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Superresolution, structured illumination microscopy (SIM) is an ideal modality for imaging live cells due to its relatively high speed and low photon-induced damage to the cells. The rate-limiting step in observing a superresolution image in SIM is often the reconstruction speed of the algorithm used to form a single image from as many as nine raw images. Reconstruction algorithms impose a significant computing burden due to an intricate workflow and a large number of often complex calculations to produce the final image. Further adding to the computing burden is that the code, even within the MATLAB environment, can be inefficiently written by microscopists who are noncomputer science researchers. In addition, they do not take into consideration the processing power of the graphics processing unit (GPU) of the computer. To address these issues, we present simple but efficient approaches to first revise MATLAB code, followed by conversion to GPU-optimized code. When combined with cost-effective, high-performance GPU-enabled computers, a 4- to 500-fold improvement in algorithm execution speed is observed as shown for the image denoising Hessian-SIM algorithm. Importantly, the improved algorithm produces images identical in quality to the original.

Super-resolution structured illumination microscopy (SR-SIM) is finding increasing application in biomedical research due to its superior ability to visualize subcellular dynamics in living cells. However, during image reconstruction artifacts can be introduced and when coupled with time-consuming postprocessing procedures, limits this technique from becoming a routine imaging tool for biologists. To address these issues, an accelerated, artifact-reduced reconstruction algorithm termed joint space frequency reconstruction-based artifact reduction algorithm (JSFR-AR-SIM) was developed by integrating a high-speed reconstruction framework with a high-fidelity optimization approach designed to suppress the sidelobe artifact. Consequently, JSFR-AR-SIM produces high-quality, super-resolution images with minimal artifacts, and the reconstruction speed is increased. We anticipate this algorithm to facilitate SR-SIM becoming a routine tool in biomedical laboratories.

Super-resolution structured illumination microscopy (SIM) routinely performs image reconstruction in the frequency domain using an approach termed frequency-domain reconstruction (FDR). Due to multiple Fourier transforms between the spatial and frequency domains, SIM suffers from low reconstruction speed, constraining its applications in real-time, dynamic imaging. To overcome this limitation, we developed a new method for SIM image reconstruction, termed spatial domain reconstruction (SDR). SDR is intrinsically simpler than FDR, does not require Fourier transforms and the theory predicts it to be a rapid image reconstruction method. Results show that SDR reconstructs a super-resolution image 7-fold faster than FDR, producing images that are equal to either FDR or the widely-used FairSIM. We provide a proof-of-principle using mobile fluorescent beads to demonstrate the utility of SDR in imaging moving objects. Consequently, replacement of the FDR approach with SDR significantly enhances SIM as the desired method for live-cell, instant super-resolution imaging. This means that SDR-SIM is a “What You See Is What You Get” approach to super-resolution imaging.

The maintenance of genome stability requires the coordinated actions of multiple proteins and protein complexes, that are collectively known as genome guardians. Within this broadly defined family is a subset of proteins that contain oligonucleotide/oligosaccharide-binding folds (OB-fold). While OB-folds are widely associated with binding to single-stranded DNA this view is no longer an accurate depiction of how these domains are utilized. Instead, the core of the OB-fold is modified and adapted to facilitate binding to a variety of DNA substrates (both single- and double-stranded), phospholipids, and proteins, as well as enabling catalytic function to a multi-subunit complex. The flexibility accompanied by distinctive oligomerization states and quaternary structures enables OB-fold genome guardians to maintain the integrity of the genome via a myriad of complex and dynamic, protein-protein; protein-DNA, and protein-lipid interactions in both prokaryotes and eukaryotes.

The rescue of stalled DNA replication forks is essential for cell viability. Impeded but still intact forks can be rescued by atypical DNA helicases in a reaction known as fork regression. This reaction has been studied at the single-molecule level using the Escherichia coli DNA helicase RecG and, separately, using the eukaryotic SMARCAL1 enzyme. Both nanomachines possess the necessary activities to regress forks: they simultaneously couple DNA unwinding to duplex rewinding and the displacement of bound proteins. Furthermore, they can regress a fork into a Holliday junction structure, the central intermediate of many fork regression models. However, there are key differences between these two enzymes. RecG is monomeric and unidirectional, catalyzing an efficient and processive fork regression reaction and, in the process, generating a significant amount of force that is used to displace the tightly-bound E. coli SSB protein. In contrast, the inefficient SMARCAL1 is not unidirectional, displays limited processivity, and likely uses fork rewinding to facilitate RPA displacement. Like many other eukaryotic enzymes, SMARCAL1 may require additional factors and/or post-translational modifications to enhance its catalytic activity, whereas RecG can drive fork regression on its own.

Super-resolution microscopy surpasses the diffraction limit to enable the observation of the fine details in sub-cellular structures and their dynamics in diverse biological processes within living cells. Structured illumination microscopy (SIM) uses a relatively low illumination light power compared with other super-resolution microscopies and has great potential to meet the demands of live-cell imaging. However, the imaging acquisition and reconstruction speeds limit its further applications. In this article, recent developments all targeted at improving the overall speed of SIM are reviewed. These comprise both hardware and software improvements, which include a reduction in the number of raw images, GPU acceleration, deep learning and the spatial domain reconstruction. We also discuss the application of these developments in live-cell imaging.