Journal of Microscopy

Speckle artifacts and isolated foreground pixels are common in fluorescence microscopy and can interfere with segmentation and subsequent quantitative image analysis. Conventional denoising methods often modify image intensities through filtering or smoothing, potentially altering biologically relevant fluorescence signals. We introduce Sparse Pixel Cluster Cleaning (SPC-Clean), a topology-aware method that removes poorly supported foreground pixels through iterative neighborhood analysis of a thresholded mask. SPC-Clean is deterministic, training-free, preserves original fluorescence intensities for practical microscopy workflows.

Researchers using electron microscopy must often balance a trade-off between obtaining high-resolution structural information and preserving sufficient cellular context. At one end of this spectrum, single particle cryo-electron microscopy and cryo-electron tomography provide near-molecular detail but are typically limited to relatively small fields of view. At the other, volume electron microscopy approaches, such as scanning electron microscopy of resin-embedded specimens, capture large cellular volumes but generally at lower resolution. Consequently, linking nanoscale structural information to larger cellular architecture remains a significant challenge. To address this gap, we optimised a transmission electron tomography workflow for resin-embedded malaria parasites that allows us to visualise targeted regions of interest at nanometre-scale resolution while retaining several micrometres of surrounding cellular context. Here, we present our current best-practice pipeline for sample preparation, tomogram acquisition, and reconstruction. In addition, we introduce VolWeaver, a data-processing framework, that integrates high-resolution tomographic datasets into serial section volume reconstructions, enabling the visualisation and interpretation of ultrastructural features within their broader cellular environment.

Modern biological imaging generates large, complex datasets that require scalable and reproducible image analysis methods. Deep learning has demonstrated strong performance on bioimage segmentation tasks, but training custom models has remained inaccessible to many researchers due to requirements for GPU infrastructure, programming expertise, and large annotated training datasets. ZEISS arivis Cloud is a browser-based platform for deep learning model training that addresses these barriers through partial annotation support, AI-assisted labeling with SAM (Segment Anything Model), pretrained model initialization, and automatically configured training pipelines requiring no machine learning expertise. The platform supports two segmentation tasks: semantic segmentation using a U-Net-style architecture with an EfficientNet encoder and PixelShuffle decoder, and instance segmentation based on Mask2Former with a Swin-Tiny backbone. Both pipelines incorporate microscopy-specific adaptations including smooth tiling, multi-channel input support, dataset-specific normalization, and partial-annotation-aware loss functions protected by patents US-20240078681-A1 and US-20250111519-A1. Trained models integrate directly with ZEISS arivis Pro for pipeline-based image analysis, ZEISS arivis Hub for parallel execution across large datasets, and ZEISS ZEN for content-aware guided acquisition. We describe the platform architecture, training methodology, segmentation architectures, reproducibility and versioning mechanisms, and FAIR compliance, and illustrate the complete workflow through two intestinal organoid imaging examples. arivis Cloud is freely accessible to student users; other users access the platform via subscription at https://www.arivis.cloud/.

Dinoflagellate chromosomes adopt a highly condensed and organized morphology, with periodic bands and arches observed by traditional Transmission Electron Microscopy (TEM). However, the limited two-dimensional field of view of TEM has prevented a precise characterization of the inherently three-dimensional organization of dinoflagellate chromosomes. Moreover, given the vast diversity among dinoflagellate species and the lack of a systematic comparison of their chromosomes, it remains unclear whether dinoflagellate chromosomes share common organizational features or instead exhibit significant cell- or species-specific differences. Here, we acquire three whole-nucleus 3D Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) datasets at 4 nm voxel size for each of four dinoflagellate species: Symbiodinium microadriaticum, Breviolum minutum, Fugacium kawagutii, and Crypthecodinium cohnii. We compile these data with previously published image volumes from four additional species and present an analysis of the largest collection of dinoflagellate FIB-SEM images to date. Common features observed across all eight species include the absence of physical confinement or spatial clustering of chromosomes in the nucleus. In addition, by decomposing each chromosome into a weighted sum of orthogonal shapes using Spherical Harmonics Expansion, we find a principal component encapsulating 88% of the total shape variance that is common to all species. However, our analysis also reveals differences in chromosome morphology across species. First, while many chromosomes exhibit surface ridges with left-handed helical twist, the proportion of chromosomes with such ridges varies extensively across species. Second, while chromosomes in most species are discrete and well-separated, chromosomes in F. kawagutii are interconnected in a single contiguous network. Lastly, to our knowledge, we report the first observation in eukaryotic cells of toroid-shaped DNA objects, whose numbers vary dramatically across cells and species. Overall, our results show that dinoflagellate chromosomes exhibit both shared organizational features and pronounced species-specific deviations.

