Microscopy (Oxford, England)最新文献

Structural observations are essential for the advancement of life science. Volume electron microscopy has recently realized remarkable progress in the three-dimensional analyses of biological specimens for elucidating complex ultrastructures in several fields of life science. The advancements in volume electron microscopy technologies have led to improvements, including higher resolution, more stability and the ability to handle larger volumes. Although human applications of volume electron microscopy remain limited, the reported applications in various organs have already provided previously unrecognized features of human tissues and also novel insights of human diseases. Simultaneously, the application of volume electron microscopy to human studies faces challenges, including ethical and clinical hurdles, costs of data storage and analysis, and efficient and automated imaging methods for larger volume. Solutions including the use of residual clinical specimens and data analysis based on artificial intelligence would address those issues and establish the role of volume electron microscopy in human structural research. Future advancements in volume electron microscopy are anticipated to lead to transformative discoveries in basic research and clinical practice, deepening our understanding of human health and diseases for better diagnostic and therapeutic strategies.

The three-dimensional (3D) anatomical structure of living organisms is intrinsically linked to their functions, yet modern life sciences have not fully explored this aspect. Recently, the combination of efficient tissue clearing techniques and light-sheet fluorescence microscopy for rapid 3D imaging has improved access to 3D spatial information in biological systems. This technology has found applications in various fields, including neuroscience, cancer research and clinical histopathology, leading to significant insights. It allows imaging of entire organs or even whole bodies of animals and humans at multiple scales. Moreover, it enables a form of spatial omics by capturing and analyzing cellome information, which represents the complete spatial organization of cells. While current 3D imaging of cleared tissues has limitations in obtaining sufficient molecular information, emerging technologies such as multi-round tissue staining and super-multicolor imaging are expected to address these constraints. 3D imaging using tissue clearing and light-sheet microscopy thus offers a valuable research tool in the current and future life sciences for acquiring and analyzing large-scale biological spatial information.

The brain is an intricate neuronal network that orchestrates our thoughts, emotions and actions through dynamic interactions between neurons. If we could record the activity of all neurons simultaneously in detail, it could revolutionize our understanding of brain function and lead to breakthroughs in treating neurological diseases. Recent technological innovations, particularly in large field-of-view two-photon microscopes, have made it possible to record the activity of tens of thousands of neurons simultaneously. However, the size and complexity of the datasets present significant challenges in extracting interpretable information. Conventional analysis methods are often insufficient, necessitating the development of new theoretical frameworks and computational efficiencies. In this review, we describe the characteristics of the data obtained from advanced imaging techniques and discuss analytical methods to facilitate mutual understanding between experimentalists and theorists. This interdisciplinary approach is crucial for effectively managing and interpreting large-scale neural activity datasets, ultimately advancing our understanding of brain function.

Recent advancements in imaging technologies have enabled the acquisition of high-quality, voluminous, multidimensional image data. Among these, light-sheet microscopy stands out for its ability to capture dynamic biological processes over extended periods and across large volumes, owing to its exceptional three-dimensional resolution and minimal invasiveness. However, handling and analyzing these vast datasets present significant challenges. Current computing environments struggle with high storage and computational demands, while traditional analysis methods relying heavily on human intervention are proving inadequate. Consequently, there is a growing shift toward automated solutions using artificial intelligence (AI), encompassing machine learning (ML) and other approaches. Although these technologies show promise, their application in extensive light-sheet imaging data analysis remains limited. This review explores the potential of light-sheet microscopy to revolutionize the life sciences through advanced imaging, addresses the primary challenges in data handling and analysis and discusses potential solutions, including the integration of AI and ML technologies.

Large-scale reconstitution of neuronal circuits from volumetric electron microscopy images is a remarkable research goal in neuroanatomy. However, the large-scale reconstruction is a result of automatic segmentation using convolutional neural networks (CNNs), which is still challenging for general researchers to perform. This review focuses on two representative CNNs for dense neuronal segmentation: flood-filling networks (FFNs) and local shape descriptors (LSDs)-predicting U-Net (LSD network). It outlines their basic mechanisms, requirements, and output segmentation using the author's example segmentation. The FFN excels in segmenting long axons, and the LSD network is adept at segmenting myelinated axons. The choice between FFN and LSD depends on the target, as neither is universally superior. A common limitation of FFN and LSD is the easy detachment of thin spines from parent dendrites, which is fundamentally unavoidable. The author also introduces CNNs that were proposed to mitigate this issue. As CNN-based automated segmentation can take months, researchers need to be aware of the selection of an appropriate CNN, required computer resources and fundamental limitations. This review serves as a guide for such dense neuronal segmentation.

