Journal of Microscopy

Nuclear segmentation in 3D microscopy remains challenging, particularly for low signal-to-noise ratio (SNR) data from timelapse imaging, in vivo experiments, or dense tissue samples. Yet accurate 3D nuclear segmentation is a prerequisite for quantitative single-cell analysis, making this a key bottleneck for high-throughput study of challenging datasets. We present NucLogic, a fast and robust 3D nuclear segmentation algorithm that combines the maturity of 2D segmentation with geometric stitching rules based on nuclear shape, orientation, and intensity, to reconstruct accurate 3D volumes. NucLogic reliably segments challenging datasets including long-term timelapse imaging of organoids, and large dense structures such as the zebrafish brain. Benchmarked against other state-of-the-art segmentation tools, NucLogic matches F1 scores at high SNR, with increasing outperformance as SNR decreases. Importantly, segmentation completes at 20-50% of the computation time, with greater speed gains on larger images. Beyond segmentation, NucLogic includes an intuitive graphical user interface allowing non-experts to segment, quantify, and visualize 3D and 4D data. This enables easy quantification of cell numbers and integration of a variety of both cell-intrinsic properties, such as phenotype, morphology, and local environment metrics, including density and spatial interactions. Together, NucLogic makes quantitative 3D nuclear analysis of previously unmanageable datasets accessible to a broad research community.

Image scanning microscopy (ISM) is emerging as the natural evolution of confocal laser-scanning microscopy, combining optical sectioning and a two-fold improvement in lateral resolution with high signal-to-noise ratio. Single-photon avalanche diode (SPAD) array detectors enable an effective implementation of ISM by recording a micro-image at each scan position with single-photon sensitivity, negligible readout noise, and without the fixed frame-readout constraints of conventional cameras. Nevertheless, SPAD array-based ISM remains largely confined to laboratories with expertise in microscope development. Here, we present BrightEyes-Scope, a reference implementation of a SPAD array-based ISM system. We provide step-by-step guidelines for optical design, system construction, alignment, calibration, data acquisition, and image reconstruction. Presented as a complete ISM microscope, BrightEyes-Scope also serves as a reference for upgrading existing laser-scanning microscopes. We anticipate that it will accelerate the adoption of ISM for quantitative super-resolution imaging.

The field of microglial research has evolved throughout the years. Microglia, the immune cells of the central nervous system, are recognized as highly heterogeneous and dynamic cells, known for modifying their structure and function based on the local context. Investigating microglial density, spatial distribution, and morphological states is important for uncovering their distinct functional states in health and pathology. However, quantifying these features accurately is often a methodological challenge. Fully automated computational approaches often fail to capture subtle biological nuances and complex structural variations. Also, the high diversity of image sets makes it difficult to maintain consistent reliability. In contrast, entirely manual quantification is labor-intensive and prone to observer bias. To bridge this gap, we propose a semi-automated framework using the open-source software CellProfiler. Our workflow is divided into two distinct pipelines designed to combine automation of batch analysis with targeted user oversight, allowing for manual intervention when necessary to ensure maximum accuracy. Both pipelines are capable of recognizing microglial soma and tracing their processes. In the density workflow, it automatically calculates cell density and provides spatial distribution measurements, such as closest-neighbor distance and spacing index. For morphological profiling, it yields extensive structural data, including area, perimeter, convex area, form factor, and various shape descriptors. Furthermore, we demonstrate how researchers can optimize the pipeline settings to accommodate varied image datasets and experimental conditions. We hope this open-source framework can standardize microglial density, distribution, and morphological analysis and reduce systematic bias, providing researchers with a robust tool to better characterize their heterogeneity.

