Journal of Microscopy

Accurate quantification of fluorescence signals in three-dimensional (3D) microscopy is often hindered by axial- and radial-position-dependent attenuation, limiting reliable measurements in live biological specimens. Here, we present a data-driven statistical correction model that compensates for signal loss arising from axial- and radial-position in 3D time-lapse imaging of Caenorhabditis elegans embryos. Our framework incorporates axial position (imaging depth, z), radial position (distance from the center of the field of view, r), together with cell cycle progression, to recover cell-specific fluorescence intensities independent of axial- and radial-positions. By leveraging repeated observations of biologically comparable states, the model infers attenuation directly from the data without requiring external calibration. Notably, the sign of the inferred radial-position-dependence in biological specimens was opposite to that observed in homogeneous fluorescent reference samples, underscoring the value of specimen-specific, data-driven correction. Validation using histone-tagged fluorescent proteins demonstrated that the method effectively removes geometric bias in nuclear fluorescence signals, enabling consistent quantification across cells and embryos. This approach provides a robust and generalizable solution for correcting intensity attenuation in volumetric microscopy datasets, thereby enabling more accurate and reproducible quantitative analyses in live imaging studies. Author summaryModern microscopy lets us watch living cells and embryos in three dimensions, but measuring brightness accurately is harder than it seems. Signals often become weaker not only when molecules are less abundant, but also when they lie deeper in the specimen or farther from the center of the image. This makes it difficult to tell whether differences in brightness reflect biology or simply the position of a cell within the microscope field. Existing correction methods typically rely on separately acquired reference measurement samples or on image-level statistical patterns. In this study, we took a different approach. Using the highly reproducible development of nematode (C. elegans) embryos, we compared cells that should be biologically equivalent across multiple embryos and used those repeated observations to estimate imaging bias directly from the biological images themselves. Our method corrects for both axial-dependent and radial attenuation simultaneously within a unified statistical framework, requiring no such reference data. Beyond simply improving consistency, we uncovered an unexpected result: the radial bias inferred from real embryos was opposite in sign to what calibration samples would predict. This underscores the need for specimen-specific, data-driven correction. Our framework should help make live imaging more quantitatively accurate for studying dynamic biological processes in complex three-dimensional specimens.

Serial Block Face - Scanning Electron Microscopy (SBF-SEM) is a volume EM method suited to investigate the 3D architecture of tissues and even entire organisms at high resolution. However, imaging large volumes in their entirety is time-consuming and not always necessary. Many research projects have a focused interest in well-defined sub-regions of the samples. The targeting and acquisition of such regions of interest (ROIs) are however currently conducted in a manual way and require heavy involvement of experienced operators. We present a workflow and an original open-source software tool (iSBEM), which allow automated targeting of ROIs in a large tissue sample, based on X-ray microscopy (XRM) maps. After an initial ROI identification and registration of the XRM map with the sample mounted on the SBF-SEM stage, iSBEM takes over the control of the microscope, triggering high resolution acquisitions at defined ROI positions, with minimal user intervention. We demonstrate the approach on two biologically distinct specimens -- malarial oocysts in infected mosquito midgut tissue, and immune cells in human kidney biopsies -- achieving significant improvement in acquisition throughput relative to manual operations, without compromising targeting precision. We also showcase the workflow in a correlative light-Xray-electron microscopy setup, which allowed us to further improve the correct target definition.

Microscopes are essential tools for discoveries on a scale invisible to the unaided human eye. The development of immuno-fluorescence followed by molecular biology techniques and fluorescent fusion proteins have revolutionised the use of optical microscopy in bioscience. The quality of the data produced is dependent upon the sample, its preparation and the instrument used. However, instruments can degrade over time without easily visible changes to the produced images and, in turn, negatively impacts results. By testing instruments and doing comparisons between results over time and between different instruments, problems can be highlighted and corrective action can be taken. Using small fluorescent beads the point spread function (PSF) of the microscope can be recorded and the image resolution measured. Beads were prepared in a concentration matched to the field of view size and dried onto coverslips and mounted on slides. The beads were then imaged as 3D Z-stacks of sufficient size to fully enclose the PSF of the system. This data was uploaded to OMERO and processed using OMERO-metrics, an OMERO plugin developed for this purpose. This paper summarizes the development of workflows and protocols to enable this process, presents the results obtained and demonstrates the detection of significant instrument issues.

