Journal of microscopy最新文献

{"title":"Demystifying HPC: Empowering life scientists in HPC use for BioImage analysis.","authors":"Camille Charoy, Ruaridh Gollifer, Camilla Harris, Courtney Hopf, Stefania Marcotti, Kimberly Meechan, John Roche, Todd Fallesen","doi":"10.1111/jmi.70178","DOIUrl":"https://doi.org/10.1111/jmi.70178","url":null,"abstract":"<p><p>High-performance computing (HPC) has become an essential tool for BioImage analysis as recent advances in microscopy are creating increasingly large and complex datasets, the analysis of which is beyond the capabilities of personal computers. Although there is a need for wet lab scientists to utilise HPC resources, HPC training is commonly designed for scientists with prior computational experience and who are comfortable with computers, as opposed to those with limited computational experience, as is often the case with wet lab scientists. We held a 1-day workshop bringing together wet lab scientists, BioImage analysts, Bioinformaticians, and HPC engineers to help determine what motivated the wet lab scientists to learn HPC and what skills in HPC they would need in order to do their own research. Based on this workshop, we created an 8-h curriculum to teach basic HPC skills to wet lab scientists, so that they can do their own analysis without being overwhelmed with specialised technical details. This curriculum was trialled at The Francis Crick Institute to positive feedback. The curriculum framework also includes three software tracks, CellProfiler, Fiji and Python, which use familiar BioImage analysis workflows to reinforce HPC concepts. We present this curriculum here as an adaptable framework for other institutions to design a course to empower wet lab scientists to use HPC resources.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038848","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":"From developer to microscopist: Improving the accessibility of volume electron microscopy software.","authors":"Piper Fowler-Wright, Gabryel Mason-Williams, Jeremy Spencer, Mariam A Demir, Martin L Jones, Michele C Darrow, Martyn D Winn","doi":"10.1111/jmi.70177","DOIUrl":"https://doi.org/10.1111/jmi.70177","url":null,"abstract":"<p><p>To maximise insight from today's high throughput microscopy experiments, it is essential for a microscopist to familiarise themselves with the latest analysis software and undertake appropriate training. This can be challenging, however, due to the rapid development of software in the field, which often has complex installation steps and requires computational resources or storage beyond the capacity of personal computers. Cloud platforms running software containers remove these barriers, particularly when the platform is customised to specific end-user needs. We have developed a platform for the Volume Electron Microscopy community, Ada-vEM, that aims to lower barriers to evaluating and adopting new software for tasks such as registration and segmentation, while also providing a mechanism to streamline the provision of training. We describe the architecture of the platform and the underlying methods that enable rapid, scalable deployment of software and courses, together with its use for the delivery of remote training. We provide guidance on good practice, and hope that our experience can serve as an exemplar for other projects.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149038919","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":"40 years of measuring properties with atomic force microscopy.","authors":"Peter Grutter","doi":"10.1111/jmi.70168","DOIUrl":"https://doi.org/10.1111/jmi.70168","url":null,"abstract":"<p><p>This paper gives a short personal retrospective of some of the AFM methods developed in the Grutter group. It gives an overview of methods developed with the aim of measuring properties with high spatial and temporal resolution. Frequency mode AFM has over the past 40 years developed into a spatially resolving technique allowing spectroscopy of quantum dots and traps in semiconductors with single charge sensitivity. Properties can also be measured on ultrafast time scales by using pump-probe techniques, resolving a 100 fs short optical pulse. Two major challenges are addressed in a brief outlook: reproducible tip fabrication and open-source automatisation to increase throughput and statistics.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149016985","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":"Artefact-aware Brillouin microscopy framework for root mechanical phenotyping.","authors":"Timm Landes, Ole Christian Hammerschmidt, Leon Pascal Wallentin, Ghulam Naseer Ud Din, Jacob Morsbach, Luis Alonso Baez, Dag Heinemann","doi":"10.1111/jmi.70173","DOIUrl":"https://doi.org/10.1111/jmi.70173","url":null,"abstract":"<p><p>Plant roots continuously sense and respond to the mechanical signals of their growth environment: root cap function, mucilage secretion, and hormonal regulation jointly adapt root tip mechanics, shaping whether roots penetrate compacted layers or reorient around obstacles. Most current measurements of root mechanical properties require removal of the root from the surrounding medium, triggering mechano-adaptive responses and confounding follow-up observations. Here we present an artefact-aware Brillouin microscopy framework that combines RoPods, a 3D-printed growing and imaging chamber with colocalised Raman microscopy, to enable repeated in situ measurements of root mechanical properties, while retaining growth-induced stresses. As a proof-of-concept application, we show that roots adapt their mechanical properties to different Gelrite concentrations, and characterise methodological biases arising from chamber geometry and sample handling.