Journal of microscopy最新文献

{"title":"ModularImageAnalysis (MIA): Assembly of modularised image and object analysis workflows in ImageJ.","authors":"Stephen J Cross, Jordan D J R Fisher, Mark A Jepson","doi":"10.1111/jmi.13227","DOIUrl":"10.1111/jmi.13227","url":null,"abstract":"<p><p>ModularImageAnalysis (MIA) is an ImageJ plugin providing a code-free graphical environment in which complex automated analysis workflows can be constructed and distributed. The broad range of included modules cover all stages of a typical analysis workflow, from image loading through image processing, object detection, extraction of measurements, measurement-based filtering, visualisation and data exporting. MIA provides out-of-the-box compatibility with many advanced image processing plugins for ImageJ including Bio-Formats, DeepImageJ, MorphoLibJ and TrackMate, allowing these tools and their outputs to be directly incorporated into analysis workflows. By default, modules support spatially calibrated 5D images, meaning measurements can be acquired in both pixel and calibrated units. A hierarchical object relationship model allows for both parent-child (one-to-many) and partner (many-to-many) relationships to be established. These relationships underpin MIA's ability to track objects through time, represent complex spatial relationships (e.g. topological skeletons) and measure object distributions (e.g. count puncta per cell). MIA features dual graphical interfaces: the 'editing view' offers access to the full list of modules and parameters in the workflow, while the simplified 'processing view' can be configured to display only a focused subset of controls. All workflows are batch-enabled by default, with image files within a specified folder being processed automatically and exported to a single spreadsheet. Beyond the included modules, functionality can be extended both internally, through integration with the ImageJ scripting interface, and externally, by developing third-party Java modules that extend the core MIA framework. Here we describe the design and functionality of MIA in the context of a series of real-world example analyses.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7616484/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10213367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of the SMLM technique and the MSSR algorithm in confocal microscopy for super-resolved imaging of cellulose fibres.","authors":"Josué David Hernández-Varela, Susana Dianey Gallegos-Cerda, José Jorge Chanona-Pérez, Liliana Edith Rojas Candelas, Eduardo Martínez-Mercado","doi":"10.1111/jmi.13287","DOIUrl":"10.1111/jmi.13287","url":null,"abstract":"<p><p>Nowadays, the use of super-resolution microscopy (SRM) is increasing globally due to its potential application in several fields of life sciences. However, a detailed and comprehensive guide is necessary for understanding a single-frame image's resolution limit. This study was performed to provide information about the structural organisation of isolated cellulose fibres from garlic and agave wastes through fluorophore-based techniques and image analysis algorithms. Confocal microscopy provided overall information on the cellulose fibres' microstructure, while techniques such as total internal reflection fluorescence microscopy facilitated the study of the plant fibres' surface structures at a sub-micrometric scale. Furthermore, SIM and single-molecule localisation microscopy (SMLM) using the PALM reconstruction wizard can resolve the network of cellulose fibres at the nanometric level. In contrast, the mean shift super-resolution (MSSR) algorithm successfully determined nanometric structures from confocal microscopy images. Atomic force microscopy was used as a microscopy technique for measuring the size of the fibres. Similar fibre sizes to those evaluated with SIM and SMLM were found using the MSSR algorithm and AFM. However, the MSSR algorithm must be cautiously applied because the selection of thresholding parameters still depends on human visual perception. Therefore, this contribution provides a comparative study of SRM techniques and MSSR algorithm using cellulose fibres as reference material to evaluate the performance of a mathematical algorithm for image processing of bioimages at a nanometric scale. In addition, this work could act as a simple guide for improving the lateral resolution of single-frame fluorescence bioimages when SRM facilities are unavailable.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139990341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Annotation and automated segmentation of single-molecule localisation microscopy data.","authors":"Oliver Umney, Joanna Leng, Gianluca Canettieri, Natalia A Riobo-Del Galdo, Hayley Slaney, Philip Quirke, Michelle Peckham, Alistair Curd","doi":"10.1111/jmi.13349","DOIUrl":"10.1111/jmi.13349","url":null,"abstract":"<p><p>Single Molecule Localisation Microscopy (SMLM) is becoming a widely used technique in cell biology. After processing the images, the molecular localisations are typically stored in a table as xy (or xyz) coordinates, with additional information, such as number of photons, etc. This set of coordinates can be used to generate an image to visualise the molecular distribution, for example, a 2D or 3D histogram of localisations. Many different methods have been devised to analyse SMLM data, among which cluster analysis of the localisations is popular. However, it can