While light-sheet microscopy is emerging as a robust method for volumetric imaging with improved axial resolution, its capability regarding two-dimensional, surface-level mapping is often hindered by limitations in data redundancy and reconstruction efficiency stemming from volumetric registration methods. We demonstrate that a multiview imaging approach in an axially-swept, dithered light-sheet microscope paired with computational image reconstruction of view projections is able to address these trade-offs to enable large-scale mapping of surface structural features, leveraging the advantages of multiview light-sheet in scalable field of view, working distance, and near isotropic resolution across the entire imaging depth. To aid in the acquisition and analysis of two-dimensional surface structures, we present a tailored surface mapping workflow and a Fiji plugin for computational reconstruction, promoting robust and comprehensive visualization of surface features of uncleared volumetric samples. Our strategy, termed projection reconstruction for imaging surface morphology (PRISM), integrates axially swept dithered light-sheet microscopy and post-processing software for multiview imaging. The imaging hardware enables near-isotropic resolution across its entire field of view, while the software implementation leverages rigid and affine transformations to align two-dimensional projections of multiview samples. It is designed to work with the BigStitcher pipeline, leveraging its robust algorithm to provide support for two-dimensional image alignment and stitching. We demonstrate the capability of PRISM in studies of lymphatic network mapping in the epicardial layer of intact mouse hearts, as well as surface profiles of FaDu spheroids labeled with antibody-nanodiamond conjugates. This method allows us to quantify cardiac lymphatic branch numbers, diameters, and lengths of a Prox1-tdTomato mouse cardiac model, as well as cluster number and diameters of epidermal growth factor receptor within a FaDu spheroid labeled with a nanodiamond-antibody conjugate, with a significant reduction of post-processing data size. PRISM leverages multiview image projections to promote studies of cardiac lymphatics in mouse models and surface receptor distributions within spheroid models, enabling efficient surface mapping of large, intact, and uncleared biological samples across a variety of scales.

The lifetime of fluorescent molecules provides an orthogonal readout to fluorescence intensity, opening experimental possibilities of measuring changes in local molecular environments, mechanical tension, and metabolism, among other factors. These changes are best studied live and in vivo; however, limitations of slow imaging speeds, high phototoxicity, and increased data size and complexity have significantly impeded progress on this front. Here, we present a complete and transferable pipeline consisting of a light sheet FLIM microscope and an accompanying machine learning model for data processing that renders long-term and/or high-speed volumetric FLIM (vFLIM) tractable in living systems. We benchmark this pipeline across several biological use cases, model systems, lifetime ranges, and spatiotemporal scales, showcasing a suite of possibilities that our workflow enables. This comprehensive pipeline from imaging to analysis is a crucial step forward towards disseminating the power of live vFLIM to the broader bioimaging community.

A cnidarian photosymbiont alga, Breviolum minutum, is an emerging model to study symbiosis due its ability to colonise host in absence of light, and amenability to genetic and physiological manipulations. This alga undergoes subcellular reorganisation in response to stress conditions such as elevated temperature and nutrient deprivation. However, subcellular visualisation of this alga is challenging because of its broad spectrum autofluorescence (400-700 nm) and relatively small size (6-8 m). We developed a super resolution imaging, Expansion Microscopy (ExM) workflow - a hydrogel-based technique for mechanical enlargement of cells, that reveals previously inaccessible subcellular features in B. minutum. This ExM workflow presents a set of thermic and enzymatic conditions which enables 4-fold expansion of B. minutum, optical clearing of autofluorescence as well as the removal of its thick cellulose rich cell wall. We implemented a recently described platinum (II)-based tri-functional linker 1, to retain in situ hybridised oligonucleotides targeted to 18S rRNA within ExM hydrogel and exploited its azide reactive group for post-ExM fluorophore labelling (DBCO modification). We observed actin patches (a cytoskeletal feature) and calmodulin (a calcium binding signalling protein) that are not previously visualised in B. minutum. This approach overcomes some of the long-standing problems in visualisation of B. minutum using commonly available reagents and commercially available low-cost ExM compatible chemistries. The broader uptake of this tool for the visualisation of diverse species of photosymbionts will pave the way for fundamental discoveries underpinning cellular reorganisation in formation and breakdown of symbiosis.

