Microscopy (Oxford, England)最新文献

Physical basics of scanning electron microscopy in volume electron microscopy. 体积电子显微镜中扫描电子显微镜的物理基础。

Microscopy (Oxford, England) Pub Date : 2025-06-26 DOI: 10.1093/jmicro/dfaf016

Mitsuo Suga, Yusuke Hirabayashi

引用次数: 0

Bioimaging and image analysis at scale. 大规模生物成像和图像分析。

Microscopy (Oxford, England) Pub Date : 2025-06-26 DOI: 10.1093/jmicro/dfaf026

Hirabayashi Yusuke, Onami Shuichi

引用次数: 0

Live-cell omics with Raman spectroscopy. 拉曼光谱的活细胞组学。

Microscopy (Oxford, England) Pub Date : 2025-06-26 DOI: 10.1093/jmicro/dfaf020

Ken-Ichiro F Kamei, Yuichi Wakamoto

{"title":"Live-cell omics with Raman spectroscopy.","authors":"Ken-Ichiro F Kamei, Yuichi Wakamoto","doi":"10.1093/jmicro/dfaf020","DOIUrl":"10.1093/jmicro/dfaf020","url":null,"abstract":"Genome-wide profiling of gene expression levels in cells, such as transcriptomics and proteomics, is a powerful experimental approach in modern biology, allowing not only efficient exploration of the genetic elements responsible for biological phenomena of interest, but also characterization of the global constraints behind plastic phenotypic changes of cells that accompany large-scale remodeling of omics profiles. To understand how individual cells change their molecular profiles to achieve specific phenotypic changes in phenomena such as differentiation, cancer metastasis and adaptation, it is crucial to characterize the dynamics of cellular phenotypes and omics profiles simultaneously at the single-cell level. Especially in the last decade, significant technical progress has been made in the in situ identification of omics profiles of cells on the microscope. However, most approaches still remain destructive and cannot unravel the post-measurement dynamics. In recent years, Raman spectroscopy-based methods for omics inference have emerged, allowing the characterization of genome-wide molecular profile dynamics in living cells. In this review, we give a brief overview of the recent development of imaging-based omics profiling methods. We then present the approach to infer omics profiles from single-cell Raman spectra. Since Raman spectra can be obtained from living cells in a non-destructive and non-staining manner, this method may open the door to live-cell omics.","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":"189-200"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203177/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A wide variety of techniques for a volume electron microscopy. 体积电子显微镜的多种技术。

Microscopy (Oxford, England) Pub Date : 2025-06-26 DOI: 10.1093/jmicro/dfaf011

Yoshiyuki Kubota, Takaaki Miyazaki, Nilton L Kamiji, Tamami Honda, Motohide Murate, Mitsuo Suga

引用次数: 0

Recent advancement and human tissue applications of volume electron microscopy. 体视电子显微镜的最新进展和人体组织应用。

Microscopy (Oxford, England) Pub Date : 2025-06-26 DOI: 10.1093/jmicro/dfae047

Makoto Abe, Nobuhiko Ohno

{"title":"Recent advancement and human tissue applications of volume electron microscopy.","authors":"Makoto Abe, Nobuhiko Ohno","doi":"10.1093/jmicro/dfae047","DOIUrl":"10.1093/jmicro/dfae047","url":null,"abstract":"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.","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":"233-243"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482450","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}

引用次数: 0

Unlocking the potential of large-scale 3D imaging with tissue clearing techniques. 利用组织清除技术释放大规模三维成像的潜力。

Microscopy (Oxford, England) Pub Date : 2025-06-26 DOI: 10.1093/jmicro/dfae046

Etsuo A Susaki

{"title":"Unlocking the potential of large-scale 3D imaging with tissue clearing techniques.","authors":"Etsuo A Susaki","doi":"10.1093/jmicro/dfae046","DOIUrl":"10.1093/jmicro/dfae046","url":null,"abstract":"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.","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":"179-188"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142333935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unraveling the neural code: analysis of large-scale two-photon microscopy data. 解开神经密码：大规模双光子显微镜数据分析。

Microscopy (Oxford, England) Pub Date : 2025-06-26 DOI: 10.1093/jmicro/dfaf010

Yoshihito Saito, Yuma Osako, Masanori Murayama

{"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":"10.1093/jmicro/dfaf010","url":null,"abstract":"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.","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":"146-163"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","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}

引用次数: 0

A guide to CNN-based dense segmentation of neuronal EM images. 基于cnn的神经元EM图像密集分割指南。

Microscopy (Oxford, England) Pub Date : 2025-06-26 DOI: 10.1093/jmicro/dfaf002

Hidetoshi Urakubo

{"title":"A guide to CNN-based dense segmentation of neuronal EM images.","authors":"Hidetoshi Urakubo","doi":"10.1093/jmicro/dfaf002","DOIUrl":"10.1093/jmicro/dfaf002","url":null,"abstract":"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.","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":"223-232"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142973854","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}

引用次数: 0

Journey from image acquisition to biological insight: handling and analyzing large volumes of light-sheet imaging data. 从图像采集到生物洞察：处理和分析大量光片成像数据。

Microscopy (Oxford, England) Pub Date : 2025-06-26 DOI: 10.1093/jmicro/dfaf013

Yuko Mimori-Kiyosue

{"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":"10.1093/jmicro/dfaf013","url":null,"abstract":"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.","PeriodicalId":74193,"journal":{"name":"Microscopy (Oxford, England)","volume":" ","pages":"164-178"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","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}

引用次数: 0

Reduction of Membrane-derived Noise Using Beam-tilt Measurement and Deep Learning in Observation using Environmental Cell. 利用波束倾斜测量和环境细胞观测中的深度学习降低膜源噪声。

Microscopy (Oxford, England) Pub Date : 2025-06-24 DOI: 10.1093/jmicro/dfaf031

Fumiaki Ichihashi, Yoshio Takahashi, Toshiaki Tanigaki

引用次数: 0