{"title":"Physical Basics of Scanning Electron Microscopy in Volume Electron Microscopy.","authors":"Mitsuo Suga, Yusuke Hirabayashi","doi":"10.1093/jmicro/dfaf016","DOIUrl":"10.1093/jmicro/dfaf016","url":null,"abstract":"<p><p>Volume electron microscopy (vEM) has become a widely adopted technique for acquiring three-dimensional structural information of biological specimens. In addition to the traditional use of transmission electron microscopy (TEM), recent advances in the resolution of scanning electron microscopy (SEM) made it suitable for vEM application. Currently, various types of SEM with different advantages have been utilized. For selecting the appropriate type of SEM to obtain optimal vEM images for the purpose of individual research, it is important to understand the physics underlying each SEM technology. This article aims to explain the physics for signal electron generation, various objective lens configurations, and detection systems, employed in SEM to enhance high-resolution imaging and improve signal detection conditions.</p>","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143582412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Cryo-STEM Tomography for Cell biology Using Thick Lamella.","authors":"Kazuhiro Aoyama, Hiroko Takazaki, Misaki Arie, Hironori Suemune, Shogo Kawai","doi":"10.1093/jmicro/dfaf017","DOIUrl":"https://doi.org/10.1093/jmicro/dfaf017","url":null,"abstract":"<p><p>Electron tomography (ET) is a powerful tool for structural studies in cell biology, but specimen thickness remains a significant limitation. Scanning transmission electron microscopy (STEM) tomography offers advantages in this regard. Recent developments in focused ion beam (FIB) slicing methods for cryo-cell biology have enabled the observation and 3D reconstruction of relatively thick specimens (300-500 nm) using cryo-STEM tomography. Organelles such as mitochondria and the nuclear membrane have been clearly reconstructed, demonstrating the promise of STEM tomography for structural studies in cell biology.</p>","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Seg and Ref: A Newly Developed Toolset for Artificial Intelligence-Powered Segmentation and Interactive Refinement for Labor-Saving Three-Dimensional Reconstruction.","authors":"Satoru Muro, Takuya Ibara, Akimoto Nimura, Keiichi Akita","doi":"10.1093/jmicro/dfaf015","DOIUrl":"https://doi.org/10.1093/jmicro/dfaf015","url":null,"abstract":"<p><p>Traditional three-dimensional reconstruction is labor-intensive owing to manual segmentation; this can be addressed by developing artificial intelligence-driven automated segmentation. However, it is limited by a lack of user-friendly tools for morphologists. We present a workflow for three-dimensional reconstruction using our artificial intelligence-powered segmentation tool. Specifically, we developed an interactive toolset, \"Seg & Ref,\" to overcome the abovementioned challenges by enabling artificial intelligence-powered segmentation and easy mask editing without requiring a command-line setup. We demonstrated a three-dimensional reconstruction workflow using serial sections of a Carnegie Stage 15 human embryo. Automated segmentation (Step 1) was performed using the graphical user interface, \"SAM2 GUI for Img Seq,\" which utilizes the Segment Anything Model 2 and supports interactive segmentation through a web-based interface. Users specify target structures via box prompts, and the results are propagated across all images for batch segmentation. The segmentation masks were reviewed and corrected (Step 2) using \"Segment Editor PP,\" a PowerPoint-based tool enabling interactive mask refinement. Finally, the corrected masks were imported into the 3D Slicer application for reconstruction (Step 3). Our three-dimensional reconstruction visualized key structures, including the spinal cord, veins, aorta, mesonephros, gut, heart, trachea, liver, and peritoneal cavity. The reconstructed models accurately represented their spatial relationships and morphologies. This provides a labor-saving approach for three-dimensional reconstruction workflows owing to their optimization for serial sections, versatility, and accessibility without programming expertise. Therefore, morphological research can be enhanced by precise segmentation using intuitive and user-friendly interfaces of \"Seg & Ref.\"</p>","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Journey from Image Acquisition to Biological Insight: Handling and Analyzing Large Volumes of Light-Sheet Imaging Data.","authors":"Yuko Mimori-Kiyosue","doi":"10.1093/jmicro/dfaf013","DOIUrl":"https://doi.org/10.1093/jmicro/dfaf013","url":null,"abstract":"<p><p>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 the high storage and computational demands, while traditional analysis methods relying heavily on human intervention are proving inadequate. Consequently, there is a growing shift towards automated solutions using artificial intelligence, encompassing machine learning 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 artificial intelligence and machine learning technologies.</p>","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ryohei Kojima, Ayhan Yurtsever, Keisuke Miyazawa, Lucas J Andrew, Mark J MacLachlan, Takeshi Fukuma
