Laboratory Investigation

Quantifying vascular architecture in histological whole slide images is needed to study tissue organisation, tumour microenvironment biology, and diseaseassociated vascular remodelling. However, vessel analysis in routine immunohistochemistry remains challenging. Available workflows are often manual, require programming expertise, or lack direct integration with digital pathology platforms. We developed VeSpA (Vessel Spatial Analysis), an open-source pipeline and QuPath extension for automated vessel segmentation and morphometric quantification in CD31-stained whole slide images. VeSpA combines configurable signal extraction, using CMYK Yellow channel extraction by default and optional DAB stain deconvolution for H-DAB images, with automatic or percentile-based thresholding, morphological refinement, contour filtering, and lumen filling to generate vessel masks from standard DAB-stained sections. The QuPath extension includes a graphical interface for selecting annotations, TMA cores, or whole images, configuring segmentation parameters, running the Python backend, and importing vessel objects directly into the QuPath hierarchy. For each detected vessel, VeSpA extracts area, major axis length, minor axis length, eccentricity, centroid, and orientation, while also appending summary measurements to parent annotations and TMA cores. Validation against independent pathologist annotations showed that VeSpA achieved segmentation performance close to inter-rater agreement and outperformed yellow channel prompt-based SAM and zero-shot YOLOv8-seg on overlap-based metrics in the tested dataset. VeSpA integrates vessel segmentation, morphometric feature extraction, and QuPath-based visualisation into a single reproducible workflow for vascular quantification in computational pathology and spatial analysis of histological tissue architecture.

ContextPancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy often diagnosed at advanced stages due to the lack of early clinical symptoms. DNA methylation alterations arise early in PDAC tumorigenesis and may serve as promising biomarkers for blood-based cancer detection. ObjectiveTo evaluate the performance of EPISEEK, a laboratory-developed blood-based multi-cancer early detection (MCED) assay, for detecting PDAC across disease stages. DesignA retrospective cohort study included 97 patients with stage I-IV PDAC and 201 asymptomatic healthy controls. Sensitivity, specificity, area under the curve (AUC), and stage-specific performance were assessed. EPISEEK-MCED performance was also compared with CA 19-9 alone and in combination with CA 19-9. ResultsEPISEEK-MCED classified 65 of 97 PDAC cases as positive, corresponding to an observed sensitivity of 70.1% (95% CI, 60.3% - 78.3%) at 99.5% specificity. The assay demonstrated strong discrimination between PDAC cases and healthy controls, with an AUC of 0.916 (95% CI, 0.88 - 0.952). Sensitivity increased with advancing stage while remaining substantial in early-stage disease, measuring 53.6% for stage I and 65.1% for stage II PDAC, 100% for stage III and 94.7% for stage IV. Across stages, EPISEEK-MCED outperformed CA 19-9 alone, particularly in early-stage disease. Combined analysis of EPISEEK-MCED and CA 19-9 further improved detection performance, achieving sensitivity of 57.1% and 81.4% for stage I and II, respectively. ConclusionsEPISEEK-MCED demonstrated high specificity and sensitivity for PDAC detection across disease stages, including early-stage disease. Combining EPISEEK-MCED with CA19-9 further improved performance, supporting its clinical utility for PDAC detection.

BackgroundClassification of central nervous system (CNS) tumors has become increasingly complex over the past decade, raising concerns about the availability, feasibility and sustainability of comprehensive molecular diagnostics. We have evaluated nanopore whole genome sequencing (nWGS) as a single workflow to replace multiple diagnostic assays. MethodsWe performed nWGS on DNA extracted from 90 adult CNS tumor samples (58 retrospective, 32 prospective) and compared the results to findings from standard of care (SoC) diagnostic work-up. Analysis was done through an automated workflow that consolidated diagnostically and therapeutically relevant genomic alterations, including copy-number variation, structural, and single-nucleotide variants, chromosomal aberrations, gene fusions and methylation-based classification. ResultsNanopore WGS enabled final diagnostic classification in all samples with >15% tumor cell content, requiring [~]3 hours of hands-on library preparation, parallel sample processing, and sequencing times within 72 hours. Methylation-based classification was available within 1 hour and was concordant with the integrated final diagnosis in 89% of cases (80/90). All diagnostically relevant copy-number variations, single-nucleotide variants, and gene fusions were concordant with standard-of-care testing, and MGMT promoter methylation status matched in 94% of cases. In addition, nWGS identified prognostic and potentially actionable variants that were not reported or covered by SoC. ConclusionsNanopore WGS delivers comprehensive genetic and epigenetic results with a fast turn-around compared to standard methods. This enables efficient, accurate, and scalable molecular diagnostics of CNS tumors using a single platform. Its broad applicability supports its implementation in routine clinical practice and may be extended to other cancer types requiring complex genomic profiling.

