Localization-based Super-resolution Microscopy Research Articles

Enhanced fluorescence blinking of AF647 fluorophores in Mowiol via violet and UV light induced recovery for superior localization microscopy

Blinking of fluorophores is essential in the context of single molecule localization-based optical super-resolution microscopy methods. To make the fluorescence molecule undergo blinking specific complex chemical mounting buffer systems, combined with suitable oxygen scavengers, and reducing agents are required. For instance to realise blinking in widely used fluorescence tags, like Alexa Fluor 647 (AF647), they are to be mounted on anti-fading buffer such as Mowiol and reducing agent such as Beta (β) - ME. However, the quality of the super-resolved images is decided by the total number of blinking events or in other words net duration for which the fluorescence blinking persists. In this paper we investigate how a violet and UV light induced fluorescence recovery mechanism can enhance the duration of fluorescence blinking. Our study uses AF647 dye conjugated with Phalloidin antibody in U87MG cell line mounted on Mowiol and β - ME. On the basis of the investigation we optimize the intensity, at the sample plane, of fluorescence excitation laser at 638 nm and fluorescence recovery beam at 405 nm or in the UV giving the maximum possible fluorescence blinking duration. We observe that the longer blinking duration, using the optimized illumination scheme, has brought down the resolution in the super-resolved image, as given by Fourier Ring Correlation method, from 168 nm to 112 nm, while the separation between two nearby resolvable filaments has been brought down to ≤ 60 nm.

Single-molecule orientation-localization microscopy resolves the nanoscale organization of self-assembled peptides.

Self-assembling peptides have remarkable applications in biomaterials and therapeutics. Amphipathic peptides with alternating polar and nonpolar residues tend to self-assemble into cross-β-rich fibrils, which are usually signatures of various protein-misfolding pathologies. Key to understanding the mechanisms of self-assembly is the ability to visualize and quantify the complex, heterogeneous organization of these peptides with single-aggregate sensitivity. Here, we utilize single-molecule orientation-localization microscopy (SMOLM), a variant of super-resolution microscopy, to measure amphipathic KFE8 (Ac-FKFEFKFE-NH2) assemblies. Using the transient binding of Nile red (NR) as a blinking mechanism, we find that the orientation distribution of NR on KFE8 is significantly different from that bound to amyloid-beta fibrils, where NR is simply oriented parallel to the coverslip and along the long axis of each fiber. A detailed analysis of NR orientations on KFE8 shows that they bind in a manner consistent with a helical ribbon model of the underlying peptide assembly. Further, the 3D orientations of NR enable us to quantify the pitch-to-diameter ratio of the underlying helix. Finally, we show that SMOLM can resolve a variety of nanoscale structures formed by model self-assembling peptides. Due to limited spatial resolution, standard localization-based super-resolution microscopy cannot resolve these details. These experiments are the first demonstration of utilizing fluorophore orientation “spectra”, i.e., the 3D orientations of dye molecules, to resolve the complex nanoscale organization of self-assembled peptides in solution.

Synthesis and Characterization of Dye-Doped Au@SiO2 Core-Shell Nanoparticles for Super-Resolution Fluorescence Microscopy.

Dye-doped nanoparticles have been investigated as bright, fluorescent probes for localization-based super-resolution microscopy. Nanoparticle size is important in super-resolution microscopy to get an accurate size of the object of interest from image analysis. Due to their self-blinking behavior and metal-enhanced fluorescence (MEF), Ag@SiO2 and Au@Ag@SiO2 nanoparticles have shown promise as probes for localization-based super-resolution microscopy. Here, several noble metal-based dye-doped core-shell nanoparticles have been investigated as self-blinking nanomaterial probes. It was observed that both the gold- and silver-plated nanoparticle cores exhibit weak luminescence under certain conditions due to the surface plasmon resonance bands produced by each metal, and the gold cores exhibit blinking behavior which enhances the blinking and fluorescence of the dye-doped nanoparticle. However, the silver-plated nanoparticle cores, while weakly luminescent, did not exhibit any blinking; the dye-doped nanoparticle exhibited the same behavior as the core fluorescent, but did not blink. Because of the blinking behavior, stochastic optical reconstruction microscopy (STORM) super-resolution analysis was able to be performed with performed on the gold core nanoparticles. A preliminary study on the use of these nanoparticles for localization-based super-resolution showed that these nanoparticles are suitable for use in STORM super resolution. Resolution enhancement was two times better than the diffraction limited images, with core sizes reduced to 15 nm using the hybrid Au-Ag cores.