Optical microscopy is fundamental to modern life-science research, yet interpreting its results requires precise modelling of point spread functions (PSFs) within complex environments. This manuscript introduces a versatile and efficient approach to wave-optical PSF calculations that extends existing frameworks by incorporating detection PSF modelling through the principle of reciprocity. Accompanying this work is a free MATLAB software package centred on a single, minimalistic core function, PlaneWaveExc.m, which utilizes a plane-wave superposition based on the Richards-Wolf model. Despite its simplicity, the framework accounts for "real-life" complexities such as systemic aberrations, arbitrary amplitude and phase modulations, and stratified media with complex-valued refractive indices. We demonstrate the softwares broad applicability through diverse case studies, including single-molecule imaging, STED microscopy, the segmented aperture of the James Webb Space Telescope, and coherent wide-field iSCAT microscopy. Each example is supported by dedicated scripts to facilitate adaptation for specific research needs.

The in vitro scratch wound healing assay is one of the most widely used methods for studying collective cell migration, but converting assay images into reproducible measurements remains a practical bottleneck of manual tracing, local software installation, parameter bookkeeping, and limited visibility into how the wound region was segmented. We present Cytomove, a browser-local software tool for reviewable scratch wound healing assay quantification. Cytomove imports local microscopy images, segments the wound region with an explainable variance-and-threshold pipeline implemented in client-side JavaScript without external image-processing dependencies, displays the segmentation as an inspectable overlay before any number is exported, supports single-image and grouped time-course analysis, and exports wound area, wound area fraction, wound width profile statistics, quality-control labels, and full analysis metadata as CSV, Excel, PNG, and ZIP. All processing runs in the browser or in a desktop package built on the same code; microscopy images never leave the users machine. In a preliminary comparison with the ImageJ/Fiji Wound Healing Size Tool (WHST) across five image sets and 31 paired measurements, Cytomove reproduced wound-area behaviour closely in a clean brightfield comparator sequence (mean absolute percentage error 4.1%, Pearson r = 0.9975) and in a phase-contrast time course approaching closure (median area error 6.6%, r = 0.9984), while surfacing near-closure and real-world acquisition difficulties through overlays and quality-control labels. Informal local testing indicates that typical single-image analysis completes within seconds in a modern browser, with no installation or dependency step. Cytomove lowers installation friction, keeps assay data local, and links every exported number to the segmentation image and parameters that produced it.

Integrating tissue-level organisation with sub-cellular resolution and molecular information often requires combining multiple microscopy modalities and scales. However, aligning images acquired with different modalities, settings, or instruments remains challenging. Here, we introduce NucleiSky, a microscopy image registration framework that utilises the spatial arrangement of nuclei or other landmarks as an intrinsic biological fingerprint. NucleiSky represents images as constellations of centroids and aligns them using geometric algorithms and spatial consensus scoring. In benchmark datasets, NucleiSky could localise query regions within larger reference images using as few as five nuclei. We show that NucleiSky can locate high-magnification fields of view within low-magnification overview scans, map these alignments to additional channels, support live brightfield-to-fixed registration using synthetic nuclear labels, and guide microscope retargeting. We further show that the same constellation-matching principle can be extended to 3D localisation and to non-nuclear landmarks. These findings establish local landmark geometry as an intrinsic spatial fingerprint that enables localisation and registration across imaging scales, modalities and microscopy platforms. NucleiSky is available as an open-source Python package and as notebook-based applications.

