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.

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.

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

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.

Correlative Light and Electron Microscopy (CLEM) integrates the molecular specificity of light microscopy (LM) with the ultrastructural detail of electron microscopy (EM), enabling comprehensive spatial analysis of biological samples. Despite growing demand, processing 3D CLEM datasets remains challenging, specifically for service provision in facilities, due to their multimodal nature and the lack of unified approaches. Typical steps include EM slice alignment, LM-EM registration, segmentation, and 3D visualization. We present a modular, end-to-end pipeline that consolidates existing and newly developed tools into a coherent workflow for 3D CLEM analysis and allows railroading the approach. Designed as interoperable modules accessible through a user-friendly interface, the pipeline is fully open-source and scales from standard workstations to high-performance computing environments to address the need for analysis of growing datasets. While some steps still require manual input, individual components can be automated to increase throughput and reproducibility. Together, this integrated solution lowers technical barriers and supports broader adoption of 3D CLEM methodologies.

The ability to detect host cell factors during Staphylococcus aureus infection in vitro by immunofluorescence microscopy is severely hampered by staphylococcal protein A (SpA), a cell wall-anchored protein that binds the fragment crystallizable (Fc) region of immunoglobulins. This interaction generates strong nonspecific fluorescent signals on the bacterial surface, complicating data interpretation and limiting the accuracy of quantitative image analysis. Several measures have been put forward to overcome this obstacle, most importantly the pre-incubation with an anti-SpA antibody (SpA) and the use of human serum (HS) as blocking agent and antibody diluent. To highlight this feature to general fluorescence microscopy users, we here systematically evaluated these two strategies. Using S. aureus coated on coverslips and S. aureus-infected A549 cells, we highlight the efficiencies of both approaches to markedly reduce nonspecific fluorescence, with HS treatment yielding the most profound suppression. Notably, HS, containing high levels of human immunoglobulins, offered a robust, cost-effective and broadly applicable solution for minimizing SpA-driven artifacts, thereby improving immunofluorescence microscopy in S. aureus infection models in vitro.

Quantitative organelle analysis is highly sensitive to image-processing choices, limiting reproducibility across microscopy studies. Here, we systematically compare automated, interactive machine learning, and deep learning-based pipelines for lipid droplet and mitochondrial quantification in live human osteosarcoma cells imaged by fluorescence microscopy and label-free holotomography. Using standardized downstream feature extraction, we evaluated script-based workflows (Fiji, Python), a modular platform (CellProfiler), interactive machine learning (ilastik), and pretrained deep learning models. Lipid droplet segmentation was qualitatively consistent across approaches; however, droplet counts, and size distributions varied substantially between pipelines and imaging modalities, with ilastik reducing background-driven detections and improving cross-modality agreement. In contrast, mitochondrial quantification proved highly sensitive to segmentation and skeletonization choices, particularly in holotomography where global intensity-threshold-based methods failed to capture network structure. Based on these cross-pipeline comparisons, we demonstrate how organelle- and modality-specific benchmarking can guide pipeline selection, illustrated by the analysis of metabolic perturbations affecting lipid droplets and mitochondria. Together, these results highlight modality- and morphology-dependent limitations in common analysis pipelines and provide practical guidance for selecting robust, reproducible strategies for quantitative organelle imaging.

