Development of a New Approach for Low-Laser-Power Super-Resolution Fluorescence Imaging

Photoswitching Reagent for Super-Resolution Fluorescence Microscopy

Single-molecule localization microscopy (SMLM) has revolutionized optical microscopy by exceeding the diffraction limit and revealing previously unattainable nanoscale details of cellular structures and molecular dynamics. This super-resolution imaging capability relies on fluorophore photoswitching, which is crucial for optimizing the imaging conditions and accurately determining the fluorophore positions. To understand the general on and off photoswitching mechanisms of single dye molecules, various photoswitching reagents were evaluated. Systematic measurement of the single-molecule-level fluorescence on and off rates (kon and koff) in the presence of various photoswitching reagents and theoretical calculation of the structure of the photoswitching reagent-fluorophore pair indicated that the switch-off mechanism is mainly determined by the nucleophilicity of the photoswitching reagent, and the switch-on mechanism is a two-photon-induced dissociation process, which is related to the power of the illuminating laser and bond dissociation energy of this pair. This study contributes to a broader understanding of the molecular photoswitching mechanism in SMLM imaging and provides a basis for designing improved photoswitching reagents with potential applications extending to materials science and chemistry.

Thiolation for Enhancing Photostability of Fluorophores at the Single-Molecule Level

Fluorescent probes are essential for single-molecule imaging. However, their application in biological systems is often limited by the short photobleaching lifetime. To overcome this, we developed a novel thiolation strategy for squaraine dyes. By introducing thiolation of the central cyclobutene of squaraine (thio-squaraine), we observed a ≈5-fold increase in photobleaching lifetime. Our single-molecule data analysis attributes this improvement to improved photostability resulting from thiolation. Interestingly, bulk measurements show rapid oxidation of thio-squaraine to its oxo-analogue under irradiation, giving the perception of inferior photostability. This discrepancy between bulk and single-molecule environments can be ascribed to the factors in the latter, including larger intermolecular distances and restricted mobility, which reduce the interactions between a fluorophore and reactive oxygen species produced by other fluorophores, ultimately impacting photobleaching and photoconversion rate. We demonstrate the remarkable performance of thio-squaraine probes in various imaging buffers, such as glucose oxidase with catalase (GLOX) and GLOX+trolox. We successfully employed these photostable probes for single-molecule tracking of CD56 membrane protein and monitoring mitochondria movements in live neurons. CD56 tracking revealed distinct motion states and the corresponding protein fractions. This investigation is expected to propel the development of single-molecule imaging probes, particularly in scenarios where bulk measurements show suboptimal performance.

Bacteria detection and species identification at the single-cell level using super-resolution fluorescence imaging and AI analysis

The skin microbiome is thought to play a critical role in maintaining skin health and protecting against infection. While most microorganisms that live on the skin are harmless or even beneficial, some can cause skin infections or other health problems, emphasizing the importance of diagnosis of the composition and diversity of the skin flora. However, conventional diagnostic methods for evaluation of the skin microbiome are not sensitive enough to detect bacteria at low concentrations and suffer from poor specificity, thus limiting early diagnosis of bacterial infections. In this study, we developed novel approaches for bacterial species detection and identification methods with single-cell sensitivity using super-resolution microscopy and AI-based image analysis: a protein quantification-based method and an AI-based bacterial image analysis method. We demonstrate that these methods can differentiate between common bacterial members of the skin flora, including Staphylococcus aureus and Staphylococcus epidermidis, and different ribotypes of Cutibacterium acnes, both in purified bacterial samples and in scaling skin samples. The advantages of these methods, including the lack of time-consuming amplification or purification steps and single-cell level detection sensitivity, allow early diagnosis of bacterial infections, even from bacterial samples at extremely low concentrations, thus showing promise as a next-generation platform for microbiome detection as single-cell diagnostics.

Benchmarking Thiolate-Driven Photoswitching of Cyanine Dyes

Carbocyanines are among the best performing dyes in single-molecule localization microscopy (SMLM), but their performance critically relies on optimized photoswitching buffers. Here, we study the versatile role of thiols in cyanine photoswitching at varying intensities generated in a single acquisition by a microelectromechanical systems (MEMS) mirror placed in the excitation path. The key metrics we have analyzed as a function of the thiolate concentration are photon budget, on-state and off-state lifetimes and the corresponding impact on image resolution. We show that thiolate acts as a concentration bandpass filter for the maximum achievable resolution and determine a minimum of ∼1 mM is necessary to facilitate SMLM measurements. We also identify a concentration bandwidth of 1-16 mM in which the photoswitching performance can be balanced between high molecular brightness and high off-time to on-time ratios. Furthermore, we monitor the performance of the popular oxygen scavenger system based on glucose and glucose oxidase over time and show simple measures to avoid acidification during prolonged measurements. Finally, the impact of buffer settings is quantitatively tested on the distribution of the glucose transporter protein 4 within the plasma membrane of adipocytes. Our work provides a general strategy for achieving optimal resolution in SMLM with relevance for the development of novel buffers and dyes.

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.

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.