Three-dimensional (3D) reconstruction is time-consuming owing to segmentation work. We evaluated the accuracy of the artificial intelligence (AI)-based segmentation and tracking model SAM-Track for segmentation of anatomical or histological structures and explored the potential of AI to enhance research efficiency. Images [obtained via computed tomography (CT) and magnetic resonance imaging (MRI)], anatomical sections from a Visible Korean Human open resource, and serial histological section images of cadavers were obtained. Six structures in the CT, MRI, and anatomical sections and seven in the histological sections were segmented using SAM-Track and compared with manual segmentation by calculating the Dice similarity coefficient. Segmented images were then reconstructed three dimensionally. The average Dice scores of CT and MRI results varied (0.13-0.83); anatomical sections showed mostly good accuracy (0.31-0.82). Clear-edged structures, such as the femur and liver, had high scores (0.69-0.83). In contrast, soft tissue structures, such as the rectus femoris and stomach, had variable accuracy (0.38-0.82). Histological sections showed high accuracy, especially for well-delineated tissues, such as the tibia and pancreas (0.95, 0.90). However, the tracking of branching structures, such as arteries and veins, was less successful (0.72, 0.52). In 3D reconstruction, high Dice scores were associated with accurate shapes, whereas low scores indicated discrepancies between the predicted and true shapes. AI-based automatic segmentation using SAM-Track provides moderate-to-good accuracy for anatomical and histological structures and is beneficial for conducting morphological studies involving 3D reconstruction.

Optimum bright-field scanning transmission electron microscopy (OBF STEM) is a recently developed low-dose imaging technique that uses a segmented or pixelated detector. While we previously reported that OBF STEM with a segmented detector has a higher efficiency than conventional STEM techniques such as annular bright field (ABF), the imaging efficiency is expected to be further improved by using a pixelated detector. In this study, we adopted a pixelated detector for the OBF technique and investigated the imaging characteristics. Because OBF imaging is based on the thick weak phase object approximation (tWPOA), a non-zero crystalline sample thickness is considered in addition to the conventional WPOA, where the pixelated OBF method can be regarded as the theoretical extension of single side band (SSB) ptychography. Thus, we compared these two techniques via signal-to-noise ratio transfer functions (SNRTFs), multi-slice image simulations, and experiments, showing how the OBF technique can improve dose efficiency from the conventional WPOA-based ptychographic imaging.

Although modern scanning electron microscope (SEM) possesses several electron detectors, it is not clear what kind of information is contained in a SEM image taken by a certain detector. Specifically, the detectors installed in the objective lens are difficult to know their characters. Thus, we propose a simple method to assess the acceptance of electron detector using a stainless steel sphere. After taking images under certain conditions, say electron beam energy, working distance (WD), etc., the image intensity of each pixel point, which is characterized by coordinate (θ, φ), is evaluated. The advantage of this method is the ease of implementation and the whole information of electron emission from the tilted surfaces is contained in the image. Using this information, the acceptance of the detector can be analyzed systematically. In this paper, the traditional Everhart-Thornley (ET) detector is analyzed with this method. It is demonstrated how the sphere image changes according to the measurement condition. The ET image quality is strongly governed by WD but not so much by the electron beam energy. We propose an alternative method to avoid the ambiguity of WD. Using a needle-type specimen stage, the ET image does not vary so much with WD and the reliability of ET image significantly improves.

A simple method that improves the resolution of phase measurement in differential phase-contrast scanning transmission electron microscopy for closed-type environmental cell applications was developed and tested using a model sample simulating environmental cell observations. Because the top and bottom membranes of an environmental cell are typically far apart, the images from these membranes are shifted widely by tilt-series acquisition, and averaging the images after alignment can effectively eliminate undesired signals from the membranes while improving the signal from the object of interest. It was demonstrated that a phase precision of 2π/100 rad is well achievable using the proposed method for the sample in an environmental cell.

The direct observation of the morphological changes in silicon-based negative electrode (Si-based negative electrode) materials during battery charging and discharging is useful for handling such materials and in electrode plate design. We developed an operando scanning electron microscopy (operando SEM) technique to quantitatively evaluate the expansion and contraction of Si-based negative electrode materials. A small all-solid-state lithium-ion battery was charged and discharged, and the expansion/contraction of particles while harnessing capacity was observed using SEM. We found that in a silicon monosilicate (SiO)/graphite negative electrode, SiO expanded first during charging, and graphite contracted first during discharging. Our study provides insights into the relationship between capacity and expansion and contraction coefficient of Si-based negative electrode materials.