Chemical imaging is a powerful branch of modern microscopy encumbered by a lack of flexible, high-throughput analysis tools. Bespoke analytical pipelines typically perform ratiometric analysis on two layers in a multispectral image to describe the relative composition of molecules in a sample. This strategy has been implemented across fields, spanning histopathology, cell biology, environmental science, and materials science. The commercialization of chemical imaging microscopes has facilitated the collection of large multispectral datasets, necessitating accessible ways to process them. This paper describes Multispectral Analysis Graphical User Interface (msaGUI), a desktop graphical user interface to analyze individual and batch datasets of multispectral images. Data is loaded as CSV, TSV, or TIFFs and processed through a user-defined sequence of modular image operations that can be flexibly combined, e.g. to reduce spectral crosstalk or background noise. After analysis, data is visualized as exportable images, histograms, and statistics. To yield publication-quality figures, outputted images are fully customizable. Written in Python with open-source libraries, the msaGUI program is packaged into an executable for Windows and Mac for a fully no-code application. Other operating systems are supported via the Python source code. In summary, msaGUI provides a rapid and user-friendly solution for analyzing and visualizing multispectral data.

Scientific user facilities routinely generate large-scale microscopy datasets across diverse instruments and vendors, differing substantially in file formats, dimensionality, and resolution. Beyond these inconsistencies, datasets are frequently fragmented living across isolated instruments and constrained by security policies and uneven metadata practices. Consequently, tracking, standardizing, processing, and visualizing these datasets in a manner compatible with modern machine learning and autonomous experimentation workflows remains a major challenge. While existing initiatives address data archiving, standardization, or analysis individually, few provide integrated solutions that bridge instrument-level acquisition and scalable ML workflows within heterogeneous, security-constrained user facilities. Here, we establish a deployable, facility-scale infrastructure that bridges instrument-level data generation with cloud-based ML analytics while remaining compliant with institutional network constraints. Our framework integrates on-premises cloud computing, the in-house Pycroscopy ecosystem, and an open-source metadata management platform to transform heterogeneous microscopy datasets into standardized, ML-ready representations. We demonstrate this approach across distinct microscopy modalities through end-to-end workflows encompassing metadata capture, format harmonization, automated database ingestion, segmentation-based ML inference, and interactive visualization. By structurally separating acquisition from cloud-based analysis services, the framework enables scalable model deployment and iterative refinement without direct connectivity to instrument computers. Together, this work provides a reproducible blueprint for facility-scale data and AI infrastructure, enabling ML-ready analytics, metadata traceability, and future autonomous experimentation workflows in microscopy-driven research.

Classical immunolabeling approaches can achieve homogeneous and continuous labeling of cellular membranes and organelles at wide-field and confocal resolution. In super-resolution and expansion microscopy, however, the lack of high-density labels hampers the localization of membrane proteins and protein complexes within their membrane context. Here we show that secondary antibodies coupled to the lipophilic dyes ATTO 647N or ATTO 550 brightly label the nuclear envelope, mitochondria, and endoplasmic reticulum of fixed, permeabilized cells, and that graded labelling intensities allow selective visualization of organelles and precise segmentation of mitochondria. Using state-of-the-art super-resolution and expansion microscopy, we achieve high-density labelling of nuclear and mitochondrial membranes, with targeting and density comparable to existing membrane-labelling approaches and a signal that can be further amplified with additional secondary antibodies. Finally, we show that these dye-conjugated IgG allow to resolve mitochondria-ER contacts and mitochondrial ultrastructure as well as precise visualization of the nuclear envelope and its invaginations. This study demonstrates that secondary antibodies conjugated to lipophilic fluorophores represent stable, convenient and affordable tools for organelle visualization in conventional microscopy and for high-density labeling of membranes in super-resolution and expansion microscopy.

Summary: Single-molecule localization microscopy techniques, such as (direct) stochastic optical reconstruction microscopy ((d)STORM) and photo-activated localization microscopy (PALM) enable the visualization of subcellular molecular organization beyond the diffraction limit of conventional light microscopy. Not only is data acquisition rather slow, but the downstream analysis of localization datasets often remains computationally challenging and time-consuming. Consequently, the complexity and duration of data processing often limit experiments to the acquisition and analysis of only small numbers of cells or regions of interest, thereby restricting the statistical power and biological reliability of SMLM studies. To address this limitation, we developed an open-source Python-based package for automated, high-throughput post-processing and quantitative analysis of SMLM localization data, enabling efficient and straightforward handling of extensive datasets with minimal manual intervention. Availability and implementation: dSTORMQuant (source code and documentation) are freely available on GitHub at https://github.com/BCMM-Bielefeld-University/dSTORMQuant under GPL v3 license.