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.

Selection of regions of interest (ROIs) is often a crucial step in spatial molecular profiling and many pathology tasks, with substantial implications for research reproducibility and biological interpretability. To provide a reproducible and adaptive framework for AI-guided ROI selection, we developed a modular generalist-specialist solution across spatial profiling platforms. In a cohort comprising 55 tumor types from 160 tissue donors profiled using NanoString Digital Spatial Profiling and multiplex immunofluorescence, we first established a protein-profiling reference atlas capturing compartment-specific immune, checkpoint, stromal, and proliferation patterns. We then developed an AI Specialist Task-Oriented Model for ROI Selection (ASTROS) and tested comprehensive benchmarks considering specialist-only (ASTROS), generalist-only (PLIP/GFM), and hybrid generalist-specialist strategies, showing that the latter provides a balanced tradeoff across slide-level signal preservation, pathologist-reference concordance, within-slide placement consistency, and large-slide computational efficiency. We further demonstrated the feasibility of virtual staining for ROI preview and modular ROI placement for other spatial omics technologies, Visium and Visium HD workflows. Together, these results support our proposed framework to enable ROI selection responding to unmet needs for reducing inter-rater variability, reproducibility, and versatility in spatial profiling experiments.

IntroductionMTAP emerges as potential predictive biomarker for MTA-cooperative PRMT5-inhibitors. Although MTAP attracts increasing attention in non-small cell lung cancer, its role in pulmonary neuroendocrine neoplasms (NENs) remains largely unexplored. MethodsHere, we assessed the prevalence of MTAP deficiency in 209 pulmonary NENs using immunohistochemistry (IHC). Additionally, we performed fluorescence in situ hybridization (FISH), whole exome sequencing (WES), deep proteomic profiling, transcriptomic, and methylation analyses of selected MTAP deficient and proficient carcinoids to further elucidate the underlying mechanisms of MTAP expression pattern. ResultsMTAP deficiency by IHC was detected in all neuroendocrine precursor lesions (n=17), 92% of typical carcinoids (n=51), 86% of atypical carcinoids (n=21), and 10% of large cell neuroendocrine carcinomas (LCNEC) (n=30). In contrast, all small cell lung cancers (SCLC) were MTAP proficient (n=90). In MTAP deficient carcinoids, FISH and WES did not detect homozygous 9p21 deletions, and methylation analysis showed no evidence of MTAP promoter hypermethylation. Comparing MTAP deficient and MTAP proficient carcinoids, proteomic data showed a clear separation between the two groups. Further, there was an inverse correlation between the expression of MTAP and OTP (orthopedia homeobox protein), which is known as a strong prognostic marker in pulmonary carcinoids. DiscussionMTAP deficiency is a novel hallmark of neuroendocrine precursors and most pulmonary carcinoids, clearly distinguishing the latter from SCLC and most LCNEC. It is neither caused by 9p21 deletion, nor by MTAP promoter hypermethylation. MTAP deficiency is a group-defining feature of carcinoids that might pave the way for new therapeutic approaches.

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.

