Journal of Microscopy

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.

Due to recent technological advances, in situ structural cell biology is becoming a high throughput microscopy technique as all the steps of the workflow, from sample preparation to data analysis, are executed faster, more reliable and more reproducible. Sample thinning by cryoFIB-SEM is an essential tool in preparing electron transparent lamellae of biological specimens suitable for further characterization by cryoET. Modern cryoFIB-SEM instruments can be operated remotely and are capable of automated and unsupervised lamellae preparation. To take full advantage of these developments they need a constant supply of LN2 to maintain cryogenic conditions inside the microscope chamber. Here, we introduce a custom automated LN2 refill system that is compatible with gas cooled cryostages, supports long-term cryoFIB-SEM operations and liberates the user from highly repetitive and manual work. We believe this solution can be utilized with other cryoSEM or cryoFIB-SEM devices requiring N2 gas-flow cooling.

The spatial resolution of optical imaging systems is fundamentally restricted by the diffraction limit. However, in widefield live-cell microscopy, the achievable resolution is further constrained by the specimen motion, which indicates the existence of a fundamental spatio-temporal resolution trade-off between signal accumulation during the full frame integration and the resulting motion blur. To improve the fidelity with which moving objects can be imaged, a quantitative understanding of this spatio-temporal trade-off is necessary. Here, we present a systematic analysis of motion-induced resolution dynamics measured with spectral signal-to-noise ratio (SSNR). We developed a simulation framework which models the image formation of objects undergoing arbitrary motion, to evaluate the degradation of the spatial resolution under translational and rotational dynamics. Our results demonstrate that for translating objects, the spatial resolution is anisotropically reduced as a function of the orientation of the object relative to the motion vector, leading to the spectral signal-to-noise ratio degrading by up to 50% and the resolution by up to 40% for a 90{degrees} change in the motion direction. Furthermore, we show that for rotational motion, conventional radially averaged metrics such as the Fourier Ring Correlation are not able to quantify the effects of angular blur. On the other hand, the SSNR is able to accurately quantify this degradation. These findings underscore the necessity of an object-oriented imaging approach, in which acquisition parameters such as exposure time are tuned to specific biological spatio-temporal characteristics to optimize the trade-off between motion blur and spatial fidelity.

High-resolution microscopy techniques are used across research and industry to analyse biological systems, from biomolecules to subcellular organelles, multicellular models and tissues. As multimodal imaging workflows and quantitative analysis of bioimaging data become increasingly widespread, there is a growing need for materials and methods to calibrate imaging systems and evaluate the fidelity of generated image data. Here, we present three-dimensional microscopy phantoms fabricated using two-photon photolithography from transparent resins that exhibit both broadband visible autofluorescence and Raman scattering across the fingerprint and C-H stretching regions. Suitable for analysis using optical profilometry, the phantoms were dimensionally calibrated with SI traceability using a metrological confocal microscope. Immersible in air and common aqueous imaging media, the phantoms are compatible with a wide variety of optical microscopy techniques, including one and two-photon excited fluorescence and coherent Raman scattering microscopy. We employed a forked wedge design to validate image deconvolution results and a stacked lattice phantom to recover image distortion matrices under realistic biological imaging conditions. We demonstrate the impact of correcting chromatic offsets and axial scaling errors for a representative application: analysis of a cell seeded scaffold using confocal laser scanning fluorescence microscopy. These phantoms provide a versatile platform for calibration, quality control and validation of multimodal imaging pipelines and improved quantitative optical microscopy.

Species-level Candida identification can inform antifungal management, but reliable identification platforms remain inaccessible in many clinical microbiology laboratories, whereas phase contrast microscopy -- a common feature of routine laboratory microscopes -- is widely available. We asked whether this ubiquitous optical tool, combined with a consumer smartphone and deep transfer learning, could provide a feasible low-cost approach for preliminary Candida species discrimination. Fifteen clinical isolates of four species (C. albicans, C. glabrata, C. tropicalis, C. krusei) were collected from a single clinical microbiology laboratory and imaged using a consumer-grade smartphone coupled to a standard phase contrast microscope. Suspensions in human serum were imaged immediately after preparation (T0) and after 2-hour incubation at 37{degrees}C (T2). Pretrained vision backbone architectures were evaluated as fixed feature extractors under strict Leave-One-Strain-Out cross-validation. The best-performing model -- EfficientNet-B0 embeddings with a Linear Support Vector Machine applied to T2 images -- achieved an apparent internally cross-validated strain-level balanced accuracy of 0.833 and an overall strain accuracy of 86.7% (13/15 strains correctly classified). C. albicans, C. glabrata, and C. tropicalis were each identified with 100% recall. Both misclassified strains belonged to C. krusei -- the species with the smallest panel representation (n=3 strains) -- with misclassification attributable to limited strain diversity and suboptimal image quality. These findings demonstrate promising feasibility for preliminary image-based Candida species discrimination from smartphone-acquired phase contrast microscopy images, and support further evaluation in larger, externally validated strain collections.

