Super-resolution Imaging Analysis Research Articles

Vessel recovery using ultrasound localisation microscopy: An in silico comparative study between minimum variance and delay-and-sum beamformers

The use of particle localisation and tracking algorithms on Contrast Enhanced Ultrasound (CEUS) or other ultrasound mode image data containing sparse microbubble (MB) populations, can produce super-resolved vascularization maps. Typically such data stem from conventional delay and sum (DAS) beamforming that is used widely in ultrasound imaging modes. Recently, adaptive beamforming has shown significant improvement in spatial resolution, but its value to super-resolution image analysis approaches is not fully understood. The in silico study here evaluates the performance of combining minimum variance beamformers (MV BF), established to provide improved lateral resolution, compared to DAS BFs with single particle detection. The isolated effect of a range of simplified image-affecting factors such as flow profile, pulse length, noise, vessel separations and data availability is considered. The study aims to assess the vessel recovery performance using the different beamformers and investigate the link with MB detection and localisation. The MV BF was shown to provide improved microvessel position accuracy compared to conventional DAS BFs. In particular, vessel separations between 0.3–4 λ provided superior localisation uncertainty with the MV. In addition, for a separation of 0.36λ, vessel recovery was achieved with both methods but the use of MV eliminated artifacts that appear as additional vessels. These results were found to be linked to improved MB detection and localisation for the MV BF, which is proposed as suitable for testing in Ultrasound Localisation Microscopy (ULM) imaging using patient data.

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.

D-SRCAGAN : DEM Super-resolution Generative Adversarial Network

High-resolution digital elevation models (DEMs) are widely used in many fields such as mapping, hydrology, meteorology and geology, where they can improve the accuracy and reliability of many geographic analysis applications as an input. However, due to the high cost and difficulty of acquiring high-resolution DEMs, as well as the problems of edge smoothing, data distortion and fractures in reconstructed ground surfaces with traditional super-resolution DEM reconstruction techniques. Inspired by the excellence of generative adversarial neural networks in super-resolution image analysis, this paper investigates an approach for DEM super-resolution reconstruction with deep residual generative adversarial network. An advanced DEM Super-resolution Generative Adversarial Network (D-SRCAGAN) is proposed in this paper, which can reconstruct a quadruple higher resolution DEM by using low-resolution DEM. Compared with the bicubic and SRGAN methods, the D-SRCAGAN method reconstruction results can retain more topographic features and obtain higher RMSE values.

(Invited) Carbon Nanotube Tracking Reveal the Landscapes of the Brain Extracellular Space Around Synapses and in Pathological Conditions

In recent years, exploration of the brain extracellular space (ECS) has made remarkable progress, including nanoscopic characterizations based on luminescent single wall carbon nanotubes imaging [1]. In this case, single nanotube tracking is used combined with super-resolution imaging analysis to provide both local measures of the nanoscale dimensions of the brain network and the ECS molecular diffusion landscape. We will show that this approach can be applied to reveal the nanoscale organization of pathological ECS in adult mice under degenerative conditions (α-synuclein-induced neurodegeneration) [2]. Another application will concern the investigation of the ECS around synapses. We will reveal specific ECS properties in terms of morphology at the nanoscale and inner diffusivity which are modulated upon synaptic activity [3].

Structure-Based Analysis of Protein Cluster Size for Super-Resolution Microscopy in the Nervous System

To overcome the diffraction limit and resolve target structures in greater detail, far-field super-resolution techniques such as stochastic optical reconstruction microscopy (STORM) have been developed, and different STORM algorithms have been developed to deal with the various problems that arise. In particular, the effect of the local structure is an important issue. For objects with closely correlated distributions, simple Gaussian-based localization algorithms often used in STORM imaging misinterpret overlapping point spread functions (PSFs) as one, which limits the ability of super-resolution imaging to resolve nanoscale local structures and leads to inaccurate length measurements. The STORM super-resolution images of biological specimens from the cluster-forming proteins in the nervous system were reconstructed for localization-based analysis. Generally, the localization of each fluorophore was determined by two-dimensional Gaussian function fitting. Further, the physical shape of the cluster structure information was incorporated into the size parameter of the localization structure analysis in order to generate structure-based fitting algorithms. In the present study, we proposed a novel, structure-based, super-resolution image analysis method: structure-based analysis (SBA), which combines a structural function and a super-resolution localization algorithm. Using SBA, we estimated the size of fluorescent beads, inclusion proteins, and subtle synaptic structures in both wide-field and STORM images. The results show that SBA has a comparable and often superior performance to the commonly used full width at half maximum (FWHM) parameter. We demonstrated that SBA is able to estimate molecular cluster sizes in far-field super-resolution STORM images, and that SBA was comparable and often superior to FWHM. We also certified that SBA provides size estimations that corroborate previously published electron microscopy data.

