Super-resolution Analysis Research Articles

A novel super-resolution STED microscopy analysis approach to observe spatial MCU and MICU1 distribution dynamics in cells.

The uptake of Ca2+ by mitochondria is an important and tightly controlled process in various tissues. Even small changes in the key proteins involved in this process can lead to significant cellular dysfunction and, ultimately, cell death. In this study, we used stimulated emission depletion (STED) microscopy and developed an unbiased approach to monitor the sub-mitochondrial distribution and dynamics of the mitochondrial calcium uniporter (MCU) and mitochondrial calcium uptake 1 (MICU1) under resting and stimulated conditions. To visualize the inner mitochondrial membrane, the STED-optimized dye called pkMitoRed was used. The study presented herein builds on the previously verified exclusive localization of MICU1 in the intermembrane space, and that MCU moves exclusively laterally along the inner mitochondrial membrane (IMM). We applied a multi-angled arrow histogram to analyze the distribution of proteins within mitochondria, providing a one-dimensional view of protein localization along a defined distance. Combining this with optimal transport colocalization enabled us to further predict submitochondrial protein distribution. Results indicate that in HeLa cells Ca2+ elevation yielded MCU translocation from the cristae membrane (CM) to the inner boundary membrane (IBM). In AC16 cardiomyocyte cell line, MCU is mainly located at the IBM under resting conditions, and it translocates to the CM upon rising Ca2+. Our data describe a novel unbiased super-resolution image analysis approach. Our showcase sheds light on differences in spatial distribution dynamics of MCU in cell lines with different MICU1:MCU abundance.

MeCP2 deficiency leads to the γH2AX nano foci expansion after ionizing radiation

DNA double-strand breaks (DSBs) trigger the recruitment of repair protein and promote signal transduction through posttranslational modifications such as phosphorylation. After DSB induction, ataxia telangiectasia mutated (ATM) phosphorylates H2AX on chromatin surrounds the mega-base pairs proximal to the DSBs. Advanced super-resolution microscopic technology has demonstrated the formation of γH2AX nano foci as a unit of nano domain comprised of multiple nucleosomes. The formation of γH2AX nano foci could be potentially affected by pre-existing chromatin structure prior to DSB induction; however, it remains unclear whether chromatin status around DSBs influences the formation of γH2AX nano foci. In this study, to investigate γH2AX nano foci formation in the context of chromatin relaxation, γH2AX nano foci were examined following the depletion of MeCP2, which is a factor promoting chromatin condensation. Remarkably, by using super resolution imaging analysis, we found that the volume of γH2AX nano foci cluster in MeCP2-depleted cells was significantly greater than that in control cells, both 5 and 30min after ionizing radiation (IR). Corresponding to the increased volume size, the number of γH2AX nano foci per cluster was greater than that in control cells, while the distance of each nano focus within foci clusters remained unchanged. These findings suggest that relaxed chromatin condition by MeCP2 depletion facilitates faster and more extensive γH2AX nano foci formation after IR. Collectively, our super-resolution analysis suggests that the chromatin status surrounding DSBs influences the expansion of γH2AX nano foci formation, thus, potentially influencing the DSB repair and signaling.

A Super-Resolution Analysis Method for Ionospheric Scattering Function

A Super-Resolution Analysis Method for Ionospheric Scattering Function

Super-resolution analysis of CT images based on HAT and self-integrated fusion method

Super-resolution analysis of CT images based on HAT and self-integrated fusion method

Super-Resolution Analysis of Remote Sensing Images Based on Cross-Attention

Super-Resolution Analysis of Remote Sensing Images Based on Cross-Attention

Structural and molecular properties of mumps virus inclusion bodies.

Viral RNA synthesis of mononegaviruses occurs in cytoplasmic membraneless organelles called inclusion bodies (IBs). Here, we report that IBs of mumps virus (MuV), which is the causative agent of mumps and belongs to the family Paramyxoviridae, displayed liquid organelle properties formed by liquid-liquid phase separation. Super-resolution microscopic analysis of MuV IBs demonstrated that nucleocapsid and phospho (P)-proteins formed a cage-like structure and that the viral polymerase adopted a reticular pattern and colocalized with viral RNAs. In addition, we characterized host RNAs localized in MuV IBs by a spatial transcriptome analysis, and found that RNAs containing G-quadruplex motif sequences (G4-RNAs) were concentrated. An in vitro phase separation assay showed that the G4-RNAs interacted with the P protein and enhanced condensation in P droplets. Together, our data show that MuV generates IBs with a characteristic cage-like structure and host G4-RNAs play an important role in forming MuV IBs.