Cell tracking in time-lapse microscopy is essential for studying dynamic biological processes such as migration, proliferation, and lineage formation. Existing benchmarks primarily focus on high-framerate imaging, where short temporal intervals simplify correspondence between cells across consecutive frames. We present the Low Frame-rate Cell Tracking dataset (LCFT), a benchmark dataset designed specifically for evaluating cell tracking methods under low-frame-rate conditions. The dataset contains multi-day live-cell microscopy sequences from four human cell lines (MCF10A, MDA-MB-231, HEK293T, and U87), acquired at 10x and 20x magnifications using phase-contrast and fluorescence imaging (nucleus). Ground-truth annotations include cell identifications, temporal linking, lineage relationships, and mitosis events. To generate reliable annotations, automated segmentation and tracking were combined with extensive manual curation. LCFT provides a comprehensive resource for developing and benchmarking robust cell tracking algorithms capable of handling sparse temporal sampling and large inter-frame motion in long-term live-cell imaging experiments.

The tissue-resident immune system involves complex 3D assemblies that interact with extended structures such as blood vessels and nerves. These interactions are difficult to study using conventional 2D profiling because they span many tissue sections. In animal tissues, volumetric imaging approaches such as light-sheet fluorescence microscopy (LSFM) are widely used to study 3D tissue organization, with labelling often aided by genetically encoded reporters and vascular dyes. In contrast, LSFM of human specimens remains underdeveloped because most clinical samples are available only as formalin-fixed paraffin-embedded (FFPE) tissue, limiting labeling strategies primarily to dyes and antibodies. Here, we present a volumetric cyclic immunofluorescence (v-CyCIF) and virtual H&E toolbox that overcomes key barriers to multiplexed imaging of immune cells and nerves in human specimens up to 1 mm thick. We use v-CyCIF to study neuroimmune interactions in normal and cancer tissues and to immunoprofile intact secondary and tertiary lymphoid structures. Re-embedding and sectioning of specimens following volumetric imaging enables high-plex high-resolution analysis of subcellular structures and cell-cell interactions associated with immune cell activity. v-CyCIF therefore provides a flexible framework for multi-scale 3D profiling of clinical specimens across imaging formats and resolutions.

Quantitative image analysis of subcellular organization requires sufficient spatial resolution to resolve individual organelles and sample size to capture heterogeneity both within cells and between cells. Existing imaging approaches often force a tradeoff between spatial resolution and throughput, limiting the ability to measure organelle-level phenotypes across cell populations. Here, we establish high-throughputs expansion microscopy (HiExM) as a scalable pipeline for single-organelle analysis. As a benchmark, we focus on mapping late endosomes and lysosomes (LELs), a heterogeneous organelle class whose small size, dense intracellular distribution, and functional diversity make it difficult to quantify accurately using conventional light microscopy. HiExM increases effective spatial resolution while preserving compatibility with large-scale image acquisition, enabling robust segmentation and quantitative profiling of individual LELs across large cell populations. Using this pipeline, we identified differences in intracellular trafficking behavior among anti-transferrin receptor antibodies that could not be captured by conventional colocalization analysis alone. We further integrate spatial and morphological features with learned image-based representations that can define relationships between LEL morphology and subcellular position as well as how these relationships respond to perturbations. Together, our work establishes HiExM as a generalizable platform for scalable single-organelle profiling, enabling an analytical framework for quantifying discrete organelles across cells and conditions.

