BMC Methods

The reproducibility of immunohistochemistry in tumor tissue analysis across reference labs remains a persistent challenge. We tested the extent to which an intra-slide calibration technology mitigated discprepencies in inter-laboratory assays of p53 immunohistochemical (IHC) reactions in brain biopsies of glioblastoma (GB), IDH-wildtype. Intra-slide calibration technologies apply a 0-100% concentration scale incorporating primary surrogate and secondary antibodies to generate a standardized curve for DAB precipitation. IHC from GB samples was performed independently by pathology departments from two different hospital laboratories and were digitalized at 40x magnification using Aperio Image Scope software. Feature extraction, including intensity and texture parameters was performed using the EBImage package in R, followed by UMAP dimensionality reduction and DBSCAN clustering analysis. Our results show significant differences in intensity and texture clustering patterns between laboratory tissue samples and intra-slide calibration technology ruler caused by the different laboratories. Intra-slide calibration technology coupled with polynomial regression analysis improved ~90% the data harmonization. Our findings demonstrate a key role for computational pathology using intra-slide calibration technology to enable intra-laboratory consistency and inter-laboratory reproducibility. These advances strengthen the reproducibility of diagnostic assessments and support more objective, data-driven decision-making in neuro-oncology.

High content imaging is being applied to achieve quantitative fluorescence readouts in increasingly complex 3-dimensional (3D) cell culture models such as spheroids and organoids. Compared to conventional 2D assays, 3D assays better represent biological heterogeneity but require more complex sample preparation, 3D imaging and 3D image analysis that can affect the accuracy and precision of such assays. We used spheroids formed from the NRAS-activated melanoma cell line 19161 modified to express an ERK kinase translocation reporter (KTR) as an exemplar 3D phenotypic assay carried out in 96-well plates. The spheroids were treated with the ERK activator TPA and a range of concentrations of the MEK inhibitor Binimetinib. 3D live-cell imaging with sub-cellular spatial resolution was performed using a dual-view oblique plane microscope (dOPM) - a form of single-objective light-sheet microscope - and the experiment was performed separately at 4 different institutes. The results were analysed using an identical 3D analysis pipeline and parameters. We assessed the variation in assay readout using a linear mixed effects model. Random variance at the well level was negligible (SD = 0.0048 relative to range of KTR biosensor readout at reference site of 0.17), indicating low technical noise. Treatment effects were dose-dependent and highly statistically significant compared to DMSO control across all sites (Dunnett-corrected p < 0.001). The range in KTR readout between the minimum (3.5 M Binimetinib) and maximum (100 nM TPA) treatments varied between 59 to 96% relative to the reference site. Measured bias in KTR readout between sites was between 6 and 12% of the range of the reference site. This study quantifies the reproducibility of a 3D live spheroid-based assay employing a fluorescence biosensor requiring readout out at the per-cell level using the dOPM platform and discusses areas where experimental protocol could be improved in the future to further improve reproducibility.

Flow imaging microscopy (FIM) is an important technology for high-throughput characterization of microscopic particles and microorganisms. However, conventional FIM relies on single-plane imaging (SPI), resulting in out-of-focus particles, reduced measurement precision, and incomplete characterization of irregularly shaped objects extending along the z-axis. To address these limitations, a volumetric flow imaging (VFI) framework was developed and implemented on the portable ARTiMiS platform. This approach captures multiple frames along the z-axis and extracts the highest fidelity image for each particle, which can also be used for single image generation with all particles in focus (i.e., all in focus image) and for three-dimensional reconstruction of irregularly shaped objects. Benchmarking VFI with microspheres, live cells (Chlorella vulgaris), and filamentous cyanobacteria demonstrated increased fraction of particles in focus, reduced variability in particle size measurement, and increased resolvability of elongated particles in comparison to conventional SPI on commercially available FIM technologies. For C. vulgaris, VFI-derived size distributions closely matched curated FlowCam measurements without requiring post-processing to exclude out-of-focus particles. All-in-focus image reconstruction enabled simultaneous visualization of particles distributed across multiple depths and consistently resolved a greater proportion of filamentous structures as compared to SPI. For Aphanizomenon sp., Dolichospermum sp., and Planktothrix agardhii, the SPI approach captured only 84%, 61%, and 58%, respectively, of the total filament length resolved by AIF reconstruction. Beyond image-based characterization, VFI enabled estimation of dynamic particle properties such as sinking velocity and mass density. Application of this framework to C. vulgaris cultures revealed distinct mass-density trajectories under nitrogen-replete and nitrogen-deplete conditions, with cell mass density increasing over time under nitrogen-replete conditions and decreasing under nitrogen deprivation. Collectively, these results establish VFI as a next-generation framework for FIM that expands its analytical capabilities beyond conventional morphometric characterization and provides new opportunities for single-cell-enabled environmental monitoring and biomanufacturing.

