Super-resolution microscopy allowed major improvements in our capacity to describe and explain biological organization at the nanoscale. Single-molecule localization microscopy (SMLM) uses the positions of molecules to create super-resolved images, but it can also provide new insights into the organization of molecules through appropriate pointillistic analyses that fully exploit the sparse nature of SMLM data. However, the main drawback of SMLM is the lack of analytical tools easily applicable to the diverse types of data that can arise from biological samples. Typically, a cloud of detections may be a cluster of molecules or not depending on the local density of detections, but also on the size of molecules themselves, the labeling technique, the photo-physics of the fluorophore, and the imaging conditions. We aimed to set an easy-to-use clustering analysis protocol adaptable to different types of data. Here, we introduce Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters. On simulated or real SMLM data, Diinamic correctly identified clusters of different sizes and densities, being performant even in noisy datasets with multiple detections per fluorophore. It also detected subdomains (“nanodomains”) in clusters with non-homogeneous distribution of detections.

Super-resolution microscopy gives researchers invaluable opportunities and continues to make great strides in terms of performance and applicability. Clever developments in stimulated emission depletion (STED) microscopy have pushed the doors open wider for many applications. Here, we discuss three examples: first, how time-resolved detection unlocks new information; then, live-cell imaging enabled by intelligent illumination schemes; and finally, deep tissue imaging with dynamic aberration correction. As an outlook, we examine MINFLUX as an approach for molecular resolution with fluorescence.

We present a fast and precise deep-learning architecture, which we term O-Net, for obtaining super-resolved images from conventional phase-modulated optical microscopical techniques, such as phase-contrast microscopy and differential interference contrast microscopy. O-Net represents a novel deep convolutional neural network that can be trained on both simulated and experimental data, the latter of which is being demonstrated in the present context. The present study demonstrates the ability of the proposed method to achieve super-resolved images even under poor signal-to-noise ratios and does not require prior information on the point spread function or optical character of the system. Moreover, unlike previous state-of-the-art deep neural networks (such as U-Nets), the O-Net architecture seemingly demonstrates an immunity to network hallucination, a commonly cited issue caused by network overfitting when U-Nets are employed. Models derived from the proposed O-Net architecture are validated through empirical comparison with a similar sample imaged via scanning electron microscopy (SEM) and are found to generate ultra-resolved images which came close to that of the actual SEM micrograph.

Stimulated emission depletion (STED) microscopy is a powerful tool for the study of sub-micron samples, especially in biology. In this article, we show a simple, straightforward method for modeling a STED microscope using Zemax geometrical optics ray tracing principles, while still achieving a realistic spot size. This method is based on scattering models, and while it employs fast ray-tracing simulations, its output behavior is consistent with the real, diffractive behavior of a STED de-excitation spot. The method can be used with Zemax Black Boxes of real objectives for analysis of their performance in a STED setup and open perspective of modeling, tolerancing performance, and then building advanced microscope systems from scratch. In an example setup, we show an analysis method for both XY and Z excitation and de-excitation intensity distributions. This method can simplify the design of custom STED setups by proper modeling of the resolution performance.

The voltage-gated sodium channel [pore-forming subunit of the neuronal voltage-gated sodium channel (NaV1.6)] has recently been found in cardiac myocytes. Emerging studies indicate a role for NaV1.6 in ionic homeostasis as well as arrhythmogenesis. Little is known about the spatial organization of these channels in cardiac muscle, mainly due to the lack of high-fidelity antibodies. Therefore, we developed and rigorously validated a novel rabbit polyclonal NaV1.6 antibody and undertook super-resolution microscopy studies of NaV1.6 localization in cardiac muscle. We developed and validated a novel rabbit polyclonal antibody against a C-terminal epitope on the neuronal sodium channel 1.6 (NaV1.6). Raw sera showed high affinity in immuno-fluorescence studies, which was improved with affinity purification. The antibody was rigorously validated for specificity via multiple approaches. Lastly, we used this antibody in proximity ligation assay (PLA) and super-resolution STochastic Optical Reconstruction Microscopy (STORM) studies, which revealed enrichment of NaV1.6 in close proximity to ryanodine receptor (RyR2), a key calcium (Ca2+) cycling protein, in cardiac myocytes. In summary, our novel NaV1.6 antibody demonstrates high degrees of specificity and fidelity in multiple preparations. It enabled multimodal microscopic studies and revealed that over half of the NaV1.6 channels in cardiac myocytes are located within 100 nm of ryanodine receptor Ca2+ release channels.

We here present two new methods for the characterization of fluorescent localization microscopy images obtained from immunostained brain tissue sections. Direct stochastic optical reconstruction microscopy images of 5-HT1A serotonin receptors and glial fibrillary acidic proteins in healthy cryopreserved brain tissues are analyzed. In detail, we here present two image processing methods for characterizing differences in receptor distribution on glial cells and their distribution on neural cells: One variant relies on skeleton extraction and adaptive thresholding, the other on k-means based discrete layer segmentation. Experimental results show that both methods can be applied for distinguishing classes of images with respect to serotonin receptor distribution. Quantification of nanoscopic changes in relative protein expression on particular cell types can be used to analyze degeneration in tissues caused by diseases or medical treatment.

Borrelia burgdorferi sensu lato, the causative agent of Lyme disease, is transmitted to humans through the bite of infected Ixodes spp. ticks. Successful infection of vertebrate hosts necessitates sophisticated means of the pathogen to escape the vertebrates’ immune system. One strategy employed by Lyme disease spirochetes to evade adaptive immunity involves a highly coordinated regulation of the expression of outer surface proteins that is vital for infection, dissemination, and persistence. Here we characterized the expression pattern of bacterial surface antigens using different microscopy techniques, from fluorescent wide field to super-resolution and immunogold-scanning electron microscopy. A fluorescent strain of B. burgdorferi spirochetes was labeled with monoclonal antibodies directed against various bacterial surface antigens. Our results indicate that OspA is more evenly distributed over the surface than OspB and OspC that were present as punctate areas.