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148990201","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":"Brillouin-Raman micro-mapping of silicone-hydrogel contact lenses: Revealing chemical and mechanical heterogeneities.","authors":"Alessandra Anna Passeri, Martina Alunni Cardinali, Erika Ponzini, Silvia Tavazzi, Daniele Fioretto","doi":"10.1111/jmi.70171","DOIUrl":"https://doi.org/10.1111/jmi.70171","url":null,"abstract":"<p><p>Joint Brillouin-Raman micro-spectroscopy (BRMS) enables simultaneous, non-destructive characterisation of the mechanical and chemical properties of soft materials at the micrometre scale, combining GHz-range viscoelastic information from acoustic modes with the vibrational fingerprint of molecular species. We present an improved BRMS platform based on an inverted microscope with a single confocal pinhole shared by both spectrometers allowing a single spectrum spanning from 0.1 GHz to 100 THz to be acquired at each point of a raster scan. Applied to Delefilcon A silicone-hydrogel contact lenses, macroscopically homogeneous and optically transparent, the platform reveals micrometric bulk heterogeneities not previously reported. Their Raman fingerprint identifies these features as clusters of the copper phthalocyanine handling tint. Two-dimensional Brillouin mapping shows a reduction of the frequency shift by up to ∼0.5 GHz near the cluster centres, corresponding to a decrease of about 12% in the longitudinal elastic modulus. These results demonstrate the ability of BRMS to resolve chemical-mechanical heterogeneities that are inaccessible to either technique alone, with potential implications for the durability of biomedical soft materials.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148920050","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":"napari-lattice: A user-friendly image processing tool for lattice light-sheet microscopy data.","authors":"Pradeep Rajasekhar, Michael Milton, Niall D Geoghegan, Ishrat Zaman, George W Ashdown, Robert Haase, Julie Iskander, Anna K Coussens, Kelly L Rogers, Lachlan Whitehead","doi":"10.1111/jmi.70167","DOIUrl":"10.1111/jmi.70167","url":null,"abstract":"<p><p>Lattice light-sheet microscopy (LLSM) has revolutionised live-cell imaging with its ability to resolve sub-cellular detail with high spatiotemporal resolution and minimal phototoxicity. However, LLSM often generates large terabyte-scale datasets that require complex preprocessing, creating bottlenecks in storage, data handling and analysis. These challenges are not unique to LLSM but also apply to oblique plane microscopy (OPM). To bridge this gap and reduce the time from large raw data acquisition to biological insight we developed napari-lattice, an open-source tool designed for scalable and modular LLSM workflows. napari-lattice provides a graphical user interface (GUI) for visualisation and analysis, and a command-line interface optimised for High-Performance Computing (HPC) environments. In this article, we demonstrate the capabilities of napari-lattice by quantifying cellular dynamics of human blood neutrophils using an LLSM. Data preprocessing and image analysis were combined into an end-to-end pipeline that used ilastik for cell segmentation and scikit-image for measuring cell morphology and intensity over time. This pipeline was then parallelised over regions of interest (ROIs) on an HPC node using napari-lattice. Restricting the analysis to ROIs reduced the deskewed data size ∼2.6-fold, allowing the full analysis pipeline to complete in ∼12 h, compared to the full field-of-view processing, which did not finish within 48 h. We also demonstrate how napari-lattice can be used with publicly available oblique plane microscopy (OPM) datasets of different configurations. Together, napari-lattice streamlines LLSM data analysis and visualisation, making analysis of complex, feature-rich datasets more accessible. This empowers researchers to fully leverage the potential of LLSM and OPM data.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148912289","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":"Development and evaluation of a deep learning framework for screening of cervical lymph node metastasis in microscopic images of oral squamous cell carcinoma: A proof of concept study.","authors":"Sourendra Nath Basu, Jeyaseelan Augustine, Nisha Chaudhary, Tanveer Ahmad, Nita Khurana, Aadithya B Urs","doi":"10.1111/jmi.70170","DOIUrl":"https://doi.org/10.1111/jmi.70170","url":null,"abstract":"<p><strong>Introduction: </strong>Lymph node (LN) metastasis serves as a critical prognostic indicator in oral squamous cell carcinoma (OSCC), profoundly influencing treatment approaches and survival outcomes. This proof-of-concept study developed and internally evaluated an economical, deep learning (DL)-assisted screening framework for identifying metastatic deposits in microscopic images of LNs from OSCC. The accessibility and operational costs of a WSI pose a major constraint in developing nations and resource-limited environments.