Super-resolution methods, such as single molecule localisation microscopy (SMLM), can determine the distribution of specific proteins at the nanometre scale. However, three-dimensional (3D) SMLM is typically limited to thin sections and requires prohibitively long acquisition times to image complete cells. Here we introduce a 3D SMLM imaging modality, Intrinsic3D, that combines the speed and optical sectioning of oblique plane microscopy (OPM) with the nanometre resolution of SMLM to determine the position of single molecule emitters throughout complete volumes more than an order-of-magnitude faster over regular DNA-PAINT. By scanning the oblique light sheet across the volume and combining localization information from two or more adjacent planes, an emitter's x, y and z position is determined, eliminating the need for post-processing steps to stitch multiple image stacks together. We demonstrate resolution and speed by imaging nuclear pore complexes in U2OS cells and Drosophila egg chambers. As a proof-of-concept, we map the 3D distribution of the cell-adhesion protein DE-cadherin in the Drosophila follicular epithelium and show that spot adherens junctions are enriched near tricellular junctions and contain more E-cadherin than the apical adherens junctions, suggesting that spot junctions play a wider role in cell-cell adhesion than previously appreciated. Finally, we examine the organization of Gliotactin, a tricellular septate junction protein relative to E-cadherin using exchange-PAINT. These investigations demonstrate that Intrinsic3D delivers rapid 3D protein mapping in whole cells. Combining Intrinsic3D with existing multiplex and resolution enhancing methods (e.g. exchange-PAINT and RESI) will enable multicolour, 3D in situ spatial proteomics in tissue samples and bridge the gap between SMLM and systems-level cell biology.

Fluorescence microscopy is routinely used in the quantitative analysis of Giant Unilamellar Vesicles (GUVs) because it makes GUVs visible and easy to detect. Nonetheless, it requires fluorescence-equipped microscopes and, especially, the addition of tagged molecules that may introduce undesired and unaccounted variations compared to the pristine formulation. Alternatively, phase-contrast microscopy can be used to observe GUVs without labelling, yet the automatic detection of GUVs in phase-contrast images by standard image processing techniques is difficult. To overcome this limitation, we developed a framework for the automated detection and size characterization of label-free GUVs based on YOLOv11 model, a state-of-art lightweight Convolutional Neural Network (CNN) for object detection. Trained on 164 phase-contrast microscope images containing 4666 manually annotated vesicles, the model enables robust detection under low contrast-to-noise ratio and heterogeneous illumination. The trained model is implemented in a stand-alone desktop application supporting multiple analysis in a single run, pixel-to-micrometer calibration, and automated size distribution analysis. This approach achieves high detection accuracy and competitive cross-microscope generalization using YOLOv11 nano on greyscale image. Retraining scripts compatible with standard hardware are also provided to enhance adaptability. This tool enables fast and reliable analysis of GUVs without altering their composition, supporting experimental reproducibility.

The visualisation of large biological samples in cryo-electron tomography requires both few-nanometre resolution and a wide, ideally >10 micrometrre, field of view. For a single image, increasing magnification trades increased resolution for a smaller field of view. Montaging, where high-magnification images are stitched together to increase field of view, addresses the trade-off but is dose-inefficient with round beams that are standard in transmission electron microscopy (TEM), since this illumination shape does not tessellate. Our workflow greatly simplifies montaging schemes for dose-sensitive specimens, allowing large montages of any shape - from individual cells or other features of interest up to whole lamellae - to be collected at high resolution without compromising electron dose efficiency. We demonstrate the workflow by acquiring montage tomograms of whole yeast (Saccharomyces cerevisiae) cells in cryo-FIB lamellae and of human neurons.

In this paper, we introduce Nagini3D, a 3D biological image segmentation method that hybridizes two high-performing yet highly complementary segmentation paradigms: deep learning-based approaches and snake-based methods. Our approach leverages both the high efficiency of data-driven techniques and the convenient shape representation provided by active surfaces, which proves highly practical for incorporating a priori knowledge as well as for the post-analysis of segmented objects. By considering a complete 3D volume, our method ensures segmentation consistency across all three dimensions, unlike many approaches that handle volumes in a slice-by-slice manner. Additionally, it theoretically enables the representation of any object with spherical topology, overcoming the limitations of common 3D methods that rely on object convexity assumptions. Furthermore, the continuous representation of objects allows for a resolution-independent description, facilitating the extraction of various geometric features of interest (e.g., local curvature, principal deformations). We demonstrate both the quality of the segmentation produced by our method and the richness of the accessible geometric features through an extensive set of experiments conducted on multiple real and simulated datasets, including objects with challenging geometries.