{"title":"Tip treatment for subnanoscale atomic force microscopy in liquid by atomic layer deposition Al2O3 coating.","authors":"Ryohei Kojima, Ayhan Yurtsever, Keisuke Miyazawa, Lucas J Andrew, Mark J MacLachlan, Takeshi Fukuma","doi":"10.1093/jmicro/dfaf014","DOIUrl":"https://doi.org/10.1093/jmicro/dfaf014","url":null,"abstract":"<p><p>Atomic force microscopy (AFM) allows direct imaging of atomic- or molecular-scale surface structures in liquid. However, such subnanoscale measurements are often sensitive to the AFM tip properties. To overcome this problem, 30 nm Si-sputter coating was proposed, and its effectiveness in improving stability and reproducibility has been demonstrated in atomic-scale imaging of various materials. However, this method involves tip blunting, enhancing the tip-induced dilation effect. As an alternative method, here we investigate atomic layer deposition (ALD) Al2O3-coating, where the film thickness is atomically well-controlled. Our transmission electron microscopy, contact angle and force curve measurements consistently suggest that as-purchased tips are covered with organic contaminants, and the initial 20 cycles gradually remove them, reducing the tip radius (Rt) and hydrophobicity. Further deposition increases Rt and hydrophilicity and forms an intact Al2O3 film over 50 cycles. We compared 50-cycle ALD-coated tips with 30 nm Si-sputter-coated tips in imaging mica and chitin nanocrystals (NCs). On mica, ALD coating gives slightly less stability and reproducibility in hydration force measurements than the Si sputter coating, yet they are sufficient in atomic-scale imaging. In imaging chitin NCs, ALD-coated tips give a less tip-induced dilation effect while maintaining molecular-scale imaging capability. We also found that 10-cycle-ALD coated tips covered with carbon give a better resolution and reproducibility in observing subnanoscale features at chitin NC surfaces. This result and our experience empirically suggest carbon-coated tips' effectiveness in observing carbon-based materials.</p>","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143560194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Segmented ring-mesh model of glycosaminoglycan chains based on the 3D analysis of normal individual and Musculocontractural Ehlers-Danlos syndrome skin using scanning transmission electron microscopy.","authors":"Naoki Takahashi, Takuya Hirose, Kiyokazu Kametani, Tomohito Iwasaki, Yasutada Imamura, Tomoki Kosho, Takafumi Watanabe","doi":"10.1093/jmicro/dfaf012","DOIUrl":"https://doi.org/10.1093/jmicro/dfaf012","url":null,"abstract":"<p><p>Collagen fibrils in the dermis are bundled by glycosaminoglycan (GAG) chains of decorin, which contribute to its strength. The three-dimensional structure of collagen fibrils and GAG chains has been discussed on the basis of observations and experiments. This study uses scanning transmission electron microscope (STEM) tomography with high Z-axis resolution to analyze the three-dimensional structure of GAG chains in the dermis from a healthy individual and a patient with Musculocontractural Ehlers-Danlos syndrome caused by pathogenic variants in CHST14 (mcEDS-CHST14). This observation revealed that the dermis from a healthy individual featured multiple GAG chains that wrapped around collagen fibrils and formed incomplete ring structures. However, in the dermis from a patient with mcEDS-CHST14, GAG chains were linear and did not form rings. Based on the relationship between collagen fibrils and GAG chains, we suggest the three-dimensional structure of normal GAG chains in a new model named the \"segmented ring-mesh model.\" The interactions between collagen fibrils and GAG chains in this model also apply to the dermis of mcEDS-CHST14 patients, in which the GAG chain composition changes to become CS-rich and more linear. This change leads to an increased inter-fibrillar space, which inhibits the dense packing of collagen fibrils. These findings suggest that this phenomenon contributes to the skin fragility observed in mcEDS-CHST14 patients. Our study suggests the \"segmented ring-mesh model\" of GAG chains is essential for the dense packing of collagen fibrils in normal dermis. STEM tomography is highly effective in analyzing the three-dimensional structure of collagen fibrils and GAG chains.</p>","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Unraveling the Neural Code: Analysis of Large-Scale Two-Photon Microscopy Data.","authors":"Yoshihito Saito, Yuma Osako, Masanori Murayama","doi":"10.1093/jmicro/dfaf010","DOIUrl":"https://doi.org/10.1093/jmicro/dfaf010","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluating accuracy in artificial intelligence-powered serial segmentation for sectional images applied to morphological studies with three-dimensional reconstruction.","authors":"Satoru Muro, Takuya Ibara, Yuzuki Sugiyama, Akimoto Nimura, Keiichi Akita","doi":"10.1093/jmicro/dfae054","DOIUrl":"https://doi.org/10.1093/jmicro/dfae054","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143416503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Derivation Method of the Dielectric Function of Amorphous Materials Using Angle-Resolved Electron Energy-Loss Spectroscopy for Exciton-Size Evaluation.","authors":"Tomoya Saito, Yohei K Sato, Masami Terauchi","doi":"10.1093/jmicro/dfae056","DOIUrl":"https://doi.org/10.1093/jmicro/dfae056","url":null,"abstract":"<p><p>Accurately deriving the momentum-transfer dependence of the dielectric function ε(q, ω) using angle-resolved electron energy-loss spectroscopy (AR-EELS) is necessary for evaluating the average electron-hole distance, i.e., the exciton size, in materials. Achieving accurate exciton-size evaluations will promote the comprehension of optical functionality in materials such as photocatalysts. However, for amorphous materials, it is difficult to accurately derive ε(q, ω) because the elastic scattering intensity originating from the amorphous structure and the inelastic scattering intensity associated with the elastic scattering overlap in the EELS spectrum. In this study, a method to remove these overlapping intensities from the EELS spectrum is proposed to accurately derive the ε(q, ω) of an amorphous material. Amorphous SiO2 (am-SiO2) was subjected to AR-EELS measurements, and ε(q, ω) of am-SiO2 was derived after removing the intensity due to the amorphous structure using the proposed method. Thereafter, the exciton absorption intensity and the exciton size were evaluated. Applying the proposed method, the exciton absorption intensity was considerably suppressed in the q-region after 1.0 Å-1, where the elastic and inelastic scattering intensities originating from the amorphous structure are dominant. The exciton size evaluated was 2 nm (1 nm), consistent with the theoretically predicted size of ~1 nm. Therefore, the proposed method is effective for deriving accurate ε(q, ω), facilitating exciton-size evaluation for amorphous materials using AR-EELS.</p>","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}