Registration and Visualization of Correlative Super-Resolution Microscopy Data

Super-resolution techniques for biomedical applications and challenges

Super-resolution (SR) techniques have revolutionized the field of biomedical applications by detailing the structures at resolutions beyond the limits of imaging or measuring tools. These techniques have been applied in various biomedical applications, including microscopy, magnetic resonance imaging (MRI), computed tomography (CT), X-ray, electroencephalogram (EEG), ultrasound, etc. SR methods are categorized into two main types: traditional non-learning-based methods and modern learning-based approaches. In both applications, SR methodologies have been effectively utilized on biomedical images, enhancing the visualization of complex biological structures. Additionally, these methods have been employed on biomedical data, leading to improvements in computational precision and efficiency for biomedical simulations. The use of SR techniques has resulted in more detailed and accurate analyses in diagnostics and research, essential for early disease detection and treatment planning. However, challenges such as computational demands, data interpretation complexities, and the lack of unified high-quality data persist. The article emphasizes these issues, underscoring the need for ongoing development in SR technologies to further improve biomedical research and patient care outcomes.

ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy

BACKGROUND

Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms are based on fitting parametric models of the point spread function (PSF) to a measured photon distribution. These algorithms make assumptions about the symmetry of the PSF and thus, do not work well with irregular, non-linear PSFs that occur for example in confocal lifetime imaging, where a laser is scanned across the sample. An alternative method for reconstructing sparse emitter sets from noisy, diffraction-limited images is compressed sensing, but due to its high computational cost it has not yet been widely adopted. Deep neural network fitters have recently emerged as a new competitive method for localization microscopy. They can learn to fit arbitrary PSFs, but require extensive simulated training data and do not generalize well. A method to efficiently fit the irregular PSFs from confocal lifetime localization microscopy combining the advantages of deep learning and compressed sensing would greatly improve the acquisition speed and throughput of this method.

RESULTS

Here we introduce ReCSAI, a compressed sensing neural network to reconstruct localizations for confocal dSTORM, together with a simulation tool to generate training data. We implemented and compared different artificial network architectures, aiming to combine the advantages of compressed sensing and deep learning. We found that a U-Net with a recursive structure inspired by iterative compressed sensing showed the best results on realistic simulated datasets with noise, as well as on real experimentally measured confocal lifetime scanning data. Adding a trainable wavelet denoising layer as prior step further improved the reconstruction quality.

CONCLUSIONS

Our deep learning approach can reach a similar reconstruction accuracy for confocal dSTORM as frame binning with traditional fitting without requiring the acquisition of multiple frames. In addition, our work offers generic insights on the reconstruction of sparse measurements from noisy experimental data by combining compressed sensing and deep learning. We provide the trained networks, the code for network training and inference as well as the simulation tool as python code and Jupyter notebooks for easy reproducibility.

Combining multiple fluorescence imaging techniques in biology: when one microscope is not enough

While fluorescence microscopy has proven to be an exceedingly useful tool in bioscience, it is difficult to offer simultaneous high resolution, fast speed, large volume, and good biocompatibility in a single imaging technique. Thus, when determining the image data required to quantitatively test a complex biological hypothesis, it often becomes evident that multiple imaging techniques are necessary. Recent years have seen an explosion in development of novel fluorescence microscopy techniques, each of which features a unique suite of capabilities. In this Technical Perspective, we highlight recent studies to illustrate the benefits, and often the necessity, of combining multiple fluorescence microscopy modalities. We provide guidance in choosing optimal technique combinations to effectively address a biological question. Ultimately, we aim to promote a more well-rounded approach in designing fluorescence microscopy experiments, leading to more robust quantitative insight.

Complementary Use of Super-Resolution Imaging Modalities to Study the Nanoscale Architecture of Inhibitory Synapses

The nanoscale architecture of synapses has been investigated using multiple super-resolution methods, revealing a common modular structure for scaffolds, neurotransmitter receptors, and presynaptic proteins. This fundamental organization of proteins into subsynaptic domains (SSDs) is thought to be important for synaptic function and plasticity and common to many types of synapses. Using 3D super-resolution Structured Illumination Microscopy (3D-SIM), we recently showed that GABAergic inhibitory synapses exhibit this nanoscale organizational principle and are composed of SSDs of GABA _A receptors (GABA _A Rs), the inhibitory scaffold gephyrin, and the presynaptic active zone protein, RIM. Here, we have investigated the use of 3D-SIM and dSTORM to analyze the nanoscale architecture of the inhibitory synaptic adhesion molecule, neuroligin-2 (NL2). NL2 is a crucial mediator of inhibitory synapse formation and organization, associating with both GABA _A Rs and gephyrin. However, the nanoscale sub-synaptic distribution NL2 remains unknown. We found that 3D-SIM and dSTORM provide complementary information regarding the distribution of NL2 at the inhibitory synapse, with NL2 forming nanoscale structures that have many similarities to gephyrin nanoscale architecture.