Light sheet fluorescence microscopy enables volumetric imaging with high imaging speed, optical sectioning capability, and reduced photobleaching and phototoxicity, and has become a workhorse in bioimaging. However, widely adopted Gaussian light sheets face an inherent trade-off between axial resolution and field-of-view due to diffraction. State-of-the-art nondiffracting light sheets--including Bessel beam, Airy beam, and lattice light sheet--alleviate this trade-off but suffer from optical aberrations that compromise performance with increasing imaging depth. While the integration of adaptive optics offers a promising solution, such integrated systems are typically complex, expensive, and slow due to the need for serial mapping and correction of spatially varying aberrations across the specimen. Here, we present polarization-engineered aberration-resilient light sheet (PEARLS), a new class of monochromatic nondiffracting light sheet with temporally invariant profile and robustness to optical aberrations. In comparison with existing light sheets, PEARLS showed significantly reduced photobleaching and enhanced aberration-resilience, permitting imaging of three-dimensional subcellular dynamics in optically complex environments. We applied PEARLS for noninvasive observations of biological dynamics in various living systems, revealing phenotypic diversity across spatial and temporal scales--from rapid membrane dynamics and organelle interactions in cultured cells to coordinated mitosis and cell migrations in developing embryos.

Multi-organelle segmentation in volumetric electron microscopy (vEM) faces several challenges, including severe class imbalance, the presence of small, rare classes, and inconsistent class coverage across crops. While recent work has focused primarily on architectural design, the impact of sampling, loss functions, and masking strategies on training effectiveness remains comparatively underexplored in vEM organelle segmentation. Here, we systematically evaluate sampling strategies, loss configurations, masking approaches, and model families (CNNs and vision transformers) on the CellMap benchmark. Using 289 annotated 3D FIB-SEM crops, we establish a 32-class segmentation benchmark with stratified train, validation, and test splits, and evaluate all the methods under the same training and inference settings. Across controlled ablations, the proposed combination of repeat-factor sampling, Tversky-BCE loss, and masking achieved the strongest rare-class performance, increasing rare-class mean Dice (mDice) from 0.3244 under uniform sampling to 0.3409. This corresponds to an absolute gain of +0.0165 mDice and a 5.1% relative improvement, while preserving comparable performance on common classes. Overall, we find that sampling, loss design, and masking contribute as much to performance variation as the choice of architecture, highlighting the importance of training-recipe design alongside model architecture in vEM organelle segmentation.

Optical fluorescence microscopy enables visualization of biological structures and dynamics. However, the intrinsic diffraction limit, especially axially, and depth-related scattering noise compromise the image resolution and fidelity. Computational 3D deconvolution is a promising approach for mitigating these issues, yet its execution is hindered by inaccurate and cumbersome theoretical modeling or experimental measurement of 3D point spread function (PSF), as well as ineffective 3D noise regularization. Furthermore, in the 3D super-resolution regime, there remains a lack of standardized tools for evaluating 3D super-resolution fidelity. Here, we present the 3D adaptive deconvolution and evaluation (3D-ADE) toolkit, which comprises 3D-Ada deconvolution with physics-oriented automatic 3D-PSF calibration, and 3D-SQUIRREL for 3D super-resolution quality assessment. It effectively resolves noise instability, eliminates the need for 3D-PSF calibration, and reliably assesses the fidelity of 3D resolution extension via deconvolution, physical, and deep-learning-based methods. Accessible via multiple software platforms, 3D-ADE enhances the versatility of 3D deconvolution and fills the gap in 3D super-resolution evaluation tools, and thereby advances volumetric fluorescence imaging applications.

