Microscopy (Oxford, England)最新文献

We investigate a one-dimensional plasmonic crystal using momentum-resolved electron energy-loss spectroscopy (EELS) and cathodoluminescence (CL) techniques, which are complementary in terms of available optical information. The plasmonic crystal sample is fabricated from large aluminum grains through the focused ion beam method. This approach allows curving nanostructures with high crystallinity, providing platforms for detailed analysis of plasmonic nanostructures using both EELS and CL. The momentum-resolved EELS visualizes dispersion curves outside the light cone, confirming the existence of the surface plasmon polaritons and local modes, while the momentum-resolved CL mapping analysis identified these surface plasmon polaritons and local modes. Such synergetic approach of two electron-beam techniques offers full insights into both radiative and non-radiative optical properties in plasmonic or photonic structures.

Cell membrane structures are supramolecular complexes that require the ordered assembly of membrane proteins and lipids. The morphology of various cell adhesion structures in multicellular organisms, such as those between epithelial cells, neural synapses and immune synapses, was initially described through electron microscopic analyses. Subsequent studies aimed to catalog their constituent proteins, which encompass transmembrane cell adhesion molecules, cytoskeletal proteins and scaffolding proteins that bind the two components. However, the diversity of plasma membrane lipids and their significance in the organization of cell adhesion structures were underappreciated until recently. It is now understood that phase separation of lipids and liquid-liquid phase separation of proteins are important driving forces for such self-assembly. In this review, we summarized recent findings on the role of lipids as scaffolds for supramolecular complexes using tight junctions in epithelial cells as an example.

Bacterial spores, known for their complex and resilient structures, have been the focus of visualization using various methodologies. In this study, we applied quick-freeze and replica electron microscopy techniques, allowing observation of Bacillus subtilis spores in high-contrast and three-dimensional detail. This method facilitated visualization of the spore structure with enhanced resolution and provided new insights into the spores and their germination processes. We identified and described five distinct structures: (i) hair-like structures on the spore surface, (ii) spike formation on the surface of lysozyme-treated spores, (iii) the fractured appearance of the spore cortex during germination, (iv) potential connections between small vesicles and the core membrane and (v) the evolving surface structure of nascent vegetative cells during germination.

Atomic-resolution scanning transmission electron microscopy combined with two-dimensional Gaussian fitting enables the accurate and precise identification of atomic column positions within a few picometers. The measurement performance significantly depends on the signal-to-noise ratio of the atomic columns. In areas with low signal-to-noise ratios, such as near surfaces, the measurement performance was lower than that of the bulk. However, previous studies evaluated the accuracy and precision only in bulk areas, underscoring the need for a method that quantitatively evaluates the accuracy and precision of each atomic column position with various signal-to-noise ratios. This study introduced Bayesian inference to assess the accuracy and precision of determining individual atomic column positions under various signals. We applied this method to simulated and experimental images and demonstrated its effectiveness in identifying statistically significant displacements, particularly near surfaces with signal degradation. The use of vector maps and kernel density estimate plots obtained from Bayesian inference provided a probabilistic understanding of the atom displacement. Therefore, this study highlighted the potential benefits of Bayesian inference in high-resolution imaging to reveal material properties.

We have developed a high-speed recordable direct electron detector based on silicon-on-insulator technology. The detector has 16 analog memories in each pixel to record 16 images with sub-microsecond temporal resolution. A dedicated data acquisition system has also been developed to display and record the results on a personal computer. The performance of the direct electron detector as an image sensor is evaluated under electron irradiation with an energy of 30 keV in a low-voltage transmission electron microscope equipped with a photocathode electron gun. We demonstrate that the detector can record images at an exposure time of 100 ns and an interval of 900 ns.

Sandwich freezing is a method of rapid freezing by sandwiching specimens between two copper disks, and it has been used for observing exquisite close-to-native ultrastructure of living yeast and bacteria. Recently, this method has been found to be useful for preserving cell images of glutaraldehyde-fixed cultured cells, as well as animal and human tissues. In the present study, this method was applied to observe the fine structure of living Arabidopsis plant tissues and was found to achieve excellent ultrastructural preservation of cells and tissues. This is the first report of applying the sandwich freezing method to observe plant tissues.

Spectral image (SI) measurement techniques, such as X-ray absorption fine structure (XAFS) imaging and scanning transmission electron microscopy (STEM) with energy-dispersive X-ray spectroscopy (EDS) or electron energy loss spectroscopy (EELS), are useful for identifying chemical structures in composite materials. Machine-learning techniques have been developed for automatic analysis of SI data and their usefulness has been proven. Recently, an extended measurement technique combining SI with a computed tomography (CT) technique (CT-SI), such as CT-XAFS and STEM-EDS/EELS tomography, was developed to identify the three-dimensional (3D) structures of chemical components. CT-SI analysis can be conducted by combining CT reconstruction algorithms and chemical component analysis based on machine-learning techniques. However, this analysis incurs high-computational costs owing to the size of the CT-SI datasets. To address this problem, this study proposed a fast computational approach for 3D chemical component analysis in an unsupervised learning setting. The primary idea for reducing the computational cost involved compressing the CT-SI data prior to CT computation and performing 3D reconstruction and chemical component analysis on the compressed data. The proposed approach significantly reduced the computational cost without losing information about the 3D structure and chemical components. We experimentally evaluated the proposed approach using synthetic and real CT-XAFS data, which demonstrated that our approach achieved a significantly faster computational speed than the conventional approach while maintaining analysis performance. As the proposed procedure can be implemented with any CT algorithm, it is expected to accelerate 3D analyses with sparse regularized CT algorithms in noisy and sparse CT-SI datasets.