2.Quantitative image analysis is central to understanding microbial growth, morphology, and spatial organisation. However, conventional metrics such as mean intensity or object count often do not capture the complex structural heterogeneity and patterning characteristic of microbial colonies and biofilms. To address this limitation, Analysis of Biofilm Complexity in 3D (ABC3D), an open-source Python framework for automated extraction of fractal, textural, and statistical descriptors from volumetric microscopy images, is reported. ABC3D computes a set of parameters including fractal dimension, lacunarity, entropy, grey-level co-occurrence matrix features, and wavelet sub-band energies from three-dimensional (3D) image datasets. ABC3D is demonstrated in macrocolony biofilms formed by cell shape mutants of Escherichia coli, where it is shown that nutrient availability accounts for the majority of structural variance, while cell shape produces additional structural variation that differs between nutrient conditions. ABC3D provides researchers with an accessible, quantitative approach to assessing biofilm morphology in microscopy datasets. SummaryAn open-source, quantitative analysis pipeline is presented that integrates fractal, lacunarity, entropy, texture and wavelet descriptors to characterise colony biofilm architecture in three dimensions. Application to Escherichia coli cell shape mutants demonstrates that macrocolony biofilm architecture is best understood as a coordinated, multiscale phenotype rather than as an aggregate of independent structural metrics. 3. Impact statementBiofilm architecture is pivotal for microbial survival, antimicrobial tolerance, and ecological function but tools to quantify structural organisation in these cell communities remain limited. The commonest metrics describe bulk properties such as width, thickness, or cell number, but they do not capture multiscale spatial heterogeneity. Here, an open-source framework for Analysis of Biofilm Complexity in 3 Dimensions (ABC3D) is reported. This software integrates measurements of fractal geometry, lacunarity, entropy, texture statistics, and wavelet energy. ABC3D is demonstrated in Escherichia coli macrocolony biofilms, where it is shown that nutrient environment has a leading role in determining colony architecture in E. coli biofilms, while cell shape has a lesser but still significant influence on structural variation. The ABC3D pipeline can be applied to any microbial communities imaged by confocal microscopy and other volumetric imaging methods and has the potential to give a deeper understanding of how cells organise in biofilms. 4. Data summaryFull code for ABC3D and data analysis is available at https://github.com/gailmcconnell/ABC3D. Image data are available upon request. The author confirms all supporting data, code and protocols have been provided within the article or through supplementary data files.

Mapping peritumoral collagen fiber directionality in solid tumors may assist in determining cancer progression and support more personalized prognoses. However, existing microscopy techniques are often limited by a restricted field of view, high cost, or incompatibility with paraffin-treated tissues. Computational scattered light imaging (ComSLI) is a cost-effective whole-slide microscopy technique that reveals fiber orientations independent of sample preparation. Using glioma, colorectal, and head and neck cancer samples, we show for the first time that ComSLI maps fiber orientations in paraffin-treated tumor tissues, visualizes tumor growth pathways and desmoplastic reactions, and allows the study of collagen orientations relative to tumor boundaries.

Dragonfly and cicada wing-inspired titanium nanopillar surfaces show promising bactericidal properties for antibacterial medical implant applications, but the precise mechanisms of bacteria-nanopillar interactions under hydrated conditions remain unclear. Cryo-electron tomography (cryo-ET) enables the visualisation of cellular organelles within their native hydrated cellular environment at molecular resolution. Visualising the bacteria-material interface on nanostructured surfaces by cryo transmission electron microscopy (cryo-TEM) requires the preparation of thin lamellae. Obtaining lamellae of bacteria directly on metal substrates while in a non-fixed and hydrated state requires cryo-focused ion beam (cryo-FIB) milling to isolate the targeted bacteria from the bulk sample. This approach faces additional challenges compared to tissues or cells on TEM grids, as titanium samples require a simultaneous cross-section of soft and hard materials at the same position and require vitrification, which embeds the sample in a thick layer of ice. Nonetheless, we demonstrate how to target a specific bacterium interacting with a titanium nanopillar surface using correlative cryo-fluorescence imaging, and how lamellae can still be prepared from vitrified samples by extracting the targeted bacterium and its surrounding as a small volume and transferring it to a receptor grid for thin lamella preparation, called targeted cryo-lift-out. Here, we outline the workflows and discuss their advantages and limitations for producing lamellae through lift-out techniques under cryogenic conditions, using methods that do not involve gas injection systems (GIS) for the lift-out transfer. These advances enhance cryo-ET applications, enabling in situ investigations of the interface between bacteria and nanopillars to effectively study the bactericidal mechanisms of biomimetic nature-inspired nanotopographies in a hydrated environment.

