Cytometry Part A

Reliable delineation of the head and tail of swine spermatozoa supports automated assessment of boar semen quality, from morphometric measurement to the quality control of insemination doses. In practice this relies on fluorescent staining, which adds chemistry, cost, and delay to every acquisition and labels only the nucleus. Recent work coupling imaging flow cytometry with machine learning has advanced rapidly, yet the segmentation stage still depends on a stained channel at inference and resolves the head alone. We present a supervised encoder decoder network that segments boar spermatozoa from brightfield images acquired on an Amnis ImageStream Mark II with no stain at inference. Training labels derive from the Hoechst 33342 nuclear channel (Ch7), recorded in registration with brightfield (Ch1); the dye serves only as an annotation source, and the network sees Ch1 alone. The best semantic segmentation model reaches a Dice coefficient of 0.940 on held-out cells. For comparison we evaluate a classical morphological pipeline, four further semantic segmentation models spanning three decoder families and two ImageNet-pretrained backbones, and two zero-shot pipelines built on the Segment Anything Model 2 (SAM 2), prompted either by a dilated box around the predicted head mask or by head and tail boxes emitted by a Gemma 4 Vision Language Model (VLM). The zero-shot route scores 0.637 against Ch7 but labels the tail, which the fluorescence protocol cannot. Cells scoring worst under the supervised model proved to be mostly registration failures rather than segmentation failures, as Ch7 is displaced relative to Ch1. Manual screening for this drift is infeasible at dataset scale, so we propose a flagging system that marks any Dice below 0.792, two standard deviations below the mean, and pairs it with a zero-shot pipeline in which a VLM l and SAM 2 cross-check the flagged cell before human review.

Hematologic diagnostics and especially cytomorphologic assessment are time-intensive and require high levels of expertise. Vision Language Models (VLM) show promise in medical image analysis in radiology and histopathology, while an evaluation on detecting acute myeloid leukemia (AML) is lacking. Our goal was to evaluate three Vision Language Models regarding their diagnostic accuracy and safety in clinical decision support in detecting AML from digitized bone marrow smears (BMS). Whole slide images were obtained from bone marrow smears of 50 AML patients and 50 bone marrow donors. Ten representative fields of view per sample were extracted manually. Three VLMs were used, two of which are considered generalist models (Qwen3.5-397B-A17B-FP8, GLM-4.6V-FP8), while the other one is a medically adapted model (Medgemma-27b-it). All models performed zero-shot analysis using two prompting strategies: First, a context-rich prompt requesting reporting of WHO/FAB diagnostic criteria in a structured manner, and secondly a minimal prompt without specific hematologic context. Overall diagnostic accuracy was poor for all models as they exhibited the overwhelming tendency to classify most samples as leukemic: With context-rich prompts, GLM4.6 identified 90% of leukemic samples while also labeling 92% of bone marrow donors as AML. The medical specialist model MedGemma-27b showed similar failure, misclassifying 86% of healthy donors and correctly detecting AML in only 66% of cases. Qwen3.5 performed best under detailed prompting, achieving a specificity of 0.26 and accuracy of 0.51. Accuracy of all models improved with context-free prompts (accuracies range 0.47-0.79), yet they still lacked the ability to correctly distinguish between leukemia and healthy bone marrow. Qwen3.5 was the only model to maintain meaningful specificity (0.64) and correctly identified 94% of AML, yielding an overall accuracy of 0.79. Morphologic feature-level agreement with human expert reports was poor across all models, indicating poor recognition of cell-level morphologies. This failure is likely driven by the fact that pathology imaging archives are vastly scraped during model training while hematological samples are not as widely available and therefore, hematology is an out-of-bounds use-case for these models, rendering them currently unsuitable for clinical decision support in hematology.

