Super-resolution Microscopy Imaging Research Articles

Advancing the clinical assessment of glomerular podocyte pathology in kidney biopsies via super-resolution microscopy and angiopoietin-like 4 staining.

Rationale: The tertiary structure of normal podocytes prevents protein from leaking into the urine. However, observing the complexity of podocytes is challenging because of the scale differences in their three-dimensional structure and the close proximity between neighboring cells in space. In this study, we explored podocyte-secreted angiopoietin-like 4 (ANGPTL4) as a potential morphological marker via super-resolution microscopy (SRM). Methods and Results: Specimens from patients with minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and membranous nephropathy (MN), along with normal controls, were analyzed via immunofluorescence and immunohistochemistry to determine the expression and localization of ANGPTL4, confirming its extensive presence in podocytes across both healthy and diseased conditions. Immunoelectron microscopy revealed that ANGPTL4 is distributed throughout the podocyte cell body, primary processes, and foot processes. Compared with conventional podocyte markers such as nephrin and synaptopodin, ANGPTL4 excels in depicting the three-dimensional structure of podocytes via SRM imaging. We then refined a protocol using tyramide signal amplification staining and confocal microscopy to uniformly enhance podocyte fluorescence, facilitating the clinical assessment of biopsies. In patients diagnosed with MCD and FSGS, measurements of slit diaphragm density, primary process width, and foot process width were taken after further co-staining with nephrin to identify patterns of podocyte morphological alterations. Distinctive patterns of foot process effacement were identified in MCD and FSGS patients, with FSGS patients showing more pronounced podocyte injury. Conclusions: ANGPTL4 serves as a reliable morphological marker for podocyte analysis, offering enhanced visualization of their three-dimensional structure and facilitating the identification of distinct pathological changes in nephrotic syndrome patients.

Applications of Nanotechnology for Spatial Omics: Biological Structures and Functions at Nanoscale Resolution.

Spatial omics methods are extensions of traditional histological methods that can illuminate important biomedical mechanisms of physiology and disease by examining the distribution of biomolecules, including nucleic acids, proteins, lipids, and metabolites, at microscale resolution within tissues or individual cells. Since, for some applications, the desired resolution for spatial omics approaches the nanometer scale, classical tools have inherent limitations when applied to spatial omics analyses, and they can measure only a limited number of targets. Nanotechnology applications have been instrumental in overcoming these bottlenecks. When nanometer-level resolution is needed for spatial omics, super resolution microscopy or detection imaging techniques, such as mass spectrometer imaging, are required to generate precise spatial images of target expression. DNA nanostructures are widely used in spatial omics for purposes such as nucleic acid detection, signal amplification, and DNA barcoding for target molecule labeling, underscoring advances in spatial omics. Other properties of nanotechnologies include advanced spatial omics methods, such as microfluidic chips and DNA barcodes. In this review, we describe how nanotechnologies have been applied to the development of spatial transcriptomics, proteomics, metabolomics, epigenomics, and multiomics approaches. We focus on how nanotechnology supports improved resolution and throughput of spatial omics, surpassing traditional techniques. We also summarize future challenges and opportunities for the application of nanotechnology to spatial omics methods.

Exploring the adhesion properties of extracellular vesicles for functional assays.

The "stickiness" of extracellular vesicles (EVs) can pose challenges for EV processing and storage, but adhesive properties may also be exploited to immobilize EVs directly on surfaces for various measurement techniques, including super-resolution microscopy. Direct adhesion to surfaces may allow examination of broader populations of EVs than molecular affinity approaches, which can also involve specialized, expensive affinity reagents. Here, we report on the interaction of EVs with borosilicate glass and quartz coverslips and on the effects of pre-coating coverslips with poly-L-lysine (PLL), a reagent commonly used to facilitate interactions between negatively charged surfaces of cells and amorphous surfaces. Additionally, we compared two mounting media conditions for super-resolution microscopy (SRM) imaging and used immobilized EVs for a B-cell interaction test. Our findings suggest that borosilicate glass coverslips immobilize EVs better than quartz glass coverslips. We also found that PLL is not strictly required for EV retention but contributes to the uniform distribution of EVs on borosilicate glass coverslips. Overall, these findings suggest that standard lab materials like borosilicate glass coverslips, with or without PLL, can be effectively used for the immobilization of EVs in specific imaging techniques.