For over a century, the goal of estimating clinical outcome from tumor biopsies has been based on histomorphology of 2D tissue slices that represent a small fraction of collected samples. Its power derives from histologys 1) unbiased representation of cell types, 2) subcellular resolution that allows the characterization of health and disease states across cell types, and 3) multi-millimeter fields of view that allow assessment of tumor heterogeneity. Histologys dependence upon physical slices, however, limits assessment of 3-dimensional cellular volumes and tissue architecture. Here, we used propagation-based phase-contrast micro-CT (PBCT) to create 3D histological images of residual formalin-fixed, paraffin-embedded (FFPE) prostate needle biopsies. The resulting isotropic, grey-scale, 0.5 micron voxel matrices were used to explore the potential of for the 3D virtual histology to distinguish diagnostic categories including benign prostatic tissue and prostatic adenocarcinoma of Gleason patterns 3, 4, and 5. Maximum intensity projections of stacks of digital slices totaling 5 microns "slices" allowed the study of virtual sections corresponding to actual serial H&E-stained sections of tissue cut after micro-CT imaging. Like histology, our PBCT reconstructions allowed us to distinguish between non-infiltrative and undulating glands of benign prostatic tissue, infiltrative round glands of Gleason pattern 3, cribriform structures of Gleason pattern 4, and comedonecrosis of Gleason pattern 5. Unlike histology, micro-CT allowed us to further probe 3D tissue architecture in volumetric context. User-friendly exploration of sample volumes was achieved using a customized Neuroglancer multiplanar and 3D rendering interface. Sparsely trained cycleGAN produced plausible virtual H&E staining from the unstained micro-CT reconstructions. Unlike tissue-section based histology, micro-CT-based virtual histology yields nondestructive 3D characterization of cancer cell and tissue architecture, including glandular spaces, without the undersampling or cutting artifacts of histology. These findings demonstrate the feasibility of PBCT-based 3D virtual histology of prostate cancer and suggest the exploration of derived quantitative analyses of tumor properties for potential contributions to patient care.

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.

Objectives. To determine whether medical machine olfaction via tracking the activation of mammalian G-Protein Coupled odorant Receptors (GPCR) stabilized by proprietary co-polymers on a photonic MZI chip can be used to diagnose prostate cancer (PCa) via urine scent. Specifically, scent character is compared against the current diagnostic PCa screening gold-standard in the US: the serum level of prostate specific antigen (PSA). The device is an artificial nose sensor built on a commercial photonic platform that reads interchangeable Mach-Zehnder interferometer (MZI) chips. These chips were functionalized with a stabilised panel of mammalian olfactory G-protein-coupled receptors (GPCRs). These samples had been characterized into POSITIVE or CONTROL for PCa six to eight years prior by standard hospital diagnostic procedures and by trained medical detection dogs, then stored at -80 Celcius. A subset of 80 patients urine samples was subsequently thawed and used for training and testing the medical machine olfaction system of RealNose as an initial validation of the novel technology and methodological approach. We posed two primary research questions: (a) whether the cancer-associated odor profile would remain detectable by machine-based systems following long-term storage and with what accuracy could it be used to cluster (YES and AUC 0.79 from scent character alone), and (b) what technical and procedural requirements would be necessary to translate such a signal into a clinically useful diagnostic assay (more training samples (500 predicted to yield 0.93) and increased breadth of receptors per chip and/or more chips per device in next iteration seen as helpful). Design, setting, participants. Retrospective diagnostic-accuracy feasibility study on 80 biobanked urine samples (40 PCa, 40 non-cancer; 368 sensor runs; a subset of unknown Gleason grade) from a single UK NHS urology service, the same collection used to train canine detectors. Main outcome measures: Patient-level Receiver Operating Characteristic (ROC) area under the curve (AUC) under patient-grouped cross-validation with a fold-honest pooled-control reference (reconstructed from training-partition controls only); sensitivity, specificity and predictive values at pre-specified operating points; 1000-fold whole-procedure label-permutation significance; patient bootstrap 95% CIs; and leave-one-day-out / leave-one-chip-out generalisation. Results. An L2-regularised linear classifier when allowed to see between three and six chips outcome on a patient sample extracted within-instrument AUC 0.79 (95% CI 0.69 to 0.88; 1000-permutation p = 0.001) from urine scent alone, exceeding this cohort own serum prostate-specific antigen (PSA) discrimination (AUC 0.645; itself within the population range for PSA 0.67) and obtained without a blood draw (at the Youden point, sensitivity 0.75, specificity 0.78, PPV 0.77, NPV 0.76). Upon allowing PSA the total AUC rose to 0.82. This was not a plateau: AUC rose from chance at 30 training samples, passed the serum-PSA range at 40, and reached 0.79 at 80 patients (0.82 if PSA was included), with an inverse-power fit projecting 0.93 by n = 500 and 0.96 by n = 1000. The discriminant was a genuine multivariate receptor pattern, independent of patient age (Spearman 0.09; the cohort is not age-matched). So at least for these data, neither age, nor collection day, ambient humidity/temperature, or overall signal amplitude (sometimes thought of as intensity of smell) were predictive of prostate cancer status, yet the scent character was. Transfer to a new sensor chip fell to AUC 0.57 without calibration, meaning the remaining obstacles are hardware portability rather than signal existence: much as a detection dog acclimatizes to a new setting, the system improves with on-site calibration prior to use. Conclusions: A genuine, confound-controlled olfactory PCa signature is recoverable from 80 samples, surpasses this cohort serum PSA (0.645) and exceeds the population PSA range, and improves monotonically with training-set size. We present this as a small-sample feasibility benchmark, not yet a validated diagnostic; the dominant remaining factor is training-set size, and the path to clinical-utility and improved AUC is clearly found to be a larger, multi-site, age-matched, and ideally prospective training cohort. A transferable small-sample lesson is also reported: adaptive feature searches (evolutionary and self-calibrating-protocol handle search) artificially inflate cross-validation and collapse under whole-procedure permutation, whereas non-adaptive averaging survives, giving a robust scent signal obtainable from the headspace of urine samples and recordable by the RealNose device that keeps improving with expanding sample training set.