In cytometry, the workhorse single-cell technology of clinical immunology, every study defines its own antibody panel and cell-type vocabulary, so a classifier trained on one cannot annotate the next. Immunologists instead annotate by manual gating, splitting one parent population at a time on a two-marker plot, down an expert-defined hierarchy. We introduce CytoGate-Bench, a benchmark that reformulates this per-step procedure as a zero-shot, panel-agnostic task for large language models. It comprises 23,646 expert-annotated instances re-curated from 11 public flow- and mass-cytometry cohorts spanning eight marker panels. Across six open- and closed-weight backbones, the strongest formulation draws one rectangular gate per candidate and falls within the range of trained, panel-specialized baselines. It degrades less under distribution shift. Walking the hierarchy stepwise outperforms predicting every cell type at once. Ablations trace the signal to the data distribution shape and curated marker priors. However, adding vision or a self-verification loop systematically tightens gates.

SignificanceRare-cell identification in fluorescence microscopy remains challenging because targets are sparse and background varies between specimens. Combining specimen-specific fluorescence enrichment with image classification may enable efficient and more specific automated detection of rare cells. AimWe developed a two-stage framework to identify and quantify candidate rare circulating hybrid neoplastic cells (CHCs, ECAD+/CD45+) in peripheral blood mononuclear cell (PBMC) preparations from tumor-bearing and tumor-naive mice. ApproachPBMCs from 28 mice were imaged by multichannel fluorescence microscopy. Matched unstained samples established animal-specific ECAD and CD45 background distributions for candidate cell enrichment. Blinded multi-annotator consensus labels were used to train a convolutional neural network (CNN) from DAPI, ECAD, and CD45 image crops. Generalization was evaluated by leave-one-animal-out validation across 10 random initializations. Final classification used a 10-model ensemble, and rare-cell burden was compared between groups using negative-binomial regression with total segmented-cell count as an exposure. ResultsOf the 1,065,512 segmented cells, enrichment retained 10,176 candidates (0.96%), reducing the search space by >99%. Four of five evaluable tumor-bearing animals showed reproducible held-out discrimination, with median quantified area under the receiver operator characteristic curve (AUROCs) of 0.918-0.951; one animal was a reproducible outlier (median AUROC, 0.338). Ensemble deployment identified 157.94 positive-consensus cells per 50,000 segmented cells in tumor-bearing animals versus 49.55 in controls. The estimated rare-cell rate was 3.15-fold higher in tumor-bearing animals (95% CI, 0.91-10.99; two-sided p=0.071; prespecified one-sided p=0.036). ConclusionsSpecimen-specific fluorescence enrichment combined with supervised image classification reduced the cellular search space and enabled automated quantification of a rare CHC (ECAD+/CD45+) phenotypes. Cross-animal validation also identified specimen-specific generalization failure, highlighting the importance of biological-specimen-level validation.

The emergence of bispecific antibodies (BsAbs) targeting T cells (CD3+) and tumor plasma B cells (BCMA+) has provided a new therapeutic option for patients with relapsed/refractory multiple myeloma cancer. However, responses to CD3xBCMA BsAb therapy remain heterogeneous, and treatment is associated with frequent immune-related adverse events. Although baseline immune characteristics have been associated with clinical outcomes, little is known about the early immune dynamics induced by this therapy. Here, we investigated whether longitudinal clinical monitoring and high-dimensional profiling of blood circulating T cells could identify early biomarkers of response or toxicity during treatment. Our results indicate that all treated patients exhibit an early depletion of circulating T cells associated with T-cell activation within the first two weeks. Integration of clinical and immunological parameters using Factorial Analysis of Mixed Data (FAMD) identified immune features associated with treatment outcome. Responders had lower plasma soluble BCMA concentrations, fewer bone lesions, higher circulating lymphocyte counts at baseline. During the first days of treatment, responders exhibited a more pronounced increase in plasma CXCL10 levels, associated with a greater decrease in T lymphocyte counts. Overall, our findings suggest that integrating clinical and immune parameters measured during the first days of treatment may enable early patient stratification and support the development of a predictive score to identify patients with multiple myeloma who are most likely to benefit from CD3xBCMA BsAb therapy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/743749v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1cb079org.highwire.dtl.DTLVardef@1860106org.highwire.dtl.DTLVardef@ad36d3org.highwire.dtl.DTLVardef@1ea5c1e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIntegrated clinical and blood T-cell immune profiling using FAMD enables patient stratification following CD3xBCMA BsAb therapy. C_LIO_LIT-cell immune activation occurs predominantly within the first two weeks of therapy. C_LIO_LIFirst-week clinical and immune parameters identify patients most likely to benefit from therapy. C_LIO_LIHigh CXCL10 levels, a profound early decline in circulating T cells, low sBCMA levels, and fewer bone lesions are candidate predictive markers of treatment response. C_LI