be useful to first segment the data, to extract the localisations in a specific region of a cell or in individual cells, prior to downstream analysis. Here we describe a pipeline for annotating localisations in an SMLM dataset in which we compared membrane segmentation approaches, including Otsu thresholding and machine learning models, and subsequent cell segmentation. We used an SMLM dataset derived from dSTORM images of sectioned cell pellets, stained for the membrane proteins EGFR (epidermal growth factor receptor) and EREG (epiregulin) as a test dataset. We found that a Cellpose model retrained on our data performed the best in the membrane segmentation task, allowing us to perform downstream cluster analysis of membrane versus cell interior localisations. We anticipate this will be generally useful for SMLM analysis.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141875098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CellProfiler plugins - An easy image analysis platform integration for containers and Python tools.","authors":"Erin Weisbart, Callum Tromans-Coia, Barbara Diaz-Rohrer, David R Stirling, Fernanda Garcia-Fossa, Rebecca A Senft, Mark C Hiner, Marcelo B de Jesus, Kevin W Eliceiri, Beth A Cimini","doi":"10.1111/jmi.13223","DOIUrl":"10.1111/jmi.13223","url":null,"abstract":"<p><p>CellProfiler is a widely used software for creating reproducible, reusable image analysis workflows without needing to code. In addition to the >90 modules that make up the main CellProfiler program, CellProfiler has a plugins system that allows for the creation of new modules which integrate with other Python tools or tools that are packaged in software containers. The CellProfiler-plugins repository contains a number of these CellProfiler modules, especially modules that are experimental and/or dependency-heavy. Here, we present an upgraded CellProfiler-plugins repository, an example of accessing containerised tools, improved documentation and added citation/reference tools to facilitate the use and contribution of the community.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10924770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10578266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging imaging users to imaging analysis - A community survey.","authors":"Suganya Sivagurunathan, Stefania Marcotti, Carl J Nelson, Martin L Jones, David J Barry, Thomas J A Slater, Kevin W Eliceiri, Beth A Cimini","doi":"10.1111/jmi.13229","DOIUrl":"10.1111/jmi.13229","url":null,"abstract":"<p><p>The 'Bridging Imaging Users to Imaging Analysis' survey was conducted in 2022 by the Center for Open Bioimage Analysis (COBA), BioImaging North America (BINA) and the Royal Microscopical Society Data Analysis in Imaging Section (RMS DAIM) to understand the needs of the imaging community. Through multichoice and open-ended questions, the survey inquired about demographics, image analysis experiences, future needs and suggestions on the role of tool developers and users. Participants of the survey were from diverse roles and domains of the life and physical sciences. To our knowledge, this is the first attempt to survey cross-community to bridge knowledge gaps between physical and life sciences imaging. Survey results indicate that respondents' overarching needs are documentation, detailed tutorials on the usage of image analysis tools, user-friendly intuitive software, and better solutions for segmentation, ideally in a format tailored to their specific use cases. The tool creators suggested the users familiarise themselves with the fundamentals of image analysis, provide constant feedback and report the issues faced during image analysis while the users would like more documentation and an emphasis on tool friendliness. Regardless of the computational experience, there is a strong preference for 'written tutorials' to acquire knowledge on image analysis. We also observed that the interest in having 'office hours' to get an expert opinion on their image analysis methods has increased over the years. The results also showed less-than-expected usage of online discussion forums in the imaging community for solving image analysis problems. Surprisingly, we also observed a decreased interest among the survey respondents in deep/machine learning despite the increasing adoption of artificial intelligence in biology. In addition, the community suggests the need for a common repository for the available image analysis tools and their applications. The opinions and suggestions of the community, released here in full, will help the image analysis tool creation and education communities to design and deliver the resources accordingly.