Pathologist-guided distinctions within histology and spatial omic images provide insights into health and disease, with digital pathology leveraging artificial intelligence to automate such assessments. To train computational models, current digital pathology methods rely on upfront manual annotations, which are time-consuming to generate. Pre-annotation is poorly suited to investigating novel spatial behaviors--a major need driven by advances in spatial profiling--for which annotation criteria and data needs will be uncertain. To address these challenges, we present DIANNE, a digital pathology approach for rapid training and inference of spatial differential attributes based on train-time Positive Class Mixup Augmentation. DIANNE can compute foundation model-derived segmentation-free localization of differential classifiers across whole slide H&E images within seconds on a workstation, enabling interactive investigation of spatial niches. Predictive models can be re-trained in real-time in response to patch or regional annotation changes, clarifying determinative biological attributes across slides from only a few dozen annotated patches. We demonstrate the effectiveness of DIANNE for tumor detection, artifact identification, and exploration of pancreatic, fetal membranes and kidney tissue structures. DIANNE also provides analogous capabilities for IHC, multiplex immunofluorescence, and registered spatial transcriptomic+H&E images. DIANNE is implemented in a Jupyter toolkit, enabling rapid development of high-resolution classifiers from weakly-supervised training. DIANNE provides a practical system to quantitatively understand known and novel spatial phenotypes.

Background: The Prosigna Breast Risk of Recurrence test is based on the PAM50 classifier and was originally validated as an in vitro diagnostic (IVD) test on the Dx enabled nCounter(R) Analysis System. The Prosigna test is intended for early-stage, hormone receptor+ (HR+) breast cancer and provides the risk of recurrence (ROR) score (0-100), intrinsic subtype (Luminal A, Luminal B, HER2-enriched, and Basal-like), and the 10-year probability of distant recurrence. We describe the performance of the Prosigna test as a whole transcriptome RNA sequencing laboratory developed test (LDT) for measuring the Prosigna ROR score and intrinsic subtypes on tissue from surgical resection and core needle biopsy as compared to the Prosigna test on the nCounter system. Methods: We evaluated three separate breast cancer cohorts to 1) bridge the IVD test on the nCounter system and NGS LDT test (n = 245), 2) validate the bridged algorithm on an independent biobank sample set (n = 187), and 3) retrospectively test performance on long-term archival samples from a previous study (n = 109). Results: Bridging analysis showed minimal score variability and robust correlation of Prosigna ROR scoring in surgical resections (SR) (2.459, SD; 0.981, R2) and core needle biopsy (CNB) (2.338, SD; 0.970, R2) samples. In the validation set, the Prosigna NGS LDT ROR scores maintained high correlation to the scores of the nCounter system (SR = 0.968, CNB = 0.966, R2), exhibited minimal score variability (SR = 2.488, CNB = 2.558, SD), and demonstrated high concordance in subtype classifications (SR = 92.3% CNB = 92.8%). Further testing demonstrated comparable performance across tumor fractions, a lower limit of detection (LLOD) of 5 ng, and robustness to exogenous ethanol or genomic DNA contamination. When testing previously extracted RNA from the clinical cohort, we observed high correlation (0.974, R2) and low variance (3.078, SD) of ROR scores with original values on the nCounter system, along with strong risk group (95.4%) and subtype (94.5%) concordance. Conclusions: This study describes the analytical validation of the Prosigna NGS-based LDT measuring the Prosigna ROR score and intrinsic subtypes with robust analytical performance on SR and CNB specimens, providing confidence for clinicians utilizing the NGS-based version of this well-established test.