The liver has a complex architecture composed of millions of lobules. Within these lobules, hepatocytes, the main hepatic cells, are organized in rows separated by blood capillaries known as sinusoids. These capillaries are lined by liver sinusoidal endothelial cells (LSEC) that form a very specific fenestrated endothelium essential for the exchange of metabolites and proteins between the blood and hepatocytes. Alterations in the size and number of LSEC fenestrations are associated with the onset and the progression of various liver diseases. The analysis of liver architecture is thus of utmost importance for advancing our knowledge of liver ultrastructure and its alterations. Liver architecture has been studied since decades, mainly using 2D electron microscopy, and more recently using advanced super-resolution fluorescence microscopy. In recent years, volume electron microscopy techniques, including focused ion beam-scanning electron microscopy (FIB-SEM) progressed and nowadays enable the 3D reconstruction of biological ultrastructures down to nanometer resolution. However, the analysis of large volumes (e.g., several tens of {micro}m3) remains challenging due to various constraints in the segmentation of large datasets. In the current study, we developed a workflow to semi-automatically segment hepatic sinusoids from FIB-SEM mice liver datasets using the CNN-based (convolutional neural network) tool known as "nnU-Net", after fine-tuning a ground truth model. We also implemented tools for semi-automatic quantification of LSEC fenestrae diameters and sinusoid porosity from segmented datasets. This workflow enabled us to compare the distribution of LSEC fenestrae diameters in wild-type versus Bmp9-deleted mice, a hepatic factor known to be involved in fenestration maintenance. Our results confirm the importance of BMP9 for LSEC differentiation. Therefore, the developed methodology represents a valuable tool for characterizing the fenestrated endothelium under various physiological and pathological conditions.

Motion is an essential component of any living system. It is rich with information, but it is often challenging to quantitatively extract biologically informative results from the motion apparent in microscopy images. This challenge is exacerbated by the wide variety in biological movement, which often takes the form of difficult-to-segment amorphous structures undergoing complex motion. An image processing technique known as optical flow can capture motion at each pixel in an image, thus bypassing the need for object segmentation or a priori definition of motion types. This makes it a powerful tool for quantitative assessment of biological systems from the protein to organism scale. However, despite its flexibility and strengths for analyzing fluorescence microscopy images, its adoption in the bioimaging community has been limited by the availability of easy-to-use tools and guidance in results interpretation. Here we describe an optical flow tool, OpticalFlow3D, that can be run in Python or MATLAB and is compatible with three-dimensional microscopy images. Using biological examples across length scales, we illustrate how OpticalFlow3D can enable new biological insight.

X-ray fluorescence microscopy (XFM) continues to develop as a powerful quantitative technique for high resolution, label-free, elemental mapping of biological, environmental, and material samples. Methods for rigorously fitting spectra, increasing throughput, accounting for background signals, and deconvoluting overlapping emission lines continue to evolve. We show here that quantitative fits of XFM data obtained after removing a baseline, calculated by connecting peak edges, can be unexpectedly dependent upon acquisition dwell-time and spectral aggregation leading to differences in apparent elemental content. Using mouse preimplantation embryos and ovarian follicles as model samples, we demonstrate how these variables influence quantitative comparisons between samples. We find that subtracting an empirically measured blank spectrum instead of a baseline provides quantitative XFM elemental mapping results that are independent of dwell time and spectral aggregation dependencies.

BackgroundLive-cell fluorescence microscopy enables the study of dynamic cellular processes. However, fluorescence microscopy can damage cells and disrupt these dynamic processes through photobleaching and phototoxicity. Reducing light exposure mitigates the effects of photobleaching and phototoxicity but results in low signal-to-noise ratio (SNR) images. Deep learning provides a solution for restoring these low-SNR images. However, these deep learning methods require large, representative datasets for training, testing, and benchmarking, as well as substantial GPU memory, particularly for denoising large images. ResultsWe present a new fluorescence microscopy dataset designed to expand the range of imaging conditions and specimens currently available for evaluating denoising methods. The dataset contains 324 paired high/low-SNR images ranging from four to 282 megapixels across 12 sub-datasets that vary in specimen, objective used, staining type, excitation wavelength, and exposure time. The dataset also includes spinning disk confocal microscopy examples and extreme-noise cases. We evaluated three state-of-the-art deep learning denoising models on the dataset: a supervised transformer-based model, a supervised CNN model, and an unsupervised single image model. We also developed an image stitching method that enables large images to be processed in smaller crops and reconstructed. ConclusionsOur dataset provides a diverse benchmark for evaluating deep learning denoising methods, and our stitching method provides a solution to GPU memory constraints encountered when processing large images. Among the evaluated deep learning models, the supervised transformer-based model had the highest denoising performance but required the longest training time.