Germination percentage is an endpoint measure and therefore does not describe when an individual seed begins visible growth or how rapidly its radicle and plumule expand. We developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture. A single industrial camera moved along a 1 m rail and imaged three culture boxes at 1 h intervals for up to 80 h. The archive comprised 1,062 full-frame images and 6,372 seed-level repeated observations under the six-seed field-of-view configuration. A physical grid maintained seed identity through time and enabled individual regions of interest to be extracted. Whole-seed foregrounds were obtained with a pretrained U2-Net, and a masked RGB intensity rule separated newly emerging tissue from the darker hull. For each tracked seed, projected emerging-tissue area and interval growth rate were calculated. Three representative normally germinating seeds first showed measurable tissue at 48 h, yet subsequently followed distinct trajectories: final projected areas ranged from 2,605 to 4,700 pixels and peak interval growth rates ranged from 106.88 to 287.92 pixels h-1. B-1 accumulated 63.71% of its final visible area during 72-80 h, whereas B-3 accumulated 73.51% during 60-72 h. Thus, seeds with the same observed emergence interval can differ substantially in the timing and magnitude of post-emergence expansion. The workflow converts repeated images into biologically interpretable temporal phenotypes and provides a basis for nondestructive studies of rice seed vigor and germination heterogeneity.

BackgroundScratch assays are widely used to study wound closure in vitro, but quantitative image analysis remains constrained by manual variability, proprietary workflows, and tools requiring programming expertise. We developed InVitroGap, a Python-based application with a browser-accessible interface for automated quantification of scratch assay closure from sequential microscopy images. MethodsRCC-ER and Renca cells were seeded in 96-well ImageLock plates and scratched using a WoundMaker device for uniform linear wounds or a 200 {micro}L pipette tip for crisscross wounds. Phase-contrast time-lapse images acquired at 0, 24, and 48 h with an IncuCyte SX5 system were independently analyzed using IncuCyte 2023A Rev2 and InVitroGap. The InVitroGap pipeline combines Gaussian smoothing, gradient-based texture mapping, adaptive percentile thresholding, and morphological post-processing to quantify wound confluence and relative wound density (RWD). Agreement was evaluated using paired comparisons, Pearson and Spearman correlations, Bland-Altman analysis, and mean absolute error (MAE). ResultsInVitroGap measurements closely tracked IncuCyte outputs across both cell lines, with no significant between-method differences (p > 0.05), strong pooled correlations (R{superscript 2} = 0.964 for RWD; R{superscript 2} = 0.983 for wound confluence), and small mean biases (absolute bias [&le;] 1.64%). The tool successfully processed crisscross wounds from brightfield image series, and a complete four-timepoint series was analyzed in approximately 10 seconds, with robust performance across distinct cell morphologies and wound geometries. ConclusionsInVitroGap provides a transparent, computationally efficient, and platform-independent alternative for scratch assay analysis, delivering performance comparable to commercial systems while remaining freely accessible at https://invitrogap.vercel.app/. HighlightsO_LIOpen-source Python tool for automated, platform-independent in vitro scratch assay analysis C_LIO_LITexture-based adaptive pipelines enable robust wound segmentation across cell types C_LIO_LIQuantifies wound confluence and relative wound density from time-lapse images C_LIO_LIStrong agreement with IncuCyte measurements in the tested datasets C_LI

