BMC Methods

BackgroundDroplet based single-cell RNA sequencing (scRNA-seq) is a powerful tool for measuring RNA abundance profiles at cell-specific resolution. Droplet-based barcoding technology allows sample multiplexing, thereby facilitating high-scale of single cell sequencing. The resulting processing complexity, sample contamination and the underlying chemistries can all contribute to cell mis-labelling and consequent spurious cell-to-sample assignment. Approaches for barcode-free de-multiplexing which leverage natural genetic variation have been developed, but generally require an external source of genotype information. ResultsWe propose a novel method to exploit genome imputation and clustering to assign cells to inferred donor groups in the absence of a priori genetic information. Using tumor-derived single-cell RNA-sequencing (scRNA-seq) data, our workflow successfully assigned individual cells to donor-of-origin with high concordance. ConclusionsThis imputation-clustering approach represents a quality-assessment and quality-control strategy for barcode-free single cell donor-origin deconvolution with the capacity to resolve cases of sample cross-contamination.

Determining when DNA recovered from a crime scene transferred from its biological source, i.e., a samples time-since-deposition (TSD), can provide critical context for biological evidence. Yet, there remains no analytical techniques for TSD that are validated for forensic casework. In this study, we investigate whether morphological and autofluorescence measurements of forensically-relevant cell populations generated with Imaging Flow Cytometry (IFC) can be used to predict the TSD of touch or trace biological samples. To this end, three different prediction frameworks for estimating the number of day(s) for TSD were evaluated: the elastic net, gradient boosting machines (GBM), and generalized linear mixed model (GLMM) LASSO. Additionally, we transformed these continuous predictions into a series of binary classifiers to evaluate the potential utility for forensic casework. Results showed that GBM and GLMM-LASSO showed the highest accuracy, with mean absolute error estimates in a hold-out test set of 29 and 21 days, respectively. Binary classifiers for these models correctly binned 94-96% and 98-99% of the age estimates as over/under 7 or 180 days, respectively. This suggests that predicted TSD using IFC measurements coupled to one or, possibly, a combination binary classification decision rules, may provide probative information for trace biological samples encountered during forensic casework.

The generation of clonal CHO cell lines is foundational to biologics manufacturing; however, labor-intensive cell culture workflows predominate in the field. We created the CLAIRE (Cell Line AI Recognition and Evaluation) tool to streamline end-to-end cell line development by integrating deep-learning image analysis with automated liquid handling. We benchmarked three object detection models for monoclonality verification and found DETR provides superior accuracy (>0.90 F1-score) in identifying single cells. To quantify the outgrowth of cell lines, we evaluated multiple zero-shot SAM2 segmentation models against a feature-based estimation method. Feature-based detection successfully identified diverse cell colony types while less robust performance was observed for SAM2 models, particularly for sparse density colonies. The pre-trained DETR and feature-based detection models were wrapped in a task-focused user interface that outputs cell line hitpick lists compatible with a Lynx LM1800 liquid handler in addition to custom scripts automating cell passaging and sampling. This approach yielded an end-to-end 36 day CLD workflow capable of generating high-titer cell lines for multiple complex antibody structures. Here, we open-access our trained models, user interface, and Lynx automation scripts to provide a modular toolkit useful for clonal cell line engineering projects. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/703387v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1f72e70org.highwire.dtl.DTLVardef@109c54dorg.highwire.dtl.DTLVardef@7867b1org.highwire.dtl.DTLVardef@dfa61e_HPS_FORMAT_FIGEXP M_FIG C_FIG

Cyclic immunofluorescence (IF) techniques enable deep phenotyping of cells and help quantify tissue organization at high resolution. Due to its high dimensionality, workflows typically rely on unsupervised clustering, followed by cell type annotation at a cluster level for cell type assignment. Most of these methods use marker expression averages that lack a statistical evaluation of cell type annotations, which can result in misclassification. Here, we propose a strategy through an end-to-end pipeline using a semi-supervised, random forests approach to predict cell type annotations. Our method includes cluster-based sampling for training data, cell type prediction, and downstream visualization for interpretability of cell annotation that ultimately improves classification results. We show that our workflow can annotate cells more accurately with a training set <5% of the total number of cells tested. In addition, our pipeline outputs cell type annotation probabilities and model performance metrics for users to decide if it could boost their existing clustering-based workflow results for complex IF data. Availability and implementationFluoro-forest is freely available on github (https://github.com/Josh-Brand/Fluoro-forest). Data used within this manuscript is hosted on Dryad (DOI: 10.5061/dryad.hqbzkh1v1) Supplementary informationSupplemental figures and methods are included in the submission.