Formalin-fixed prostate tissue samples were imaged by propagation-based synchrotron phase contrast micro computed tomography ({micro}CT) with a 3D spatial resolution of ca. 3 {micro}m. Post-{micro}CT, samples were prepared for histology with sections close to coplanar with the transverse {micro}CT image planes. Haematoxylin and eosin stained sections were examined by an expert prostate histopathologist and compared qualitatively with corresponding {micro}CT-visible microstructure features. There is potential for {micro}CT to provide complimentary information to conventional histology and light microscopy without the need for preparation of stained thin sections. For the imaging conditions and spatial resolution of our study, {micro}CT may provide tissue architectural features similar to those used in Gleason grading, albeit without clear subcellular microstructure detail. At the spatial resolution of our study {micro}CT may provide novel 3D microstructure information for validation of diffusion weighted magnetic resonance imaging (MRI) methods. As an example, we demonstrate a qualitative correlation between {micro}CT-derived stromal fibre orientation and preferential water diffusion direction measured by diffusion tensor MRI microscopy of the same sample.

Spectroscopic single-molecule localization microscopy (sSMLM) enables multiplexed super-resolution imaging by simultaneously acquiring the spatial position and spectral information of individual fluorophores. Dual-wedge prism (DWP)-based implementations provide a compact, alignment-stable approach to spectral dispersion, but trade-offs between localization precision, spectral precision, and experimental complexity remain. We systematically compare five DWP-based sSMLM configurations, including two-dimensional (2D) and three-dimensional (3D) implementations using single DWP (DWP-sSMLM) and symmetrically-dispersed DWP (SDDWP-sSMLM). We evaluate lateral precision, spectral precision, and ease of use. SDDWP configurations acquire spectral images in both channels and utilize both for spatial localization, yielding the highest lateral and spectral precision. However, for applications that do not require axial information, 2D-DWP provides a simple, plug-and-play solution with robust performance. This work offers a guideline for selecting DWP configurations based on experimental needs.

Fluorescence microscopy is central to the study of bacterial cell biology, multicellular behaviours, and host-pathogen interactions. Bright, robust and photostable labelling is required for bacterial identification, sorting and quantitative analysis, driving continuous development of state-of-the-art labelling tools. Here, we developed a multicolor fluorescent cell labelling toolkit for Gram-negative bacteria carrying the attTn7 site, using the opportunistic human pathogen Pseudomonas aeruginosa as a model. Cell labelling is achieved by constitutive chromosomal expression of genes encoding a choice of four novel fluorescent proteins, mNeonGreen, mJuniper, mLychee and mScarlet-I3, codon-optimised for P. aeruginosa. These reporters provide bright, stable fluorescence with minimal photobleaching and excellent spectral separation during long-term imaging of single cells, macrocolonies and biofilms. Chromosomal expression of mNeonGreen yielded brighter and more homogeneous labelling than expression of the same construct from a plasmid. Importantly, dual-color labelling of macrocolonies uncovered previously unrecognised phenomena of collective motility when two isogenic swarming populations interact. Finally, we demonstrate the applicability of our constructs in biologically relevant host-pathogen contexts by imaging both live and fixed P. aeruginosa-infected human airway epithelial cells. This versatile cell labelling platform enables reliable bacterial identification, segmentation, tracking, and quantitative fluorescence imaging across spatial and temporal scales, and is readily adaptable to most other Gram-negative bacteria as the attTn7 integration site is well conserved.

Quantitative analysis of single-molecule localisation microscopy (SMLM) data remains challenging because biologically diverse, well-annotated datasets are limited, whilst nanoscale protein organisation is heterogeneous and difficult to describe with hand-tuned metrics. We present SynthMLM, a framework that infers interpretable structural descriptors from experimental SMLM data and uses these descriptors to generate synthetic localisation datasets. We demonstrate SynthMLM by generating descriptor-matched synthetic datasets corresponding to diverse experimental SMLM datasets and evaluating their agreement with real data using descriptor-level and embedding-based measures. By enabling controlled generation of synthetic localisation data, SynthMLM provides a practical resource for benchmarking SMLM analysis methods, testing algorithm failure modes, and developing machine-learning workflows where large, labelled datasets are required.

