Challenges facing quantitative large-scale optical super-resolution, and some simple solutions

An UltraMNIST classification benchmark to train CNNs for very large images

Current convolutional neural networks (CNNs) are not designed for large scientific images with rich multi-scale features, such as in satellite and microscopy domain. A new phase of development of CNNs especially designed for large images is awaited. However, application-independent high-quality and challenging datasets needed for such development are still missing. We present the 'UltraMNIST dataset' and associated benchmarks for this new research problem of 'training CNNs for large images'. The dataset is simple, representative of wide-ranging challenges in scientific data, and easily customizable for different levels of complexity, smallest and largest features, and sizes of images. Two variants of the problem are discussed: standard version that facilitates the development of novel CNN methods for effective use of the best available GPU resources and the budget-aware version to promote the development of methods that work under constrained GPU memory. Several baselines are presented and the effect of reduced resolution is studied. The presented benchmark dataset and baselines will hopefully trigger the development of new CNN methods for large scientific images.

Small Peptide-Based Nanodelivery Systems for Cancer Therapy and Diagnosis

Developing nano-biomaterials with tunable topology, size, and surface characteristics has shown tremendously favorable benefits in various biologic and clinical applications. Among various nano-biomaterials, peptide-based drug delivery systems offer multiple merits over other synthetic systems due to their enhanced bio- and cytocompatibility and desirable biochemical and biophysical properties. Currently, around 100 peptide-based drugs are clinically available for numerous therapeutic purposes. In conjugation with chemotherapeutic moieties, peptides demonstrate a remarkable ability to reduce nonspecific drug effects by improving drug targetability at cancer sites. This review encompasses a wide-ranging role played by different peptide-based nanostructures in cancer theranostics. Section 1 introduces the rising concern about cancer as a disease and further describes peptide-based nanomaterials as biomedical agents to tackle the ailment. The subsequent section explores the mechanistic pathways behind the self-assembly of peptides to form hierarchically distinct assemblies. The crux of our review lies in an exhaustive exploration of the applications of various types of peptide-based nanostructures in cancer therapy and diagnosis. SIGNIFICANCE STATEMENT: Peptide-based drug delivery systems possess superior biocompatibility, biochemical, and biophysical properties compared to other synthetic alternatives. The development of these nano-biomaterials with customizable topology, size, and surface characteristics have shown promising outcomes in biomedical contexts. Peptides in conjunction with chemotherapeutic agents exhibit the ability to enhance drug targetability at cancer sites, reducing nonspecific drug effects. This comprehensive review emphasizes the pivotal role of diverse peptide-based nanostructures as cancer theranostics, elucidating their potential in revolutionizing cancer therapy and diagnosis.

Characterizing the blood-brain barrier and gut barrier with super-resolution imaging: opportunities and challenges

Brain and gut barriers have been receiving increasing attention in health and diseases including in psychiatry. Recent studies have highlighted changes in the blood-brain barrier and gut barrier structural properties, notably a loss of tight junctions, leading to hyperpermeability, passage of inflammatory mediators, stress vulnerability, and the development of depressive behaviors. To decipher the cellular processes actively contributing to brain and gut barrier function in health and disease, scientists can take advantage of neurophotonic tools and recent advances in super-resolution microscopy techniques to complement traditional imaging approaches like confocal and electron microscopy. Here, we summarize the challenges, pros, and cons of these innovative approaches, hoping that a growing number of scientists will integrate them in their study design exploring barrier-related properties and mechanisms.

Iterative immunostaining combined with expansion microscopy and image processing reveals nanoscopic network organization of nuclear lamina

Investigation of nuclear lamina architecture relies on superresolved microscopy. However, epitope accessibility, labeling density, and detection precision of individual molecules pose challenges within the molecularly crowded nucleus. We developed iterative indirect immunofluorescence (IT-IF) staining approach combined with expansion microscopy (ExM) and structured illumination microscopy to improve superresolution microscopy of subnuclear nanostructures like lamins. We prove that ExM is applicable in analyzing highly compacted nuclear multiprotein complexes such as viral capsids and provide technical improvements to ExM method including three-dimensional-printed gel casting equipment. We show that in comparison with conventional immunostaining, IT-IF results in a higher signal-to-background ratio and a mean fluorescence intensity by improving the labeling density. Moreover, we present a signal-processing pipeline for noise estimation, denoising, and deblurring to aid in quantitative image analyses and provide this platform for the microscopy imaging community. Finally, we show the potential of signal-resolved IT-IF in quantitative superresolution ExM imaging of nuclear lamina and reveal nanoscopic details of the lamin network organization-a prerequisite for studying intranuclear structural coregulation of cell function and fate.