1.Aggressive cancers, such as pancreatic ductal adenocarcinoma (PDAC), are often characterized by a complex and desmoplastic tumor microenvironment rich in stroma, a supportive connective tissue composed primarily of extracellular matrix (ECM) and non-cancerous cells. Desmoplasia, which is a dense deposition of stroma, is a major reason for therapy resistance, acting both as a physical barrier that interferes with drug penetration and as a supportive niche that protects cancer cells through diverse mechanisms. A precise understanding of spatial cell interactions within the tumor microenvironment in stroma-rich cancers is essential for optimizing therapeutic responses. It allows detailed mapping of stromal-tumor interfaces, comprehensive phenotyping of diverse cell types and their functional states, and insights into changes in cellular distribution and tissue architecture, thus leading to an improved assessment of drug responses. Recent advances in multiplexed immunofluorescence imaging have enabled the acquisition of large batches of whole-slide tumor images, but scalable and reproducible methods to analyze the spatial distribution of cell states relative to stromal regions remain limited. To address this gap, we developed an open-source computational pipeline that integrates QuPath (Bankhead et al. 2017), StarDist (Schmidt et al. 2018), and custom Python scripts to quantify biomarker expression at a single- and sub-cellular resolution across entire tumor sections. Our workflow includes: (i) automated nuclei segmentation using StarDist, (ii) machine learning-based cell classification using multiplexed marker expression, (iii) modeling of stromal regions based on fibronectin staining, (iv) sensitivity analyses on classification thresholds to ensure robustness across heterogeneous datasets, and (v) distance-based quantification of the proximity of each cell to the stromal border. To improve consistency across slides with variable staining intensities, we introduce a statistical strategy that translates classification thresholds by propagating a chosen reference percentile across the distribution of marker-related cell measurement in each image. We apply this approach to quantify spatial patterns of distribution of the phosphorylated form of the N-Myc downregulated gene 1 (NDRG1), a novel DNA repair protein that conveys signals from the ECM to the nucleus to maintain replication fork homeostasis, and a known cell proliferation marker Ki67 in fibronectin-defined stromal regions in PDAC xenografts. The pipeline is applicable for the analysis of various stroma-rich tissues and is publicly available: https://github.com/HMS-IAC/stroma-spatial-analysis-web. 2. Summary paragraphOur study introduces a scalable and reproducible image analysis pipeline that quantifies spatial biomarker distributions relative to the stroma in tumor tissues using open-source tools. By modeling cell-level intensity distributions and calibrating classification thresholds across heterogeneous images, we uncover spatially organized patterns of stroma sensing, DNA damage, and proliferative response in pancreatic tumors. This approach enables robust, quantitative analysis of tumor-stroma interactions and is readily adaptable to other tumor types and biomarker panels, providing a valuable resource for spatial pathology and tumor microenvironment research.

Our goal is to identify and understand cellular behaviors using 3D live imaging of cell organization. To do this, we image human inducible pluripotent stem cell (hiPSC) lines expressing fluorescently tagged protein representing specific cellular organelles and structures. To produce large numbers of standardized cell images, we developed an automated hiPSC culture procedure, to maintain, passage and Matrigel coat 6-well plastic plates and 96-well glass plates compatible with high-resolution 3D microscopy. Here we describe this system including optimization procedures and specific values for plate movement, angle of tips, speed of aspiration and dispense, seeding strategies and timing of every step. We validated this approach through a side-by-side comparison of quality control results obtained from manual and automated methods. Additionally, we developed an automated image-based colony segmentation and feature extraction pipeline to predict cell count and select wells with consistent morphology for high resolution 3D microscopy.

New technological advances have enabled high-throughput phenotyping at the single-cell level, yet analyzing the large amount of data generated by high throughput phenotyping experiments automatically and accurately is a considerable challenge. Here we introduce Processing Images Easily (PIE), software that automatically tracks growth of microbial colonies in low-magnification brightfield images by combining adaptive object-center recognition with gradient-based object-outline recognition. PIE recognizes colony outlines very robustly and accurately across a wide range of image brightnesses, focal depths, and organisms. Beyond accurate colony recognition, PIE is designed to easily integrate with complex experiments, allowing colony tracking across multiple experimental phases and classification based on fluorescence intensity. We show that PIE can be used to accurately measure the growth rates of large numbers (>90,000) of bacterial or yeast microcolonies in a single-time-lapse experiment, allowing calculation of population-wide growth properties. Finally, PIE is able to track individual colonies across multiple experimental phases, measuring both growth and fluorescence properties of the microcolonies. Author SummaryHigh-throughput microscopy has enabled automated collection of large amounts of growth and gene-expression data in microbes. Computational methods that can precisely recognize and track organisms in images are essential to performing measurements at scale using automated microscopy. We have developed PIE, software that automatically recognizes microbial colonies in microscopy images, tracks them in imaging time-series, and performs measurements of growth and, potentially, gene expression. PIE is highly effective on low-resolution images, outperforming current state-of-the-art approaches in both speed and accuracy, and works well in microbes of varying shapes and sizes. In addition, PIE allows tracking microcolonies across arbitrary sequences of experimental phases, each collecting data in different modalities. We show that PIE allows measurement of growth and fluorescence properties in tens of thousands of microbial colonies in a single experiment, and that in turn the scale of these measurements can lead to important insights about interindividual differences in growth and stress response. PIE is available as a Python package (https://doi.org/10.5281/zenodo.4987127) with documentation currently at https://pie-image.readthedocs.io/; users can also run analysis on individual images or time-series without the need to install PIE by using our web application, currently available at http://pie.hpc.nyu.edu/.