Galaxy morphologies revealed with Subaru HSC and super-resolution techniques. II. Environmental dependence of galaxy mergers at z ∼ 2–5

Abstract We super-resolve the seeing-limited Subaru Hyper Suprime-Cam (HSC) images for 32187 galaxies at $z\sim 2$–5 using three techniques, namely, the classical Richardson–Lucy (RL) point spread function (PSF) deconvolution, sparse modeling, and generative adversarial networks, to investigate the environmental dependence of galaxy mergers. These three techniques generate overall similar high spatial resolution images but with some slight differences in galaxy structures; for example, more residual noises are seen in the classical RL PSF deconvolution. To alleviate the disadvantages of each technique, we create combined images by averaging over the three types of super-resolution images, resulting in galaxy substructures resembling those seen in the Hubble Space Telescope images. Using the combined super-resolution images, we measure the relative galaxy major merger fraction corrected for the chance projection effect, $f_{\rm merger}^{\rm rel,col}$, for galaxies in the $\sim$300 deg$^2$ area data of the HSC Strategic Survey Program and the CFHT Large Area U-band Survey. Our $f_{\rm merger}^{\rm rel,col}$ measurements at $z\sim 3$ validate previous findings showing that $f_{\rm merger}^{\rm rel,col}$ is higher in regions with a higher galaxy overdensity $\delta$ at $z\sim 2$–3. Thanks to the large galaxy sample, we identify a nearly linear increase in $f_{\rm merger}^{\rm rel,col}$ with increasing $\delta$ at $z\sim 4$–5, providing the highest-z observational evidence that galaxy mergers are related to $\delta$. In addition to our $f_{\rm merger}^{\rm rel,col}$ measurements, we find that the galaxy merger fractions in the literature also broadly align with the linear $f_{\rm merger}^{\rm rel,col}$–$\delta$ relation across a wide redshift range of $z\sim 2$–5. This alignment suggests that the linear $f_{\rm merger}^{\rm rel,col}$–$\delta$ relation can serve as a valuable tool for quantitatively estimating the contributions of galaxy mergers to various environmental dependences. This super-resolution analysis can be readily applied to datasets from wide field-of-view space telescopes such as Euclid and Roman.

Characterization and visualization of gas–liquid two-phase flow based on wire-mesh sensor

Gas–liquid two-phase flow is prevalent in both industrial production and the natural environment, making the exploration of its flow behavior critically important. In this study, we design a wire-mesh sensor and measurement system to conduct dynamic experiments on vertical upward gas–liquid two-phase flow, obtaining multi-channel data under various conditions. We develop an integrated limited penetrable visibility graph network (ILPVGNet) to quantitatively assess the dynamic transition from slug flow to bubble flow using network metrics. The results demonstrate that this approach effectively integrates multi-channel measurements, revealing the evolution dynamics and internal coupling mechanisms of gas–liquid two-phase flow. To better observe fine flow structures and details, we perform visualization and super-resolution analysis on the multi-channel data, significantly enhancing image clarity and restoring detailed features, thereby improving our ability to intuitively analyze complex gas–liquid flow behaviors.

MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

This paper presents a novel MRI super-resolution analysis model, MRISR. Through the utilization of generative adversarial networks for the estimation of degradation kernels and the injection of noise, we have constructed a comprehensive dataset of high-quality paired high- and low-resolution MRI images. The MRISR model seamlessly integrates VMamba and Transformer technologies, demonstrating superior performance across various no-reference image quality assessment metrics compared with existing methodologies. It effectively reconstructs high-resolution MRI images while meticulously preserving intricate texture details, achieving a fourfold enhancement in resolution. This research endeavor represents a significant advancement in the field of MRI super-resolution analysis, contributing a cost-effective solution for rapid MRI technology that holds immense promise for widespread adoption in clinical diagnostic applications.

Expanding Insights: Harnessing Expansion Microscopy for Super-Resolution Analysis of HIV-1–Cell Interactions

Expansion microscopy has recently emerged as an alternative technique for achieving high-resolution imaging of biological structures. Improvements in resolution are achieved by physically expanding samples through embedding in a swellable hydrogel before microscopy. However, expansion microscopy has been rarely used in the field of virology. Here, we evaluate and characterize the ultrastructure expansion microscopy (U-ExM) protocol, which facilitates approximately four-fold sample expansion, enabling the visualization of different post-entry stages of the HIV-1 life cycle, focusing on nuclear events. Our findings demonstrate that U-ExM provides robust sample expansion and preservation across different cell types, including cell-culture-adapted and primary CD4+ T-cells as well as monocyte-derived macrophages, which are known HIV-1 reservoirs. Notably, cellular targets such as nuclear bodies and the chromatin landscape remain well preserved after expansion, allowing for detailed investigation of HIV-1–cell interactions at high resolution. Our data indicate that morphologically distinct HIV-1 capsid assemblies can be differentiated within the nuclei of infected cells and that U-ExM enables detection of targets that are masked in commonly used immunofluorescence protocols. In conclusion, we advocate for U-ExM as a valuable new tool for studying virus–host interactions with enhanced spatial resolution.