Decoupling actin assembly from microtubule disassembly by TBC1D3C-mediated direct GEF-H1 activation.

Actin and microtubules are essential cytoskeletal components and coordinate their dynamics through multiple coupling and decoupling mechanisms. However, how actin and microtubule dynamics are decoupled remains incompletely understood. Here, we identified TBC1D3C as a new regulator that can decouple actin filament assembly from microtubule disassembly. We showed that TBC1D3C induces the release of GEF-H1 from microtubules into the cytosol without perturbing microtubule arrays, leading to RhoA activation and actin filament assembly. Mechanistically, we found that TBC1D3C directly binds to GEF-H1, disrupting its interaction with the Tctex-DIC-14-3-3 complex and thereby displacing GEF-H1 from microtubules independently of microtubule disassembly. Super-resolution microscopy and live-cell imaging further confirmed that TBC1D3C triggers GEF-H1 release and actin filament assembly while maintaining microtubule integrity. Therefore, our findings demonstrated that TBC1D3C functions as a direct GEF activator and a novel regulator in decoupling actin assembly from microtubule disassembly, providing new insights into cytoskeletal regulation.

Unlocking Intracellular Protein Delivery by Harnessing Polymersomes Synthesized at Microliter Volumes using Photo-PISA.

Efficient delivery of therapeutic proteins and vaccine antigens to intracellular targets is challenging due to generally poor cell membrane permeation and endolysosomal entrapment causing degradation. Herein, these challenges are addressed by developing an oxygen-tolerant photoinitiated polymerization-induced self-assembly (Photo-PISA) process, allowing for the microliter-scale (10µL) synthesis of protein-loaded polymersomes directly in 1536-well plates. High-resolution techniques capable of analysis at a single particle level are employed to analyze protein encapsulation and release mechanisms. Using confocal microscopy and super-resolution stochastic optical reconstruction microscopy (STORM) imaging, their ability to deliver proteins into the cytosol following endosomal escape is subsequently visualized. Lastly, the adaptability of these polymersomes is exploited to encapsulate and deliver a prototype vaccine antigen, demonstrating its ability to activate antigen-presenting cells and support antigen cross-presentation for applications in subunit vaccines and cancer immunotherapy. This combination of ultralow volume synthesis and efficient intracellular delivery holds significant promise for unlocking the high throughput screening of a broad range of otherwise cost-prohibitive or early-stage therapeutic protein and vaccine antigen candidates that can be difficult to obtain in large quantities. The versatility of this platform for rapid screening of intracellular protein delivery can result in significant advancements across the fields of nanomedicine and biomedical engineering.

This Microtubule Does Not Exist: Super-Resolution Microscopy Image Generation by a Diffusion Model.

Generative models, such as diffusion models, have made significant advancements in recent years, enabling the synthesis of high-quality realistic data across various domains. Here, the adaptation and training of a diffusion model on super-resolution microscopy images are explored. It is shown that the generated images resemble experimental images, and that the generation process does not exhibit a large degree of memorization from existing images in the training set. To demonstrate the usefulness of the generative model for data augmentation, the performance of a deep learning-based single-image super-resolution (SISR) method trained using generated high-resolution data is compared against training using experimental images alone, or images generated by mathematical modeling. Using a few experimental images, the reconstruction quality and the spatial resolution of the reconstructed images are improved, showcasing the potential of diffusion model image generation for overcoming the limitations accompanying the collection and annotation of microscopy images. Finally, the pipeline is made publicly available, runnable online, and user-friendly to enable researchers to generate their own synthetic microscopy data. This work demonstrates the potential contribution of generative diffusion models for microscopy tasks and paves the way for their future application in this field.

