Journal of microscopy最新文献

Doughnut-shaped vortex beams are widely used to enhance lateral resolution in super-resolution fluorescence microscopy and subtractive second harmonic generation microscopy. The influence of polarisation states on the axial point spread function is investigated theoretically and experimentally in subtractive second harmonic generation microscopy using a first-order Laguerre–Gaussian vortex beam. The influence of left-handed circular, right-handed circular and linear polarised states are analysed for second harmonic generation imaging and compared with results of fluorescence imaging. The results exhibit great agreement with theoretical predictions, and demonstrate the superiority of left-handed circular polarisation in achieving a complete dark central spot and an extended axial point spread function.

Mesenchymal stem cells (MSC) undergo replicative senescence, a state of irreversible cell cycle arrest that limits their utility in regenerative medicine applications. To identify novel markers of senescence useful to enhance the quality of MSC-based therapies, we compared young and senescent human bone marrow-derived mesenchymal stem cells (hMSCs) using a non-invasive, label-free approach based on quantitative phase imaging (QPI) with the Livecyte microscope. Senescent hMSCs demonstrated substantial morphological alterations, including a threefold increase in cell area, elevated dry mass, reduced thickness, and decreased sphericity compared to their younger counterparts. Additionally, motility metrics such as instantaneous velocity and displacement were significantly reduced in senescent cells, underscoring functional impairments that could hinder their therapeutic potential in regenerative medicine. The application of QPI offers a promising tool for monitoring cellular health, identifying, and potentially eliminating, senescent cells to improve the quality and effectiveness of MSC-based therapies.

Musculoskeletal tissues present complex hierarchical structures and mechanical heterogeneity across multiple length scales, making them difficult to characterise accurately. Digital volume correlation (DVC) is a non-destructive imaging technique that enables quantification of internal 3D strain fields under realistic loading conditions, offering a unique tool to investigate the biomechanics of biological tissues and biomaterials. This review highlights recent advancements in DVC, focusing on its applications across scales ranging from organ- to tissue-level mechanics in both mineralised and soft tissues. Instead of a traditional systematic review, we identify key technical challenges including the treatment of tissue interfaces, border effects, and the quantification of uncertainty in DVC outputs. Strategies for improving measurement accuracy and reliability are discussed. We also report on the increasing use of DVC in in vivo applications, its coupling with computational modelling to inform and validate biomechanical simulations, and its recent integration with data-driven methods such as deep learning to directly predict displacement and strain fields. Additionally, we examine its application in tissue engineering and implant–tissue interface assessment. By addressing such areas, we outline current limitations and emerging opportunities for future research. These include advancing precision, enabling clinical translation, and leveraging machine learning to create more robust, automated, and predictive DVC workflows for musculoskeletal health and tissue engineering.

The Pearson Correlation Coefficient can be calculated using three different criteria for pixel selection. These criteria carry different implications for interpretation of the metric. Different image analysis tools use these different criteria, and it is important that users know which criteria they have used. We maintain that this is important information for the community to consider and that we accurately addressed this topic. Adler and Parmryd have identified a flaw in our analysis in Figure 2 and here we explain how this came about and provide a correction. Alder and Parmryd's letter maligns our intent and competency – we refute all accusations and we object to the use of ad hominem arguments in scientific discourse.

Malaria is one of the deadliest infectious diseases in the world, annually responsible for over 400,000 deaths. It is caused by parasites of the genus Plasmodium, which undergo remarkable structural changes during their development within different cells across various hosts. An important approach to understand the structural basis of biochemical and physiological processes during Plasmodium infection has been the quantitative measurement of dimensional parameters obtained by different microscopy techniques. In this regard, sample preparation, particularly electron microscopy protocols that rely on room-temperature chemical fixation, has posed significant challenges, as it is known to produce artefacts such as shrinking, swelling and displacement of structures and osmolytes. In contrast, specimen immobilisation by cryofixation followed by freeze substitution minimises these artefacts and provides better sample preservation. Nevertheless, the composition of the freeze substitution medium may vary depending on the cell type, making it a critical factor for achieving optimal sample preparation. In this work, we optimised a freeze substitution protocol for the structural analysis of intraerythrocytic stages of the murine malaria models Plasmodium chabaudi and P. berghei. We tested different freeze substitution recipes, considering the biochemical composition of malaria membranes, and compared the results with those obtained through conventional chemical fixation. Overall, the results showed a significant improvement on the preservation of cell morphology and haemozoin crystals. Establishing an efficient and reproducible freeze substitution protocol for murine malaria models provides an important tool for advancing our understanding of the structural organisation of Plasmodium spp.

Volume Correlative Light and Electron Microscopy (vCLEM) is a powerful method for assessing the ultrastructure of molecularly defined subcellular domains. A central challenge in vCLEM has been the efficient navigation of Regions of Interest (ROIs) across multimodal and multiscale imaging datasets. We developed two key tools to overcome this challenge. First, we developed a multimodal image registration tool (SegReg) that utilizes segmentation of common objects across modalities and uses a Graphical Processing Unit (GPU) for registration of imaging datasets in two and three dimensions. Secondly, we developed a dedicated image viewer to visualize multimodal image registration in three dimensions (NavROI). Here, we demonstrate the integrated use of SegReg and NavROI to navigate large mouse tissue blocks with preserved fluorescent signals to allow selective targeting for TEM tomography of ROIs containing synapses and the cisternal organelle on the proximal region of the axon of a selected pyramidal neuron. By providing real time guidance to precise X-Y trimming of selected ROIs, reliable estimates of cutting depth relative to ROIs and a clear visual navigation of multimodal and multiscale images, our integrated workflow significantly improves the efficiency and accessibility of vCLEM analysis.

Deep learning (DL) has transformed image analysis, enabling breakthroughs in segmentation, object detection, and classification. However, a gap persists between cutting-edge DL research and its practical adoption in electron microscopy (EM) labs. This is largely due to the inaccessibility of DL methods for EM specialists and the expertise required to interpret model outputs.

To bridge this gap, we introduce DeepEM Playground, an interactive, user-friendly platform designed to empower EM researchers – regardless of coding experience – to train, tune, and apply DL models. By providing a guided, hands-on approach, DeepEM Playground enables users to explore the workings of DL in EM, facilitating both first-time engagement and more advanced model customisation.

The DeepEM Playground lowers the barrier to entry and fosters a deeper understanding of deep learning, thereby enabling the EM community to integrate AI-driven analysis into their workflows more confidently and effectively.