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.

Direct measurements of luminal Ca 2+ with endo-lysosomal GFP-aequorin reveal functional IP 3 receptors.

Endo-lysosomes are considered acidic Ca 2+ stores but direct measurements of luminal Ca 2+ within them are limited. Here we report that the Ca 2+ -sensitive luminescent protein aequorin does not reconstitute with its cofactor at highly acidic pH but that a significant fraction of the probe is functional within a mildly acidic compartment when targeted to the endo-lysosomal system. We leveraged this probe (ELGA) to report Ca 2+ dynamics in this compartment. We show that Ca 2+ uptake is ATP-dependent and sensitive to blockers of endoplasmic reticulum Ca 2+ pumps. We find that the Ca 2+ mobilizing messenger IP 3 which typically targets the endoplasmic reticulum evokes robust luminal responses in wild type cells, but not in IP 3 receptor knock-out cells. Responses were comparable to those evoked by activation of the endo-lysosomal ion channel TRPML1. Stimulation with IP 3 -forming agonists also mobilized the store in intact cells. Super-resolution microscopy analysis confirmed the presence of IP 3 receptors within the endo-lysosomal system, both in live and fixed cells. Our data reveal a physiologically-relevant, IP 3 -sensitive store of Ca 2+ within the endo-lysosomal system.

The phenol red compound: A potential artifact in pharmacological induction of ferroptosis

Phenol red (PR) is a commonly used compound in culture media as a pH indicator. However, it is unknown whether this compound can interfere with the pharmacological induction of ferroptosis. Here, using high-content live-cell imaging death analysis, we determined that the presence of PR in the culture medium preconditioned normal and tumor cells to ferroptosis induced by system xc− inhibition mediated by imidazole ketone erastin (IKE) or GPX4 blockade in response to RSL-3, but had no significant effects against treatment with the endoperoxide FINO2. Mechanistically, we revealed that PR decreases the levels of the antiferroptotic genes Slc7a11, Slc3a2, and Gpx4, while promoting the overexpression de Acls4, a key inducer of ferroptosis. Additionally, through superresolution analysis, we determined that the presence of PR mislocalizes the system xc− from the plasma membrane. Thus, our results show that the presence of PR in the culture medium can be a problematic artifact for the accurate interpretation of cell sensitivity to IKE or RSL-3-mediated ferroptosis induction.

Nonlinear super-resolution signal processing allows intracellular tracking of calcium dynamics

Objective. Traditional quantification of fluorescence signals, such as ΔF/F , relies on ratiometric measures that necessitate a baseline for comparison, limiting their applicability in dynamic analyses. Our goal here is to develop a baseline-independent method for analyzing fluorescence data that fully exploits temporal dynamics to introduce a novel approach for dynamical super-resolution analysis, including in subcellular resolution. Approach. We introduce ARES (Autoregressive RESiduals), a novel method that leverages the temporal aspect of fluorescence signals. By focusing on the quantification of residuals following linear autoregression, ARES obviates the need for a predefined baseline, enabling a more nuanced analysis of signal dynamics. Main result. We delineate the foundational attributes of ARES, illustrating its capability to enhance both spatial and temporal resolution of calcium fluorescence activity beyond the conventional ratiometric measure ( ΔF/F ). Additionally, we demonstrate ARES’s utility in elucidating intracellular calcium dynamics through the detailed observation of calcium wave propagation within a dendrite. Significance. ARES stands out as a robust and precise tool for the quantification of fluorescence signals, adept at analyzing both spontaneous and evoked calcium dynamics. Its ability to facilitate the subcellular localization of calcium signals and the spatiotemporal tracking of calcium dynamics—where traditional ratiometric measures falter—underscores its potential to revolutionize baseline-independent analyses in the field.

OneFlowTraX: a user-friendly software for super-resolution analysis of single-molecule dynamics and nanoscale organization.

Super-resolution microscopy (SRM) approaches revolutionize cell biology by providing insights into the nanoscale organization and dynamics of macromolecular assemblies and single molecules in living cells. A major hurdle limiting SRM democratization is post-acquisition data analysis which is often complex and time-consuming. Here, we present OneFlowTraX, a user-friendly and open-source software dedicated to the analysis of single-molecule localization microscopy (SMLM) approaches such as single-particle tracking photoactivated localization microscopy (sptPALM). Through an intuitive graphical user interface, OneFlowTraX provides an automated all-in-one solution for single-molecule localization, tracking, as well as mobility and clustering analyses. OneFlowTraX allows the extraction of diffusion and clustering parameters of millions of molecules in a few minutes. Finally, OneFlowTraX greatly simplifies data management following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. We provide a detailed step-by-step manual and guidelines to assess the quality of single-molecule analyses. Applying different fluorophores including mEos3.2, PA-GFP, and PATagRFP, we exemplarily used OneFlowTraX to analyze the dynamics of plant plasma membrane-localized proteins including an aquaporin, the brassinosteroid receptor Brassinosteroid Insensitive 1 (BRI1) and the Receptor-Like Protein 44 (RLP44).