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

O_LIThe effect of some bioactive compounds on living organisms is dependent on their concentration and gradients, as is the case of hormones and signalling peptides, determining cell identity, activity and organism development. C_LIO_LIThere are a handful of methods that allow to produce spatially confined peaks of concentration local application of biochemicals on plants, such as agar blocks and microinjection, but they lack in precision, throughput and/or simplicity. C_LIO_LIWe developed the MicroTron, a microfluidics-based method specifically for filamentous organisms or life cycle stages, like the moss plant Physcomitrium patens protonemata, that serves as a platform for the application of chemicals on single cells and study the cell response. C_LIO_LIWe show how chemical applications could be performed on cells, either on the side or apically with dyes and hormones, targeting the cell wall, cell membrane, cytosol and nucleus. C_LIO_LITreatments could be applied on single filaments and with a precision of up to single cells in optimal conditions. C_LIO_LIThis method could be used to study live responses to chemicals with high spatiotemporal resolution. C_LI

Hydractinia symbiolongicarpus is a powerful model for stem cell research and maintains a population of pluripotent adult stem cells throughout its lifetime. Here we describe a gene expression-agnostic FACS technique to isolate a live cell population from Hydractinia feeding polyps that appear to be stem cells. This technique utilizes only the general cellular component stains DAPI, DRAQ5, Calcein AM, and Pyronin Y. The stem cell population was identified via subtractive gating based on samples whose stem cell populations had been selectively depleted with the DNA-alkylating agent Mitomycin C. To validate the identity of the isolated population, a colorimetric cytological assay capable of simultaneously discriminating between all major Hydractinia cell types in a live-dissociated cell solution was developed using May-Grunwald and Giemsa stains. The isolated cell population was significantly depleted by Mitomycin C administration, had a high RNA content, was proliferative, had a cytological profile that matched that of Piwi1+ stem cells, and was [~]10x enriched with Piwi1+ stem cells compared to whole cell suspension, all of which support the conclusion that the isolated population is indeed comprised of stem cells. This gene-agnostic FACS technique will serve future research into Hydractinia stem cell biology by enabling the use of isolated populations of live stem cells in transplantation, cell culture, and spheroid experimentation, and may serve as a reference for the development of new methods in other cnidarian species.

Pathogenic missense variants in the X chromosome gene UBQLN2 cause amyotrophic lateral sclerosis (ALS), often accompanied by frontotemporal dementia (FTD). As an X-linked gene, UBQLN2 is subject to X chromosome inactivation (XCI), a process wherein one X chromosome in each cell is randomly inactivated to a Barr body throughout the body in females, creating a mosaic of allelic expression in the tissues of heterozygotes. Despite heterozygous females constituting a majority of reported cases of UBQLN2-linked ALS/FTD, and the known influence of XCI on neurological disorders at large, no current disease models account for XCI. Here we report the characterisation of 12 iPSC clones carrying the ALS/FTD-causing p.T487I (c.1460C>T) UBQLN2 variant. These clones, originally derived from 3 heterozygous carrier fibroblast lines, underwent validation of homeostatic Barr body retention. Erosion of XCI in a subset of the lines was correlated with biallelic expression (of both wildtype and mutant UBQLN2), as measured through a novel allele-selective qPCR (AS-qPCR) assay and verified by amplicon-based Illumina sequencing and Sanger chromatogram quantification, enabling selection of iPSC clones best retaining XCI. Together, this UBQLN2 AS-qPCR assay and selected iPSC clones will enable studies of the role of XCI and its skew in female resilience to UBQLN2 p.T487I-linked ALS/FTD and enable development of allele-selective therapies.