A Picture Worth a Thousand Molecules-Integrative Technologies for Mapping Subcellular Molecular Organization and Plasticity in Developing Circuits.

A key challenge in developmental neuroscience is identifying the local regulatory mechanisms that control neurite and synaptic refinement over large brain volumes. Innovative molecular techniques and high-resolution imaging tools are beginning to reshape our view of how local protein translation in subcellular compartments drives axonal, dendritic, and synaptic development and plasticity. Here we review recent progress in three areas of neurite and synaptic study in situ—compartment-specific transcriptomics/translatomics, targeted proteomics, and super-resolution imaging analysis of synaptic organization and development. We discuss synergies between sequencing and imaging techniques for the discovery and validation of local molecular signaling mechanisms regulating synaptic development, plasticity, and maintenance in circuits.

Synucleinopathy alters nanoscale organization and diffusion in the brain extracellular space through hyaluronan remodeling

In recent years, exploration of the brain extracellular space (ECS) has made remarkable progress, including nanoscopic characterizations. However, whether ECS precise conformation is altered during brain pathology remains unknown. Here we study the nanoscale organization of pathological ECS in adult mice under degenerative conditions. Using electron microscopy in cryofixed tissue and single nanotube tracking in live brain slices combined with super-resolution imaging analysis, we find enlarged ECS dimensions and increased nanoscale diffusion after α-synuclein-induced neurodegeneration. These animals display a degraded hyaluronan matrix in areas close to reactive microglia. Furthermore, experimental hyaluronan depletion in vivo reduces dopaminergic cell loss and α-synuclein load, induces microgliosis and increases ECS diffusivity, highlighting hyaluronan as diffusional barrier and local tissue organizer. These findings demonstrate the interplay of ECS, extracellular matrix and glia in pathology, unraveling ECS features relevant for the α-synuclein propagation hypothesis and suggesting matrix manipulation as a disease-modifying strategy.

Nanoscale exploration of the extracellular space in the live brain by combining single carbon nanotube tracking and super-resolution imaging analysis.

The brain extracellular space (ECS) is a system of narrow compartments whose intricate nanometric structure has remained elusive until very recently. Understanding such a complex organisation represents a technological challenge that requires a technique able to resolve these nanoscopic spaces and simultaneously characterize their rheological properties. We recently used single-walled carbon nanotubes (SWCNTs) as near-infrared fluorescent probes to map with nanoscale precision the local organization and rheology of the ECS. Here we expand our method by tracking single nanotubes through super-resolution imaging in rat organotypic hippocampal slices and acute brain slices from adult mice, pioneering the exploration of the adult brain ECS at the nanoscale. We found a highly heterogeneous ECS, where local rheological properties can change drastically within few nanometres. Our results suggest differences in local ECS diffusion environments in organotypic slices when compared to adult mouse slices. Data obtained from super-resolved maps of the SWCNT trajectories indicate that ECS widths may vary between brain tissue models, with a looser, less crowded nano-environment in organotypic cultured slices.

Fast Frequency-Domain Compressed Sensing Analysis for High-Density Super-Resolution Imaging Using Orthogonal Matching Pursuit

Single-molecule localization methods play a vital role in a localization-based super-resolution fluorescence microscopy. However, it is difficult for conventional localization schemes based on the Gaussian fitting to locate overlapped high-density fluorescent emitters. Currently, in the spatial domain, the compressive-sensing-based algorithm (CSSTORM) can localize high-emitter-density images. However, the computational cost of this approach is extremely high, which limits its practical application. Here, we propose an alternative frequency-domain compressed sensing (FD-CS) technique for fast super-resolution imaging. Unlike the CSSTORM method, which is a measurement matrix based on the point spread function, a Fourier dictionary designed in the frequency domain and orthogonal matching pursuit is used to reliably recover the original signal. The simulation and experimental results prove that the FD-CS is 1000 times faster than CSSTORM with CVX and ten times faster than that with L1-Homotopy with almost the same localization accuracy and recall rate. This drastic reduction in computational time should allow the compressed sensing approach to be routinely applied to a super-resolution image analysis.