There is a constant pressure to reduce timelines in mammalian cell line development (CLD) for biotherapeutic protein production. Demonstration of clonal derivation of the generated cell lines is key for health authorities approval. To meet these regulatory and process-oriented demands, single-cell dispensers have become vital instruments for single-cell cloning. We conducted validation experiments with the UP.SIGHT (CYTENA GmbH) to determine this instruments single-cell dispensing efficiency (SCDE) and probability of clonal derivation (p(clonal)). Process optimization to maximize clone recovery with several cell lines was also performed, focusing on cloning media and plate type. With a SCDE >97%, p(clonal) >99.99% and clone recovery values of up to 80%, the data reported here support the notion that the UP.SIGHT covers all steps in the single-cell dispensing process with assurance of clonality and colony tracking, leading to faster and more efficient CLD workflows. This work also serves as a guideline for instrument validation and guidance towards process optimization.

Extracellular vesicles (EVs) are lipid spheres released from cells. Research utilizing EVs has met several hurdles owing to the small size of the majority of EVs and other nanoparticles (<150 nm) and the lack of detection technologies capable of providing high-throughput single particle measurements at this scale. The use of high-throughput single particle measurements is critical for the assessment of EV heterogeneity and abundance which are features often used to assess the development of isolation protocols or particle characterization. The Coulter principle, known in the field as resistive pulse sensing (RPS), has been used for several decades to size and count cells. More recently, this technology has evolved to accommodate nanoparticle analysis. In the last decade a platform utilizing microfluidic resistive pulse sensing (MRPS) has been demonstrated for nanoparticles, offering ergonomic characterization of nanoparticles along with utilizing open format data. To date, assessment of MRPS accuracy and reporting standards have not been assessed. With the aim of increasing data accuracy, ergonomics, and reporting transparency, we developed a microfluidic resistive pulse sensing post-acquisition analysis software (RPSPASS) application for automated cohort calibration, population gating, statistical output, QC plot generation, alternative data file outputs, and standardized reporting templates.

Sequential multiplex methodologies such as Akoya CODEX, Miltenyi MACSima, Rarecyte Orion, and others require modification of the antibodies by conjugation to an oligo or a specific fluorophore which means the use of off-the-shelf reagents is not possible. Modifications of these antibodies are typically performed via reduction chemistry and thus require verification and validation post-modification. Fixed panels are therefore developed due to various limitations including spectral overlap that creates spectral unmixing issues, steric hindrance, harsh antibody removal, and tissue degradation throughout the labeling. As such, a complex interrogation evaluating multiple study hypotheses and/or endpoints requires the development of sequential panels, reconstruction, and realignment of the tissue that necessitate a z-stack strategy. Standardized antibody panels are typically fixed and require substantial validation efforts to modify a single target and thus do not evolve with the pace of research interests. To increase the throughput of profiling cells within the human central nervous system (CNS), we developed and validated a CNS-specific library with an associated analysis platform using the newly developed Lunaphore COMETTM platform. The COMETTM is an automated staining/imaging instrument integrating a reagent deck for staining buffers and off-the-shelf label-free primary antibodies and fluorophore-labeled secondary antibodies, which feed into a circular plate holding up to 4 slides that are automatically imaged in microscope-operated control software. For this study, standard formalin fixed paraffin embedded histology slides are used. However, the COMET is capable of imaging fresh-frozen samples using specialized settings. Our methodologies address an unmet need in the neuroscience field while leveraging prior developmental efforts in the domain of immunology spatial profiling. Cataloging and validating a large series of antibodies on the COMET along with developing CNS autofluorescence management strategies while optimizing standard operating procedures have allowed for the visualization at the subcellular level. Forty analytes can be used to analyze one specimen which has clinical utility in cases in which the CNS can only be sampled by biopsy. CNS biopsies, depending on the anatomical location, can have limited available volume to a degree that requires prioritization and restriction to select analysis. In-depth bioinformatic imaging analysis can be done using standard bioinformatic tools and software such as Visiopharm(R). These results establish a general framework for imaging and quantifying cell populations and networks within the CNS while providing the scientific community with standard operating procedures.