Actin nano-architecture of phagocytic podosomes

Podosomes are actin-enriched adhesion structures important for multiple cellular processes, including migration, bone remodeling, and phagocytosis. Here, we characterize the structure and organization of phagocytic podosomes using interferometric photoactivated localization microscopy, a super-resolution microscopy technique capable of 15–20 nm resolution, together with structured illumination microscopy and localization-based super-resolution microscopy. Phagocytic podosomes are observed during frustrated phagocytosis, a model in which cells attempt to engulf micropatterned IgG antibodies. For circular patterns, this results in regular arrays of podosomes with well-defined geometry. Using persistent homology, we develop a pipeline for semi-automatic identification and measurement of podosome features. These studies reveal an hourglass shape of the podosome actin core, a protruding knob at the bottom of the core, and two actin networks extending from the core. Additionally, the distributions of paxillin, talin, myosin II, α-actinin, cortactin, and microtubules relative to actin are characterized.

Precise measurement of nanoscopic septin ring structures with deep learning-assisted quantitative superresolution microscopy.

The combination of image analysis and superresolution microscopy methods allows for unprecedented insight into the organization of macromolecular assemblies in cells. Advances in deep learning (DL)-based object recognition enable the automated processing of large amounts of data, resulting in high accuracy through averaging. However, while the analysis of highly symmetric structures of constant size allows for a resolution approaching the dimensions of structural biology, DL-based image recognition may introduce bias. This prohibits the development of readouts for processes that involve significant changes in size or shape of amorphous macromolecular complexes. Here we address this problem by using changes of septin ring structures in single molecule localization-based superresolution microscopy data as a paradigm. We identify potential sources of bias resulting from different training approaches by rigorous testing of trained models using real or simulated data covering a wide range of possible results. In a quantitative comparison of our models, we find that a trade-off exists between measurement accuracy and the range of recognized phenotypes. Using our thus verified models, we find that septin ring size can be explained by the number of subunits they are assembled from alone. Furthermore, we provide a new experimental system for the investigation of septin polymerization.

Prior information improves resolution

Statistical parameter estimation (1) has enabled nearly two decades of localization-based superresolution microscopy: fluorescent probes are imaged one by one, yielding estimates of molecular positions by fitting point spread function (PSF) models to the data. The map of fluorescent probe locations comprises an image in its own right, with resolution set by the precision of the molecular position estimates (2). A newer generation of superresolution techniques improve the resolution by augmenting image data with prior information on the likely positions of fluorescent molecules (3,4).

Localization-Based Super-Resolution Microscopy Reveals Relationship between SARS-CoV2 Spike and Phosphatidylinositol (4,5)-bisphosphate.

Localization microscopy circumvents the diffraction limit by identifying and measuring the positions of numerous subsets of individual fluorescent molecules, ultimately producing an image whose resolution depends on the uncertainty and density of localization, and whose capabilities are compatible with imaging living specimens. Spectral resolution can be improved by incorporating a dichroic or dispersive element in the detection path of a localization microscope, which can be useful for separation of multiple probes imaged simultaneously and for detection of changes in emission spectra of fluorophores resulting from changes in their environment. These methodological advances enable new biological applications, which in turn motivate new questions and technical innovations. As examples, we present fixed-cell imaging of the spike protein SARS-CoV2 (S) and its interactions with host cell components. Results show a relationship between S and the lipid phosphatidylinositol (4,5)-bisphosphate (PIP2). These findings have ramifications for several existing models of plasma membrane organization.

Parametric comparison between sparsity-based and deep learning-based image reconstruction of super-resolution fluorescence microscopy.