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.

We introduce a workflow to identify oligomeric structures that are recorded with single-molecule localization microscopy (SMLM) under cryogenic conditions. Typically, these oligomers are assumed to consist of protomers arranged as equilateral two-dimensional polygons and every protomer is labeled with a dye molecule for visualization. Unlike previous work, we consider scenarios in which the sample plane has an unknown orientation relative to the focal plane. Our contribution is a high-precision plane-fitting algorithm to determine the sample plane, combined with geometrical transformations and two circle-fitting algorithms to identify the oligomeric structures. Our simulations on synthetic data demonstrate that the proposed workflow achieves high accuracy in estimating both the unknown tilted plane and the oligomer size.

Understanding the behavior of - and {beta}-cells within intact human islets is essential for elucidating mechanisms of metabolic control in diabetes. Current cell-type identification strategies rely on destructive labeling or on advanced imaging modalities such as Fluorescence Lifetime Imaging Microscopy (FLIM), which provide rich metabolic information but require specialized instrumentation and acquisition protocols. Here we show that structured intracellular intensity patterns derived from endogenous autofluorescence are sufficient to discriminate and {beta} cells in living human islets. Using rotation-invariant Local Ternary Pattern (LTP) descriptors combined with morphological features, we achieve highly accurate classification (AUC = 0.92), improving upon previously reported benchmarks. The resulting framework is lightweight, interpretable, and compatible with standard imaging configurations, enabling accessible and scalable analysis of label-free microscopy data. Interpretability analyses demonstrate that discrimination is driven predominantly by fine-scale intracellular intensity organization rather than global morphology. In the spectral window employed, cytoplasmic autofluorescence is prominently shaped by lipofuscin-rich granules. Consistent with prior reports of higher lipofuscin accumulation in {beta}-cells, the dominant features identified here likely reflect differences in granule abundance and spatial organization between endocrine cell types. These findings indicate that endogenous intensity patterns encode sufficient structural information for reliable /{beta} discrimination, providing a biologically grounded and fully non-destructive framework for the identification of pancreatic islet cell types.

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.

Cryo-electron tomography (cryo-ET) is a powerful approach for visualizing macromolecular structures directly within cells, but its broader application is limited by the difficulty of reliably targeting specific structures for imaging. In particular, capturing small or rare objects within FIB-milled lamellae remains a major bottleneck. Here, we establish fluorescence-guided cryo-FIB milling workflows that overcome key sources of targeting error and enable routine capture of structures across a wide size range. For larger objects (>500 nm), we develop a single step registration-based targeting strategy that combines FIB-milled fiducials with physically grounded depth correction to account for focal shifts arising from refractive index mismatch. For smaller targets (150-500 nm), we implement real-time fluorescence-guided milling on a commercially available FIB SEM instrument with an integrated cryo fluorescence microscope allowing dynamic monitoring and precise termination of milling at the onset of target ablation. Using this strategy, we achieve consistent recovery of lamellae containing the targeted structure, including small single-copy organelles such as centrioles and cilia. Together, these workflows expand the accessible target space for cryo-ET and provide practical solutions for studying cellular structures that have previously been difficult to capture.