A method for estimating relative changes in the synaptic density in Drosophila central nervous system

BackgroundSynapse density is an essential indicator of development and functioning of the central nervous system. It is estimated indirectly through the accumulation of pre and postsynaptic proteins in tissue sections. 3D reconstruction of the electron microscopic images in serial sections is one of the most definitive means of estimating the formation of active synapses in the brain. It is tedious and highly skill-dependent. Confocal imaging of whole mounts or thick sections of the brain provides a natural alternative for rapid gross estimation of the synapse density in large areas. The optical resolution and other deep-tissue imaging aberrations limit the quantitative scope of this technique.ResultsHere we demonstrate a simple sample preparation method that could enhance the clarity of the confocal images of the neuropil regions of the ventral nerve cord of Drosophila larvae, providing a clear view of synapse distributions. We estimated the gross volume occupied by the synaptic junctions using 3D object counter plug-in of Fiji/ImageJ®. It gave us a proportional estimate of the number of synaptic junctions in the neuropil region. The method is corroborated by correlated super-resolution imaging analysis and through genetic perturbation of synaptogenesis in the larval brain.ConclusionsThe method provides a significant improvement in the relative estimate of region-specific synapse density in the central nervous system. Also, it reduced artifacts in the super-resolution images obtained using the stimulated emission depletion microscopy technique.

Single cell analysis revealed distinct B-cell subpopulations that produce or respond to type I interferon in SLE

Abstract We previously showed that B-cell endogenous expression of interferon beta (IFNβ) is associated with the development of autoreactive B cells in BXD2 autoimmune mice. As among all type I IFNs, IFNβ exhibits the highest affinity to IFNAR1 and IFNAR2 and IFNβ also can stimulate IFNαs, there is a pressing need for a better understanding of the source and responses of IFNβ in systemic lupus erythematosus (SLE). Flow cytometry and super-resolution imaging analysis showed increased expression of IFNβ in B cells obtained from PBMCs of SLE patients compared to healthy controls. The importance of SLE B-cell endogenous IFNβ in regulating TLR7-mediated responses was identified when CL264 (a TLR7 ligand) plus anti-Ig-induced CD69 and survival of purified B cells were significantly and equivalently suppressed by an anti-IFNβ or an anti-IFNAR1 blocking antibody. We next carried out single cell qRT-PCR analysis to determine if B-cell endogenous IFNβ acts in an autocrine or paracrine manner to modulate IFN stimulated genes (ISGs). Hierarchical analysis revealed three prominent and consistent clusters, consisting of an IFNB+ cluster, an ISG+ cluster, and an IFNA+ cluster in naïve B cells isolated from 3 SLE patients. Cells within the IFNB+ cluster expressed higher levels of IFNB, IFNA1, IFNA8, and TLR7. Cells within the ISG+ cluster expressed higher levels of IFNAR1, IFNAR2, BAFFR, MX1, RIG1 and TLR9. Cells within the IFNA+ cluster expressed higher levels of IFNA4, IFNA10, IFNA14, IFNA16, IFNA17, and TLR3. Together, the results reveal (1) B cells as an important source of IFNβ in modulating TLR7 responses in SLE; (2) a well-orchestrated program in modulating IFNβ donor versus responder B cells; and (3) some patients may benefit from specific targeting of IFNβ.