Sparsity-based and deep learning-based image reconstruction algorithms are two promising approaches to accelerate the image acquisition process for localization-based super-resolution microscopy, by allowing a higher density of fluorescing emitters to be imaged in a single frame. Despite the surging popularity, a comprehensive parametric study guiding the practical applications of sparsity-based and deep learning-based image reconstruction algorithms is yet to be conducted. In this study, we examined the performance of sparsity- and deep learning-based algorithms in reconstructing super-resolution images using simulated fluorescent microscopy images. The simulated images were synthesized with varying levels of sparsity and connectivity. We found the deep learning-based VDSR recovers image faster, with a higher recall rate and localization accuracy. The sparsity-based SPIDER recovers more zero pixels truthfully. We also compared the two algorithms using images acquired from a real super-resolution experiment, yielding results agreeing with the results from the evaluation using simulated images. We concluded that VDSR is preferable when accurate emitter localization is needed while SPIDER is more suitable when evaluation of the number of emitters is critical.

Application of convolutional neural networks towards nuclei segmentation in localization-based super-resolution fluorescence microscopy images

BackgroundAutomated segmentation of nuclei in microscopic images has been conducted to enhance throughput in pathological diagnostics and biological research. Segmentation accuracy and speed has been significantly enhanced with the advent of convolutional neural networks. A barrier in the broad application of neural networks to nuclei segmentation is the necessity to train the network using a set of application specific images and image labels. Previous works have attempted to create broadly trained networks for universal nuclei segmentation; however, such networks do not work on all imaging modalities, and best results are still commonly found when the network is retrained on user specific data. Stochastic optical reconstruction microscopy (STORM) based super-resolution fluorescence microscopy has opened a new avenue to image nuclear architecture at nanoscale resolutions. Due to the large size and discontinuous features typical of super-resolution images, automatic nuclei segmentation can be difficult. In this study, we apply commonly used networks (Mask R-CNN and UNet architectures) towards the task of segmenting super-resolution images of nuclei. First, we assess whether networks broadly trained on conventional fluorescence microscopy datasets can accurately segment super-resolution images. Then, we compare the resultant segmentations with results obtained using networks trained directly on our super-resolution data. We next attempt to optimize and compare segmentation accuracy using three different neural network architectures.ResultsResults indicate that super-resolution images are not broadly compatible with neural networks trained on conventional bright-field or fluorescence microscopy images. When the networks were trained on super-resolution data, however, we attained nuclei segmentation accuracies (F1-Score) in excess of 0.8, comparable to past results found when conducting nuclei segmentation on conventional fluorescence microscopy images. Overall, we achieved the best results utilizing the Mask R-CNN architecture.ConclusionsWe found that convolutional neural networks are powerful tools capable of accurately and quickly segmenting localization-based super-resolution microscopy images of nuclei. While broadly trained and widely applicable segmentation algorithms are desirable for quick use with minimal input, optimal results are still found when the network is both trained and tested on visually similar images. We provide a set of Colab notebooks to disseminate the software into the broad scientific community (https://github.com/YangLiuLab/Super-Resolution-Nuclei-Segmentation).

Probing Chromatin Compaction and Its Epigenetic States in situ With Single-Molecule Localization-Based Super-Resolution Microscopy.

Chromatin organization play a vital role in gene regulation and genome maintenance in normal biological processes and in response to environmental insults. Disruption of chromatin organization imposes a significant effect on many cellular processes and is often associated with a range of pathological processes such as aging and cancer. Extensive attention has been attracted to understand the structural and functional studies of chromatin architecture. Biochemical assays coupled with the state-of-the-art genomic technologies have been traditionally used to probe chromatin architecture. Recent advances in single molecule localization microscopy (SMLM) open up new opportunities to directly visualize higher-order chromatin architecture, its compaction status and its functional states at nanometer resolution in the intact cells or tissue. In this review, we will first discuss the recent technical advantages and challenges of using SMLM to image chromatin architecture. Next, we will focus on the recent applications of SMLM for structural and functional studies to probe chromatin architecture in key cellular processes. Finally, we will provide our perspectives on the recent development and potential applications of super-resolution imaging of chromatin architecture in improving our understanding in diseases.