Serial-sectioning electron microscopy allows the exploration of the 3D ultrastructure of cells, tissues or whole organisms. Sample preparation is a critical step for this technique, which determines the quality of downstream imaging and analysis. Here we optimize a high-pressure freezing sample-preparation workflow for larvae of the marine annelid Platynereis dumerilii. We tested several fixation and staining methods and screened a large number of specimens by synchrotron X-ray imaging to evaluate specimen integrity and contrast. We further evaluated selected blocks by array tomography SEM to select the best performing protocols. Our protocols and robust workflow could be adapted to similar marine planktonic organisms including ciliated invertebrate larvae.

Maximizing the morphological and molecular fidelity of preserved mammalian brain tissue is essential for basic neuroscience and brain banking, where tissue quality determines the reliability of downstream analyses. Aldehyde fixation and cryogenic storage are the most powerful preservation techniques available. However, how to best combine them is incompletely characterized. Here, we tested twelve different approaches to preserve the murine brain, including standard aldehyde perfusion fixation, variants of aldehyde-stabilized cryopreservation (ASC) using ethylene glycol (EG), aldehyde fixation followed by unprotected freezing, and several cryopreservation protocols without preceding aldehyde fixation, such as interleaved equilibration with vitrification solution. Preservation quality was assessed using light microscopy, transmission electron microscopy, and patch-level feature analysis with the DINOv2 vision foundation model. We found that ASC with sodium dodecyl sulfate (SDS)-mediated blood-brain barrier permeabilization preserved nuclear morphology, myelin periodicity, and neuropil texture comparable to standard aldehyde perfusion fixation, providing the first independent replication of ASC. Omitting SDS caused severe parenchymal dehydration, due to a mismatch between water and cryoprotectant transport. Aldehyde fixation followed by unprotected slow freezing confined ice damage primarily to perivascular zones, while fast freezing led to intranuclear clefts and cavities. Among non-fixation protocols, interleaved equilibration with vitrification solution preserved myelinated axon profiles and chromatin patterns, but induced perivascular edema. High concentration glycerol perfusion caused significant osmotic dehydration. Straight freezing of unfixed tissue without cryoprotectant was followed by membrane disruption, but resulted in the visualization of more structure than expected, likely as a result of structural restitution upon thawing. Taken together, our results provide a framework for matching preservation strategies to the needs of different types of brain banking and neuroscience research.

Centriolar satellites are membrane-less organelles that store, modify and traffic proteins, in particular those of the centrosome and the primary cilium, and their deregulation is linked to ciliopathies, neurodevelopmental and neurodegenerative disorders. Satellite number, size, intensity and spatial distribution vary with cell type and cell state, yet satellites are usually quantified by a single readout, the pericentrosomal fluorescence intensity in maximum-projected images, which cannot distinguish changes in satellite number, size and position. Here we present SatelliteQ, an open-source Fiji and Python workflow for object-based quantification of satellites in 2D, 3D and time-lapse images. SatelliteQ segments satellites, detects centrosomes or primary cilia in the same cells, and reports satellite number, size, intensity, distance to the centrosome, shape and spatial distribution per satellite and per cell, with interactive graph and spatial viewers. Its measurements correlate closely with Imaris-based quantification; absolute values depend on segmentation settings and analysis mode, whereas the biological differences tested here were preserved across matched settings. Using SatelliteQ, we resolved distinct spatial signatures of satellite dispersal induced by microtubule depolymerization and by disruption of weak hydrophobic interactions, quantified differences between cell types and the scaling of satellite number with cell size, captured PCM1 granule dynamics in living cells, and compared satellite organization between ciliated and unciliated cells. Depletion of the ciliopathy-associated satellite protein CEP290 altered satellite abundance and pericentrosomal organization, while patient-derived cells carrying CEP290 mutations displayed divergent satellite phenotypes that were not explained by ciliation alone. SatelliteQ thus establishes satellite properties as a multidimensional cellular phenotype and provides an accessible framework for exploring satellites as an organelle-level readout in disease.