Light microscopy imaging with histological stains is central to disease diagnosis and research. It is enhanced with immunostaining to reveal cellular composition and complexity linked to clinical utility and biological mechanisms. Emerging multiplex imaging technologies like Phenocycler markedly increase the coverage to capture the cellular diversity but are costly, technically demanding, and inaccessible to most clinical laboratories. We developed DigitAb, a deep learning framework that classifies cell types directly from hematoxylin and eosin (H&E) stained slides, eliminating the need for specialized assays. Using Phenocycler imaging, we generated highlZlresolution ground truths for [~]3.5 million cells from 29 human kidney samples across four multi-institutional datasets to train a semantic segmentation model for 10 cell types, achieving a balanced accuracy of 0.78. By employing an integrated adversarial domain adaptation module, we tested DigitAb on unlabeled and untested biopsy samples from kidney transplant and diabetic samples. We were able to predict several cell types just from histology images, without using any special technology or immunostains, and demonstrate high concordance with clinical gold-standard Banff schema in kidney transplant rejection, and clinical characteristics of diabetic nephropathy. Our cloudlZlbased tool, DigitAb, provides scalable, accessible, labellZlfree cellular segmentation for research and clinical pathology.

Aims Active surveillance (AS) allows selected men with localized prostate cancer to defer curative therapy and reduce treatment morbidity. Conversion from AS to treatment is commonly triggered by Gleason grade group (GGG) upgrading on confirmatory biopsy. We developed and validated a digital pathology artificial intelligence (DPAI) biomarker to predict GGG upgrading in AS-eligible patients. Materials & Methods The DPAI model was trained using histopathology image features from diagnostic biopsies of 998 patients and validated in an independent cohort of 296 patients meeting criteria for AS. Logistic regression estimated the probability of confirmatory-biopsy GGG increase, and feature selection identified the most predictive variables. Results AI-GUR (Artificial Intelligence-Gleason Upgrade Risk) predicted GGG reclassification at confirmatory biopsy (OR 1.60; p=0.0003), and provided information beyond conventional stratification (risk group, CAPRA) and cribriform morphology (all p<0.01). Predicted risks were similar across time from diagnosis (~10-15% to ~85% at 1, 1.5, or 2 years; p for time=0.50), consistent with initial biopsy mischaracterization rather than time-dependent progression. Conclusions AI-GUR provides individualized estimates of confirmatory-biopsy GGG upgrading for AS candidates. Using DPAI may improve shared decision-making by complementing standard clinicopathologic tools and molecular testing using the same biopsy specimen, while informing the likelihood of grade upgrade at confirmation.

