Journal of Microscopy

Backscattered electron scanning electron microscopy (BSE-SEM) provides compositional image contrast but has found limited application to biological samples due to the low atomic number difference between constituent elements, the thickness of the surrounding environment, and the need for complex sample preparation. Here, we demonstrate the use of room temperature liquid phase BSE-SEM (LPBSEM) for imaging Bacillus subtilis spores encapsulated in graphene liquid cells, preserving native hydration and reducing the thickness of the sample environment. This approach eliminates the need for staining and enables high-contrast visualisation of subcellular structures. Distinct structural layers within B. subtilis spores have been observed with a contrast similar to conventional thin-section transmission electron microscopy but without the need for sample preparation that potentially compromises sample integrity. We further investigate the influence of beam energy on the interaction volume depth and image contrast and propose optimal conditions for subsurface visualisation. Monte Carlo simulations have been used to validate our experimental observations and provide a quantitative framework for understanding BSE generation from hydrated, low atomic number specimens.

Microscopy images are frequently downsampled due to acquisition and computational constraints, requiring reconstruction before downstream analysis. While super-resolution (SR) is typically assessed using pixel-level fidelity metrics, its impact on deep learning (DL) model behavior remains insufficiently understood. In this work, we present a study that examines how different upsampling strategies affect image quality and classification performance. Using the BloodMNIST dataset, we construct matched 224x224 datasets from 64x64 images via bicubic interpolation, SwinIR Classical, and SwinIR RealGAN DL SR models, alongside the original 224 ground-truth images. We evaluate reconstruction quality using the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) scores and assess downstream classification performance using ResNet-50 and Vision Transformer models, with accuracy, macro-F1 score, and a confidence-aware metric, the area under the receiver operating curve for successful prediction (AUPR Success). Our results demonstrate that bicubic interpolation significantly degrades classification performance, whereas SR methods can recover class-relevant information, even better than the ground-truth data. These findings emphasize the importance of confidence-aware evaluation and unambiguous reporting of reconstruction pipelines in microscopy-based DL studies.

Smart microscopy is transforming life sciences by automating experimental imaging workflows and enabling real-time adaptation based on feedback from images and other data streams. This shift increases throughput, improves reproducibility, and expands the functional capabilities of microscopes. However, the current landscape is highly fragmented. Academic researchers often develop custom solutions for specific scientific needs, while industry offerings are typically proprietary and tied to specific hardware. This diversity, while fostering innovation, also creates major challenges in interoperability, reproducibility, and standardization, which slows progress and adaption. This article presents a collaborative effort between academic and industry leaders to survey the current state of smart microscopy, highlight representative implementations, and identify common technical and organizational barriers. We propose a framework for greater interoperability based on shared standards, modular software design, and community-driven development. Our goal is to support collaboration across the field and lay the groundwork for a more connected, reusable, and accessible smart microscopy ecosystem. We conclude with a call to action for researchers, hardware developers, and institutions to join in building an open, interoperable foundation that will unlock the full potential of smart microscopy in life science research.

Candida albicans, a commensal fungal pathogen, is a major cause of opportunistic infections in immunocompromised individuals. Understanding its cellular structures and pathogenic mechanisms is critical for developing targeted antifungal therapies. Stimulated emission depletion (STED) microscopy enables nanoscale visualization of cellular components, surpassing the diffraction limit of conventional light microscopy. In this study, we employed STED microscopy to investigate the ultrastructural organization of C. albicans in live specimens. We showed that dyes commonly used in STED microscopy of mammalian cells are ineffective for the study of C. albicans, and we showed the utility of Nile Red staining for visualising the organisation of dynamic cellular components, including tracking of lipid droplets, using time-lapse recording in experiments exceeding 12 hours. STED microscopy offered more than a two-fold improvement in resolution compared to confocal laser scanning microscopy applied to the same specimens with negligible photobleaching. This study demonstrates the utility of STED microscopy in advancing our understanding of C. albicans biology at the nanoscale, providing a platform for future investigations into fungal pathogenicity and antifungal development. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/625149v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@d46837org.highwire.dtl.DTLVardef@105f661org.highwire.dtl.DTLVardef@728d11org.highwire.dtl.DTLVardef@8b65ae_HPS_FORMAT_FIGEXP M_FIG C_FIG We present an optimised fluorescence staining method for super-resolution live-cell imaging of Candida albicans, using Stimulated Emission Depletion (STED) microscopy to resolve and track sub-cellular structures. We compare the performance of conventional confocal laser scanning microscopy (CLSM) to STED imaging, providing a three-fold resolution improvement beyond the diffraction limit. Finally, we perform live cell tracking to visualise and quantify the trajectories of multiple sub-diffraction limit-sized objects over a period of 12 hours, demonstrating the potential for live-cell STED imaging of Candida to visualise key processes involved in pathogenesis, drug resistance and infection.