Volumetric microscopy produces datasets too large to share as files and too rich to convey as still images, yet exploring them interactively means installing desktop software or building web infrastructure. Here we present Luxar, an open-source Python framework that compiles multidimensional data into compact archives that any modern browser renders from static file hosting. Fluorescence volumes are represented as mixtures of anisotropic Gaussians (Gaussian splats): the image becomes geometry that the GPU draws directly, with no voxel grid to rebuild. Splats share one scene with points, trajectories and surfaces. Blind-spot cross-validation sets the number of Gaussians without noise-free references. Across 17 volumes from 12 datasets and four modalities, this yields a median 99-fold (6- to 340-fold) size reduction at 26 to 67 dB peak signal-to-noise ratio (PSNR), in minutes per volume on one GPU. We demonstrate Luxar on a 500-timepoint embryogenesis recording, super-resolution localisations, single-cell atlases and lineage tracking, each explorable through a shareable link.

High-speed atomic force microscopy (HS-AFM) experiments provide unique insights into nanoscale biological processes by directly visualizing the surface topography dynamics of biomolecules, such as proteins, over extended periods of time and generating large imaging data sets. However, the lack of computational methods for automated data analysis has thus far limited the full utilization of these data for the quantitative understanding of single-molecule dynamic. Here, we present the AFM Analysis Arena (A3) within BioAFMviewer as a software platform for automated, high-throughput quantitative analysis of AFM topographic imaging data within a user-friendly interactive graphical interface, implementing a convenient workflow from raw HS-AFM (AFM) imaging to statistical analysis. In several applications to HS-AFM imaging data, we demonstrate methods of automatized molecule detection and dynamic tracking to highlight the available capacities, aiming to transform HS-AFM measurements from observations towards an analytical tool in nanoscale biology.

Protein labelling using genetically encoded fluorescent protein fusions has revolutionized the study of protein localization and function. However, visualizing endogenous proteins in specific cell types remains challenging, as existing approaches often require overexpression or laborious genetic engineering. To address these limitations, we developed a library of fluorescent protein binders using existing nanobodies and single-chain variable fragments that recognize fluorescent proteins and short peptide tags, allowing for modular, cell type-specific labelling of genetically tagged endogenous proteins. We found that fluorescent protein binders generate background fluorescence in the absence of the target protein, reducing signal specificity. To overcome this limitation, we generated destabilized fluorescent protein binders directed against GFP, ALFA, GCN4 peptide and V5. These binders display increased antigen-dependent stability and reduced background fluorescence, thereby improving the signal-to-noise ratio of endogenous protein visualization. We demonstrate the detection of endogenous proteins in drosophila larvae and embryos, zebrafish embryos, as well as in specific neuronal populations of the adult drosophila brain. In the brain, tissue-specific labelling revealed previously unknown protein expression and subcellular localization in sleep-regulatory neurons. Together, our panel of tag-stabilized fluorescent protein binders provides a versatile, ready-to-use toolkit for background-free, cell type-specific visualization of endogenous proteins.

Preprocessing of spatial transcriptomics (ST) count matrices is critical for removing technical variation while preserving biological signals, but optimal strategies remain unclear across diverse platforms. We systematically benchmarked 371 preprocessing workflows across 45 data spanning 11 mainstream ST platforms, evaluating their performance using specifically designed complementary metrics. To further assess preprocessing factors upstream of count-matrix generation, we additionally evaluated image preprocessing, including cell segmentation and post-segmentation transcript processing, across representative imaging-based ST platforms. We found that no universal count-matrix preprocessing workflow performed optimally across all platforms, while data from the same platforms exhibited striking consistency in optimal count-matrix preprocessing workflows. We identified the key data structure characteristics and count-matrix preprocessing steps that critically influence preprocessing performance. We developed platform-specific and data structure-guided recommendations to assist users in selecting the optimal count-matrix preprocessing workflow, validated across a broader range of downstream tasks. We demonstrated that our data-driven recommendations uncover subtle biologically meaningful spatial patterns in new data that were obscured by suboptimal count-matrix preprocessing. Our findings suggest best practices for ST data count-matrix preprocessing.