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

Imaging Mass Cytometry (IMC) combines metal-tagged antibody labelling with laser ablation mass spectrometry to generate highly multiplexed spatial images of tissue sections. However, the area that can be acquired within a single region of interest (ROI) is limited by hardware and software constraints, requiring large tissues to be imaged as multiple tiled ROIs. Reconstructing these ROIs into whole-slide images requires additional processing, while the proprietary .mcd file format can hinder integration with standard bioimage analysis workflows. Here, we present MCD Stitcher, an open-source Python package for converting .mcd files into OME-TIFF images with automated whole-slide stitching. The tool supports rectangular and polygonal ROIs, accommodates variable pixel sizes between ROIs, and uses memory-aware chunked reading during data ingestion to process large datasets on standard workstations. The generated OME-TIFF outputs preserve spatial, channel, and acquisition metadata for downstream analysis in tools such as QuPath, napari, and ImageJ/Fiji. MCD Stitcher provides a reproducible workflow for converting raw IMC data into interoperable image formats, enabling whole-slide spatial analysis without reliance on vendor-specific software.

BackgroundRecombinant expression of the SARS-CoV-2 receptor-binding domain (RBD) is essential for vaccine development, serological diagnostics, and mechanistic studies. Primary human fibroblasts offer physiologically relevant protein folding and post-translational modification, yet their short lifespan limits scalable production. We used an immortalized human splenic fibroblast cell line to stably express RBD-sfGFP for longitudinal characterization and downstream studies. MethodsImmortalized human primary splenic fibroblasts were transfected by electroporation with a plasmid encoding SARS-CoV-2 RBD fused to superfolder GFP (sfGFP), with a neomycin resistance cassette (neoR) for G418 selection. Four independent G418-resistant cultures (n=4), designated HPSF-IM-RBD-BHSKPU T1-T4, were established from distinct selection flasks. Based on previous screenings, two cultures (T1, T3) were monitored for 98 days (14 passages, P1-P14); two cultures (T2, T4) were monitored for 42 days (6 passages, P1-P6). RBD-sfGFP expression was assessed by fluorescence microscopy at 7-day intervals. For each timepoint, 2 fields were imaged and analyzed for relative fluorescence intensity (normalized to global maximum = 100%) and mean fluorescence intensity (MFI, normalized to global maximum = 100%). Coefficient of variation (CV), linear regression, and Pearson correlation were calculated. ResultsAll four cultures exhibited robust GFP fluorescence, confirming stable transgene retention. Expression ranking: T1 (93.1% +/- 3.6%) > T3 (89.2% +/- 3.4%) > T2 (84.2% +/- 3.2%) > T4 (79.7% +/- 3.9%). Long-term cultures T1 and T3 retained [~]100% of Day 7 signal at Day 98 (T1: 100.7%; T3: 100.0%). Expression exhibited passage-dependent oscillation rather than progressive silencing. CV increased over time in T1 (1.5% -> 8.5%), indicating growing inter-cellular heterogeneity. A strong positive correlation between fluorescence and MFI (Pearson r = 0.823, p = 7.44 x 10-11) suggested coherent population-level regulation. ConclusionsHPSF-IM-RBD-BHSKPU cells stably retain RBD-sfGFP expression for over 3 months, validating their utility as a recombinant protein production platform. However, oscillatory dynamics and increasing heterogeneity are consistent with position-effect variegation at distinct integration loci. Consequently, early passages (P1-P4) are optimal for applications requiring maximal uniformity. Ultimately, these cells provide a practical tool for RBD production and a valuable model for studying epigenetic regulation of transgene expression in human primary fibroblast backgrounds.

Extracellular Vesicles (EVs) are small membrane-bound particles secreted by cells that play key roles in intercellular communication, gene regulation and modulation of cell function. They are involved in both physiological and pathological processes and, due to their ability to transport biomolecules across biological barriers, have emerged as promising tools for use as drug delivery vehicles and biomarkers with diagnostic and prognostic applications. Various methodologies are currently employed for the isolation, characterization, and analysis of EVs, including Ultracentrifugation (UC), Transmission Electron Microscopy (TEM), Nanoparticle Tracking Analysis (NTA), and Flow Cytometry. Flow Cytometry has emerged as a powerful technique capable of providing a multiparametric analysis of individual EVs. Recent advancements have led to the development of cytometers with higher sensitivity and increased limit of detection, enabling the detection and sorting of nanoscale particles--a technique known as Nano-Flow Cytometry. In this study, we show the optimization of small particle sorting, termed nanoFACS, via the CytoFLEX SRT. This method enables sorting based on size or fluorescence, enhancing reproducibility and broadening the potential for application in biological and clinical assays. Furthermore, we demonstrate the utility of nanoFACS in isolating nanoparticles from complex biofluids and in detecting miRNA using molecular beacons (MBs) highlighting its potential in both basic research and translational applications.