Spaced training activates Miro/Milton-dependent mitochondrial dynamics in neuronal axons to sustain long-term memory

Neurons have differential and fluctuating energy needs across distinct cellular compartments, shaped by brain electrochemical activity associated with cognition. Invitro studies show that mitochondria transport from soma to axons is key to maintaining neuronal energy homeostasis. Nevertheless, whether the spatial distribution of neuronal mitochondria is dynamically adjusted invivo in an experience-dependent manner remains unknown. In Drosophila, associative long-term memory (LTM) formation is initiated by an early and persistent upregulation of mitochondrial pyruvate flux in the axonal compartment of neurons in the mushroom body (MB). Through behavior experiments, super-resolution analysis of mitochondria morphology in the neuronal soma and invivo mitochondrial fluorescence recovery after photobleaching (FRAP) measurements in the axons, we show that LTM induction, contrary to shorter-lived memories, is sustained by the departure of some mitochondria from MB neuronal soma and increased mitochondrial dynamics in the axonal compartment. Accordingly, impairing mitochondrial dynamics abolished the increased pyruvate consumption, specifically after spaced training and in the MB axonal compartment, thereby preventing LTM formation. Our results thus promote reorganization of the mitochondrial network in neurons as an integral step in elaborating high-order cognitive processes.

Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables

Nonlinear machine learning for turbulent flows can exhibit robust performance even outside the range of training data. This is achieved when machine-learning models can accommodate scale-invariant characteristics of turbulent flow structures. This study presents a data-driven approach to reveal scale-invariant vortical structures across Reynolds numbers that provide insights for supporting nonlinear machine-learning-based studies of turbulent flows. To uncover conditions for which nonlinear models are likely to perform well, we use a Buckingham-Pi-based sparse nonlinear scaling to find the influence of the Pi groups on the turbulent flow data. We consider nonlinear scalings of the invariants of the velocity gradient tensor for an example of three-dimensional decaying isotropic turbulence. The present scaling not only enables the identification of vortical structures that are interpolatory and extrapolatory for the given flow field data but also captures non-equilibrium effects of the energy cascade. As a demonstration, the present findings are applied to machine-learning-based super-resolution analysis of three-dimensional isotropic turbulence. We show that machine-learning models reconstruct vortical structures well in the interpolatory space with reduced performance in the extrapolatory space revealed by the nonlinearly scaled invariants. The present approach enables us to depart from labelling turbulent flow data with a single parameter of Reynolds number and comprehensively examine the flow field to support training and testing of nonlinear machine-learning techniques.

In-section Click-iT detection and super-resolution CLEM analysis of nucleolar ultrastructure and replication in plants

Correlative light and electron microscopy (CLEM) is an important tool for the localisation of target molecule(s) and their spatial correlation with the ultrastructural map of subcellular features at the nanometre scale. Adoption of these advanced imaging methods has been limited in plant biology, due to challenges with plant tissue permeability, fluorescence labelling efficiency, indexing of features of interest throughout the complex 3D volume and their re-localization on micrographs of ultrathin cross-sections. Here, we demonstrate an imaging approach based on tissue processing and embedding into methacrylate resin followed by imaging of sections by both, single-molecule localization microscopy and transmission electron microscopy using consecutive CLEM and same-section CLEM correlative workflow. Importantly, we demonstrate that the use of a particular type of embedding resin is not only compatible with single-molecule localization microscopy but shows improvements in the fluorophore blinking behavior relative to the whole-mount approaches. Here, we use a commercially available Click-iT ethynyl-deoxyuridine cell proliferation kit to visualize the DNA replication sites of wild-type Arabidopsis thaliana seedlings, as well as fasciata1 and nucleolin1 plants and apply our in-section CLEM imaging workflow for the analysis of S-phase progression and nucleolar organization in mutant plants with aberrant nucleolar phenotypes.

SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis

The morphology of protein assemblies impacts their behaviour and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system- and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and a morphology fingerprinting module for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data: time-resolved insulin aggregates, and published data of dSTORM imaging of nuclear pore complexes, fibroblast growth receptor 1, sptPALM of Syntaxin 1a and dynamic live-cell PALM of ryanodine receptors. SEMORE extracts and quantifies all protein assemblies, their temporal morphology evolution and provides quantitative insights, e.g. classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being a time-aware framework can also support the rise of 4D super-resolution data.