Super-resolution Optical Microscopy Research Articles

Nanosilver-Enhanced Far-Field Fluorescence Fluctuations for Super-Resolution Microscopy.

Fluctuation-based super-resolution microscopy enhances image resolution using signal fluctuations, yet the inherent fluctuations of fluorophores limit its spatiotemporal resolution. In this work, we reveal a far-field enhancement (FFE) effect via a nanosilver film that significantly boosts fluorescence fluctuations of fluorophores positioned up to 10 μm away. The FFE effect arises from the interference of scattered light from the nanosilver film and photothermal-induced refractive index changes in the imaging medium, which create periodic auxiliary illumination on the sample. This phenomenon enabled the development of far-field enhanced super-resolution microscopy (FFE-SRM), a technique compatible with commonly used fluorophores. FFE-SRM improves temporal resolution up to 10-fold and enhances spatial resolution by about 2-fold over various SRM methods, including stochastic optical fluctuation imaging, super-resolution radial fluctuation, mean-shift super-resolution, and direct stochastic optical reconstruction microscopy. We demonstrated the potential of FFE-SRM by revealing mitochondrial dynamics in live-cell imaging, advancing super-resolution imaging, and cellular process exploration.

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.

Optical Nanoscopy of Cytokine-Induced Structural Alterations of the Endoplasmic Reticulum and Golgi Apparatus in Insulin-Secreting Cells.

Pro-inflammatory cytokines play a role in the failure of β cells in type 1 and type 2 diabetes. While existing data from 'omics' experiments allow for some understanding of the molecular mechanisms behind cytokine-induced dysfunction in β cells, no report thus far has provided information on the direct imaging of the β cell landscape with nanoscale resolution following cytokine exposure. In this study, we use Airyscan-based optical super-resolution microscopy of Insulinoma 1E (INS-1E) cells to investigate the structural properties of two subcellular membranous compartments involved in the production, maturation and secretion of insulin-containing granules, the endoplasmic reticulum (ER) and the Golgi apparatus (GA). Our findings reveal that exposure of INS-1E cells to IL-1β and IFN-γ for 24 h leads to significant structural alterations of both compartments. In more detail, both the ER and the GA fragment and give rise to vesicle-like structures with markedly reduced characteristic area and perimeter and increased circularity with respect to the original structures. These findings complement the molecular data collected thus far on these compartments and their role in β cell dysfunction and lay the groundwork for future optical microscopy-based ex vivo and in vivo investigations.

G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework.

Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multiomics datasets provide opportunities to use computational approaches for such studies. Here, we devised a novel disease gene identification (digID) computational framework that consists of a semi-supervised deep learning classifier to predict AD-associated genes and a protein-protein interaction (PPI) network-based analysis to prioritize the importance of these predicted genes in AD. digID predicted 1,529 AD-associated genes and revealed potentially new AD molecular mechanisms and therapeutic targets including GNAI1 and GNB1, two G-protein subunits that regulate cell signaling, and KNG1, an upstream modulator of CDC42 small G-protein signaling and mediator of inflammation and candidate coregulator of amyloid precursor protein (APP). Analysis of mRNA expression validated their dysregulation in AD brains but further revealed the significant spatial patterns in different brain regions as well as among different subregions of the frontal cortex and hippocampi. Super-resolution STochastic Optical Reconstruction Microscopy (STORM) further demonstrated their subcellular colocalization and molecular interactions with APP in a transgenic mouse model of both sexes with AD-like mutations. These studies support the predictions made by digID while highlighting the importance of concurrent biological validation of computationally identified gene clusters as potential new AD therapeutic targets.

Achieving Single Cell Acoustic Localisation with Deactivation Super Resolution.

