Super‐resolution fluorescence microscopy‐based single‐molecule spectroscopy

Spectral Hadamard microscopy with metasurface-based patterned illumination

Hadamard matrices, composed of mutually orthogonal vectors, are widely used in various applications due to their orthogonality. In optical imaging, Hadamard microscopy has been applied to achieve optical sectioning by separating scattering and background noise from desired signals. This method involves sequential illumination using Hadamard patterns and subsequent image processing. However, it typically requires costly light modulation devices, such as digital micromirror devices (DMDs) or spatial light modulators (SLMs), to generate multiple illumination patterns. In this study, we present spectral Hadamard microscopy based on a holographic matasurface. We noticed that certain patterns repeat within other Hadamard patterns under specific condition, allowing the entire set to be reproduced from a single pattern. This finding suggests that generating a single pattern is sufficient to implement Hadamard microscopy. To demonstrate this, we designed a metasurface to generate an illumination pattern and conducted imaging simulations. Results showed that holographic metasurface-based Hadamard microscopy effectively suppressed scattering signals, resulting in clear fluorescent images. Furthermore, we demonstrated that hyperspectral imaging can be achieved with Hadamard microscopy using dispersive optical elements, as the orthogonality of the Hadamard pattern enables to resolve spectral information. The reconstructed hyperspectral images displayed a color distribution closely matching the synthetic hyperspectral images used as ground truth. Our findings suggest that optical sectioning and hyperspectral imaging can be accomplished without light modulation devices, a capability typically unattainable with standard wide-field microscopes. We showed that sophisticated metasurfaces have the potential to replace and enhance conventional optical components, and we anticipate that this study will contribute to advancements in metasurface-based optical microscopy.

Oxygen-excluded nanoimaging of polymer blend films

Polymer blend films exhibit unique properties and have applications in various fields. However, understanding their nanoscale structures and polymer component distributions remains a challenge. To address this limitation, we have developed a super-resolution fluorescence microscopy-based technique called oxygen-excluded nanoimaging. By using point accumulation for imaging in nanoscale topography with sulfonate-based dye molecules, we achieved nanoscale imaging of polymer blend films while specifically labeling non-oxygen domains and excluding oxygen-containing domains. This selectivity is attributed to the electrostatic repulsion between the negatively charged sulfonate groups in the dye molecules and the oxygen atoms in the polymer side chains. We demonstrate the applicability of oxygen-excluded nanoimaging to various polymer blend films, enabling domain identification and visualization of nanoscale structures. Our oxygen-excluded nanoimaging technique provides unique insights into the complex phase separation behavior of polymer blends at the nanoscale, opening possibilities for the nanoscale characterization of a wide range of materials beyond polymer blends.

Artificial Intelligence-Empowered Spectroscopic Single Molecule Localization Microscopy

Spectroscopic single-molecule localization microscopy (SMLM) has revolutionized the visualization and analysis of molecular structures and dynamics at the nanoscale level. The technique of combining high spatial resolution of SMLM with spectral information, enables multicolor super-resolution imaging and provides insights into the local chemical environment of individual molecules. However, spectroscopic SMLM faces significant challenges, including limited spectral resolution and compromised localization precision because of signal splitting and the difficulties in analyzing complex, multidimensional datasets, that limit its application in studying intricate biological systems and materials. The recent integration of artificial intelligence (AI) with spectroscopic SMLM has emerged as a powerful approach for addressing these challenges. Here, it is reviewed how AI-based methods applied to spectroscopic SMLM enhance and expand the capabilities of these applications. Recent advancements in AI-driven data analysis for spectroscopic SMLM, including improved spectral classification, localization precision, and extraction of rich spectral information from unmodified point-spread functions are discussed, further examining their applications in biological studies, materials science, and single-molecule reaction analysis, which highlight how AI provides new insights into molecular behavior and interactions. The AI-empowered approach adds new dimensions of information and provides new opportunities and insights into the nanoscale world of rapidly evolving field of spectroscopic SMLM.

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.