Multimodal 3D imaging has emerged as a powerful approach for investigating complex tissue architecture in pathological specimens. Techniques such as propagation-based phase-contrast computed tomography (PCT), light-sheet microscopy (LSM), and three-photon microscopy (3PM) provide complementary information on unlabeled tissue morphology based on distinct intrinsic contrast mechanisms. However, integrating these heterogeneous datasets into a unified spatial framework remains challenging due to differences in imaging geometry, spatial resolution, and modality-specific distortions. In this study, we present a registration pipeline for spatially aligning volumetric datasets acquired with PCT, LSM, and 3PM from formalin-fixed paraffin-embedded (FFPE) human colon cancer specimens. Biopsies from theses specimens were optically cleared and imaged sequentially using the three high-resolution modalities. To compensate for large positional differences between acquisitions, a three-stage cascade registration strategy was developed, consisting of coarse global alignment on down-sampled data, followed by rigid refinement at intermediate resolution. Mutual information was used as the similarity metric to ensure robust multimodal registration. The resulting framework enables the generation of spatially aligned multi-channel 3D datasets that combine structural information from X-ray phase-contrast imaging with complementary optical contrast signals. Beyond registration, we demonstrate that the fused six-dimensional feature space can be further exploited for unsupervised tissue characterization using a Gaussian Mixture Model (GMM), enabling data-driven identification of spatially coherent tissue regions without manual annotation. Qualitative evaluation confirms consistent alignment of major anatomical structures across modalities, while the unsupervised clustering reveals biologically meaningful patterns despite modality-specific noise and resolution differences. While further optimization and validation across larger datasets will enhance its computational efficiency and breadth of application, the approach already demonstrates strong potential for comprehensive tissue analysis and enables scalable, label-free 3D characterization of colon cancer tissue architecture.

Treatment of advanced head and neck squamous cell carcinoma (HNSCC) often involves radiotherapy combined with chemotherapy, targeted therapy, or immunotherapy. However, due to its anatomical and molecular heterogeneity, identifying the most effective treatment for each patient remains a major clinical challenge. To address this need, we developed a high-throughput organoid-based drug screening platform that uses patient-derived organoids to assess candidate treatment regimens. We validated the platform by establishing bioprinted 3D organoids of human HNSCC cell lines and exposing them to X-ray radiation in combination with various small-molecule drugs and biologics. We quantified viability using ATP release assays and assessed extracellular matrix (ECM) invasion with a machine learning-based brightfield image analysis pipeline. Proof-of-concept experiments with HPV-negative HNSCC lines (HN30 and HN31, established from primary and metastatic disease from the same patient) and HPV-positive HNSCC cells (SCC154) revealed different therapy agents that can radiosensitize each cell line. Image analysis showed that copanlisib, afatinib, and ibrutinib could limit ECM invasion of HN31, while the AKT inhibitor ipatasertib promotes invasion of HN30 cells, consistent with previous studies. Application of the platform to patient-derived HPV+ oropharyngeal tumor organoids showed that they shared sensitivity to several agents while also exhibiting differences against certain therapies. Cetuximab, sorafenib, and nedisertib significantly radiosensitized organoids from two clinical samples. This work demonstrates the feasibility of performing sensitivity screening by integrating bioprinting, conventional viability assays, and advanced image analysis techniques. This platform has the potential to enable a personalized therapeutic pipeline for patients with advanced HNSCC, optimizing responses to radiotherapy and targeted agents to improve clinical outcomes while avoiding modulators that may promote tumor invasion.

Nephrons are the functional units of the kidney; within each nephron, the glomerulus is the initial site of selective filtration that allows removal of waste products while preserving proteins in the bloodstream. Each glomerulus consists of a network of capillaries surrounded by specialized epithelial cells, podocytes, which mediate selective filtration. Abnormalities in glomerular structure impair renal function, resulting in proteinuria and kidney disease. Although several microscopy-based approaches exist to characterize glomerular architecture and structural abnormalities, quantitative analysis is often limited by labor-intensive image segmentation. In this study we present a semi-automated approach for segmentation and analysis of glomerular architecture from three-dimensional confocal microscopy data. Using mTmG transgenic mice that express membrane-associated EGFP in podocytes and membrane-associated tdTomato across all other cell types, we reconstruct podocyte processes and glomerular capillaries from volumetric renal images. This semi-automated approach reduces manual segmentation effort and supports more efficient, standardized analysis of glomerular architecture in three-dimensional confocal microscopy datasets.

