Microscopy (Oxford, England)最新文献

Morphogenesis is a developmental process of organisms being shaped through complex and cooperative cellular movements. To understand the interplay between genetic programs and the resulting multicellular morphogenesis, it is essential to characterize the morphologies and dynamics at the single-cell level and to understand how physical forces serve as both signaling components and driving forces of tissue deformations. In recent years, advances in microscopy techniques have led to improvements in imaging speed, resolution and depth. Concurrently, the development of various software packages has supported large-scale, analyses of challenging images at the single-cell resolution. While these tools have enhanced our ability to examine dynamics of cells and mechanical processes during morphogenesis, their effective integration requires specialized expertise. With this background, this review provides a practical overview of those techniques. First, we introduce microscopic techniques for multicellular imaging and image analysis software tools with a focus on cell segmentation and tracking. Second, we provide an overview of cutting-edge techniques for mechanical manipulation of cells and tissues. Finally, we introduce recent findings on morphogenetic mechanisms and mechanosensations that have been achieved by effectively combining microscopy, image analysis tools and mechanical manipulation techniques.

In this study, a new method for the phase retrieval of electron rocking curves observed using convergent-beam electron diffraction, which is applicable to the determination of three-dimensional lattice displacement fields along the beam direction, is proposed. Total variation and total squared variation regularizations are introduced for phase retrieval to suppress overfitting to noise or background signals in the rocking curves and to reproduce the sparse characteristics of displacement fields, which exist only near lattice defects. The results show that the proposed algorithm is effective for rocking curves modulated by the dynamical effect of electron diffraction. The accuracy of phase reconstruction using the proposed method is also discussed. Phase retrieval of the experimental rocking curves obtained from a stacking fault in stainless steel is demonstrated.

We present a graph neural network (GNN)-based framework applied to large-scale microscopy image segmentation tasks. While deep learning models, like convolutional neural networks (CNNs), have become common for automating image segmentation tasks, they are limited by the image size that can fit in the memory of computational hardware. In a GNN framework, large-scale images are converted into graphs using superpixels (regions of pixels with similar color/intensity values), allowing us to input information from the entire image into the model. By converting images with hundreds of millions of pixels to graphs with thousands of nodes, we can segment large images using memory-limited computational resources. We compare the performance of GNN- and CNN-based segmentation in terms of accuracy, training time and required graphics processing unit memory. Based on our experiments with microscopy images of biological cells and cell colonies, GNN-based segmentation used one to three orders-of-magnitude fewer computational resources with only a change in accuracy of ‒2 % to +0.3 %. Furthermore, errors due to superpixel generation can be reduced by either using better superpixel generation algorithms or increasing the number of superpixels, thereby allowing for improvement in the GNN framework's accuracy. This trade-off between accuracy and computational cost over CNN models makes the GNN framework attractive for many large-scale microscopy image segmentation tasks in biology.

In recent years, the technique of scanning electron microscopy (SEM) observation with low landing energy of a few keV or less has become common. We have especially focused on the drastic contrast change at near 0 eV. Using a patterned sample consisting of Si, Ni and Pt, threshold energies where the total reflection of incident electrons occurs were investigated by SEM at near 0 eV. In both the cases of in-situ and ex-situ sample cleaning, drastic changes in the brightness of each material were observed at near 0 eV, with threshold energies in the order Si < Ni < Pt. This order agreed with the order of the literature values of the work functions and the surface potentials measured by Kelvin force probe microscopy. This result suggests that the difference of the threshold energy is caused by the difference in surface potential due to the work function difference of each material. Although the order of the threshold energies also agreed with those of work functions reported in the literature, the work functions of air-exposed surfaces should be rather considered as 'modified work functions', since they could be significantly altered by the adsorbates, etc. Nevertheless, the difference of the threshold energy for each material was observed with commercial SEM at landing energy near 0 eV, which opens a new possibility to distinguish materials, although the difference should be rather recognized as 'fingerprints', since surface potentials are sensitive to conditions of surface treatments and atmospheric exposure.

Scanning/transmission electron microscopy (STEM) is a powerful characterization tool for a wide range of materials. Over the years, STEMs have been extensively used for in situ studies of structural evolution and dynamic processes. A limited number of STEM instruments are equipped with a secondary electron (SE) detector in addition to the conventional transmitted electron detectors, i.e. the bright-field (BF) and annular dark-field (ADF) detectors. Such instruments are capable of simultaneous BF-STEM, ADF-STEM and SE-STEM imaging. These methods can reveal the 'bulk' information from BF and ADF signals and the surface information from SE signals for materials <200 nm thick. This review first summarizes the field of in situ STEM research, followed by the generation of SE signals, SE-STEM instrumentation and applications of SE-STEM analysis. Combining with various in situ heating, gas reaction and mechanical testing stages based on microelectromechanical systems (MEMS), we show that simultaneous SE-STEM imaging has found applications in studying the dynamics and transient phenomena of surface reconstructions, exsolution of catalysts, lunar and planetary materials and mechanical properties of 2D thin films. Finally, we provide an outlook on the potential advancements in SE-STEM from the perspective of sample-related factors, instrument-related factors and data acquisition and processing.