Accelerating 3D single-molecule localization microscopy using blind sparse inpainting.

.Significance: Single-molecule localization-based super-resolution microscopy has enabled the imaging of microscopic objects beyond the diffraction limit. However, this technique is limited by the requirements of imaging an extremely large number of frames of biological samples to generate a super-resolution image, thus requiring a longer acquisition time. Additionally, the processing of such a large image sequence leads to longer data processing time. Therefore, accelerating image acquisition and processing in single-molecule localization microscopy (SMLM) has been of perennial interest.Aim: To accelerate three-dimensional (3D) SMLM imaging by leveraging a computational approach without compromising the resolution.Approach: We used blind sparse inpainting to reconstruct high-density 3D images from low-density ones. The low-density images are generated using much fewer frames than usually needed, thus requiring a shorter acquisition and processing time. Therefore, our technique will accelerate 3D SMLM without changing the existing standard SMLM hardware system and labeling protocol.Results: The performance of the blind sparse inpainting was evaluated on both simulation and experimental datasets. Superior reconstruction results of 3D SMLM images using up to 10-fold fewer frames in simulation and up to 50-fold fewer frames in experimental data were achieved.Conclusions: We demonstrate the feasibility of fast 3D SMLM imaging leveraging a computational approach to reduce the number of acquired frames. We anticipate our technique will enable future real-time live-cell 3D imaging to investigate complex nanoscopic biological structures and their functions.

Super-beacons: Open-source probes with spontaneous tuneable blinking compatible with live-cell super-resolution microscopy.

Localization‐based super‐resolution microscopy relies on the detection of individual molecules cycling between fluorescent and non‐fluorescent states. These transitions are commonly regulated by high‐intensity illumination, imposing constrains to imaging hardware and producing sample photodamage. Here, we propose single‐molecule self‐quenching as a mechanism to generate spontaneous photoswitching. To demonstrate this principle, we developed a new class of DNA‐based open‐source super‐resolution probes named super‐beacons, with photoswitching kinetics that can be tuned structurally, thermally and chemically. The potential of these probes for live‐cell compatible super‐resolution microscopy without high‐illumination or toxic imaging buffers is revealed by imaging interferon inducible transmembrane proteins (IFITMs) at sub‐100 nm resolutions.

Combining super-resolution microscopy with neuronal network recording using magnesium fluoride thin films as cover layer for multi-electrode array technology

We present an approach for fabrication of reproducible, chemically and mechanically robust functionalized layers based on MgF2 thin films on thin glass substrates. These show great advantages for use in super-resolution microscopy as well as for multi-electrode-array fabrication and are especially suited for combination of these techniques. The transparency of the coated substrates with the low refractive index material is adjustable by the layer thickness and can be increased above 92%. Due to the hydrophobic and lipophilic properties of the thin crystalline MgF2 layers, the temporal stable adhesion needed for fixation of thin tissue, e.g. cryogenic brain slices is given. This has been tested using localization-based super-resolution microscopy with currently highest spatial resolution in light microscopy. We demonstrated that direct stochastic optical reconstruction microscopy revealed in reliable imaging of structures of central synapses by use of double immunostaining of post- (homer1 and GluA2) and presynaptic (bassoon) marker structure in a 10 µm brain slice without additional fixing of the slices. Due to the proven additional electrical insulating effect of MgF2 layers, surfaces of multi-electrode-arrays were coated with this material and tested by voltage-current-measurements. MgF2 coated multi-electrode-arrays can be used as a functionalized microscope cover slip for combination with live-cell super-resolution microscopy.

QUANTIFICATION OF MITOCHONDRIAL MEMBRANE CURVATURE BY THREE-DIMENSIONAL LOCALIZATION MICROSCOPY.