BackgroundTraditional immunohistochemistry (IHC) with chromogen detection has limited multiplex capacity, detecting at most 4 protein markers per tissue section simultaneously, thereby restricting comprehensive spatial analysis of valuable human biopsies. We developed and validated a robust serial IHC (sIHC) staining method to detect multiple antigens on a single kidney biopsy slide, maximizing data yield for diagnosing and studying complex kidney diseases. MethodsFormalin-fixed, paraffin-embedded kidney biopsy sections were subjected to repeated IHC/imaging cycles with antibody removal using an optimized sodium dodecyl sulfate-glycerol buffer stripping protocol. Images were then co-registered, and analysis was performed using a variety of methodologies, including color deconvolution, cell segmentation, and spatial clustering. ResultsThis optimized sIHC method successfully detected up to 20 antigens on a single slide. Combining image analysis and artificial intelligence software, for example with HALO (Indica Labs), the assay assembles high-dimensional images and enables quantitative histology and single-cell spatial analysis. Using this advanced method, we were able to identify rare cell populations, such as double-negative T cells, that are challenging to detect conventionally. ConclusionWe have developed a validated, high-capacity sIHC protocol that uses standard IHC procedures with commercially available, clinically validated off-the-shelf antibodies. This method is a valuable, cost-effective tool for obtaining extensive, high-dimensional single-cell-resolved spatial data from limited pathology samples, such as a human kidney biopsy.

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.

Pre-infusion cerebrospinal fluid (CSF) proteomics may enable risk stratification for immune effector cell-associated neurotoxicity syndrome (ICANS) after chimeric antigen receptor T-cell therapy, but disease-specific baseline variation may influence biomarker interpretation. We compared pre-infusion CSF proteomic profiles from 28 patients with diffuse large B-cell lymphoma (DLBCL) and 9 with multiple myeloma (MM). Although principal component analysis showed substantial overlap, orthoPLS-DA identified significant disease-associated discrimination supported by permutation testing. Proteins contributing to this separation were enriched for plasma cell-related, extracellular, and metabolic signatures. ICANS occurred in 7 of 28 DLBCL patients but in none of the 9 MM patients. MM cases aligned with the ICANS-negative group in binary analysis while remaining distinct from both DLBCL subgroups in three-group analysis. These findings indicate that pre-infusion CSF proteomics captures disease-specific molecular structure that should be considered when developing and interpreting biomarkers of CAR-T-associated neurotoxicity.

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

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

The erythrocyte sedimentation rate (ESR) is one of the most common and widely used laboratory diagnostic parameters in connection with inflammatory reactions and it is probable that every reader has already experienced a determination of their ESR. A rapid ESR is a non-specific parameter that provides information about the inflammatory process. Although the origins of this methodology date back to antiquity, the description of the process as the collapse of a percolating gel formed from erythrocytes has only recently been achieved. It was not yet known whether slow ESR has any medically relevant significance. Here we show a variety of clinical pictures that exhibit a systematically slow ESR (e.g., sickle cell disease, neuroacanthocytosis syndromes, chronic mountain sickness). Using a combination of measured data and physical modelling, we show how the accuracy and significance of ESR data can be increased. With this improved ESR (supraESR), we introduce a completely new, cost-effective diagnostic parameter, based on an established and easily automated measurement method, that enables low-cost screening for neuroacanthocytosis syndrome, a group of rare neurodegenerative diseases previously detectable only through complex diagnostic tests.