Evaluating single molecule detection methods for microarrays with high dynamic range for quantitative single cell analysis

Single molecule microarrays have been used in quantitative proteomics, in particular, single cell analysis requiring high sensitivity and ultra-low limits of detection. In this paper, several image analysis methods are evaluated for their ability to accurately enumerate single molecules bound to a microarray spot. Crucially, protein abundance in single cells can vary significantly and may span several orders of magnitude. This poses a challenge to single molecule image analysis. In order to quantitatively assess the performance of each method, synthetic image datasets are generated with known ground truth whereby the number of single molecules varies over 5 orders of magnitude with a range of signal to noise ratios. Experiments were performed on synthetic datasets whereby the number of single molecules per spot corresponds to realistic single cell distributions whose ground truth summary statistics are known. The methods of image analysis are assessed in their ability to accurately estimate the distribution parameters. It is shown that super-resolution image analysis methods can significantly improve counting accuracy and better cope with single molecule congestion. The results highlight the challenge posed by quantitative single cell analysis and the implications to performing such analyses using microarray based approaches are discussed.

Mitochondrial Ca2+ Uniporter Is a Mitochondrial Luminal Redox Sensor that Augments MCU Channel Activity

Ca2+ dynamics and oxidative signaling are fundamental mechanisms for mitochondrial bioenergetics and cell function. The MCU complex is the major pathway by which these signals are integrated inmitochondria. Whether and how these coactive elements interact with MCU have not been established. As an approach toward understanding the regulation of MCU channel by oxidative milieu, weadapted inflammatory and hypoxia models. Weidentified the conserved cysteine 97 (Cys-97) to be the only reactive thiol in human MCU that undergoes S-glutathionylation. Furthermore, biochemical, structural, and superresolution imaging analysis revealed that MCU oxidation promotes MCU higher order oligomer formation. Both oxidation and mutation of MCU Cys-97 exhibited persistent MCU channel activity with higher [Ca2+]m uptake rate, elevated mROS, and enhanced [Ca2+]m overload-induced cell death. In contrast, these effects were largely independent of MCU interaction with its regulators. These findings reveal a distinct functional role for Cys-97 in ROS sensing and regulation of MCU activity.

Alterations in thin filament length during postnatal skeletal muscle development and aging in mice.

The lengths of the sarcomeric thin filaments vary in a skeletal muscle-specific manner and help specify the physiological properties of skeletal muscle. Since the extent of overlap between the thin and thick filaments determines the amount of contractile force that a sarcomere can actively produce, thin filament lengths are accurate predictors of muscle-specific sarcomere length-tension relationships and sarcomere operating length ranges. However, the striking uniformity of thin filament lengths within sarcomeres, specified during myofibril assembly, has led to the widely held assumption that thin filament lengths remain constant throughout an organism's lifespan. Here, we rigorously tested this assumption by using computational super-resolution image analysis of confocal fluorescence images to explore the effects of postnatal development and aging on thin filament length in mice. We found that thin filaments shorten in postnatal tibialis anterior (TA) and gastrocnemius muscles between postnatal days 7 and 21, consistent with the developmental program of myosin heavy chain (MHC) gene expression in this interval. By contrast, thin filament lengths in TA and extensor digitorum longus (EDL) muscles remained constant between 2 mo and 2 yr of age, while thin filament lengths in soleus muscle became shorter, suggestive of a slow-muscle-specific mechanism of thin filament destabilization associated with aging. Collectively, these data are the first to show that thin filament lengths change as part of normal skeletal muscle development and aging, motivating future investigations into the cellular and molecular mechanisms underlying thin filament adaptation across the lifespan.

Fast compressed sensing analysis for super-resolution imaging using L1-homotopy

In super-resolution imaging techniques based on single-molecule switching and localization, the time to acquire a super-resolution image is limited by the maximum density of fluorescent emitters that can be accurately localized per imaging frame. In order to increase the imaging rate, several methods have been recently developed to analyze images with higher emitter densities. One powerful approach uses methods based on compressed sensing to increase the analyzable emitter density per imaging frame by several-fold compared to other reported approaches. However, the computational cost of this approach, which uses interior point methods, is high, and analysis of a typical 40 µm x 40 µm field-of-view super-resolution movie requires thousands of hours on a high-end desktop personal computer. Here, we demonstrate an alternative compressed-sensing algorithm, L1-Homotopy (L1H), which can generate super-resolution image reconstructions that are essentially identical to those derived using interior point methods in one to two orders of magnitude less time depending on the emitter density. Moreover, for an experimental data set with varying emitter density, L1H analysis is ~300-fold faster than interior point methods. This drastic reduction in computational time should allow the compressed sensing approach to be routinely applied to super-resolution image analysis.