Mitochondrial membrane organization is important for many biological functions, and is implicated in a number of diseases, but conventional microscopy has insufficient resolution to image biologically relevant structures. We present methods to quantify nanoscale membrane curvature using three-dimensional localization-based super-resolution microscopy. Localizations are analyzed using a cluster algorithm followed by principal component analysis to determine local membrane curvature. Results are shown for mitochondria in C2C12 mouse myotubes labeled with Tom20-Dendra2.

Modulation and study of photoblinking behavior in dye doped silver-silica core–shell nanoparticles for localization super-resolution microscopy

Blinking of fluorescent nanoparticles is a compelling phenomenon with widely debated mechanisms. The ability to inhibit or control blinking is important for applications in the field of optical, semiconductor and fluorescent imaging. Self-blinking nanomaterials are also attractive labels for localization-based super-resolution microscopy. In this work, we have synthesized silver core silica nanoparticles (Ag@SiO2) doped with Rhodamine 110 and studied the parameters that affect blinking. We found that under nitrogen rich conditions the nanoparticles shifted towards higher duty cycles. Also, it was found that hydrated nanoparticles showed a less drastic response to nitrogen rich conditions as compared to dried nanoparticles, indicating that surrounding matrix played a role in the response of nanoparticles to molecular oxygen. Further, the blinking is not a multi-body phenomena, super-resolution localization combined with intensity histogram analysis confirmed that single particles are emitting.

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.

Photo-Induced Depletion of Binding Sites in DNA-PAINT Microscopy.

The limited photon budget of fluorescent dyes is the main limitation for localization precision in localization-based super-resolution microscopy. Points accumulation for imaging in nanoscale topography (PAINT)-based techniques use the reversible binding of fluorophores and can sample a single binding site multiple times, thus elegantly circumventing the photon budget limitation. With DNA-based PAINT (DNA-PAINT), resolutions down to a few nanometers have been reached on DNA-origami nanostructures. However, for long acquisition times, we find a photo-induced depletion of binding sites in DNA-PAINT microscopy that ultimately limits the quality of the rendered images. Here we systematically investigate the loss of binding sites in DNA-PAINT imaging and support the observations with measurements of DNA hybridization kinetics via surface-integrated fluorescence correlation spectroscopy (SI-FCS). We do not only show that the depletion of binding sites is clearly photo-induced, but also provide evidence that it is mainly caused by dye-induced generation of reactive oxygen species (ROS). We evaluate two possible strategies to reduce the depletion of binding sites: By addition of oxygen scavenging reagents, and by the positioning of the fluorescent dye at a larger distance from the binding site.

A Photoactivatable BODIPY Probe for Localization‐Based Super‐Resolution Cellular Imaging

The synthesis and application of a photoactivatable boron-alkylated BODIPY probe for localization-based super-resolution microscopy is reported. Photoactivation and excitation of the probe is achieved by a previously unknown boron-photodealkylation reaction with a single low-power visible laser and without requiring the addition of reducing agents or oxygen scavengers in the imaging buffer. These features lead to a versatile probe for localization-based microscopy of biological systems. The probe can be easily linked to nucleophile-containing molecules to target specific cellular organelles. By attaching paclitaxel to the photoactivatable BODIPY, in vitro and in vivo super-resolution imaging of microtubules is demonstrated. This is the first example of single-molecule localization-based super-resolution microscopy using a visible-light-activated BODIPY compound as a fluorescent probe.

Correlation analysis framework for localization-based superresolution microscopy

Superresolution images reconstructed from single-molecule localizations can reveal cellular structures close to the macromolecular scale and are now being used routinely in many biomedical research applications. However, because of their coordinate-based representation, a widely applicable and unified analysis platform that can extract a quantitative description and biophysical parameters from these images is yet to be established. Here, we propose a conceptual framework for correlation analysis of coordinate-based superresolution images using distance histograms. We demonstrate the application of this concept in multiple scenarios, including image alignment, tracking of diffusing molecules, as well as for quantification of colocalization, showing its superior performance over existing approaches.

The Role of Probe Photophysics in Localization-Based Superresolution Microscopy

The Role of Probe Photophysics in Localization-Based Superresolution Microscopy