Volumetric imaging is increasingly important in fields such as neuroscience, where quantifying structure and mapping features are inherently three-dimensional problems. Block-face serial two-photon tomography (STPT) has emerged as a powerful approach for high-resolution, whole-organ volumetric imaging. STPT avoids complex sample preparation and balances image quality, speed, and scalability. However, STPT remains niche, in part because of the absence of flexible, general-purpose control software. Here we present BakingTray, an open-source software platform for STPT that extends ScanImage. BakingTrays modular framework accommodates diverse hardware configurations and allows users to build systems customised to their needs. Twelve such systems, which we call BrainSaws, have been constructed worldwide since 2016, with more under construction, and have so far contributed to 35 research papers. The three systems at the Sainsbury Wellcome Centre alone have acquired over 7,000 samples in a busy multi-user facility. We demonstrate the platform across its core neuroscience applications: electrode track localisation, brain-wide cell counting, and bulk projection mapping. Beyond these, we map the cell-type-specific synaptic output of inhibitory neurons in the ventral lateral geniculate nucleus and, with whole-mount staining, amyloid plaques brain-wide in a mouse model of Alzheimers disease. BakingTray is a mature platform for groups or facilities needing a customised volumetric imaging solution, whether built new or converted cheaply from an existing two-photon microscope.

Vasculogenic mimicry (VM) describes the ability for tumor cells to mimic endothelial cells and form tumor-lined blood vessels. Although VM is associated with poor prognosis, resistance to therapeutics and metastasis, its presence, relevance and functionality have been debated, largely due to the techniques employed to determine its presence. Dual CD31 and periodic acid-Schiff (PAS) histological staining, the method widely used to detect VM within tumor tissue, is hampered by issues with artifacts of 2D sections and does not assess active blood flow through vessels and hence cannot confirm vessel functionality. Here, we present a 3D imaging pipeline to detect functionally perfused VM and host vessels within mouse tumor models. Mice tumor models are intravenously injected with fluorescently-labelled tomato lectin which binds to the lumen of functional blood vessels. These tumors are thickly sliced, optically-cleared, stained with endothelial markers and imaged using light-sheet microscopy. We apply this imaging modality to image tumors from six breast cancer patient-derived tissue xenograft models, an orthotopic mammary cancer model and two small cell lung cancer circulating-tumor cell explant models, where VM vessels, mosaic vessels and host-VM vessel junctions can be detected. To our knowledge, this is the largest collection of 3D VM vessel images to date, providing convincing evidence of the presence and functionality of VM within tumors and defining the level of evidence necessary to detect VM in vivo.

Extracellular signal-regulated kinase (ERK) governs many cellular processes, and its spatiotemporal activity dynamics are important for downstream signaling specificity. Existing fluorescent ERK activity biosensors, however, fall short in enabling spectrally multiplexed, spatially resolved, or deep tissue measurements. Here, we introduce a chemigenetic, single-fluorophore biosensor, WHaloEKAR, that reports ERK activities through changes in fluorescence lifetime. WHaloEKAR combines large lifetime changes (-0.81 ns) with the spectral versatility of the self-labeling protein HaloTag giving access to orange, red, and far-red ERK biosensors. The latter enabled the investigation of heterogeneous ERK activity at distinct subcellular locations in cultured cell lines and primary cortical rat neurons via fluorescence lifetime imaging microscopy (FLIM). Moreover, multiplexed FLIM measurements allowed to study ERK activity in parallel to the second messengers calcium and cyclic adenosine monophosphate. Finally, WHaloEKAR enabled the investigation of ERK activity in 3D samples as well as in live animals.

In the murine osteochondral unit, morphology of chondrocyte lacunae (CCL) within calcified articular cartilage and osteocyte lacunae (OCL) within the underlying subchondral bone reflects local biomechanical conditions that are altered in disease, making reliable discrimination of both lacunar populations important for studying osteochondral physiology and pathology. Histology is generally considered the gold standard for identifying CCL and OCL. However, it is inherently limited to two-dimensional assessment and a relatively small tissue section, restricting comprehensive characterisation of three-dimensional lacunar structure. Synchrotron-radiation micro-computed tomography (SR-microCT) enables volumetric quantification of lacunae; where classification is typically based on the position of each lacuna relative to the cement line separating calcified cartilage from bone, requiring manual delineation of this convoluted interface. Such approaches are labour-intensive, operator-dependent, and rely on access to specialised synchrotron infrastructure that is not widely available. To address these limitations, we present a fully automated, annotation-free pipeline for classifying OCL and CCL in the mouse osteochondral unit based on desktop micro-computed tomography (microCT), independent of cement line identification. The proposed unsupervised workflow requires neither tissue separation nor labelled training data, enabling objective and scalable discrimination of lacunar populations directly from volumetric image data. Seven morphometric and positional features per lacuna were extracted from SR-microCT and desktop microCT datasets and analysed using Uniform Manifold Approximation and Projection (UMAP). Validation of the pipeline outcome on SR-microCT against manual classification across specimens yielded very-high agreement, with Gwet's AC1, the Matthews correlation coefficient, and balanced accuracy each approaching 0.99, in both healthy and post-traumatic osteoarthritis specimens. Such a workflow can be readily adapted for the investigation of osteochondral interface in various other species.