In contemporary bio-imaging-based research, computer-based assessment is becoming crucial for the characterisation of biological structures, as it minimises the need for time-consuming human annotation, which is prone to human error. Furthermore, it allows for the use of optical techniques that use lower photon intensities, thereby reducing reliance on high-intensity excitation and mitigating adverse effects on their activities. This study details the development and evaluation of sophisticated deep-learning models for amoeba detection using phase-contrast imaging. Using a single-class annotated dataset comprising 88 images and 4,131 annotations, we developed nine object detection models based on Detectron 2 and six variants based on YOLO v10. The diversity of the dataset, acquired under varying setup parameters, facilitated a comprehensive evaluation of the strengths and limitations of each model. A comparative analysis of speed and accuracy was performed to identify the most efficient models for real-time detection, providing critical insights for future microscopic analyses.

Correlative microscopy techniques are used for many different applications in the biological sciences because the comparison of different imaging methods allows researchers to gain more insight and data from samples. Correlative light and electron microscopy (CLEM) methods have been developed to preserve biological samples to withstand the harsh environments necessary for electron microscopy. After first being imaged using widefield (WF) and super-resolution structured illumination fluorescence microscopy (SIM), a NanoSuit chemical treatment was applied to a mammalian testis sample before imaging with scanning electron microscopy (SEM). This was done to compare the image quality and resolution of each technique. SEM yields higher resolution and offers validation of results from SIM.

Oblique plane microscopy (OPM) is a light sheet microscopy technique that uses a single high numerical aperture (NA) objective for both illuminating the sample and collecting emission fluorescence from a tilted plane within the specimen. OPM has become indispensable in biological and biomedical research, providing rapid, high-resolution volumetric fluorescence imaging of live cells and tissues while minimising phototoxicity and photobleaching. It also overcomes the sample mounting challenges associated with conventional light sheet microscopes that require two orthogonally placed objectives. However, the application of OPM has been limited by the complex design and the intricate optical alignment and characterisation needed, particularly with the remote-refocusing system (RFS) in the emission path. This protocol offers a detailed, step-by-step guide for constructing an OPM setup using commercially available components and for characterising its performance to ensure optimal imaging quality. We aim to deliver the unique merits of OPM to researchers in life science and medicine, enabling them to visualise the spatiotemporal organisation of key biomolecules, structures, and cells in 3D at high resolutions.

Antimicrobial resistance remains a global existential threat. Given that antimicrobial therapy commonly starts before pathogen identification, rapid and scalable methods capable of determining effective antimicrobial compounds are needed. In this paper, we demonstrate a 2 x 2 array of parallelised microscopes that uses low numerical aperture (NA=0.25) detection optics and LED excitation to determine bacterial viability based on their fluorescence response to an electrical stimulus. Following a 2-hour incubation, the fluorescent viability readout requires less than one minute. We use K-means clustering to classify pixels in a time lapse sequence of widefield fluorescence images and extract changes seen within bacterial clusters. We demonstrate sufficient sensitivity to measure fluorescence changes after electrical stimulation in a bacterial monolayer. To capture these subtle fluorescence changes at high signal-to-background ratios, we place a limit on the minimum optical density of the bacterial sample. This novel approach is scalable to 96-well formats using a suitable consumable electrode array.

Light-sheet fluorescence microscopy enables deep optical sectioning of large, cleared biological tissues. However, effective clearing of collagen-rich tissues remains a persistent technical challenge. Moreover, standardized workflows integrating three-dimensional imaging with computational analysis of collagen architecture are currently unavailable. Here, we present an integrated pipeline combining optimized tissue clearing, volumetric light-sheet imaging, and deep learning-based feature extraction of collagen architecture. Using experimental desmoid tumors as a proxy for collagen-dense tissues, we optimised DISCO-based clearing incorporating Fast Green FCF for collagen labelling. We achieved optical transparency and full 3D visualization of collagen architectures in desmoid tumors, human skin biopsies, and fibrotic mouse lung & liver tissues, including FFPE samples. Using the Benchtop mesoSPIM platform, we acquired high-resolution volumetric datasets and validated multimodal collagen imaging through two-photon microscopy with the Schmidt-Voigt objective. To enable automated feature extraction from these large volumetric datasets, we developed ColNet, a U-Net model for automated collagen fiber segmentation. ColNet demonstrated robust generalization across diverse human and mouse tissues without retraining or hyperparameter adjustment. This integrated workflow provides a foundation for future quantitative assessment of cell-extracellular matrix dynamics in a fibrotic context.