Background. Scanning electron microscopy (SEM) is widely used to determine fibrin fiber structural properties such as fiber diameter and fiber length. However, conventional SEM preparation protocols are time-consuming and typically require conductive sputter coating. The coating process introduces an additional layer onto the sample surface and may influence measurements of nanoscale fiber structure. Furthermore, preparation of purified fibrinogen clots often follows protocols originally developed for plasma clots, resulting in unnecessary processing steps. Objective. To evaluate indium tin oxide (ITO) as a flat, conductive substrate for SEM imaging of fibrin fibers, investigate the effects of sputter coating on measured fiber diameter, and develop a simplified SEM preparation protocol for purified fibrinogen clots. Methods. Platelet-poor plasma clots and purified fibrinogen clots were formed on ITO substrates and imaged by SEM following 0 s, 45 s, or 90 s sputter coating. Fibrin fiber diameters were quantified and compared across coating conditions. For purified fibrinogen clots, an ITO-based simplified preparation protocol, in which clots were formed and imaged directly on the conductive ITO surface, was compared with a previously developed, standardized SEM protocol, in which clots were formed in microtube lids and subsequently transferred onto carbon tape for imaging. Results. Fiber diameter measurements were affected by sputter coating duration, with increasing coating time resulting in larger apparent fiber diameters. Plasma and purified fibrinogen clots exhibited distinct fiber diameter distributions and coating responses. For purified fibrinogen clots, the simplified ITO-based protocol produced fiber diameter measurements that were not significantly different from those obtained using the standardized lid-to-carbon-tape workflow when identical coating times were applied. Conclusions. ITO provides a practical conductive substrate for SEM imaging of fibrin fibers and enables substantial simplification of purified fibrinogen clot preparation. When coating conditions are matched, the simplified ITO-based protocol yields fiber diameter measurements comparable to those obtained using the previously standardized lid-to-carbon-tape workflow. These findings support the use of ITO as an alternative conductive imaging substrate and provide a simplified workflow for SEM analysis of purified fibrinogen clots. By reducing washing and transfer steps, this workflow may also provide a useful platform for future controlled studies of fibrin interactions with added proteins or other associated components.

BackgroundInvestigating the subcellular distribution of proteins is crucial for understanding complex cell behaviours and disease mechanisms, and fluorescence microscopy has become a key tool for visualising protein localisation. Endogenous protein tagging, where the sequence for a tag (typically a peptide or fluorescent protein) is integrated into the native genetic sequence encoding a protein of interest, enables proteins to be visualised without the need for antibodies against the target protein. ORANGE (Open Resource for the Application of Neuronal Genome Editing) is a CRISPR-Cas9-based endogenous protein tagging technique which relies on homology-independent targeted integration (HITI)-mediated gene editing. Utilising HITI as the DNA repair pathway of choice gives ORANGE the advantage of being more efficient than classical homology-directed repair (HDR)-based endogenous protein tagging techniques and additionally, means it can be used in post-mitotic cells. ResultsWe applied the ORANGE system to tag three proteins, CYFIP1, JAKMIP1, and STAT3, and confirmed that the expressed fusion proteins demonstrate expected subcellular localisations through fluorescence microscopy. Unexpectedly, the efficiency of ORANGE editing was less than 1% in HEK293 cells, despite high transfection efficiency. To improve the editing efficiency associated with ORANGE, we combined the ORANGE method with an established Sleeping Beauty transposase/CRISPR-Cas9 fusion technique, which has been shown to enhance HITI-mediated gene editing. Using this new method, which we term Sleeping ORANGE, we successfully tagged CYFIP1 with the fluorescent protein mNeonGreen. Importantly, quantitative analysis by fluorescence microscopy and flow cytometry demonstrated an increase in editing efficiency using Sleeping ORANGE, with an approximately 12.85-fold increase in the percentage of mNeonGreen-expressing cells at 72 hours post-transfection relative to populations of cells edited with the ORANGE method. ConclusionsWe have incorporated the DNA-binding domain of the Sleeping Beauty transposase to create a new system that improves the gene-editing efficiency of the ORANGE technique. With further developments to optimise CRISPR gRNA design and reduce off-target effects, the Sleeping ORANGE technique may form a valuable tool for researchers to better understand subcellular localisation and dynamics.