Stimulated emission depletion (STED) microscopy is a super-resolution fluorescence imaging technique that achieves high spatial and temporal resolution by exploiting stimulated emission to induce fluorescence depletion (FD) and is expected to have substantial utility for imaging applications using fluorescent proteins. However, the compatibility of fluorescent proteins with STED microscopy systems has been understood primarily through empirical observations, and there is no established methodology for the rational selection of fluorescent proteins for STED microscopy. In this study, we systematically evaluated the compatibility of commonly used fluorescent proteins with STED microscopy systems by measuring FD properties using transient absorption spectroscopy and fluorescence dip spectroscopy, both of which are classified as two-color spectroscopy (TCS). Fluorescent proteins identified as compatible with the STED microscopy system based on the TCS measurements were employed for three-dimensional STED imaging of cellular samples expressing each protein. In all samples, three-dimensional spatial resolution was improved relative to confocal laser microscopy, with particularly marked improvements in z-axis resolution. These findings demonstrate that measurements of FD properties via TCS provide a robust approach for evaluating the compatibility of fluorescent proteins with the STED microscopy system and for selecting suitable fluorescent proteins for STED imaging.

We present an ultrahigh-field magic-angle spinning (MAS) solid-state NMR (ssNMR) study to characterize intact nontuberculous mycobacteria (NTM) at the molecular level. Hydrated and dried whole-cell Mycobacterium abscessus samples were investigated by combining conventional high-field ssNMR at 750 MHz with ultrahigh-field ssNMR at 1.2 GHz and ultrafast MAS at 100 kHz. To improve sensitivity and enable multidimensional experiments, 13C/15N isotope labeling was performed after growth in synthetic cystic fibrosis medium (SCFM). We utilized 1D 13C and multidimensional 1H-13C and 13C-13C ssNMR experiments to characterize the chemical composition, dynamics, and structural organization of the M. abscessus cell envelope. The isotope-labeling efficiency was found to be non-uniform across different molecular classes, with high incorporation into polysaccharides and lower incorporation into lipid and peptide-associated signals. INEPT- and CP-based experiments selectively probed flexible and rigid fractions of the samples, revealing substantial differences in linewidth, dynamics, and sensitivity between hydrated and dried preparations. Conventional 750 MHz experiments provided high-resolution multidimensional spectra and enabled identification of distinct chemical environments associated with peptidoglycan, arabinogalactan, mycolic acids, lipids, and peptide-associated components. Ultrahigh-field ssNMR at 1.2 GHz combined with ultrafast MAS and 1H detection substantially improved spectral resolution and sensitivity in particular per mg of sample amount, allowing detection of weak and previously unresolved resonances, including polysaccharide and possible nucleic-acid-associated signals. Together, these results demonstrate that ultra-high-field and ultrafast-MAS ssNMR enables detailed characterization of intact NTM cell envelopes under near-native conditions and provides a framework for future molecular investigations of antimicrobial interactions.

During forebrain development, inhibitory interneurons and oligodendrocyte progenitor cells migrate long distances into the developing dorsal cortex. Human induced pluripotent stem cell-derived forebrain assembloids (FAs) provide direct experimental access to this migratory process in vitro. Using viral labeling to express yellow fluorescent protein (EYFP) and tandem-dimer tomato (tdTomato) driven by EF1 or SOX10 promoters, respectively, we tracked cells in FAs over 15-17h using spinning disk confocal microscopy. We developed an end-to-end processing pipeline for 4D volumetric imaging data, consisting of background subtraction and drift correction, manual cell coordinate tracking, and an analysis workflow to describe migratory cell behavior. Image preprocessing significantly improved data quality for subsequent manual tracking in datasets with heterogeneous labeling density and brightness. Trajectory analysis of 336 EYFP- and 337 tdTomato-labeled cells from twelve FAs indicates that most cells show super-diffusive directed motility. Our pipeline represents a key resource for cell tracking in FAs and similar three-dimensional platforms. This pipeline represents the first open tracking resource for iPSC-derived FAs and can be used as a ground-truth resource for the development of automated cell detection and tracking algorithms.

Altair-dvOPM is an open-access direct-view oblique plane microscope designed for large-field, three-dimensional imaging of cleared and expanded tissue sections. By combining photographic-lens-based detection, externally launched oblique illumination and precision-registered modular baseplates, the system achieves micrometer-scale lateral resolution over a ~5.4 mm field of view without custom objectives or highly specialized alignment procedures. We demonstrate imaging across scales, from subcellular structures in expanded cells to centimeter-scale expanded tissue sections, and provide documentation, CAD files, Zemax models and open-source control software to support replication and extension.