Super-Resolution Fluorescence Microscopy Methods for Assessing Mouse Biology

Super-resolution (diffraction unlimited) microscopy was developed 15 years ago; the developers were awarded the Nobel Prize in Chemistry in recognition of their work in 2014. Super-resolution microscopy is increasingly being applied to diverse scientific fields, from single molecules to cell organelles, viruses, bacteria, plants, and animals, especially the mammalian model organism Mus musculus. In this review, we explain how super-resolution microscopy, along with fluorescence microscopy from which it grew, has aided the renaissance of the light microscope. We cover experiment planning and specimen preparation and explain structured illumination microscopy, super-resolution radial fluctuations, stimulated emission depletion microscopy, single-molecule localization microscopy, and super-resolution imaging by pixel reassignment. The final section of this review discusses the strengths and weaknesses of each super-resolution technique and how to choose the best approach for your research. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC.

SSNOMBACTER: A collection of scattering-type scanning near-field optical microscopy and atomic force microscopy images of bacterial cells

BACKGROUND

In recent years, a variety of imaging techniques operating at nanoscale resolution have been reported. These techniques have the potential to enrich our understanding of bacterial species relevant to human health, such as antibiotic-resistant pathogens. However, owing to the novelty of these techniques, their use is still confined to addressing very particular applications, and their availability is limited owing to associated costs and required expertise. Among these, scattering-type scanning near field optical microscopy (s-SNOM) has been demonstrated as a powerful tool for exploring important optical properties at nanoscale resolution, depending only on the size of a sharp tip. Despite its huge potential to resolve aspects that cannot be tackled otherwise, the penetration of s-SNOM into the life sciences is still proceeding at a slow pace for the aforementioned reasons.

RESULTS

In this work we introduce SSNOMBACTER, a set of s-SNOM images collected on 15 bacterial species. These come accompanied by registered Atomic Force Microscopy images, which are useful for placing nanoscale optical information in a relevant topographic context.

CONCLUSIONS

The proposed dataset aims to augment the popularity of s-SNOM and for accelerating its penetration in life sciences. Furthermore, we consider this dataset to be useful for the development and benchmarking of image analysis tools dedicated to s-SNOM imaging, which are scarce, despite the high need. In this latter context we discuss a series of image processing and analysis applications where SSNOMBACTER could be of help.

Ultradian Secretion of Growth Hormone in Mice: Linking Physiology With Changes in Synapse Parameters Using Super-Resolution Microscopy

Neuroendocrine circuits are orchestrated by the pituitary gland in response to hypothalamic hormone-releasing and inhibiting factors to generate an ultradian and/or circadian rhythm of hormone secretion. However, mechanisms that govern this rhythmicity are not fully understood. It has been shown that synaptic transmission in the rodent hypothalamus undergoes cyclical changes in parallel with rhythmic hormone secretion and a growing body of evidence suggests that rapid rewiring of hypothalamic neurons may be the source of these changes. For decades, structural synaptic studies have been utilizing electron microscopy, which provides the resolution suitable for visualizing synapses. However, the small field of view, limited specificity and manual analysis susceptible to bias fuel the search for a more quantitative approach. Here, we apply the fluorescence super-resolution microscopy approach direct Stochastic Optical Reconstruction Microscopy (dSTORM) to quantify and structurally characterize excitatory and inhibitory synapses that contact growth hormone-releasing-hormone (GHRH) neurons during peak and trough values of growth hormone (GH) concentration in mice. This approach relies on a three-color immunofluorescence staining of GHRH and pre- and post-synaptic markers, and a quantitative analysis with a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. With this method we confirm our previous findings, using electron microscopy, of increased excitatory synaptic input to GHRH neurons during peak levels of GH. Additionally, we find a shift in synapse numbers during low GH levels, where more inhibitory synaptic inputs are detected. Lastly, we utilize dSTORM to study novel aspects of synaptic structure. We show that more excitatory (but not inhibitory) pre-synaptic clusters associate with excitatory post-synaptic clusters during peaks of GH secretion and that the numbers of post-synaptic clusters increase during high hormone levels. The results presented here provide an opportunity to highlight dSTORM as a valuable quantitative approach to study synaptic structure in the neuroendocrine circuit. Importantly, our analysis of GH circuitry sheds light on the potential mechanism that drives ultradian changes in synaptic transmission and possibly aids in GH pulse generation in mice.