Modern fluorescence and multiphoton microscopy workflows operate within a heterogeneous ecosystem of file formats, partially overlapping metadata standards, and reader-specific conventions. In practice, this frequently leads to silent axis misinterpretations, loss or corruption of physical voxel size information, and laboratory-specific glue code that is fragile, poorly documented, and difficult to reproduce. OMIO, short for Open Microscopy Image I/O, addresses these issues by providing a lightweight, policy-driven image I/O layer for Python that enforces a canonical, OME-compatible data representation at the API boundary. The central contribution of OMIO is the explicit separation of low-level format access from semantic normalization. Existing reader libraries are used as interchangeable backends for extracting pixel data and available metadata, while OMIO enforces axis conventions, metadata interpretation, and fallback decisions in a centralized and auditable policy layer. This design allows heterogeneous microscopy inputs to be converted into a stable representation without propagating backend-specific assumptions into downstream analysis code. The core design principles of OMIO include canonical axis semantics (TZCYX), robust metadata normalization with explicit and auditable fallbacks, memory-aware operation via optional Zarr-based backends, and workflow-level semantics that extend beyond individual files to folder stacks and BIDS-like project structures. This architecture allows OMIO to orchestrate existing reader libraries into a coherent and reproducible I/O pipeline without replacing or duplicating their functionality. OMIO is implemented as an open-source and community-oriented system in which support for additional file formats and metadata conventions can be added incrementally through modular reader backends. By encouraging the contribution of example datasets, backend extensions, and feature requests, OMIO is designed to evolve alongside emerging acquisition systems while preserving strict semantic guarantees at the interface level. The resulting standardized OME-TIFF outputs are immediately suitable for downstream quantitative analysis and interactive inspection in scientific Python workflows, including workflows based on ImageJ and Napari.

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables the acquisition of diffraction patterns at every probe position in a dense array. For imaging applications this approach offers significant benefits in terms of spatial resolution and contrast enhancement. In this work, we present the development of a synchronous scan generator integrated with SerialEM software to enable automation of complex experimental protocols such as tomography. The proposed hardware functions as an interface between SerialEM, the scan controls of the microscope, a fast annular dark-field detector, and a synchronized trigger for a pixelated detector. Our previous implementation, named SavvyScan, relied on a dedicated computer equipped with a multichannel acquisition and signal-generation cards, as well as a separate microcontroller for synchronization. Here, we report a low-cost implementation based on a Red Pitaya board, utilizing direct programming of its embedded FPGA and Linux server components. We provide detailed instructions for system installation and operation, along with practical guidance for modifying the source code. System performance is validated through oscilloscope measurements and imaging of a replica grating sample. The utility of the approach is further demonstrated by generating a 3D electron tomogram of a cryogenic sample of mitochondria from a tilt series of shadow montage projections.

Coherent structured illumination microscopy (c-SIM) is a synthetic aperture optical technique for sub-diffraction limit imaging that extends the utility of traditional SIM to non-fluorescent samples. Here, we present a complementary 5-beam implementation of c-SIM that provides enhanced optical sectioning compared to conventional quadrupolar illumination. Since our approach detects intensity images due to coherent light scattering, it avoids the complications associated with detecting complex fields. Through comparative measurements on calibration samples and live microalgae, we show that 5-beam c-SIM effectively suppresses coherent defocus effects, improving image quality while simultaneously providing a 2-fold lateral resolution improvement.

Deep learning neural networks provide a powerful approach for segmenting low-contrast cryogenic electron microscopy (cryoEM) images. However, model performance can vary significantly across imaging conditions and may hinder downstream quantitative analyses. Here, we present a structured evaluation workflow to systematically screen segmentation models based on performance, inference speed, robustness across imaging conditions, and reliability of downstream quantitative measurements. Using the Bacterial Cell Envelope Thickness Tool (BCET) as a test case, we evaluate multiple architectures (YOLOv11, YOLO26, U-Net, Detectron2, and SAM3) under low-dose and ultralow-dose cryoEM conditions. While several models achieve high metrics, model choice strongly influences downstream measurements of envelope thickness. Models optimized for high F1-scores may produce unreliable segmentation masks from object crowding, interpolation artifacts or imaging conditions. Our results reveal distinct trade-offs between performance, speed, and robustness amongst models. YOLOv11 provides the highest fidelity membrane segmentation for quantitative measurements and the Meta-based model SAM3 offers improved robustness under ultralow-dose conditions with competitive inference performance. This work provides practical guidance for model selection in cryoEM workflows, emphasizing that optimal choice depends on experimental priorities and downstream analysis requirements rather than metrics alone. These findings are broadly relevant to cryoEM workflows as AI-based analysis expands beyond the biological sciences. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/730486v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@f29df4org.highwire.dtl.DTLVardef@601d6eorg.highwire.dtl.DTLVardef@2c5023org.highwire.dtl.DTLVardef@1413f76_HPS_FORMAT_FIGEXP M_FIG C_FIG