Overview of the super-resolution and advanced microscopy imaging session at the 21st IUPAB and the 62nd Biophysics Society of Japan joint congress.

Recent advancements in microscopic techniques have significantly progressed, with improvements in fundamental parameters such as resolution, as well as the emergence of novel imaging techniques for measuring cellular information. In this session, six invited speakers introduced recent advancements in super-resolution and advanced microscopy imaging, covering both the development of novel microscopy techniques and their biological applications.

Spatial organization of translation and translational repression in two phases of germ granules

Most RNA-protein condensates are composed of heterogeneous immiscible phases. However, how this multiphase organization contributes to their biological functions remains largely unexplored. Drosophila germ granules, a class of RNA-protein condensates, are the site of mRNA storage and translational activation. Here, using super-resolution microscopy and single-molecule imaging approaches, we show that germ granules have a biphasic organization and that translation occurs in the outer phase and at the surface of the granules. The localization, directionality, and compaction of mRNAs within the granule depend on their translation status, translated mRNAs being enriched in the outer phase with their 5′end oriented towards the surface. Translation is strongly reduced when germ granule biphasic organization is lost. These findings reveal the intimate links between the architecture of RNA-protein condensates and the organization of their different functions, highlighting the functional compartmentalization of these condensates.

A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features

Cellular phenotypic heterogeneity is an important hallmark of many biological processes and understanding its origins remains a substantial challenge. This heterogeneity often reflects variations in the chromatin structure, influenced by factors such as viral infections and cancer, which dramatically reshape the cellular landscape. To address the challenge of identifying distinct cell states, we developed artificial intelligence of the nucleus (AINU), a deep learning method that can identify specific nuclear signatures at the nanoscale resolution. AINU can distinguish different cell states based on the spatial arrangement of core histone H3, RNA polymerase II or DNA from super-resolution microscopy images. With only a small number of images as the training data, AINU correctly identifies human somatic cells, human-induced pluripotent stem cells, very early stage infected cells transduced with DNA herpes simplex virus type 1 and even cancer cells after appropriate retraining. Finally, using AI interpretability methods, we find that the RNA polymerase II localizations in the nucleoli aid in distinguishing human-induced pluripotent stem cells from their somatic cells. Overall, AINU coupled with super-resolution microscopy of nuclear structures provides a robust tool for the precise detection of cellular heterogeneity, with considerable potential for advancing diagnostics and therapies in regenerative medicine, virology and cancer biology.

Nanoscale single-vesicle analysis: High-throughput approaches through AI-enhanced super-resolution image analysis

The analysis of membrane vesicles at the nanoscale level is crucial for advancing the understanding of intercellular communication and its implications for health and disease. Despite their significance, the nanoscale analysis of vesicles at the single particle level faces challenges owing to their small size and the complexity of biological fluids. This new vesicle analysis tool leverages the single-molecule sensitivity of super-resolution microscopy (SRM) and the high-throughput analysis capability of deep-learning algorithms. By comparing classical clustering methods (k-means, DBSCAN, and SR-Tesseler) with deep-learning-based approaches (YOLO, DETR, Deformable DETR, and Faster R–CNN) for the analysis of super-resolution fluorescence images of exosomes, we identified the deep-learning algorithm, Deformable DETR, as the most effective. It showed superior accuracy and a reduced processing time for detecting individual vesicles from SRM images. Our findings demonstrate that image-based deep-learning-enhanced methods from SRM images significantly outperform traditional coordinate-based clustering techniques in identifying individual vesicles and resolving the challenges related to misidentification and computational demands. Moreover, the application of the combined Deformable DETR and ConvNeXt-S algorithms to differently labeled exosomes revealed its capability to differentiate between them, indicating its potential to dissect the heterogeneity of vesicle populations. This breakthrough in vesicle analysis suggests a paradigm shift towards the integration of AI into super-resolution imaging, which is promising for unlocking new frontiers in vesicle biology, disease diagnostics, and the development of vesicle-based therapeutics.