Super-Resolution Imaging Strategies for Cell Biologists Using a Spinning Disk Microscope

In this study we use a spinning disk confocal microscope (SD) to generate super-resolution images of multiple cellular features from any plane in the cell. We obtain super-resolution images by using stochastic intensity fluctuations of biological probes, combining Photoactivation Light-Microscopy (PALM)/Stochastic Optical Reconstruction Microscopy (STORM) methodologies. We compared different image analysis algorithms for processing super-resolution data to identify the most suitable for analysis of particular cell structures. SOFI was chosen for X and Y and was able to achieve a resolution of ca. 80 nm; however higher resolution was possible >30 nm, dependant on the super-resolution image analysis algorithm used. Our method uses low laser power and fluorescent probes which are available either commercially or through the scientific community, and therefore it is gentle enough for biological imaging. Through comparative studies with structured illumination microscopy (SIM) and widefield epifluorescence imaging we identified that our methodology was advantageous for imaging cellular structures which are not immediately at the cell-substrate interface, which include the nuclear architecture and mitochondria. We have shown that it was possible to obtain two coloured images, which highlights the potential this technique has for high-content screening, imaging of multiple epitopes and live cell imaging.

Ultrafast, accurate, and robust localization of anisotropic dipoles

The resolution of single molecule localization imaging techniques largely depends on the precision of localization algorithms. However, the commonly used Gaussian function is not appropriate for anisotropic dipoles because it is not the true point spread function. We derived the theoretical point spread function of tilted dipoles with restricted mobility and developed an algorithm based on an artificial neural network for estimating the localization, orientation and mobility of individual dipoles. Compared with fitting-based methods, our algorithm demonstrated ultrafast speed and higher accuracy, reduced sensitivity to defocusing, strong robustness and adaptability, making it an optimal choice for both two-dimensional and three-dimensional super-resolution imaging analysis.

Super-resolution image analysis as a means of monitoring bracken (Pteridium aquilinum) distributions

The bracken (Pteridium aquilinum) fern is environmentally significant due to its great abundance and swift colonisation, and its perception as a problem plant in degrading agricultural or ecologically sensitive land. Various attempts have been made to map bracken using remote sensing, but these have proved relatively unsuccessful, often apparently constrained by the lack of spatial detail associated with medium spatial resolution satellite sensors such as the Landsat series. In this study, bracken was characterised using a combination of 30m Landsat sensor imagery and 4m IKONOS imagery. Different classification techniques were compared, including hard maximum likelihood classification and a super-resolution approach comprising soft classification and sub-pixel contouring. These techniques were applied to a range of image dates, including summer, winter and multitemporal images. Image analysis was supported by extensive field data collection, comprising both a land cover survey and stakeholder interviews. For the hard classified Landsat sensor imagery, the summer image proved least able to characterise bracken, due largely to the spectral similarity between (green) growing bracken and grasses and other vegetation. The winter images were more successful for identifying bracken due to the strong contrast between dead (brown/red) bracken and other vegetation. However, the multitemporal Landsat image was considerably more accurate than any of the single date images. The hard classified IKONOS image was more accurate overall than the Landsat sensor images for classifying land cover. Surprisingly, though, it was not comprehensively more accurate for mapping the bracken class. Notably, the producers accuracy of bracken was lower for the IKONOS image than the Landsat sensor images. This suggests image spatial resolution, although influential on the success of bracken characterisation, is not necessarily the sole or main determinant of classification accuracy. Also important are the temporal nature of image acquisition (here the multitemporal Landsat sensor image proved of considerable benefit) and the spectral characteristics of the imagery (here IKONOS’s four visible and near infrared spectral wavebands proved limited compared to the Landsat sensors’ six visible, near and shortwave infrared bands). Following soft classification of the multitemporal Landsat image, super-resolution sub-pixel contouring was applied to identify the boundary of bracken patches. Predicted bracken boundaries were assessed against actual boundaries identified using field observation and IKONOS image interpretation. For comparison, the bracken boundaries identified through hard classification (i.e. using pixel edges) were also assessed against the actual boundaries. Overall, the spatial accuracy of the super-resolution approach proved considerably higher than that of hard classification.