</p><p><strong>Methods: </strong>An in-house dataset of conventional microscopic images (×400 magnification) was prepared from slides of LNs collected from 77 patients (one slide from each patient, 40 metastatic and 37 non-metastatic cases). The 'Training and Validation' subset of this dataset contained 16,426 metastasis-positive and 14,192 non-metastatic image patches of 256 × 256 pixels prepared from the microscopic images and were later augmented and used for training five deep-learning image classification models using the transfer learning approach. Among the five, fine-tuned convolutional neural network model, 'Xception', achieved the best performance metrics without evident overfitting. The in-house dataset also had a strictly separate EVALUATION component/EV subset that contained a total of 1938 metastasis-positive and 1604 metastasis-free microscopic images of LNs of size 1024 × 768 pixels (×400 magnification). Using a custom Python-based framework and the fine-tuned 'Xception' model, a software tool META_DETECT-AI was developed and tested over the whole microscopic images of the EV subset.</p><p><strong>Results: </strong>The proposed tool demonstrated 97.94% specificity, 99.85% sensitivity, and 98.98% accuracy with visually explainable heatmap generation capabilities over the microscopic images of the EV subset. At the slide level, it showed 100% sensitivity, 71.43% specificity, and 88.24% accuracy.</p><p><strong>Conclusion: </strong>The developed framework is intended as a decision-support tool for pathologist support and screening and prioritisation of suspicious microscopic images and not as a replacement for slide-level diagnosis or immunohistochemistry. Upon further validation on a larger, heterogeneous, multicentre dataset, this framework may be integrated with augmented reality microscopy/digital pathology workflow.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148920028","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":"Bioimage management and analysis in Galaxy: Tools, workflows, training, and community practices.","authors":"Leonid Kostrykin, Riccardo Massei, Diana Chiang, Thomas Wollmann, Jean-Marie Burel, David Lopez Tabernero, Yi Sun, Qi Gao, Maarten W Paul, Pavankumar Videm, Cameron Watson, Daniel Franco-Barranco, Anup Kumar, Nadia Goué, Reyhaneh Tavakoli Koopaei, Vladimír Ulman, Khaled Jum'ah, Krzysztof Poterlowicz, Martin Etzrodt, Arrate Muñoz-Barrutia, Sylvia E Le Dévédec, Josh Moore, Jeremy Goecks, Bjoern Gruening, Karl Rohr, Beatriz Serrano-Solano","doi":"10.1111/jmi.70165","DOIUrl":"https://doi.org/10.1111/jmi.70165","url":null,"abstract":"<p><p>Image analysis in the life sciences is constrained by fragmented software ecosystems, heterogeneous data formats, and limited reproducibility. These barriers hinder the reuse of image analysis methods and the sustainability of tools. In this article, we describe how the Galaxy platform enables FAIR (Findable, Accessible, Interoperable, and Reusable) image analysis by providing an integrated environment for data access, workflow execution, provenance tracing, and training. We present Galaxy as a computational workbench that supports diverse image formats and integrates with public, institutional, and private repositories. We describe a reference structure for FAIR image analysis workflows and illustrate how this pattern supports reproducibility, interoperability, and reuse. We also describe community-driven training and sustainability practices that embed FAIR principles directly into executable tutorials and shared workflows. Together, these foundations position Galaxy as a reproducible, scalable, and community-maintained platform for FAIR image analysis across the life sciences and beyond.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148906113","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":"Geometry-dependent migration dynamics of fibroblasts on ECM protein micropatterns.","authors":"Yuge Zhang, Shuhe Zhang, Lasse Hyldgaard Klausen, Mingdong Dong","doi":"10.1111/jmi.70166","DOIUrl":"https://doi.org/10.1111/jmi.70166","url":null,"abstract":"<p><p>Extracellular matrix (ECM) micropatterns provide well-defined adhesive environments for investigating how geometric confinement influences cell migration. Here, we combined ECM protein micropatterning with quantitative live-cell microscopy to examine NIH 3T3 fibroblast migration on straight and sinusoidal adhesive tracks. Time-lapse bright-field imaging, actin fluorescence imaging, centroid tracking, migration-rate quantification and aspect-ratio analysis revealed distinct migration behaviours under confined adhesion. On straight tracks, cells exhibited either oscillatory migration, characterised by repeated cycles of protrusion, retraction, and migration-rate bursts, or confined migration, characterised by limited centroid displacement and slow morphological remodelling. Sinusoidal tracks further introduced curvature-associated changes in migration dynamics, with cells adjusting leading-edge orientation along the curved path or becoming locally confined within curved regions. These observations demonstrate that adhesive geometry influences cell migration beyond simple path guidance and highlight the value of engineered ECM micropatterns combined with live-cell microscopy for resolving dynamic migration behaviours under confined conditions.</p>","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":" ","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148891904","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.70158","DOIUrl":"https://doi.org/10.1111/jmi.70158","url":null,"abstract":"","PeriodicalId":16484,"journal":{"name":"Journal of microscopy","volume":"303 3","pages":""},"PeriodicalIF":1.9,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jmi.70158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148862367","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}