Hydrogen is attracting attention as an energy carrier for realizing a low-carbon society, because it can directly convert the energy obtained from chemical reactions into electrical energy without carbon dioxide emissions. This paper presents in situ transmission electron microscopy (TEM) observations related to hydrogen storage in metal and metal hydrides, hydrogen embrittlement of metallic materials used for storing and transporting hydrogen in containers and pipes, and fuel cells and water electrolysis using metal catalysts and oxides as electrode materials. All of these processes are important for practical applications of hydrogen. Numerous in situ TEM studies have revealed the microscopic structural changes when hydrogen reacts with the materials, when hydrogen is solidly dissolved in the materials and during the operation of the material. This review is expected to facilitate further development of TEM operando observations of hydrogen-related materials.

In-situ observation has expanded the application of transmission electron microscopy (TEM) and has made a significant contribution to materials research and development for energy, biomedical, quantum, etc. Recent technological developments related to in-situ TEM have empowered the incorporation of three-dimensional observation, which was previously considered incompatible. In this review article, we take up heating as the most commonly used external stimulus for in-situ TEM observation and overview recent in-situ TEM studies. Then, we focus on the electron tomography (ET) and in-situ heating combined observation by introducing the authors' recent research as an example. Assuming that in-situ heating observation is expanded from two dimensions to three dimensions using a conventional TEM apparatus and a commercially available in-situ heating specimen holder, the following in-situ heating-and-ET observation procedure is proposed: (i) use a rapid heating-and-cooling function of a micro-electro-mechanical system holder; (ii) heat and cool the specimen intermittently and (iii) acquire a tilt-series dataset when the specimen heating is stopped. This procedure is not too technically challenging and can have a wide range of applications. Essential technical points for a successful 4D (space and time) observation will be discussed through reviewing the authors' example application.

Herein, we review notable points from observations of electrochemical reactions in a liquid electrolyte by liquid-phase electron microscopy. In situ microscopic observations of electrochemical reactions are urgently required, particularly to solve various battery issues. Battery performance is evaluated by various electrochemical measurements of bulk samples. However, it is necessary to understand the physical/chemical phenomena occurring in batteries to elucidate the reaction mechanisms. Thus, in situ microscopic observation is effective for understanding the reactions that occur in batteries. Herein, we focus on two methods, of the liquid phase (scanning) transmission electron microscopy and liquid phase scanning electron microscopy, and summarize the advantages and disadvantages of both methods.

The advent of single-molecule atomic-resolution time-resolved electron microscopy (SMART-EM) has created a new field of 'cinematic chemistry,' allowing for the cinematographic recording of dynamic behaviors of organic and inorganic molecules and their assembly. However, the limited electron dose per frame of video images presents a major challenge in SMART-EM. Recent advances in direct electron counting cameras and techniques to enhance image quality through the implementation of a denoising algorithm have enabled the tracking of stochastic molecular motions and chemical reactions with sub-millisecond temporal resolution and sub-angstrom localization precision. This review showcases the development of dynamic molecular imaging using the SMART-EM technique, highlighting insights into nanomechanical behavior during molecular shuttle motion, pathways of multistep chemical reactions, and elucidation of crystallization processes at the atomic level.

In situ transmission/scanning transmission electron microscopy (TEM/STEM) measurements have taken a central stage for establishing structure-chemistry-property relationship over the past couple of decades. The challenges for realizing 'a lab-in-gap', i.e. gap between the objective lens pole pieces, or 'a lab-on-chip', to be used to carry out experiments are being met through continuous instrumental developments. Commercially available TEM columns and sample holder, that have been modified for in situ experimentation, have contributed to uncover structural and chemical changes occurring in the sample when subjected to external stimulus such as temperature, pressure, radiation (photon, ions and electrons), environment (gas, liquid and magnetic or electrical field) or a combination thereof. Whereas atomic resolution images and spectroscopy data are being collected routinely using TEM/STEM, temporal resolution is limited to millisecond. On the other hand, better than femtosecond temporal resolution can be achieved using an ultrafast electron microscopy or dynamic TEM, but the spatial resolution is limited to sub-nanometers. In either case, in situ experiments generate large datasets that need to be transferred, stored and analyzed. The advent of artificial intelligence, especially machine learning platforms, is proving crucial to deal with this big data problem. Further developments are still needed in order to fully exploit our capability to understand, measure and control chemical and/or physical processes. We present the current state of instrumental and computational capabilities and discuss future possibilities.