Improving correlation based super-resolution microscopy images through image fusion by self-supervised deep learning.

Super-resolution imaging is a powerful tool in modern biological research, allowing for the optical observation of subcellular structures with great detail. In this paper, we present a deep learning approach for image fusion of intensity and super-resolution optical fluctuation imaging (SOFI) microscopy images. We construct a network that can successfully combine the advantages of these two imaging methods, producing a fused image with a resolution comparable to that of SOFI and an SNR comparable to that of the intensity image. We also demonstrate the effectiveness of our approach experimentally, specifically on cell samples where microtubules were stained with ATTO647N and imaged using a confocal microscope with a single photon fiber bundle camera, allowing for the simultaneous acquisition of an image scanning microscopy (ISM) image and a SOFISM (ISM and SOFI) image. Our network is designed as a self-supervised network and shows the ability to train on a single pair of images and to generalize to other image pairs without the need for additional training. Our approach offers a flexible and efficient way to combine the strengths of correlation based imaging techniques along with traditional intensity based microscopy, and can be readily applied to other fluctuation based imaging modalities.

Far-Field Super-Resolution Microscopy Using Evanescent Illumination: A Review

The resolution of conventional optical microscopy is restricted by the diffraction limit. Light waves containing higher-frequency information about the sample are bound to the sample surface and cannot be collected by far-field optical microscopy. To break the resolution limit, researchers have proposed various far-field super-resolution (SR) microscopy imaging methods using evanescent waves to transfer the high-frequency information of samples to the low-frequency passband of optical microscopy. Optimization algorithms are developed to reconstruct a SR image of the sample by utilizing the high-frequency information. These techniques can be collectively referred to as spatial-frequency-shift (SFS) SR microscopy. This review aims to summarize the basic principle of SR microscopy using evanescent illumination and introduce the advances in this research area. Some current challenges and possible directions are also discussed.

Single image Fourier ring correlation

We address resolution assessment for (light super-resolution) microscopy imaging. In modalities where imaging is not diffraction limited, correlation between two noise independent images is the standard way to infer the resolution. Here we take away the need for two noise independent images by computationally splitting one image acquisition into two noise independent realizations. This procedure generates two Poisson noise distributed images if the input is Poissonian distributed. As most modern cameras are shot-noise limited this procedure is directly applicable. However, also in the presence of readout noise we can compute the resolution faithfully via a correction factor. We evaluate our method on simulations and experimental data of widefield microscopy, STED microscopy, rescan confocal microscopy, image scanning microscopy, conventional confocal microscopy, and transmission electron microscopy. In all situations we find that using one image instead of two results in the same computed image resolution.

Prolonged Activity Deprivation Causes Pre- and Postsynaptic Compensatory Plasticity at Neocortical Excitatory Synapses.

Homeostatic plasticity stabilizes firing rates of neurons, but the pressure to restore low activity rates can significantly alter synaptic and cellular properties. Most previous studies of homeostatic readjustment to complete activity silencing in rodent forebrain have examined changes after 2 d of deprivation, but it is known that longer periods of deprivation can produce adverse effects. To better understand the mechanisms underlying these effects and to address how presynaptic as well as postsynaptic compartments change during homeostatic plasticity, we subjected mouse cortical slice cultures to a more severe 5 d deprivation paradigm. We developed and validated a computational framework to measure the number and morphology of presynaptic and postsynaptic compartments from super-resolution light microscopy images of dense cortical tissue. Using these tools, combined with electrophysiological miniature excitatory postsynaptic current measurements, and synaptic imaging at the electron microscopy level, we assessed the functional and morphological results of prolonged deprivation. Excitatory synapses were strengthened both presynaptically and postsynaptically. Surprisingly, we also observed a decrement in the density of excitatory synapses, both as measured from colocalized staining of pre- and postsynaptic proteins in tissue and from the number of dendritic spines. Overall, our results suggest that cortical networks deprived of activity progressively move toward a smaller population of stronger synapses.