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10950841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41157191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In situ isotropic 3D imaging of vasculature perfusion specimens using x-ray microscopic dual-energy CT.","authors":"Stephan Handschuh, Ursula Reichart, Stefan Kummer, Martin Glösmann","doi":"10.1111/jmi.13369","DOIUrl":"https://doi.org/10.1111/jmi.13369","url":null,"abstract":"<p><p>Ex vivo x-ray angiography provides high-resolution, three-dimensional information on vascular phenotypes down to the level of capillaries. Sample preparation for ex vivo angiography starts with the removal of blood from the vascular system, followed by perfusion with an x-ray dense contrast agent mixed with a carrier such as gelatine or a polymer. Subsequently, the vascular micro-architecture of harvested organs is imaged in the intact fixed organ. In the present study, we present novel microscopic dual-energy CT (microDECT) imaging protocols that allow to visualise and analyse microvasculature in situ with reference to the morphology of hard and soft tissue. We show that the spectral contrast of µAngiofil and Micropaque barium sulphate in perfused specimens allows for the effective separation of vasculature from mineralised skeletal tissues. Furthermore, we demonstrate the counterstaining of perfused specimens using established x-ray dense contrast agents to depict blood vessels together with the morphology of soft tissue. Phosphotungstic acid (PTA) is used as a counterstain that shows excellent spectral contrast in both µAngiofil and Micropaque barium sulphate-perfused specimens. A novel Sorensen-buffered PTA protocol is introduced as a counterstain for µAngiofil specimens, as the polyurethane polymer is susceptible to artefacts when using conventional staining solutions. Finally, we demonstrate that counterstained samples can be automatically processed into three separate image channels (skeletal tissue, vasculature and stained soft tissue), which offers multiple new options for data analysis. The presented microDECT workflows are suited as tools to screen and quantify microvasculature and can be implemented in various correlative imaging pipelines to target regions of interest for downstream light microscopic investigation.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to 'Non-fitting FLIM-FRET facilitates analysis of protein interactions in live Zebrafish embryos'.","authors":"","doi":"10.1111/jmi.13368","DOIUrl":"https://doi.org/10.1111/jmi.13368","url":null,"abstract":"","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142566993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative sample preparation using alcohol dehydration and high refractive index medium enables acquisition of two-channel super-resolution 3D STED image of an entire oocyte.","authors":"Michaela Frolikova, Michaela Blazikova, Martin Capek, Helena Chmelova, Jan Valecka, Veronika Kolackova, Eliska Valaskova, Martin Gregor, Katerina Komrskova, Ondrej Horvath, Ivan Novotny","doi":"10.1111/jmi.13363","DOIUrl":"https://doi.org/10.1111/jmi.13363","url":null,"abstract":"<p><p>Super-resolution (SR) microscopy is a cutting-edge method that can provide detailed structural information with high resolution. However, the thickness of the specimen has been a major limitation for SR methods, and large biological structures have posed a challenge. To overcome this, the key step is to optimise sample preparation to ensure optical homogeneity and clarity, which can enhance the capabilities of SR methods for the acquisition of thicker structures. Oocytes are the largest cells in the mammalian body and are crucial objects in reproductive biology. They are especially useful for studying membrane proteins. However, oocytes are extremely fragile and sensitive to mechanical manipulation and osmotic shocks, making sample preparation a critical and challenging step. We present an innovative, simple and sensitive approach to oocyte sample preparation for 3D STED acquisition. This involves alcohol dehydration and mounting into a high refractive index medium. This extended preparation procedure allowed us to successfully obtain a unique two-channel 3D STED SR image of an entire mouse oocyte. By optimising sample preparation, it is possible to overcome current limitations of SR methods and obtain high-resolution images of large biological structures, such as oocytes, in order to study fundamental biological processes. Lay Abstract: Super-resolution (SR) microscopy is a cutting-edge tool that allows scientists to view incredibly fine details in biological samples. However, it struggles with larger, thicker specimens, as they need to be optically clear and uniform for the best imaging results. In this study, we refined the sample preparation process to make it more suitable for SR microscopy. Our method includes carefully dehydrating biological samples with alcohol and then transferring them into a mounting medium that enhances optical clarity. This improved protocol enables high-resolution imaging of thick biological structures, which was previously challenging. By optimizing this preparation method, we hope to expand the use of SR microscopy for studying large biological samples, helping scientists better understand complex biological structures.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142400441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microscopy at a glance: New poster article series exploring the intersection of art, science and imaging","authors":"Kirti Prakash,&nbsp;Christian Franke,&nbsp;Fei Xia,&nbsp;Nabanita Chatterjee,&nbsp;Carlas Smith","doi":"10.1111/jmi.13357","DOIUrl":"10.1111/jmi.13357","url":null,"abstract":"","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142391206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TOC - Issue Information","authors":"","doi":"10.1111/jmi.13204","DOIUrl":"https://doi.org/10.1111/jmi.13204","url":null,"abstract":"","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":null,"pages":null},"PeriodicalIF":1.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.13204","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142429632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}