Cryo-Electron Microscopy (CryoEM) Single Particle Analysis (SPA) is pivotal for elucidating protein structures and their conformational dynamics. Several critical steps are required to obtain accurate maps. First, the \textit{ab initio} relative orientations and shifts between images must be determined. Second, the common issue of specimens presenting a preferred orientation should be adequately addressed. Third, all of the above must account for particle flexibility and sample heterogeneity. Additionally, these steps are interrelated, making the search for all these parameters a real challenge when considering convergence robustness. In this context, we present ReconSIREN, a novel deep learning framework that addresses the key steps outlined before in an \textit{ab initio} manner while greatly minimizing one of the most important practical burdens in SPA, namely the errors introduced in the CryoEM maps by the existence of preferred orientations; the latter is achieved by a change of the optimization approach that avoids the so-called ``attraction problem'', and by the introduction of a new type of geometrical priors. It does so fully \textit{ab initio}, using deep learning training/inference steps without resorting to more traditional optimization approaches, and generating highly accurate initial reconstructions. We present maps as both homogeneous (consensus) maps and a full conformational landscape for analyzing flexibility and heterogeneity. Thanks to its Mixture-of-Experts (MoE) architecture, ReconSIREN distributes the estimation workload across independent experts, improving optimization efficiency and simplifying the process.

Focal adhesions (FAs) and actin stress fibers (SFs) are mechanically coupled but often analyzed separately. We present FilAdh, a unified framework for FA instance segmentation, SF instance reconstruction, morphometry, and FA-anchorage-based SF classification. Across 57 held-out HeLa, SUIT-2, and MRC-5 cell line images, FilAdh achieved the highest FA Dice, intersection over union (IoU), and recall and the highest SF clDice and skeleton precision among benchmarked methods; paired t tests with Holm correction indicated significant gains over all compared methods for FA Dice, IoU, and recall, and over classical methods for SF centerline metrics, with no significant differences detected relative to the deep-learning comparators. Conditional on expert-curated FA and SF masks, SFs were assigned to dorsal, ventral, and peripheral classes with 81.5%-87.3% accuracy. Controlled Poisson-Gaussian noise, Gaussian defocus, and point-spread-function blur revealed distinct, structure-specific errors in overlap and object-level measurements. By linking segmentation and reconstruction to count, area, length, apparent width, and structural class, FilAdh offers an interpretable view of the FA-actin structural system and makes degradation-dependent measurement biases explicit. BIGGER PICTURECells sense and respond to their physical environment through focal adhesions and actin stress fibers. The two form a mechanically coupled system, yet they are usually analyzed separately, with workflows re-tuned for each new set of images. FilAdh analyzes both within a single framework, tailoring segmentation, reconstruction, and classification to each structures geometry: compact adhesion patches versus thin, extended fiber networks. Testing on expert-annotated references and under controlled degradations that mimic low signal, focus errors, and optical blur shows that these common imaging imperfections distort adhesion and fiber measurements in different ways. The same design principle--matching the analysis to each structures shape and judging methods by how errors propagate into the final biological measurements--may benefit other bioimage-analysis problems. Released code, trained weights, and fixed data splits make all comparisons reproducible and extensible. HIGHLIGHTSO_LIFilAdh jointly quantifies focal adhesions and actin stress fibers C_LIO_LIFilAdh achieves the highest benchmarked FA overlap and comparable SF topology C_LIO_LIMatched synthetic degradations reveal geometry-specific measurement errors C_LIO_LIFA-SF spatial context enables dorsal, ventral, and peripheral SF classification C_LI