Different omics types such as genomics and proteomics all contribute to deciphering biology. Applying these omics approaches in a spatial context helps reveal biology in situ at a single cell level. Here we present a protocol for the combined multiplexed detection of targeted genes using DNA FISH, and proteins using multiplexed immunofluorescence. The protocol is integrated on the commercial PhenoCycler platform and generates one single dataset with gene and protein readout at a single cell level in large tissue sections, allowing for a throughput of thousands to millions of cells. The workflow can be used for characterising malignant cells in large tumor areas based on genetic aberrations, while deciphering the cellular landscape and microenvironment from multiplexed protein detection using immunofluorescence.

To properly respond to their environment, cells adjust the activity of key regulatory proteins and rates of gene expression. Methods to detect and quantify these forms of regulatory dynamics in living cells are of central importance for understanding cellular signaling events in both physiological and pathological conditions. Current technologies in this field make use of fluorescent probes to track cell signaling dynamics. Although these technologies have been used for decades, challenges remain. In particular, the segmentation, tracking, and interpretation of single cell dynamic data are time-consuming, prone to subjective errors, and often lacking in standardization across experiments. Here, we present SPIFEE, a data pipeline that uses experiment-dependent parameters to smooth noise and quantify key features of fluorescence data from time-lapse imaging studies. Processing data in this manner enhances and accelerates quantification of live-cell gene and protein expression, simplifies data analysis, and facilitates hypothesis generation. Author SummaryCells adjust protein activity and gene expression levels over time to respond to changes in their environment, a process referred to as cell signaling dynamics. Quantifying cell signaling dynamics in living cells often uses fluorescent probes, such as green fluorescent protein (GFP) and its spectral variants, to track changes in gene expression or protein activity over time. Challenges inherent in analyzing fluorescence data from single cells stem from biological and experimental noise, time-consuming quantification, and subjective errors. To address these challenges, we developed a computational tool called Signal Processing and Integrated Feature Extraction (SPIFEE). The pipeline improves the quality of fluorescence data analysis by reducing noise and extracting signal features in a way that is both intuitive and objective. The pipeline provides more accurate, rapid, and unbiased quantification of time-lapse microscopy data.

Tumor pseudo-progression (PsP) refers to an initial increase in tumor size or the appearance of new lesions. These pseudo-progressive lesions are predominantly composed of infiltrative inflammatory cells, such as macrophages. This phenomenon commonly occurs in patients undergoing radiation therapy or immunotherapy and typically indicates a positive treatment response. However, it often leads to premature treatment cessation due to misinterpretation as disease progression. Non-invasive imaging biomarkers capable of distinguishing pseudo-progression from true progression would greatly aid in treatment decision-making. In our preliminary study, we explored the potential of gadoterate meglumine (Gd-DOTA, a macrocyclic Gd-contrast) in combination with amine chemical-exchange saturation transfer (amine-CEST) imaging to differentiate tumor from radiation necrosis by assessing Gd-DOTA uptake by infiltrating immune cells, such as macrophages. To evaluate whether amine-CEST, in combination with Gd-DOTA, can differentiate macrophages from cancer cells, we incubated them with Gd-DOTA for 30 minutes. Subsequently, the cells were processed, and amine-CEST imaging was performed on a 9.4 Tesla preclinical scanner. Upon treatment with Gd-DOTA, we did not observe a significant change in amine-CEST contrast in F98 cells compared with untreated cells, whereas treated macrophages exhibited a marked decrease (~40%) in amine-CEST signal compared with untreated macrophages. This reduction in signal was attributed to the uptake of Gd-DOTA by macrophages, which notably shortened water T1 relaxation, thereby quenching the amine-CEST signal. Conversely, cancer cells showed no appreciable change in the amine-CEST signal, indicating no Gd-DOTA uptake. Furthermore, to validate that T1 shortening influences amine-CEST signal, cancer cells were also treated with manganese chloride (MnCl2) for 30 minutes. The uptake of MnCl2 by cancer cells similarly induced T1 shortening, as observed in macrophages, resulting in a decrease in the amine-CEST signal from these cells. Next, we performed the amin-CEST imaging on F98 tumor-bearing rats and radiation necrotic rats. Post-injection with Gd-DOTA showed no appreciable change in the amine-CEST contrast in the tumor-bearing rat, whereas a significant decrease in contrast was observed in the radiation necrotic rat. This further demonstrates that no change in the amine-CEST contrast in tumor-bearing rats is due to cancer cells failing to take up Gd-DOTA. The decrease in amine-CEST contrast in radiation-treated rats reflects the uptake of Gd-DOTA by macrophages infiltrating the radiation-necrotic regions. This straightforward imaging approach holds promise for clinical translation. It offers a novel method for characterizing pseudo-progressive lesions and monitoring diverse treatment responses in cancer patients using standard clinical scanners.