Tracking the Message: Applying Single Molecule Localization Microscopy to Cellular RNA Imaging

RNA function is increasingly appreciated to be more complex than merely communicating between DNA sequence and protein structure. RNA localization has emerged as a key contributor to the intricate roles RNA plays in the cell, and the link between dysregulated spatiotemporal localization and disease warrants an exploration beyond sequence and structure. However, the tools needed to visualize RNA with precise resolution are lacking in comparison to methods available for studying proteins. In the past decade, many techniques have been developed for imaging RNA, and in parallel super resolution and single-molecule techniques have enabled imaging of single molecules in cells. Of these methods, single molecule localization microscopy (SMLM) has shown significant promise for probing RNA localization. In this review, we highlight current approaches that allow super resolution imaging of specific RNA transcripts and summarize challenges and future opportunities for developing innovative RNA labeling methods that leverage the power of SMLM.

Advanced image-free analysis of the nano-organization of chromatin and other biomolecules by Single Molecule Localization Microscopy (SMLM)

The cell as a system of many components, governed by the laws of physics and chemistry drives molecular functions having an impact on the spatial organization of these systems and vice versa. Since the relationship between structure and function is an almost universal rule not only in biology, appropriate methods are required to parameterize the relationship between the structure and function of biomolecules and their networks, the mechanisms of the processes in which they are involved, and the mechanisms of regulation of these processes. Single molecule localization microscopy (SMLM), which we focus on here, offers a significant advantage for the quantitative parametrization of molecular organization: it provides matrices of coordinates of fluorescently labeled biomolecules that can be directly subjected to advanced mathematical analytical procedures without the need for laborious and sometimes misleading image processing. Here, we propose mathematical tools for comprehensive quantitative computer data analysis of SMLM point patterns that include Ripley distance frequency analysis, persistent homology analysis, persistent 'imaging', principal component analysis and co-localization analysis. The application of these methods is explained using artificial datasets simulating different, potentially possible and interpretatively important situations. Illustrative analyses of real complex biological SMLM data are presented to emphasize the applicability of the proposed algorithms. This manuscript demonstrated the extraction of features and parameters quantifying the influence of chromatin (re)organization on genome function, offering a novel approach to study chromatin architecture at the nanoscale. However, the ability to adapt the proposed algorithms to analyze essentially any molecular organizations, e.g., membrane receptors or protein trafficking in the cytosol, offers broad flexibility of use.

Single Molecule Localization Microscopy for Studying Small Extracellular Vesicles

Small extracellular vesicles (sEVs) are 30-200 nm nanovesicles enriched with unique cargoes of nucleic acids, lipids, and proteins. sEVs are released by all cell types and have emerged as a critical mediator of cell-to-cell communication. Although many studies have dealt with the role of sEVs in health and disease, the exact mechanism of sEVs biogenesis and uptake remain unexplored due to the lack of suitable imaging technologies. For sEVs functional studies, imaging has long relied on conventional fluorescence microscopy that has only 200-300 nm resolution, thereby generating blurred images. To break this resolution limit, recent developments in super-resolution microscopy techniques, specifically single-molecule localization microscopy (SMLM), expanded the understanding of subcellular details at the few nanometer level. SMLM success relies on the use of appropriate fluorophores with excellent blinking properties. In this review, the basic principle of SMLM is highlighted and the state of the art of SMLM use in sEV biology is summarized. Next, how SMLM techniques implemented for cell imaging can be translated to sEV imaging is discussed by applying different labeling strategies to study sEV biogenesis and their biomolecular interaction with the distant recipient cells.

The synaptic basis of activity-dependent eye-specific competition

Binocular vision requires proper developmental wiring of eye-specific inputs to the brain. In the thalamus, axons from the two eyes initially overlap in the dorsal lateral geniculate nucleus and undergo activity-dependent competition to segregate into target domains. Here, we combine eye-specific tract tracing with volumetric super-resolution imaging to measure the nanoscale molecular reorganization of developing retinogeniculate eye-specific synapses in the mouse brain. We show there are eye-specific differences in presynaptic vesicle pool size and vesicle association with the active zone at the earliest stages of retinogeniculate refinement but find no evidence of eye-specific differences in subsynaptic domain number, size, or transsynaptic alignment across development. Genetic disruption of spontaneous retinal activity decreases retinogeniculate synapse density, delays the emergence eye-specific differences in vesicle organization, and disrupts subsynaptic domain maturation. These results suggest that activity-dependent eye-specific presynaptic maturation underlies synaptic competition in the mammalian visual system.