Volume electron microscopy encompasses a set of electron microscopy techniques that can be used to examine the ultrastructure of biological tissues and cells in three dimensions. Two block face techniques, focussed ion beam scanning electron microscopy (FIB-SEM) and serial block face scanning electron microscopy (SBF-SEM) have often been used to study biological tissue samples. More recently, these techniques have been adapted to in vitro tissue culture samples. Here we describe detailed protocols for two sample embedding methods for in vitro tissue culture cells intended to be studied using SBF-SEM. The first protocol focuses on cell pellet embedding and the second on en face embedding. En face embedding can be combined with light microscopy, and this CLEM workflow can be used to identify specific biological events in a light microscope, which can then be imaged using SBF-SEM. We systematically outline the steps necessary to fix, stain, embed and image adherent tissue culture cell monolayers by SBF-SEM. In addition to sample preparation, we discuss optimization of parameters for data collection. We highlight the challenges and key steps of sample preparation, and the consideration of imaging variables that will facilitate the acquisition of high quality datasets. Users experienced with electron microscopy sample preparation methodology will be able to complete this protocol in 10-11 days from initial seeding of cells in tissue culture to image acquisition.

The most common procedure to reveal the location of specific (sub)cellular elements in biological samples is via immunostaining followed by optical imaging. This is typically performed with target-specific primary antibodies (1.Abs), which are revealed by fluorophore-conjugated secondary antibodies (2.Abs). However, at high resolution this methodology can induce a series of artifacts due to the large size of antibodies, their bivalency, and their polyclonality. Here we use STED and DNA-PAINT super-resolution microscopy or light sheet microscopy on cleared tissue to show how monovalent secondary reagents based on camelid single-domain antibodies (nanobodies; 2.Nbs) attenuate these artifacts. We demonstrate that monovalent 2.Nbs have four additional advantages: 1) they increase localization accuracy with respect to 2.Abs; 2) they allow direct pre-mixing with 1.Abs before staining, reducing experimental time, and enabling the use of multiple 1.Abs from the same species; 3) they penetrate thick tissues efficiently; and 4) they avoid the artificial clustering seen with 2.Abs both in live and in poorly fixed samples. Altogether, this suggests that 2.Nbs are a valuable alternative to 2.Abs, especially when super-resolution imaging or staining of thick tissue samples are involved.

Structured illumination microscopy (SIM) is widely used for fast, long-term, live-cell super-resolution imaging. However, SIM images can contain substantial artifacts if the sample does not conform to the underlying assumptions of the reconstruction algorithm. Here we describe a simple, easy to implement, process that can be combined with any reconstruction algorithm to alleviate many common SIM reconstruction artifacts and briefly discuss possible extensions.

Single Molecule Localization Microscopy (SMLM) is a straightforward approach to reach sub-50 nm resolution using techniques such as Stochastic Optical Reconstruction Microscopy (STORM) or DNA-Point Accumulation for Imaging in Nanoscale Topography (PAINT), and to resolve the arrangement of cellular components in their native environment. However, SMLM acquisitions are slow, particularly for multicolor experiments where channels are usually acquired in sequence. In this work, we evaluate two approaches to speed-up multicolor SMLM using a module splitting the fluorescence emission toward two cameras: simultaneous 2-color PAINT (S2C-PAINT) that images spectrally-separated red and far-red imager strands on each camera, and spectral demixing STORM (SD-STORM) that uses spectrally-close far-red fluorophores imaged on both cameras before assigning each localization to a channel by demixing. For each approach, we carefully evaluate the crosstalk between channels using three types of samples: DNA origami nanorulers of different sizes, single-target labeled cells, or cells labeled for multiple targets. We then devise experiments to assess how crosstalk can potentially affect the detection of biologically-relevant subdiffraction patterns. Finally, we show how these approaches can be combined with astigmatism to obtain three-dimensional data, and how SD-STORM can be extended three-color imaging, making spectral separation and demixing attractive options for robust and versatile multicolor SMLM investigations.