BackgroundImaging flow cytometry (IFC) provides a high quantity of single-cell morphological data, yet the field lacks open access tools for designing interpretable, bespoke parameters. In particular, rare and atypical cell populations where well annotated data is limited, are negatively affected. ResultsWe present Flow cytometry Feature Importance (FlowFI), an open-source graphical software for bespoke image parameter design and analysis. FlowFI provides a suite of image parameter options combining data across multiple channels and markers, tailored digital noise reduction (reducing noise resulting from common flow cytometry ultra-high image acquisition modalities), and a scalable, unsupervised feature selection pipeline that allows experimentalists to refine image-derived parameters iteratively, with a novel ensemble subsampling approach that provides robust feature importance scoring. We validated FlowFI using data from a rare and heterogenous bone marrow cell type, megakaryocytes, demonstrating that the tool can successfully identify novel, discriminatory morphological features to improve the purity of selected cell populations and gating strategy. ConclusionFlowFIs core functionalities are interacted with through an intuitive user interface for researchers with options to export data directly to common image and flow cytometry software formats. With this in mind, FlowFI offers a scalable way to both feature design, and feature refinement using a range of approaches to manifold learning, augmented by a data efficient bootstrap subsampling approach for unsupervised parameter recommendations in the big data regime. The software also introduces a new feature selection measures based on common manifold learning methods in the space inspired by the Uniform Manifold Approximation and Projection (UMAP) algorithm and finds performance comparable to existing methods. FlowFI provides a versatile testing ground for future developments in broad and dynamically developing areas of research including single cell analysis, label-free sorting and intra- and inter-cellular interaction analysis, while ensuring interoperability with current research workflows. Desktop installation options as well as detailed documentation can be found at https://github.com/EarlhamInst/FlowFI

For decades, the photon counting histogram (PCH) was used as the sole method to quantify fluorophore numbers in a diffraction-limited focal volume. This technique combines spatial excitation profiles, and the distribution of photon counts to register the photon emission statistics of individual fluorophores. However, this approach has not yet been transferred to widefield fluorescent imaging due to the lack of fast and single photon sensitive camera sensors which can capture the photon emission statistics of a single fluorophore. Here, we explore avenues towards quantitative analysis of the active fluorophore number by leveraging recent advancements in single photon avalanche diode (SPAD) array technology. Binary exposures of a SPAD array can be synchronized with picosecond laser pulses to measure the PCH in a widefield setting. Then, by modeling the statistical relationship between the active fluorophore number and the PCH in a region of interest following a laser pulse, we can perform Bayesian inference of this number. The model is demonstrated experimentally by counting quantum dots and various numbers of fluorescent dye molecules bound to DNA origamis. We find that this method has several important applications in widefield microscopy, including enhanced localization microscopy and constrained fitting of multiple unresolvable fluorescent emitters.

Atomic force microscopy (AFM) enables nanometre-scale, label-free imaging of biomolecules and surfaces under near-native conditions, yet quantitative analysis of AFM data remains limited compared to other bioimaging modalities. This limitation largely arises from the absence of open, automated tools capable of addressing AFM-specific artefacts, data formats, and topographical outputs. Here, we present the latest version of TopoStats, an open-source Python package for automated and quantitative AFM image analysis, developed as a deep-learning enabled advancement of our original TopoStats software to support more complex samples and richer molecular characterisation. The pipeline integrates all key processing stages, including image flattening and noise correction, object detection and segmentation, morphometric feature extraction, and strand tracing with topological classification. Designed for accessibility and reproducibility, TopoStats adheres to the FAIR for Research Software (FAIR4RS) principles and provides configurable workflows adaptable to diverse biological samples. Combining high-resolution AFM and our analysis pipeline allows the quantification of subtle structural changes within a heterogeneous sample set, revealing properties not accessible with other structural biology techniques. We demonstrate the effectiveness of our pipeline to differentiate between plasmids with both different topology and sequence, by extracting meaningful quantitative descriptors that distinguish the samples with statistical significance. Collectively, these developments establish TopoStats as a versatile framework for high-throughput, quantitative AFM analysis, advancing AFM from a fundamentally qualitative visualisation technique toward a quantitative analytical tool.