Condensation of RNase L promotes its rapid activation in response to viral infection in mammalian cells.

Oligoadenylate synthetase 3 (OAS3) and ribonuclease L (RNase L) are components of a pathway that combats viral infection in mammals. Upon detection of viral double-stranded RNA (dsRNA), OAS3 synthesizes 2'-5'-oligo(A), which activates the RNase domain of RNase L by promoting the homodimerization and oligomerization of RNase L monomers. Activated RNase L rapidly degrades all cellular mRNAs, shutting off several cellular processes. We sought to understand the molecular mechanisms underlying the rapid activation of RNase L in response to viral infection. Through superresolution microscopy and live-cell imaging, we showed that OAS3 and RNase L concentrated into higher-order cytoplasmic complexes known as dsRNA-induced foci (dRIF) in response to dsRNA or infection with dengue virus, Zika virus, or West Nile virus. The concentration of OAS3 and RNase L at dRIF corresponded with the activation of RNase L-mediated RNA decay. We showed that dimerized/oligomerized RNase L concentrated in a liquid-like shell surrounding a core OAS3-dRIF structure and dynamically exchanged with the cytosol. These data establish that the condensation of dsRNA, OAS3, and RNase L into dRIF is a molecular switch that promotes the rapid activation of RNase L upon detection of dsRNA in mammalian cells.

Supported Biomembrane Systems Incorporating Multiarm Polymers and Bioorthogonal Tethering.

To functionalize interfaces with supported biomembranes and membrane proteins, the challenge is to build stabilized and supported systems that mimic the native lipid microenvironment. Our objective is to control substrate-to-biomembrane spacing and the tethering chemistry so proteoliposomes can be fused and conjugated without perturbation of membrane protein function. Furthermore, the substrates need to exhibit low protein and antibody nonspecific binding to use these systems in assays. We have employed protein orthogonal coupling schemes in concert with multiarm poly(ethylene glycol) (PEG) technology to build supported biomembranes on microspheres. The lipid bilayer structures and tailored substrates of the microsphere-supported biomembranes were analyzed via flow cytometry, confocal fluorescence, and super-resolution imaging microscopy, and the lateral fluidity was quantified using fluorescence recovery after photobleaching (FRAP) techniques. Under these conditions, the 4-arm-PEG20,000-NH2 based configuration gave the most desirable tethering system based on lateral diffusivity and coverage.

KCBC – a correlation-based method for co-localization analysis of super-resolution microscopy images using bivariate Ripley's K functions

Motivated by the high demand for co-localization analysis methods for super-resolution microscopy images which are featured with nanoscale precise locational information of molecules, this paper establishes a novel correlation-based method, KCBC, named after the Coordinated-Based Colocalization (CBC) method proposed by Malkusch et al. in 2012, by using bivariate Ripley's K functions. The local KCBC values are to quantify the local spatial co-localization of molecules between two species by measuring the correlation of bivariate Ripley's K functions over equal-area concentric rings around the base species within a near distance. The mean of local KCBC values is proposed to quantify the co-localization degree of cross-channel to base-channel molecules for the whole image. It could effectively correct the false positives with reduced variance and increased power within the user-defined proximity size. We provide extensive simulation studies under different scenarios to demonstrate the unbiasedness of the KCBC method, and its ability to filter noise signals and random over-counting. Our real data application for super-resolution mitochondria image data illustrates the applicability of our methods with increased effectiveness and power.