Expansion microscopy of neutrophil nuclear structure and extracellular traps

Neutrophils are key players of the immune system and possess an arsenal of effector functions, including the ability to form and expel neutrophil extracellular traps (NETs) in a process termed NETosis. During NETosis, the nuclear DNA/chromatin expands until it fills the whole cell and is released into the extracellular space. NETs are composed of DNA decorated with histones, proteins, or peptides, and NETosis is implicated in many diseases. Resolving the structure of the nucleus in great detail is essential to understand the underlying processes, but so far, superresolution methods have not been applied. Here, we developed an expansion-microscopy-based method and determined the spatial distribution of chromatin/DNA, histone H1, and nucleophosmin with an over fourfold improved resolution (<40-50 nm) and increased information content. It allowed us to identify the punctate localization of nucleophosmin in the nucleus and histone-rich domains in NETotic cells with a size of 54-66 nm. The technique could also be applied to components of the nuclear envelope (lamins B1 and B2) and myeloperoxidase, providing a complete picture of nuclear composition and structure. In conclusion, expansion microscopy enables superresolved imaging of the highly dynamic structure of nuclei in immune cells.

Glyoxal Fixation Is Optimal for Immunostaining of Brain Vessels, Pericytes and Blood-Brain Barrier Proteins

Brain vascular staining is very important for understanding cerebrovascular pathologies. 4% paraformaldehyde is considered the gold standard fixation technique for immunohistochemistry and it revolutionized the examination of proteins in fixed tissues. However, this fixation technique produces inconsistent immunohistochemical staining results due to antigen masking. Here, we test a new fixation protocol using 3% glyoxal and demonstrate that this method improves the staining of the brain vasculature, pericytes, and tight junction proteins compared to 4% paraformaldehyde. Use of this new fixation technique will provide more detailed information about vascular protein expressions, their distributions, and colocalizations with other proteins at the molecular level in the brain vasculature.

Modeling Non-additive Effects in Neighboring Chemically Identical Fluorophores

Quantitative fluorescence analysis is often used to derive chemical properties, including stoichiometries, of biomolecular complexes. One fundamental underlying assumption in the analysis of fluorescence data─whether it be the determination of protein complex stoichiometry by super-resolution, or step-counting by photobleaching, or the determination of RNA counts in diffraction-limited spots in RNA fluorescence in situ hybridization (RNA-FISH) experiments─is that fluorophores behave identically and do not interact. However, recent experiments on fluorophore-labeled DNA origami structures such as fluorocubes have shed light on the nature of the interactions between identical fluorophores as these are brought closer together, thereby raising questions on the validity of the modeling assumption that fluorophores do not interact. Here, we analyze photon arrival data under pulsed illumination from fluorocubes where distances between dyes range from 2 to 10 nm. We discuss the implications of non-additivity of brightness on quantitative fluorescence analysis.

Blood Cell Analysis: From Traditional Methods to Super-Resolution Microscopy

Blood cell analysis is essential for the diagnosis and identification of hematological malignancies. The use of digital microscopy systems has been extended in clinical laboratories. Super-resolution microscopy (SRM) has attracted wide attention in the medical field due to its nanoscale spatial resolution and high sensitivity. It is considered to be a potential method of blood cell analysis that may have more advantages than traditional approaches such as conventional optical microscopy and hematology analyzers in certain examination projects. In this review, we firstly summarize several common blood cell analysis technologies in the clinic, and analyze the advantages and disadvantages of these technologies. Then, we focus on the basic principles and characteristics of three representative SRM techniques, as well as the latest advances in these techniques for blood cell analysis. Finally, we discuss the developmental trend and possible research directions of SRM, and provide some discussions on further development of technologies for blood cell analysis.

LOCAN: a python library for analyzing single-molecule localization microscopy data

SUMMARY

Single-molecule localization microscopy has become an important part of the super-resolution microscopy toolbox in biomedical research. Software platforms for applying analytical methods to the point-based data structures are needed that offer both routine application and flexible customization of analysis procedures. We present a python library called LOCAN that consists of well-defined data structures and analysis methods for analyzing localization data in a script or computable notebook.

AVAILABILITY AND IMPLEMENTATION

The package source code is released open-source under a BSD-3 license at https://github.com/super-resolution/Locan. It can be installed form the Python Package Index at https://pypi.org/project/locan. Documentation is available at https://locan.readthedocs.io.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.