Three-dimensional live-cell fluorescence imaging of yeast cells is crucial for studying cell-cycle mechanics and regulation. However, extracting multi-channel phenotypes within dense cell clusters remains an image-processing bottleneck. Standard deep-learning models segment cells but fail to track mother-bud boundaries, mitotic spindle shapes and spindle-localizing proteins. Investigators rely on labour-intensive manual coordinate plotting, introducing observer bias and often exclude clustered cell data due to visual complexity. Here, we present an open-source Fiji pipeline for automated yeast cell image processing and deterministic classification of cell-cycle, spindle and protein dynamics. The workflow utilizes a dual-segmentation architecture via custom Cellpose models to capture the mother-bud cell boundaries. Extracted masks are integrated with multi-channel fluorescence data using a Difference-of-Gaussians framework to resolve SPB coordinates and localized protein kinetics, which a rule-based decision-tree maps to precise mitotic phenotypes. Validation demonstrates a 50-fold acceleration with ~6% deviation from manual analysis. Availability: Zenodo at https://doi.org/10.5281/zenodo.22083016.

Recent technological advances allow for the large-scale acquisition of genetic and morphological data: high-throughput sequencing has transformed the field of genomics while synchrotron X-ray microtomography enables rapid, noninvasive 3D imaging. However, integrating these approaches for the same specimens is challenging because X-rays can fragment DNA, and DNA extraction damages internal morphology, particularly relevant for small bodied organisms, such as insects. We systematically tested multiple extraction protocols and irradiation conditions across three model insect species. We irradiated more than 1,000 specimens under varying conditions and tested DNA quality through DNA barcoding and UCE sequencing. Our results demonstrate that high-quality DNA and high-resolution tomograms can be obtained from the same individuals, provided that the parameters are carefully optimized and rapid SR-CT scanning precedes DNA extraction. In this respect, our findings establish practical guidelines for combining genomics and phenomics, paving the way for comprehensive integrative digitization of biodiversity.

The sea anemone Aiptasia is a model system for understanding cnidarian loss of symbiotic algae under heat stress (bleaching). While Aiptasia polyps have been widely used to study this process, accurate symbiosis phenotyping grapples with discordant length scales: fine spatial resolution (~100 um) is needed across a whole organism (~5 mm). To address this, we consider small (~100 um), optically transparent Aiptasia larvae as a bleaching model suitable for whole-organism phenotyping by fluorescence microscopy with larvae classified as symbiotic when algae are localized within gastrodermal cells. To expedite phenotyping, we introduce a machine-learning (ML) image-analysis pipeline (SYMPHONY) designed for single-larva resolution analysis of intact larvae. SYMPHONY efficiently identifies the cellular location of internalized algae (accuracy: 79%, precision: 82%, recall: 79%, F1 score: 79%; training dataset composed of 1611 total objects). Additionally, SYMPHONY reports statistically significant larval bleaching under heat stress and corroborates manual phenotyping results, while significantly reducing operator labor from hours to minutes. The combination of the Aiptasia larvae model and the SYMPHONY pipeline aims to accelerate our understanding of symbiosis breakdown.