Quantitative analysis of bacterial dynamics in time-lapse microscopy requires robust tracking pipelines, yet selecting and optimizing algorithms for specific experiments remains challenging. Indeed, Microbiologists are confronted with numerous algorithms that must be carefully chosen and parameterized to achieve optimal tracking for their experiments. We present an automated methodology to determine optimal tracking configurations for microbiological applications. It is based on TrackMate 8, a novel version of the TrackMate Fiji plugin extended with microbiology-specific tools. Our approach systematically evaluates algorithm-parameter combinations optimizing biologically relevant metrics (e.g., cell-cycle accuracy, bacteria morphology) and includes: (1) integration of deep-learning algorithms (Omnipose, YOLO, Trackastra) adequate for bacteria images in TrackMate, (2) a TrackMate-Helper extension for parameter optimization, and (3) a tracking and segmentation editor for tracking ground-truth generation. We demonstrate the effectiveness of the methodology on two use cases showing its adaptability to diverse experimental conditions. This methodology enables microbiologists with a widely applicable, automated framework to optimize tracking pipelines, facilitating quantitative analysis in bacterial imaging.

Super-resolution microscopy overcomes the diffraction limit of light to achieve higher spatial resolutions than are typically available when using light microscopy techniques. However, these methods are usually restricted to imaging a very small field of view (FOV). Here, we have applied one of these super-resolution techniques, Super-Resolution Radial Fluctuations (SRRF) in conjunction with the Mesolens, which has the unusual combination of a low-magnification and high numerical aperture, to obtain super-resolved images over a FOV of 4.4 mm x 3.0 mm. We assessed the accuracy of these SRRF images through error maps calculated using a secondary analysis method, Super-resolution Quantitative Image Rating and Reporting of Error Locations (SQUIRREL). We demonstrate it is possible to achieve images with a resolution of 446.3 {+/-} 10.9 nm, providing a [~]1.6-fold improvement in spatial resolution over a uniquely large field, with consistent structural agreement between raw data and SRRF processed images. MotivationCurrent super-resolution imaging techniques allow for a greater understanding of cellular structures however they are often complex or only have the ability to image a few cells at once. This small field of view may not represent the behaviour across the entire sample and the manual selection of which restricted ROI to use may introduce bias. Currently, this is often circumvented by stitching and tiling methods which stitch many small ROI together, however this can result in artefacts across an image which poses an issue when analysing data. To combat this, we have used the Mesolens alongside Super-Resolution Radial Fluctuations analysis, to obtain super-resolved images over a field of view of 4.4 mm x 3.0 mm with minimal error.

Automation in microscopy is the key to success in long and complex experiments. Most microscopy manufacturers provide Application Programming Interfaces (API) to enable communication between a user-defined program and the hardware. Although APIs effectively allow the development of complex routines involving hardware control, the developers need to build the applications from basic commands. Here we present a Software Development Kit (SDK) for easy control of Focussed Ion Beam Scanning Electron Microscopes (FIB/SEM) microscopes. The SDK, which we named OpenFIBSEM consists of a suite of building blocks for easy control that simplify the development of complex automated workflows.

Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.

Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

Cellular organelles are not just static structures; they are highly dynamic and directly linked to cellular functions. Changes in their morphology can be early indicators of diseases. Recent advancements in light microscopy techniques have transformed organelle research from qualitative descriptions to precise, quantitative measurements, enabling nanoscale resolution, high-throughput image analysis, and live-cell compatibility. This enables accurate measurement of organelle morphology, dynamics, and spatial organization using modern imaging and analysis techniques. By quantifying organelles, we go beyond simply visualizing to measuring and statistically comparing cellular features across different samples. This protocol addresses a wide range of cellular organelles across all major experimental systems, specifically mentioning mitochondria, myofibers, actin filaments, endoplasmic reticulum, and Golgi apparatus, by integrating experimental design, optimized sample preparation, high-resolution imaging, and validated Fiji/ImageJ-based analysis workflows. For each organelle, step-by-step methods specify reagents, equipment, acquisition parameters, and expected results. While recent advances, such as expansion microscopy, correlative light-electron microscopy, and AI-powered segmentation, offer gains in throughput and resolution, this workflow demonstrates that Fiji-based analysis remains fully capable of delivering high-precision organelle quantification. The entire workflow can be completed within 2-4 weeks, from initial design through validation and the production of measurements suitable for cross-study comparisons. Overall, this protocol establishes a flexible approach to standardize organelle quantification to understand multiple organelles simultaneously in their cellular contexts. Basic Protocol 1: Mitochondrial Quantification Basic Protocol 2: Myofibril Quantification Basic Protocol 3: Golgi Apparatus Morphometry Basic Protocol 4: Endoplasmic Reticulum Network Analysis Alternate Protocol 1: Super-Resolution Imaging Protocol