Machine learning-based 3D segmentation of mitochondria in polarized epithelial cells

Mitochondria are dynamic organelles that alter their morphological characteristics in response to functional needs. Therefore, mitochondrial morphology is an important indicator of mitochondrial function and cellular health. Reliable segmentation of mitochondrial networks in microscopy images is a crucial initial step for further quantitative evaluation of their morphology. However, 3D mitochondrial segmentation, especially in cells with complex network morphology, such as in highly polarized cells, remains challenging. To improve the quality of 3D segmentation of mitochondria in super-resolution microscopy images, we took a machine learning approach, using 3D Trainable Weka, an ImageJ plugin. We demonstrated that, compared with other commonly used methods, our approach segmented mitochondrial networks effectively, with improved accuracy in different polarized epithelial cell models, including differentiated human retinal pigment epithelial (RPE) cells. Furthermore, using several tools for quantitative analysis following segmentation, we revealed mitochondrial fragmentation in bafilomycin-treated RPE cells.

NanoFeatures: a cross-platform application to characterize nanoparticles from super-resolution microscopy images.

Super-resolution microscopy and Single-Molecule Localization Microscopy (SMLM) are powerful tools to characterize synthetic nanomaterials used for many applications such as drug delivery. In the last decade, imaging techniques like STORM, PALM, and PAINT have been used to study nanoparticle size, structure, and composition. While imaging has progressed significantly, image analysis has often not advanced accordingly and many studies remain limited to qualitative and semi-quantitative analyses. Therefore, it is imperative to have a robust and accurate method to analyze SMLM images of nanoparticles and extract quantitative features from them. Here, we introduce nanoFeatures, a cross-platform Matlab-based app for the automatic and quantitative analysis of super-resolution images. nanoFeatures makes use of clustering algorithms to identify nanoparticles from the raw data (localization list) and extract quantitative information about size, shape, and molecular abundance at the single-particle and single-molecule levels. Moreover, it applies a series of quality controls, increasing data quality and avoiding artifacts. nanoFeatures, thanks to its intuitive interface, is also accessible to non-experts and will facilitate analysis of super-resolution microscopy for materials scientists and nanotechnologies. This easy accessibility to expansive feature characterization at the single particle level will bring us one step closer to understanding the relationship between nanostructure features and their efficiency (https://github.com/n4nlab/nanoFeatures).

Isolation of Viral Biofilms From HTLV-1 Chronically Infected T Cells and Integrity Analysis.

The human T-lymphotropic virus type-1 (HTLV-1) is an oncogenic retrovirus that predominantly spreads through cell-to-cell contact due to the limited infectivity of cell-free viruses. Among various modes of intercellular transmission, HTLV-1 biofilms emerge as adhesive structures, polarized at the cell surface, which encapsulate virions within a protective matrix. This biofilm is supposed to facilitate simultaneous virion delivery during infection. Yet, the molecular and functional intricacies of viral biofilms remain largely unexplored, despite their pivotal role in understanding retroviral pathogenesis. In this study, we optimized a protocol to isolate HTLV-1 biofilms from chronically infected T cells, facilitating their structural and molecular characterization using proteomic and super-resolution microscopy analyses. This protocol involves cultivating HTLV-1 chronically infected T cells at high density to facilitate the natural detachment of viral biofilms into the supernatant. Then, employing successive centrifugations, the cells are separated from the detached biofilms, and these structures are pelleted at medium speed (10,000× g). This method circumvents the need for mechanical, chemical, or enzymatic biofilm detachment, bypasses the use of ultracentrifugation, and enables us to resuspend the biofilms in the appropriate buffer for subsequent analyses such as western blotting or super-resolution microscopy imaging as presented. Key features • Isolation of viral biofilms from HTLV-1 chronically infected T cells after 4 days of culture at high cellular density. • Structural analysis of viral biofilms using super-resolution microscopy techniques. • Experiments performed in vitro within a confined biosafety level 3 (BSL3) environment. • This protocol requires at least five days to complete.