Magnetic field effects (MFEs) in biological systems are typically small and experimentally challenging to measure reproducibly across large sample populations. Existing approaches to measure such effects often rely on low-throughput microscopy or custom-built magnetic stimulation systems that provide limited control over magnetic field geometry, synchronization, or experimental automation. Here, we present an open-source magnetofluorescence imaging platform designed for bacterial plate-scale screening of MFEs in live colonies. The instrument integrates a programmable three-axis vector electromagnet, synchronized fluorescence excitation and imaging, and integrated acquisition software with per-frame metadata logging on a hardware-synchronized data acquisition card. An extensive calibration procedure enables accurate generation of arbitrary magnetic field vectors, while synchronized triggering ensures deterministic alignment between field application, illumination, and image acquisition. The system images an entire 100 mm Petri dish in a single acquisition. Typical experiments monitor hundreds of bacterial colonies simultaneously over multi-hour acquisition sequences. Control software, calibration routines, mechanical design files, and acquisition workflows are provided openly to facilitate replication. Instrument performance is demonstrated through detection of magnetic field-dependent fluorescence changes in E. coli expressing the engineered magnetosensitive fluorescent protein MagLOV2. This instrument provides a flexible and scalable platform for high-throughput magnetobiology, synthetic biology, and quantum biology experiments.

Recent advances in spatial transcriptomics have enabled the profiling of increasingly larger numbers of genes while retaining single-cell and subcellular resolution in situ. However, standardized bioinformatics workflows for analyzing these datasets have lagged behind, with existing pipelines focusing primarily on image processing and cell segmentation. To address this gap, we present nf_xpatial, a best-practices Nextflow pipeline for the downstream analysis of 10x Genomics Xenium data. The pipeline performs quality control, filtering, log and cell area normalization, multi-sample integration, and both expression-driven and spatially informed clustering across systematic parameter sweeps, allowing users to evaluate and compare clustering resolutions and spatial modeling parameters within a single reproducible run. Overall, nf_xpatial streamlines the processing of Xenium data from platform outputs to integrated single-cell and spatial clustering datasets, providing a standardized starting point from which biologists can fine-tune parameters and proceed to hypothesis-driven spatial analyses.

Alterations of fluorescence properties in retinal pigment epithelium (RPE) cells caused by diseases such as age-related macular degeneration (AMD) highlight the need for detailed analysis of the fluorescent RPE granules at the individual level. Precise segmentation and classification of these granules remain challenging due to their limited visual separability. In this study, we present Classi4RPE, a computational algorithm designed to accurately segment RPE granules and classify them into three categories -- lipofuscin (L), melanolipofuscin (ML), and melanin (M) -- based on fluorescence lifetime imaging data, which provide distinctive contrast. The method is implemented in a custom Python framework and employs seeded watershed segmentation to isolate individual granules. Lipofuscin granules are identified as hyperfluorescent structures with longer lifetimes, while granules with shorter lifetimes are further analyzed based on their spatial lifetime distribution from the center to edge, enabling discrimination of ML from other melanin-rich granules. Our approach achieves high performance, with mean sensitivities of 0.99 for L granules and 0.90 for ML granules, and corresponding specificities of 0.93 and 0.98, respectively, compared to manually annotated ground truth. These results demonstrate the potential of Classi4RPE to surpass human visual limitations and provide a robust tool for quantitative RPE analysis.