Super-resolution optical microscopy enables optical imaging of cells, molecules and other biological structures beyond the diffraction limit. However, no similar method exists to super-resolve specific cells with ultrasound. Here we introduce Deactivation Super Resolution (DSR), an ultrasound imaging method that uses the acoustic deactivation of genetically encodable contrast agents to super-resolve individual cells with ultrasound as they navigate through structures that cannot be resolved by conventional imaging methods. DSR takes advantage of gas vesicles, which are air-filled sub-micron protein particles that can be expressed in genetically engineered cells to produce ultrasound contrast. Our experimental results show that DSR can distinguish sub-wavelength microstructures that standard B-mode ultrasound images fail to resolve by super- localizing individual mammalian cells. This study provides a proof of concept for the potential of DSR to serve as a super- resolution ultrasound technique for individual cell localization, opening new horizons in the field.

Surpassing the Diffraction Limit in Label-Free Optical Microscopy.

Super-resolution optical microscopy has enhanced our ability to visualize biological structures on the nanoscale. Fluorescence-based techniques are today irreplaceable in exploring the structure and dynamics of biological matter with high specificity and resolution. However, the fluorescence labeling concept narrows the range of observed interactions and fundamentally limits the spatiotemporal resolution. In contrast, emerging label-free imaging methods are not inherently limited by speed and have the potential to capture the entirety of complex biological processes and dynamics. While pushing a complex unlabeled microscopy image beyond the diffraction limit to single-molecule resolution and capturing dynamic processes at biomolecular time scales is widely regarded as unachievable, recent experimental strides suggest that elements of this vision might be already in place. These techniques derive signals directly from the sample using inherent optical phenomena, such as elastic and inelastic scattering, thereby enabling the measurement of additional properties, such as molecular mass, orientation, or chemical composition. This perspective aims to identify the cornerstones of future label-free super-resolution imaging techniques, discuss their practical applications and theoretical challenges, and explore directions that promise to enhance our understanding of complex biological systems through innovative optical advancements. Drawing on both traditional and emerging techniques, label-free super-resolution microscopy is evolving to offer detailed and dynamic imaging of living cells, surpassing the capabilities of conventional methods for visualizing biological complexities without the use of labels.

The Effects of the Carrier and Ligand Spatial Conformation on RNA Nanodrug Cell Delivery.

Small interfering RNA (siRNA) highlights the immense therapeutic potential for cancer treatment. The major challenge in siRNA therapy is the effective RNA nanodrug delivery system, which is facilitated by the ligand and the carrier. In this study, we analyzed the binding specificity of linear RGD and circular RGD to αVβ3 integrins by mapping the morphology using super-resolution direct stochastic optical reconstruction microscopy. Meanwhile, the binding dynamics was investigated using single-molecule force spectroscopy. Then, the effects of the ligand and carrier on RNA nanodrug cell entry dynamic parameters were evaluated at the single particle level by the force tracing technique. Furthermore, the delivery efficiency of RNA nanodrugs was assessed using AFM-based nanoindentation at the single cell level. This report will provide valuable insights for rational design strategies aiming to achieve improved efficiency for nanodrug delivery systems.

Symmetry-Guided Engineering of Polarization by 2D Moiré Metasurfaces.

Cylindrical vector (CV) beams exhibit spatially varying polarization important in optical communication, super-resolution microscopy, and high-throughput information processing. Compared to radially or azimuthally polarized CV beams that are cylindrically symmetric, hybrid-electric (HE) beams offer increased optical tunability because of their polygonally symmetric polarizations. However, efforts to generate and isolate HE beams have relied on bulky optical assemblies or devices with complex and stringent fabrication requirements. Here, we report a moiré-based metasurface approach to engineer HE polarization states with high degrees of rotational symmetry. Importantly, polarization symmetries can be tailored based only on the reciprocal lattice of the metasurface and not the real-space patterns. Our modular method outlines important design principles for shaping light at the nanoscale.

β-Galactosidase- and Photo-Activatable Fluorescent Probes for Protein Labeling and Super-Resolution STED Microscopy in Living Cells.