Advances in immunophenotyping of tumour-infiltrating lymphocytes (TILs) have improved our understanding of prognostic biomarkers and immune targets in human melanoma. However, whether the tumour-immune landscape in canine oral malignant melanoma (COMM) is concordant with that of human melanoma has not been properly defined. To address this gap, we developed a 16-colour spectral flow cytometry panel to characterise TIL phenotypic and functional profiles in COMM. Validation using mitogen-stimulated peripheral blood mononuclear cells from healthy dogs (n = 5) demonstrated robust identification of major T cell lineages, including regulatory T cells (Tregs) and memory subsets, and reliable evaluation of their activation and exhaustion status. COMM patients (n = 8) displayed a distinct protumour microenvironment, characterised by an increased proportion of Tregs, enrichment of tumour-specific exhausted-like T cells co-expressing programmed cell death protein 1 (PD-1) and tumour necrosis factor receptor 2 (TNFR2), and a reduction in cytotoxic CD8 and natural killer T (NKT) cell populations compared with tissue-resident (n = 4) and circulating (n = 8) lymphocytes. Analysis of additional solid tumours, including a mast cell tumour, nerve sheath tumour and adrenal cortical carcinoma, further supported the capability of the panel to identify similar patterns of immune dysregulation across diverse canine tumour landscapes. Collectively, this work describes the first detailed evaluation of canine TIL immunophenotyping using spectral flow cytometry and provides insights into the immunosuppressive mechanisms shaping the tumour microenvironment in COMM. These findings not only increase our understanding of canine tumour immunology but also identify potential immune targets and support ongoing comparative immuno-oncology efforts.

Uniform, reproducible blood smears are critical for reliable hematological evaluation. Manual smear preparation, however, is user-dependent and introduces variability that limits quantitative microscopy. Here we developed BUsmear, a low-cost, 3D-printed, motorized blood smear device that prepares two smears simultaneously from a printed stage and a micro-motor drive with tunable linear velocity, controlled through a joystick-operated driver module. By spreading two slides in parallel with fully repeatable slide-to-slide motion, the device doubles throughput while eliminating operator-dependent motion artifacts, and can be fabricated on any benchtop 3D printer in under one day. To validate smear quality, we analyzed blood films from three donors together with a manual smear prepared by an expert from the blood of one of the same donors, giving a matched device-versus-manual pair. Automated Cellpose segmentation of 14,046 red blood cells across 12 bright-field fields showed that cell diameter was preserved and closely matched the expert smear (6.7-7.8 um across groups, within the 6.2-8.2 um human reference range; 6.7 vs 6.9 um in the matched pair), and that all films formed non-aggregated monolayers (Clark-Evans index of aggregation 1.04-1.24). Critically, red blood cells in the device films were markedly more circular than in the expert manual smear (mean eccentricity 0.405 vs 0.502; 0.443 vs 0.502 in the matched pair), with complete separation between the two methods at the level of whole fields of view. Because eccentricity reports smear-induced cell distortion, this indicates that a constant, mechanically controlled spreading velocity preserves red blood cell morphology better than skilled manual technique. BUsmear offers an accessible route to standardized smear geometry for quantitative analysis, including AI-based morphometry, and its low cost may be particularly advantageous in low-income countries with a high prevalence of malaria.

Accurate, reproducible interpretation of kidney allograft biopsies is critical for diagnosis of graft injury to guide prognosis and management. The international Banff classification is a consensus diagnostic system based on semiquantitative histological lesion scoring on either extent or severity of kidney transplant biopsies. However, pathologist scoring is limited by substantial interobserver variability, constrained scalability, and the inherent nature of the scoring system itself. Here we present BanffNET, a weakly supervised, probabilistic deep learning framework that combines self-supervised feature extraction with a novel Bayesian multiple-instance learning framework to predict (continuously) the full spectrum of Banff lesion scores directly from whole-slide images (WSIs). Using lesion-specific aggregation functions tailored to localized (modeling lesion severity) and diffuse pathologies (modeling lesion extent), BanffNET generates interpretable, patch-level probability maps and calibrated slide-level scores. BanffNET's performance was assessed relative to consensus, biological correlates of rejection and clinical outcome, demonstrating superior consistency, transportability and generalization. Trained on 7,249 WSIs from three cohorts, BanffNET demonstrates consistent performance on 11,028 WSIs across five external test sets, performing on par or exceeding expert consensus across lesions. BanffNET scores align more closely than pathologist Banff scores with molecular profiles of rejection, offering a transparent, biologically grounded framework for computational pathology with relevance beyond transplantation.