Fundamental cellular decisions of life and death are governed by intricate and tightly regulated intracellular signalling pathways that determine whether cells proliferate, enter quiescence, or undergo programmed cell death (apoptosis). Live-cell fluorescence imaging enables these processes to be observed in real time at single-cell resolution, but two problems limit their study. First, existing biosensors do not allow apoptotic status and cell cycle progression to be resolved in tandem within the same cell. Second, interpreting live-cell imaging data is challenging even where multiplex reporters exist, as the biological meaning of fluorescent signals depends on their temporal ordering, and large-scale imaging experiments generate complex, multidimensional data that are difficult to analyse systematically and at scale. Here we address both problems. We present FluoroFate, a generalisable and user-friendly graphical interface-driven tool for time-resolved single-cell analysis of multiplex live-cell imaging datasets, which integrates existing, robust deep learning-based segmentation, cell tracking, and temporal classification methods to quantify fluorescent reporter dynamics in individual cells across time without the need for specialist computational expertise. Alongside FluoroFate, we develop tricistronic Fluorescent Ubiquitination-based Cell Cycle Indicator (Fucci) and apoptosis biosensors, enabling simultaneous monitoring of cell cycle progression and caspase activation within the same cell. Applying FluoroFate, we resolve apoptotic and non-apoptotic cell death at the single-cell level based on the temporal ordering of Annexin V and propidium iodide signals, identifying distinct kinetic and phenotypic cell death profiles in response to pharmacological perturbation. We highlight divergent temporal dynamics and modes of cell death between birinapant and cycloheximide treatment, reflecting differences in how TNF/TNFR1 signalling is disrupted by these agents. At the single-cell level, we uncover parallel, independently regulated death programmes, demonstrating that loss of RIPK1 selectively impairs apoptotic cell death whilst leaving non-apoptotic death largely unaffected. We then use FluoroFate to analyse timelapse images of our combined Fucci-apoptosis reporters, resolving cell cycle progression and caspase activation within the same cell over time. Together, FluoroFate and our new cell cycle and apoptosis biosensors represent a broadly applicable platform for extracting mechanistic insight from live-cell imaging data.

Preparing electron-transparent cryo-lamellae is inherently a serial, low-throughput process. During sample handling, milling, and transfer, cryo-fixed cells and their supporting films are subjected to mechanical forces as well as thermal stresses caused by temperature fluctuations. After milling, these extremely thin lamellae remain vulnerable to both mechanical and thermal stress, often leading to cracking or complete disintegration. Consequently, the loss of valuable lamellae is frequently an unavoidable aspect of working with such fragile specimens. In this work, we reconsider the conventional lamella geometry, which is typically a flat, thin cross-sectional slab. During milling, lamellae often become unintentionally bent, complicating the final polishing step required to achieve uniform thinning across their width. To address this limitation, we propose deliberately fabricating lamellae in a pre-bent configuration, i.e. specifically, adopting an arch-shaped profile instead of the traditional flat geometry. The arch shape is intrinsically more mechanically stable than a flat structure, thereby reducing lamella loss due to mechanical failure. Moreover, pre-bent milling patterns facilitate uniform thinning of bent lamellae, which is difficult to achieve using conventional flat milling approaches. In addition to the arch geometry, we investigate corrugated lamellae, characterised by a sinusoidal variation around the plane of a conventional flat lamella. Similarly to the arch shape, the corrugated design offers enhanced mechanical stability compared to traditional flat lamellae. We fabricated a series of test lamellae incorporating both arches and corrugations. High-resolution cryo-TEM imaging was performed to evaluate these structures, demonstrating that non-flat geometries do not compromise cryo-electron tomography performance. Furthermore, finite element method (FEM) simulations were conducted to provide insight into stress distributions within bent and corrugated lamellae.

Background: Accurate sampling of suspected peripheral lung cancers depends on access to the lesion and confirmation that the biopsy tool is in contact with target tissue. Current bronchoscopic navigation and imaging techniques can guide instruments to a target but do not provide real-time biological confirmation at the point of sampling. Fluorescence lifetime imaging microscopy (FLIM) provides molecular contrast by measuring fluorescence decay - how long photons continue to be emitted from fluorescent molecules. In the Precision Lung clinical study (ISRCTN15093468), the Prothea Imaging System (Generation 1) identified a candidate tumour-associated phenotype of spatially overlapped low fluorescence lifetime and low intensity (LLLI) from in-vivo imaging. We used this observation as the basis for a reverse-translational study to determine whether the LLLI phenotype is linked to cancer pathology; reproducible with the Imaging System (Generation 2); and distinguishable from normal lung tissue. Methods: Previously reported Precision Lung findings were used as the clinical starting observation and were not re-analysed. Validation was then performed using: (i) pathology linked benchtop FLIM of early-stage non-small-cell lung tissue microarrays encompassing malignant cell clusters of approximately 300 um2, matched to the EoT imaging scale; (ii) five sequential fresh lung-cancer resections imaged at tumour and comparator regions, including visibly blood-rich contact sites, using the (Generation 2) Imaging System; and (iii) systematic mapping of two ventilated non-cancer donor lungs, one from a smoker and one from a non-smoker, across all available lobes. The LLLI phenotype was defined as spatial co-localisation of low intensity and short lifetime. Results: Using a real time fibre based FLIM system, capable of deployment through a working channel of a bronchoscope, the LLLI tumour phenotype was optically identified in freshly resected tumour tissue. The same phenotype was identified in fixed tissue samples with known pathology, and with images taken in the Precision Lung clinical study. Whole human lung controls did not show evidence of the tumour phenotype. Conclusions: This evidence forms a reverse-translational chain that supports the concept of the Prothea Imaging System - as a platform that confirms that the tool is in contact with a region of cancer in the lesion, while preserving continuous access for biopsy or intervention.