Fluorescence lifetime imaging microscopy (FLIM) translates the duration of excited states of fluorophores into lifetime information as additional source of contrast in images of biological samples. This offers the possibility to separate fluorophores particularly beneficial in case of similar excitation spectra. Here, we demonstrate the distinction of fluorescent molecules based on FLIM phasor analysis, called lifetime unmixing, in live-cell imaging using open-source software for analysis. We showcase two applications using Caenorhabditis elegans as a model system. First, we unmixed the highly spectrally overlapping fluorophores mCherry and mKate2 to distinctively track tagged proteins in six-dimensional datasets to investigate cell division in the developing early embryo. Second, we unmixed fluorescence of tagged proteins of interest from masking natural autofluorescence in adult hermaphrodites. For FLIM data handling and workflow implementation, we developed the open-source plugin napari-FLIM-phasor-plotter to implement conversion, visualization, analysis and reuse of FLIM data of different formats. Our work thus advances technical applications and bioimage data management and analysis in FLIM microscopy for life science research.

The combination of image analysis and fluorescence superresolution microscopy methods allows for unprecedented insight into the organization of macromolecular assemblies in cells. Advances in deep learning-based object recognition enables the automated processing of large amounts of data, resulting in high accuracy through averaging. However, while the analysis of highly symmetric structures of constant size allows for a resolution approaching the dimensions of structural biology, deep learning methods are prone to different forms of bias. A biased recognition of structures may prohibit the development of readouts for processes that involve significant changes in size or shape of amorphous macromolecular complexes. What is required to overcome this problem is a detailed investigation of potential sources of bias and the rigorous testing of trained models using real or simulated data covering a wide dynamic range of possible results. Here we combine single molecule localization-based superresolution microscopy of septin ring structures with the training of several different deep learning models for a quantitative investigation of bias resulting from different training approaches and finally quantitative changes in septin ring structures. We find that trade-off exists between measurement accuracy and the dynamic range of recognized phenotypes. Using our trained models, we furthermore find that septin ring size can be explained by the number of subunits they are assembled from alone. Our work provides a new experimental system for the investigation of septin polymerization.

Live-cell imaging (LCI) provides researchers the opportunity to understand biological phenomena at a temporal resolution and is achieved using dedicated imaging systems. These studies enable insight into dynamic phenotypic changes occurring in cells, which may otherwise be missed when studying fixed samples. Access to advanced microscopy is disproportionately available to researchers in high-income countries, whereas researchers in low-to middle-income countries (LMICs) are severely underrepresented in the adoption of such technologies. A major barrier to the dissemination of advanced microscopy centres around economic inequalities, with the cost of high-end imaging systems often being prohibitively expensive. Recognition of such disparities has motivated the wider microscopy community to manufacture frugal microscopes that are accessible to researchers in resource-constrained settings. The OpenFlexure Microscope (OFM) is an open source, customisable, 3D-printed microscope suitable for medical research and field-diagnostics. We have made adaptations to the OFM to enable its use for live-cell imaging in humid tissue culture incubators. By moving major electronic components outside of the microscope, we remove the risk of corrosion of the Raspberry Pi and Sangaboard used to operate the instrument. We tested four common 3D-printing polymer materials for increased thermal robustness and found ASA is the best plastic to print the main body of the microscope, offering both durability and image stability in 24- to 48-hour time course experiments. We have also created an optional 3D-printable weighted-hammock system to reduce external vibration artefacts during image acquisition. Critically, electronic modifications included custom extension cables from the motors and camera to the Raspberry Pi and Sangaboard, and the inclusion of 22 ohm ({Omega}) resistors to reduce the current to the stepper motors, preventing detrimental temperature increases inside sealed incubators during prolonged powering of the instrument. To remove dependence on WiFi connections for setting up timelapse experiments, we generated a simple application with a graphical user interface (GUI) that can be installed locally on a Raspberry Pi and is specifically designed for setting up timelapse experiments without extensive computational knowledge or experience. We validated our LCI-OFM adaptations with a 48-hour treatment of MDA-MB-231 breast cancer cells with the chemotherapeutic drug docetaxel, showcasing how the modified microscope can seamlessly feed into established bioimaging pipelines and generate biologically meaningful results. For researchers in LMICs, this adapted LCI-OFM provides new opportunities to study locally-relevant health challenges with timelapse microscopy, enabling deeper insight into biological dynamics and supporting the generation of preliminary data critical for securing grant funding and access to more advanced imaging systems in purpose-built regional imaging hubs.