Background: Blood culture-negative infective endocarditis (BCNIE) poses a significant diagnostic challenge. This study evaluated a multimodal diagnostic algorithm combining serological and molecular methods at the Argentine National Reference Laboratory. Methods: A prospective analysis was conducted on 53 consecutive patients with suspected BCNIE referred between January 2019 and December 2024. The diagnostic workflow included indirect immunofluorescence for Bartonella spp. and Coxiella burnetii, species-specific PCR for Bartonella spp. and Tropheryma whipplei, and broad-range 16S rRNA PCR with Sanger sequencing on available blood and valvular tissue specimens. Results: An etiological diagnosis was established in 17 of 53 patients (32.1%). Bartonella spp. was the predominant pathogen (47.1%; 8/17), followed by T. whipplei (35.3%; 6/17) and Streptococcus spp. (17.6%; 3/17). All Bartonella cases were initially detected via serology, with molecular confirmation achieved exclusively through valvular tissue analysis. Conclusions: Implementing a standardized multimodal diagnostic algorithm significantly enhances etiological yields in BCNIE. The findings emphasize the complementary value of frontline serology and targeted molecular testing, highlighting that simultaneous submission of serum, blood, and valvular tissue is essential for optimal diagnosis.

Multiple myeloma (MM) is a plasma cell malignancy that frequently harbors activating mutations in NRAS and KRAS oncogenes. Previous clinical trials targeting the Ras/MAPK oncogenic pathway with MEK inhibitors (MEKi) were met with limited efficacy, and newer generation of Ras inhibitors (RASi) have not been specifically evaluated in MM patients. To investigate the vulnerabilities of Ras-mutant MM to targeted therapies, we examined the sensitivity of a panel of human MM cell lines to the RASi RMC-6236 (daraxonrasib) and the MEKi trametinib. Although Ras-mutant MM cells are responsive to oncogenic Ras signaling and are sensitive to RAS inhibition, their sensitivity to MEK inhibition is heterogeneous. Mechanistic studies revealed that c-Myc protein is destabilized by MEK inhibition only in MEKi-sensitive MM cells but not in MEKi-resistant cells, and pharmacological and genetic stabilization of c-Myc is sufficient to confer MEKi resistance. In contrast, Ras inhibition reduced c-Myc protein across all MM cell lines tested, regardless of their dependency on the MAPK pathway, and c-Myc expression was insufficient to promote RASi resistance. Together, these findings demonstrate that c-Myc protein stability differentiates the response of Ras-mutant MM cells to Ras and MEK inhibition, and suggest that direct targeting of the Ras oncoprotein, rather than its downstream MAPK pathway, may present a more effective strategy.

Stain normalization reduces color variability in histopathology whole-slide images, but cohort-scale pipelines lack fused multi-image batch transforms for classical methods. We present StainX, a GPU-accelerated batch stain normalization framework built around a two-stage fit/transform interface. It implements histogram matching, Macenko, and Reinhard normalizers through a portable PyTorch backend and an optional CUDA backend that fuses per-pixel operations for batch throughput. On NVIDIA GPUs, the fused CUDA path outperforms the torch CPU backend by 168x, 70x, and 48x for Reinhard, histogram matching, and Macenko respectively, and exceeds the fastest GPU peers by 7-8x (Reinhard) and 2x (Macenko) at comparable accuracy. StainX also provides user-selectable precision modes, a documented Python API, continuous integration testing, and online documentation. Source code available at https://github.com/rendeirolab/stainx, and documentation at https://stainx.readthedocs.io. Implemented in Python. Runs on Linux, macOS, and Windows.