The ultrastructure of endothelial cells (ECs) "in situ" is of great interest due to their involvement in many physiological processes. In some organs, these cells form transcellular pores or fenestrae, allowing for the rapid exchange of molecules between blood and interstitium. Despite their importance, no optical images of these dynamic morphological structures have yet been acquired in situ. Major obstacles to their in-situ imaging are the lack of specifical labels for fenestrae and their size well below the optical diffraction limit. Here, we report how we have overcome these challenges and managed to visualize the EC ultrastructure in situ in 25 {micro}m thick liver sections. To enable this, a lipophilic, fluorescent membrane dye was infused into the portal vein of murine livers to stain the sinusoidal ECs before the organ was harvested. Tissue sections were subsequently imaged using a novel, super-resolution optical-sectioning structured illumination microscope (OS-SIM), providing approx. 170 nm spatial resolution with significantly faster image acquisition compared to confocal microscopy.

Cryo-electron tomography (cryo-ET) enables near-native visualization of subcellular architectures, yet applying it to moderately thick, multilayered tissues such as the retina is hampered by inadequate vitrification and inaccurate depth-targeting. Here, we developed PLCT, an integrated approach combining modified high-pressure freezing, cryo-ultramicrotome trimming, and plasma-based cryo-FIB milling to overcome these barriers. PLCT reliably vitrified <100 m retinal strips with minimal ice artifacts, navigates precisely to the outer plexiform layer using morphological landmarks, and produces high-quality lamellae suitable for high-resolution cryo-ET. Subtomogram averaging (STA) analysis identified microtubules at 16.33 [A] within retinal horizontal cell processes. Importantly, STA also resolved a 10-nm-diameter filamentous structure at 24.81 [A] in the same processes, featuring six peripheral strands surrounding an elongated central density with continuous intervening cavities, an architecture consistent with intermediate filaments. Together with its native localization and immunoreactivity, these features collectively identify the filaments as neurofilaments. Separately, 3D reconstruction of synaptic ribbons uncovered a previously unrecognized "mahjong tile"-like fine ultrastructure. These results demonstrate that PLCT-produced lamellae are of sufficient quality to support structural analysis in native tissue. Although demonstrated on retinal photoreceptor synapses as a proof-of-principle, PLCT is inherently generalizable, with its depth-navigation and vitrification strategies directly applicable to any multilayered tissues. This work establishes PLCT as a robust, reproducible platform for depth-resolved in situ cryo-ET of multilayered tissues.

High-speed three-dimensional imaging in scattering tissues remains challenging because volumetric microscopy generally requires scanning, whereas snapshot light-field approaches divide limited detector pixels among multiple views. This constraint is particularly severe in the second near-infrared window (NIR-II), where commonly used InGaAs cameras typically have relatively small sensor formats and high detector noise. Here we introduce NIR-II squeezed light-field microscopy (NIR-II SLIM), which optically rotates and compresses multiple perspective views before detection, allowing efficient use of camera pixels while retaining complementary spatial information for three-dimensional reconstruction. NIR-II SLIM acquires volumes at up to 600 volumes s-1 with a reconstructed lateral sampling grid of 512 x 512 pixels. We use the method for label-free four-dimensional imaging of cardiac dynamics in pigmented late-larval zebrafish, resolving chamber deformation and millisecond-scale atrioventricular-valve motion, and for NIR-II fluorescence imaging of vascular and lymphatic transport in mice. NIR-II SLIM provides a detector-efficient approach for high-speed volumetric imaging of rapid biological dynamics in scattering tissues.