Multiplex staining is a technique that allows the identification of cell types within a single tissue section by simultaneously detecting multiple molecular markers. Generally, multiplex staining is performed using several combinations of probes, including specific antibodies, nucleic acid probes, and lectins. Here, a novel multiplex staining strategy that relies exclusively on lectin probes that target glycans is presented. Glycans have a vast variety of structural forms that vary depending on cell type-specific modifications. Furthermore, an enormous number of glycan-binding molecules, collectively known as lectins, exist in the biological world. Each lectin displays specificity for a particular glycan motif while maintaining broad affinity. Although lectin-based cell staining has been used in various applications, the partial and limited specificity of lectins has hindered the use of glycan-targeted multiplex staining with lectins. In addition, lectin probes have largely been avoided for cell-type identification because of the absence of strict cell-type-specific glycans. Here, a novel staining method, Glycan Painting, is introduced. Rather than viewing the partial specificity of lectins and the broad, non-cell-type-specific distribution of glycans as drawbacks, this approach turns these features into advantages by generating distinct color patterns that comprehensively visualize cell-type-specific glycan combinations and enable full-color imaging of tissues.

Tumour-educated platelets (TEPs) carry cancer-type-specific RNA signatures accessible through whole-blood RNA sequencing, but systematic multi-algorithm benchmarking with quantified statistical uncertainty had not been applied to the GSE68086 dataset, the fields primary reference cohort. We applied an end-to-end transcriptomic and machine learning framework to 280 whole-blood platelet RNA-seq samples from six cancer types (non-small cell lung cancer, colorectal cancer, glioblastoma multiforme, hepatobiliary cancer, breast cancer, and pancreatic cancer) and healthy donors. After a standardised preprocessing and normalisation pipeline, seven supervised classifiers - Logistic Regression, SVM (RBF), XGBoost, LightGBM, Random Forest, K-Nearest Neighbours, and a Multilayer Perceptron were benchmarked using stratified 5-fold cross-validation and a held-out test set. Statistical uncertainty was quantified via 2,000-resample percentile bootstrap confidence intervals. Multinomial Logistic Regression achieved the highest test macro F1-score (0.522) and macro-averaged ROC-AUC (0.869), both substantially above the seven-class chance level (1/7 {approx} 0.14). SHAP analysis of the Random Forest classifier identified IFITM3 as the globally dominant TEP biomarker; cancer-type-specific discriminators included ATP5PD (hepatobiliary cancer), C6orf62 (NSCLC and pancreatic cancer), VPS13C (healthy donors), and TMSB4Y (breast cancer). Gene Ontology and KEGG pathway enrichment corroborated the biological specificity of identified transcriptomic signatures. These results support the diagnostic potential of TEP transcriptomics as a multi-class liquid biopsy platform and provide a methodologically transparent, reproducible reference framework for future blood-based cancer classification studies.

The spatial arrangement of immune cells in the tumor microenvironment (TME) varies widely, from dispersed to clustered and tumor excluded to infiltrating. Multiplexed spatial profiling is an effective means of characterizing tumor-infiltrating lymphocytes (TILs) and immune complexes such as tertiary lymphoid structures (TLS) in the TME. However, few approaches have been described for objectively parametrizing patterns of immune organization and assessing their association with biological or clinical variables. This makes it difficult to evaluate whether a set of tumors is relatively immunologically cold or hot. Here we describe an intuitive set of statistical tools (available in the R package, tlsR) for characterizing lymphocyte patterns in the TME of solid cancers. We apply tlsR to primary prostate cancer (PCa), which is often described as immunologically cold. Using a cohort of 29 radical prostatectomy specimens stratified into low Gleason-grade (LGG; n=15) and high Gleason-grades (HGG; n =14) we show that HGG PCa is significantly more infiltrated than LGG PCa with lymphocytes organized into B cell or T cell enriched immune clusters (BICs and TICs). A subset of these ICs have the B and T cell zonation and follicular dendritic cells characteristic of a bona fide TLS. HGGs are also enriched with ICs containing precursor exhausted T cells (Tpex) and proliferating B cells and their tumor compartments harbor granzyme-B+ cytotoxic T cells in contact with cancer cells. Thus, far from being cold, a subset of HGG PCa has features associated with active immune surveillance, a finding with implications for emerging PCa immunotherapies.