Three-dimensional (3D) spheroid models have become essential in cancer biology, drug screening, and tissue engineering. However, their small size, fragile structure, and tendency to disintegrate during routine histoprocessing present persistent technical challenges. Conventional paraffin embedding often results in tissue fragmentation, loss of spatial orientation, and poor section quality, whereas cryosectioning often compromises cellular morphology. Here, we present a robust, cost-effective protocol for preserving and sectioning fragile 3D spheroids, resulting in high-quality histological sections with intact architecture and excellent cellular detail. The method involves optimized handling and embedding procedures that stabilize spheroids during standard formalin fixation, paraffin infiltration, and microtomy, eliminating mechanical distortion and preserving spherical integrity for consistent sectioning. We demonstrate successful application across different cell line spheroids, with subsequent compatibility with hematoxylin and eosin (H&E) staining protocols. Compared to conventional methods, our approach significantly reduces sample loss, improves inter-section reproducibility, and preserves fine structural features such as necrotic cores, proliferative zones, and extracellular matrix components. This protocol provides a reliable, accessible solution for routine histological analysis of fragile 3D spheroids, facilitating more accurate morphological and molecular assessment in translational research settings. Key featuresO_LIMaintains spheroid integrity: Prevents mechanical distortion, fragmentation, and loss of spatial orientation during processing. C_LIO_LISignificantly reduces sample loss: Decreases failure rate compared to traditional methods, conserving valuable samples. C_LIO_LIBroad spheroid compatibility: Works effectively with primary tumor-derived, stem cell-derived, and co-culture spheroid models. C_LIO_LIEnables high-quality sectioning and staining: Delivers consistent, reproducible sections that are fully compatible with H&E, IHC, and IF. C_LI Graphical overview O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/743094v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1670c4org.highwire.dtl.DTLVardef@145810aorg.highwire.dtl.DTLVardef@1accb1org.highwire.dtl.DTLVardef@17481c0_HPS_FORMAT_FIGEXP M_FIG C_FIG

Spatial transcriptomic and proteomic technologies provide complementary insights into tissue organisation, cellular phenotype and function, yet integrating these modalities on the same tissue section remains technically challenging. Sequential workflows must preserve RNA integrity, antigenicity and tissue morphology while maintaining accurate spatial registration. At present, publicly available multimodal datasets suitable for computational method development remain limited. Here, we present a workflow for sequential 10x Genomics Xenium spatial transcriptomics, COMET cyclic immunofluorescence, and haematoxylin and eosin (H&E) histological staining on the same formalin-fixed paraffin-embedded tissue section. We demonstrate this approach across multiple biologically distinct human tissues, including tonsil, hepatocellular adenoma, and matched tumour and non-tumour hepatocellular carcinoma, illustrating the widespread applicability of the workflow beyond a single tissue type. Following image registration, Xenium-derived cell segmentations were applied to protein images to generate integrated single-cell transcriptomic and proteomic measurements for downstream analyses. To facilitate community reuse, we publicly release four representative aligned tissue cores together with transcript coordinates, multiplex protein images, H&E images, cell segmentations, and integrated single-cell datasets. We additionally introduce UnumLocalia, an open-source visualisation and data extraction tool that enables interactive exploration of aligned multimodal images, supports user-defined cell segmentation, and allows export of integrated single-cell data for downstream analyses. Together, this technical protocol, workflow, software, and openly available dataset provide a reusable resource for multimodal spatial biology, supporting advances in biological discovery, computational method development, multimodal data integration, and validation of emerging analytical approaches across complementary spatial technologies.

MYCN amplification has long been a prognostic marker in paediatric neuroblastoma, yet is typically assayed in bulk, alongside rather than within the heterogeneous tissue architecture pathologists assess. This leaves a gap: MYCN status alone cannot localise MYCN-associated biology, while morphology alone cannot assign molecular risk. Motivated by our finding that the two together identify high-risk cases missed by either, we developed Pheno-MYCN, a weakly supervised framework linking slide-level MYCN prediction to interpretable morphological sub-populations on routine H&E whole-slide images. The aim is not a stronger classifier: prediction probes what MYCN amplification does to the tissue, its evidence open to pathological scrutiny. Across 189 slides, Pheno-MYCN resolved each into phenotypic clusters that expert review mapped to neuroblastoma morphologies. Cell-level profiling revealed MYCN amplification "marked" every sub-population, through a different feature in each: densely cellular yet disorganised tumour with sparser, less diverse networks; chiefly abundance in necrotic and haemorrhagic regions. MYCN-amplified-like tissue was identifiable per slide from these features alone (AUC 0.93-1.00, leave-one-slide-out) and traced as a continuous gradient within tumours. Thus MYCN amplification leaves a concrete, interpretable footprint that can be read and localised on routine H&E, offering a low-cost means to flag and map it where molecular testing is limited.