provide a framework and guidelines on how the modulation contrast, which depends on laser coherence, polarization, beam displacement or sample movements, can be mapped over the entire field of view. In addition, we characterize the influence of the signal-to-noise ratio and the Wiener filtering on the quality of reconstructed SIM images, both in real and frequency space. Our results are supported by theoretical descriptions containing the parameters leading to image artifacts. This study aims to help microscopists to better understand and adjust optical parameters for structured illumination, thereby leading to more trustworthy measurements and analyses of biological dynamics.

Tracking and Analyzing the Brownian Motion of Nano-objects Inside Hollow Core Fibers

Tracking and analyzing the individual diffusion of nanoscale objects such as proteins and viruses is an important methodology in life science. Here, we show a sensor that combines the efficiency of light line illumination with the advantages of fluidic confinement. Tracking of freely diffusing nano-objects inside water-filled hollow core fibers with core diameters of tens of micrometers using elastically scattered light from the core mode allows retrieving information about the Brownian motion and the size of each particle of the investigated ensemble individually using standard tracking algorithms and the mean squared displacement analysis. Specifically, we successfully measure the diameter of every gold nanosphere in an ensemble that consists of several hundreds of 40 nm particles, with an individual precision below 17% (±8 nm). In addition, we confirm the relevance of our approach with respect to bioanalytics by analyzing 70 nm λ-phages. Overall these features, together with the strongly reduced demand for memory space, principally allows us to record thousands of frames and to achieve high frame rates for high precision tracking of nanoscale objects.

Simultaneous co-localized super-resolution fluorescence microscopy and atomic force microscopy: combined SIM and AFM platform for the life sciences

Correlating data from different microscopy techniques holds the potential to discover new facets of signaling events in cellular biology. Here we report for the first time a hardware set-up capable of achieving simultaneous co-localized imaging of spatially correlated far-field super-resolution fluorescence microscopy and atomic force microscopy, a feat only obtained until now by fluorescence microscopy set-ups with spatial resolution restricted by the Abbe diffraction limit. We detail system integration and demonstrate system performance using sub-resolution fluorescent beads and applied to a test sample consisting of human bone osteosarcoma epithelial cells, with plasma membrane transporter 1 (MCT1) tagged with an enhanced green fluorescent protein (EGFP) at the N-terminal.