From Homogeneity to Turing Pattern: Kinetically Controlled Self-Organization of Transmembrane Protein

Understanding the spatial organization of membrane proteins is crucial for unraveling key principles in cell biology. The reaction-diffusion model is commonly used to understand biochemical patterning; however, applying reaction-diffusion models to subcellular phenomena is challenging because of the difficulty in measuring protein diffusivity and interaction kinetics in the living cell. In this work, we investigated the self-organization of the plasmalemma vesicle-associated protein (PLVAP), which creates regular arrangements of fenestrated ultrastructures, using single-molecule tracking. We demonstrated that the spatial organization of the ultrastructures is associated with a decrease in the association rate by actin destabilization. We also constructed a reaction-diffusion model that accurately generates a hexagonal array with the same 130 nm spacing as the actual scale and informs the stoichiometry of the ultrastructure, which can be discerned only through electron microscopy. Through this study, we integrated single-molecule experiments and reaction-diffusion modeling to surpass the limitations of static imaging tools and proposed emergent properties of the PLVAP ultrastructure.

Recent development of computational cluster analysis methods for single-molecule localization microscopy images

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.

Single-Molecule Imaging of Membrane Proteins on Vascular Endothelial Cells

Transporting substances such as gases, nutrients, waste, and cells is the primary function of blood vessels. Vascular cells use membrane proteins to perform crucial endothelial functions, including molecular transport, immune cell infiltration, and angiogenesis. A thorough understanding of these membrane receptors from a clinical perspective is warranted to gain insights into the pathogenesis of vascular diseases and to develop effective methods for drug delivery through the vascular endothelium. This review summarizes state-of-the-art single-molecule imaging techniques, such as super-resolution microscopy, single-molecule tracking, and protein-protein interaction analysis, for observing and studying membrane proteins. Furthermore, recent single-molecule studies of membrane proteins such as cadherins, integrins, caveolins, transferrin receptors, vesicle-associated protein-1, and vascular endothelial growth factor receptor are discussed.

Visualizing extracellular vesicle biogenesis in gram-positive bacteria using super-resolution microscopy

BACKGROUND

Recently, bacterial extracellular vesicles (EVs) have been considered to play crucial roles in various biological processes and have great potential for developing cancer therapeutics and biomedicine. However, studies on bacterial EVs have mainly focused on outer membrane vesicles released from gram-negative bacteria since the outermost peptidoglycan layer in gram-positive bacteria is thought to preclude the release of EVs as a physical barrier.

RESULTS

Here, we examined the ultrastructural organization of the EV produced by gram-positive bacteria using super-resolution stochastic optical reconstruction microscopy (STORM) at the nanoscale, which has not been resolved using conventional microscopy. Based on the super-resolution images of EVs, we propose three major mechanisms of EV biogenesis, i.e., membrane blebbing (mechanisms 1 and 2) or explosive cell lysis (mechanism 3), which are different from the mechanisms in gram-negative bacteria, despite some similarities.

CONCLUSIONS

These findings highlight the significant role of cell wall degradation in regulating various mechanisms of EV biogenesis and call for a reassessment of previously unresolved EV biogenesis in gram-positive bacteria.

Polarity Nano-Mapping of Polymer Film Using Spectrally Resolved Super-Resolution Imaging

With the rapid development of the nanofabrication of polymer materials, the local measurement of the chemical properties of polymer nanostructures has become crucial because they can be highly heterogeneous at the nanoscale. We developed a spectroscopic imaging approach to characterize the nanoscale local polarity of polymer films via spectrally resolved super-resolution microscopy. We demonstrate the capability of the recently developed single-molecule sensing and imaging method to probe the polarity of polymers either inside a polymer matrix or on the external surface of a polymer. The nanoscale polarity sensing capability of our method facilitates the differentiation of various polymer surfaces based on chemical polarities, and it can further differentiate the polarity of functional side chain groups. Moreover, we demonstrate that a two-component polymer mixture can be locally distinguished based on the contrasting polarities of the lateral phase separation, further allowing for the investigation of nanoscale phase separation depending on the composition of the polymer blend film. This approach is anticipated to open the door to further characterizations of various nanocomposite materials.

Development of Deep-Learning-Based Single-Molecule Localization Image Analysis

Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is significantly restricted by the image analysis method, as the final super-resolution image is reconstructed from identified localizations through computational analysis. With recent advancements in deep learning, many researchers have employed deep learning-based algorithms to analyze SMLM image data. This review discusses recent developments in deep-learning-based SMLM image analysis, including the limitations of existing fitting algorithms and how the quality of SMLM images can be improved through deep learning. Finally, we address possible future applications of deep learning methods for SMLM imaging.