We report on the synthesis of two fluorescent probes which can be activated by β-Galactosidase (β-Gal) enzymes and/or light. The probes contained 2-nitro-4-oxybenzyl and 3-nitro-4-oxybenzyl fragments, with β-Gal residues linked to C-4. We performed the enzymatic and photoactivation of the probes in a cuvette and compared them, prior to the labeling of Vimentin-Halo fusion protein in live cells with overexpressed β-galactosidase. The dye fluorescence afforded the observation of enzyme activity by means of confocal and super-resolution optical microscopy based on stimulated emission depletion (STED). The tracing of enzymatic activity with the retention of activated fluorescent products inside cells was combined with super-resolution imaging as a tool for use in biomedicine and life science.

Self-Quenched Fluorophore-DNA Labels for Super-Resolution Fluorescence Microscopy.

Protein labeling through transient and repetitive hybridization of short, fluorophore-labeled DNA oligonucleotides has become widely applied in various optical super-resolution microscopy methods. The main advantages are multitarget imaging and molecular quantification. A challenge is the high background signal originating from the presence of unbound fluorophore-DNA labels in solution. Here, we report the self-quenching of fluorophore dimers conjugated to DNA oligonucleotides as a general concept to reduce the fluorescence background. Upon hybridization, the fluorescence signals of both fluorophores are restored. We expand the toolbox of fluorophores suitable for self-quenching and report their spectra and hybridization equilibria. We apply self-quenched fluorophore-DNA labels to stimulated emission depletion microscopy and single-molecule localization microscopy and report improved imaging performances.

Amphiphilic coumarin-based probes for live-cell STED nanoscopy of plasma membrane

Plasma membranes are vital biological structures, serving as protective barriers and participating in various cellular processes. In the field of super-resolution optical microscopy, stimulated emission depletion (STED) nanoscopy has emerged as a powerful method for investigating plasma membrane-related phenomena. However, many applications of STED microscopy are critically restricted by the limited availability of suitable fluorescent probes. This paper reports on the development of two amphiphilic membrane probes, SHE-2H and SHE-2N, specially designed for STED nanoscopy. SHE-2N, in particular, demonstrates quick and stable plasma membrane labelling with negligible intracellular redistribution. Both probes exhibit outstanding photostability and resolution improvement in STED nanoscopy, and are also suited for two-photon excitation microscopy. Furthermore, microscopy experiments and cytotoxicity tests revealed no noticeable cytotoxicity of probe SHE-2N at concentration used for fluorescence imaging. Spectral analysis and fluorescence lifetime measurements conducted on probe SHE-2N using giant unilamellar vesicles, revealed that emission spectra and fluorescence lifetimes exhibited minimal sensitivity to lipid composition variations. These novel probes significantly augment the arsenal of tools available for high-resolution plasma membrane research, enabling a more profound exploration of cellular processes and dynamics.

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.

Ultrastructural 3D Microscopy for Biomedicine: Principles, Applications, and Perspectives.

Modern biomedical research often requires a three-dimensional microscopic analysis of the ultrastructure of biological objects and materials. Conceptual technical and methodological solutions for three-dimensional structure reconstruction are needed to improve the conventional optical, electron, and probe microscopy methods, which to begin with allow one to obtain two-dimensional images and data. This review discusses the principles and potential applications of such techniques as serial section transmission electron microscopy; techniques based on scanning electron microscopy (SEM) (array tomography, focused ion beam SEM, and serial block-face SEM). 3D analysis techniques based on modern super-resolution optical microscopy methods are described (stochastic optical reconstruction microscopy and stimulated emission depletion microscopy), as well as ultrastructural 3D microscopy methods based on scanning probe microscopy and the feasibility of combining them with optical techniques. A comparative analysis of the advantages and shortcomings of the discussed approaches is performed.