BackgroundThe quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standardization and being used as a proxy for disease severity. Historically, spore quantification has relied on manual hemocytometry, which remains the most precise counting process to date, where chambers such as the Malassez slide are used to count a subsample of the inoculum. However, this method applied manually is highly labor-intensive, time-consuming, and can be prone to operator-dependent variability. To overcome these limitations, we introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms. Featuring a user-friendly graphical interface, MIRA is adaptable to multiple camera systems and supports advanced object detection models, including YOLOv11 and YOLOv26. ResultsWe demonstrate that MIRA can be used to accurately detect and count spores from several phytopathogenic fungi, automatically measure spore surface area, and to differentiate spores across different genera. In an exhaustive comparative analysis using Pyricularia oryzae spores as an example, MIRA was benchmarked against manual gold-standard counting slides (Malassez and Kova) and indirect spectrophotometric methods (SPARK). The P. oryzae model loaded via MIRA achieved a strong correlation (R = 0.96) with manual gold standards while reducing processing time by over 90% for high-concentration samples (10 spores/mL). Beyond this benchmark, we also successfully tested specific YOLO models designed to recognize macro- and microconidia of Fusarium oxysporum f. sp. cubense, a model for Pseudocercospora fijiensis, and a single multiclass model capable of identifying six different rice pathogenic fungi. We provide comprehensive tutorials for operating the software and training custom detection models for free using Roboflow and Google Colab. MIRA is available both as open-source Python code and as standalone executables for Windows and Linux. ConclusionsMIRA provides a rapid, accurate, and highly reproducible alternative to manual spore counting, effectively removing a major bottleneck in phytopathology workflows. By combining advanced YOLO-based deep learning with an accessible interface and comprehensive training resources, MIRA makes accessible automated image analysis for researchers without programming expertise. Moreover, MIRA drastically improves the efficiency of high-throughput disease phenotyping and can be adapted for a wide range of microscopic quantification tasks across various biological disciplines.

Melanoma incidence continues to rise globally, with formalin-fixed paraffin-embedded (FFPE) tissue archives representing an invaluable resource for large-scale retrospective proteomic studies. However, inconsistent deparaffinization remains a critical pre-analytical bottleneck limiting protein yield, reproducibility, and downstream data quality. In this study, we developed and validated a fully automated FFPE deparaffinization workflow using the Fluent(R) 780 liquid handling workstation (Tecan (C)) and evaluated its performance against a conventional manual protocol in a cohort of 54 patients with primary cutaneous melanoma, predominantly at early AJCC 8th edition stage I-II. The automated workflow achieved superior protein identification (6,146 {+/-} 860 vs. 4,941 {+/-} 1,091 proteins; p < 0.0001) with lower technical variability, while maintaining highly comparable global proteomic profiles as confirmed by principal component analysis and hierarchical clustering. A total of 8,305 proteins (96.1%) were identified by both methods, supporting the reproducibility and equivalence of the automated approach. Patients were stratified by the presence (N=21) or absence (N=33) of histological regression in the primary tumor. Proteomic comparison revealed 97 upregulated and 226 downregulated proteins in regressing melanomas, with pathway enrichment analysis demonstrating elevated mitochondrial and translational activity alongside reduced innate immune and complement pathway activation in the regression group. No statistically significant differences in overall, disease-free, or progression-free survival were observed between groups, consistent with the early-stage composition of the cohort. Digital pathology validated tissue morphology preservation across processing conditions. These findings support the integration of automated FFPE processing with proteomic and digital pathology workflows as a scalable platform for precision melanoma research. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=133 SRC="FIGDIR/small/744404v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@1d51629org.highwire.dtl.DTLVardef@a1f126org.highwire.dtl.DTLVardef@1df1b0aorg.highwire.dtl.DTLVardef@686f1c_HPS_FORMAT_FIGEXP M_FIG C_FIG