Expansion microscopy (ExM) has revolutionized super-resolution imaging in cell biology due to its simple and inexpensive workflow. The use of ExM has revealed several novel insights into the nanoscale architectures of cellular protein complexes, especially the microtubule cytoskeleton in model and non-model systems. Despite tremendous progress in expansion microscopy protocols that preserve cellular ultrastructure (U-ExM), compatible probes for imaging actin isoforms with U-ExM are still lacking and have hindered the study of diverse actin isoforms and networks across model systems. Here, we use IntAct, an internally tagged actin that incorporates into cellular actin networks, to develop and optimize U-ExM of diverse actin network types in both yeast and mammalian cells. Using expression of ALFA-tagged IntAct variants in yeast and mammalian cells, we show robust visualization of actin patches, cables, and rings in yeast and diverse actin networks such as actin cortex, stress fibers, filopodia, lamellipodium in mammalian cells at improved resolution. We also detect transient nuclear actin filaments using IntAct-U-ExM underscoring the advantages offered by our approach to image understudied actin structures. Overall, we demonstrate the effectiveness of IntAct-U-ExM for performing super-resolution imaging of various actin structures in an isoform-specific manner and highlight the potential of IntAct to study the nanoscale organization of diverse actin cytoskeletal networks across species. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/654030v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@169cd82org.highwire.dtl.DTLVardef@1334af3org.highwire.dtl.DTLVardef@7d9d43org.highwire.dtl.DTLVardef@dfc50b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Nuclei segmentation is an important step for quantitative analysis of fluorescence microscopy images. A large volume generally has many different regions containing nuclei with varying spatial characteristics. Automatically identifying nuclei that are challenging to segment can speed up the analysis of biological tissues. Here we show a segmentation technique that provides a metric of segmentation "confidence" for each segmented object in an image volume. This confidence metric can be used either to generate a "confidence map" for visual distinction of reliable from unreliable regions, or in the data space to identify questionable measurements that can be analyzed separately or eliminated from analysis. In an analysis of nuclei in a 3-dimensional image volume, we show that the confidence map correlates well with visual evaluations of segmentation quality, and that the confidence metric correlates well with F1 scores within subregions of the image volume. In addition, we also describe three visualization methods that can visualize the segmentation differences between a segmented volume and a reference volume.

Stimulated Raman Scattering (SRS) microscopy enables label-free imaging of cells and tissues in their native state with chemical specificity. However, there are often experimental advantages of chemical fixation of samples prior to imaging, which can introduce perturbations that may alter the native state of the samples, and possibly impact the SRS signal. In this study, we systematically characterize the effects of common fixatives (Paraformaldehyde, Formalin, Glutaraldehyde, Methanol, Ethanol) on the preservation of cellular integrity and molecular composition in a Hela cell model as observed by SRS microscopy. We demonstrate how the different fixatives can influence lipid and protein content, and overall cell morphology, with significant implications for the accuracy of quantitative SRS microscopy. Our findings indicate that Paraformaldehyde (PFA) imposes minimal disruption to cellular and molecular states compared to the other fixatives tested, and suggest Glutaraldehyde (GA) as a suitable alternative for SRS imaging. This study provides insights for the choice of the optimal sample preparation, enabling more reliable SRS-based studies for the evaluation of cellular processes and disease mechanisms where fixation is used.