Scanning quantum correlation microscopy with few emitters

Optical superresolution microscopy is an important field, where nonlinear optical processes or prior information is used to defeat the classical diffraction limit of light. Quantum correlation microscopy uses photon arrival statistics from single photon emitters to aid in the determination of properties including the number of emitters and their relative brightness. Here we model quantum correlation microscopy in the few emitter regime, i.e. around four single photon emitters below the diffraction limit. We use the Akaike Information Criterion to determine the number of emitters and we vary the relative contributions of intensity to quantum correlation information to determine contribution that provides optimal imaging. Our results show diffraction unlimited performance and a change in localisation scaling behaviour dependent on emitter closeness.

Reconstruction of an enigmatic Pennsylvanian cone reveals a relationship to Sphenophyllales.

We studied the 3D morphology of a small, well-preserved cone from the Pennsylvanian Mazon Creek Lagerstätte to characterize its structure and determine its systematic affinity. Previously tentatively assigned to the enigmatic Tetraphyllostrobus, we show that it differs in key respects from that genus as described. We systematically compared the new fossil with relevant Paleozoic cone genera and employed advanced imaging techniques, including scanning electron microscopy, Airyscan confocal super-resolution microscopy, optical microscopy, and X-ray microcomputed tomography to visualize and reconstruct the fossil cone in 3D. The analyses demonstrate unequivocally that the sporophylls of the new Mazon Creek cone are arranged in whorls of six and have characters typical of Sphenophyllales, including epidermal cells with undulatory margins and in situ spores assignable to Columinisporites. The combination of characters, including sporophyll arrangement, anatomy, and spore type, supports the establishment of Hexaphyllostrobus kostorhysii gen. et sp. nov. within Sphenophyllales. Furthermore, we show that Tetraphyllostrobus, although originally described as possessing smooth monolete spores, actually possesses Columinisporites-type spores, indicating that it, too, was most likely a sphenophyll. The recognition of Hexaphyllostrobus contributes to our knowledge of Pennsylvanian sphenophyll diversity, and in particular increases the number of species with in situ Columinisporites-type spores. Attribution of Hexaphyllostrobus to Sphenophyllales calls into question current interpretations of Tetraphyllostrobus suggesting that future research on better-preserved macrofossil material may demonstrate a sphenophyllalean relationship.

Computational drug development for membrane protein targets.

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learning structure-based design and the evaluation of big data. Recent protein structure predictions based on machine learning tools have delivered surprisingly reliable results for water-soluble and membrane proteins but have limitations for development of drugs that target membrane proteins. Structural transitions of membrane proteins have a central role during transmembrane signaling and are often influenced by therapeutic compounds. Resolving the structural and functional basis of dynamic transmembrane signaling networks, especially within the native membrane or cellular environment, remains a central challenge for drug development. Tackling this challenge will require an interplay between experimental and computational tools, such as super-resolution optical microscopy for quantification of the molecular interactions of cellular signaling networks and their modulation by potential drugs, cryo-electron microscopy for determination of the structural transitions of proteins in native cell membranes and entire cells, and computational tools for data analysis and prediction of the structure and function of cellular signaling networks, as well as generation of promising drug candidates.

Mechanism of Viral DNA Packaging in Phage T4 Using Single-Molecule Fluorescence Approaches.

In all tailed phages, the packaging of the double-stranded genome into the head by a terminase motor complex is an essential step in virion formation. Despite extensive research, there are still major gaps in the understanding of this highly dynamic process and the mechanisms responsible for DNA translocation. Over the last fifteen years, single-molecule fluorescence technologies have been applied to study viral nucleic acid packaging using the robust and flexible T4 in vitro packaging system in conjunction with genetic, biochemical, and structural analyses. In this review, we discuss the novel findings from these studies, including that the T4 genome was determined to be packaged as an elongated loop via the colocalization of dye-labeled DNA termini above the portal structure. Packaging efficiency of the TerL motor was shown to be inherently linked to substrate structure, with packaging stalling at DNA branches. The latter led to the design of multiple experiments whose results all support a proposed torsional compression translocation model to explain substrate packaging. Evidence of substrate compression was derived from FRET and/or smFRET measurements of stalled versus resolvase released dye-labeled Y-DNAs and other dye-labeled substrates relative to motor components. Additionally, active in vivo T4 TerS fluorescent fusion proteins facilitated the application of advanced super-resolution optical microscopy toward the visualization of the initiation of packaging. The formation of twin TerS ring complexes, each expected to be ~15 nm in diameter, supports a double protein ring-DNA synapsis model for the control of packaging initiation, a model that may help explain the variety of ring structures reported among pac site phages. The examination of the dynamics of the T4 packaging motor at the single-molecule level in these studies demonstrates the value of state-of-the-art fluorescent tools for future studies of complex viral replication mechanisms.

Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network

As a supplement to optical super-resolution microscopy techniques, computational super-resolution methods have demonstrated remarkable results in alleviating the spatiotemporal imaging trade-off. However, they commonly suffer from low structural fidelity and universality. Therefore, we herein propose a deep-physics-informed sparsity framework designed holistically to synergize the strengths of physical imaging models (image blurring processes), prior knowledge (continuity and sparsity constraints), a back-end optimization algorithm (image deblurring), and deep learning (an unsupervised neural network). Owing to the utilization of a multipronged learning strategy, the trained network can be applied to a variety of imaging modalities and samples to enhance the physical resolution by a factor of at least 1.67 without requiring additional training or parameter tuning. Given the advantages of high accessibility and universality, the proposed deep-physics-informed sparsity method will considerably enhance existing optical and computational imaging techniques and have a wide range of applications in biomedical research.

Deep-learning-augmented microscopy for super-resolution imaging of nanoparticles.

Conventional optical microscopes generally provide blurry and indistinguishable images for subwavelength nanostructures. However, a wealth of intensity and phase information is hidden in the corresponding diffraction-limited optical patterns and can be used for the recognition of structural features, such as size, shape, and spatial arrangement. Here, we apply a deep-learning framework to improve the spatial resolution of optical imaging for metal nanostructures with regular shapes yet varied arrangement. A convolutional neural network (CNN) is constructed and pre-trained by the optical images of randomly distributed gold nanoparticles as input and the corresponding scanning-electron microscopy images as ground truth. The CNN is then learned to recover reversely the non-diffracted super-resolution images of both regularly arranged nanoparticle dimers and randomly clustered nanoparticle multimers from their blurry optical images. The profiles and orientations of these structures can also be reconstructed accurately. Moreover, the same network is extended to deblur the optical images of randomly cross-linked silver nanowires. Most sections of these intricate nanowire nets are recovered well with a slight discrepancy near their intersections. This deep-learning augmented framework opens new opportunities for computational super-resolution optical microscopy with many potential applications in the fields of bioimaging and nanoscale fabrication and characterization. It could also be applied to significantly enhance the resolving capability of low-magnification scanning-electron microscopy.

Pushing the Resolution Limit of Stimulated Emission Depletion Optical Nanoscopy.

Optical nanoscopy, also known as super-resolution optical microscopy, has provided scientists with the means to surpass the diffraction limit of light microscopy and attain new insights into nanoscopic structures and processes that were previously inaccessible. In recent decades, numerous studies have endeavored to enhance super-resolution microscopy in terms of its spatial (lateral) resolution, axial resolution, and temporal resolution. In this review, we discuss recent efforts to push the resolution limit of stimulated emission depletion (STED) optical nanoscopy across multiple dimensions, including lateral resolution, axial resolution, temporal resolution, and labeling precision. We introduce promising techniques and methodologies building on the STED concept that have emerged in the field, such as MINSTED, isotropic STED, and event-triggered STED, and evaluate their respective strengths and limitations. Moreover, we discuss trade-off relationships that exist in far-field optical microscopy and how they come about in STED optical nanoscopy. By examining the latest developments addressing these aspects, we aim to provide an updated overview of the current state of STED nanoscopy and its potential for future research.