Manual microscopy is the gold standard for phytoplankton monitoring in diverse engineered and natural environments. However, it is both labor-intensive and requires specialized training for accuracy and consistency, and therefore difficult to implement on a routine basis without significant time investment. Automation can reduce this burden by simplifying the measurement to a single indicator (e.g., chlorophyll fluorescence) measurable by a probe, or by processing samples on an automated cytometer for more granular information. The cost of commercially available flow imaging cytometers, however, poses a steep financial barrier to adoption. To overcome these labor and cost barriers, we developed ARTiMiS: the Autonomous Real-Time Microbial Scope. The ARTiMiS is a low-cost flow imaging microscopy-based platform with onboard software capable of providing species-level quantitation of phytoplankton communities in real-time. ARTiMiS leverages novel multi-modal imaging and onboard machine learning-based data processing that is currently optimized for a curated and expandable database of industrially relevant microalgae. We demonstrate its operational limits, performance in identification of laboratory-cultivated microalgae, and potential for continuous monitoring of complex microalgal communities in full-scale cultivation systems. SynopsisWe introduce a platform for low-cost real-time imaging monitoring of phytoplankton and demonstrate its utility in real-time monitoring of laboratory- and full-scale microalgal cultivation systems.

Image-based spatial transcriptomics depends on cell segmentation to assign transcripts to individual cells, but how segmentation algorithms perform across tissues with distinct cellular architectures is poorly understood. This study presents the broadest independent benchmark to date of cell segmentation algorithms for spatial transcriptomics, comparing five approaches across ten mouse tissues using a 5,006-gene Xenium panel. To quantify segmentation errors, Co-expression Rejection in Segmentation Purity (CRISP) was developed, an open-source tool available in R and Python that measures cell purity through tissue-specific mutually exclusive marker co-expression without requiring ground truth annotations. This benchmark revealed that segmentation algorithms face a fundamental tradeoff between maximizing transcript capture and maintaining cell purity, and that the severity of this tradeoff is tissue-dependent. Proseg achieved the highest average performance across tissues, though the magnitude of its advantage varies with tissue architecture. Overall, CRISP provides per-tissue performance profiles as a practical resource for algorithm selection.

Multiplex imaging technologies have revolutionized our ability to study cellular behavior within the tissue microenvironment. Translating this complex data into meaningful biological insights requires a unified analytical framework. To address this, we developed SPACEMAP (Spatial Phenotyping And Classification with Enhanced Multiplex Analysis Pipeline), a comprehensive Python and Qupath-based platform for multiplex imaging analysis. SPACEMAP integrates image registration, segmentation, artifact removal, tissue and zone classification, spatial feature extraction, and a consolidated phenotyping approach into a single system. A core feature of SPACEMAP is its high-fidelity phenotyping. To evaluate classification performance, we benchmarked our method RESOLVE, against three established approaches, Leiden clustering, Self-Organizing Maps, and SCIMAP revealing substantial disagreement among them. SPACEMAP overcomes this through two complementary workflows: a machine learning model trained on expert-labeled cells, and a consensus classifier that integrates high-confidence cells across methods. Here, we validated SPACEMAP on in-house colorectal cancer samples and a public dataset, demonstrating its robustness.

Appropriately tailored segmentation techniques can extract detailed quantitative information from biological image datasets to characterize and better understand sample distributions. Practically, high-resolution characterization of biological samples such as cell populations can provide insights into the sources of variance in biomarker expression, drug resistance, and other phenotypic aspects, but it is still unclear what is the best method for extracting this information from large image-based datasets. We present a software pipeline and comparison of multiple image segmentation methods to extract single-cell morphological and fluorescence quantitation from time lapse images of clonal growth rates using a recently reported microfluidic system. The inputs in all pipelines consist of thousands of unprocessed images and the outputs are the detection of cell counts, chamber identifiers, and individual morphological properties of each clone over time detected through multi-channel fluorescence and bright field imaging. Our conclusion is that unsupervised learning methods for cell segmentation substantially outperform supervised statistical methods with respect to accuracy and have key advantages including individual cell instance detection and flexibility through model training. We expect this system and software to have broad utility for researchers interested in high-throughput single-cell biology.

Determining the relationship between biomarkers via fluorescence microscopy is a key step in the characterisation of cellular phenotypes. We define a simple distance-based measurement termed a perimeter distance mean (PDmean) which quantifies the relative proximity of objects in one fluorescent channel to objects in a second fluorescent channel in 2D or 3D microscopy datasets. PDmean measurements were able to accurately identify known changes in colocalisation in computer-generated and real-world microscopy datasets. We argue that this approach provides substantial advantages over currently used distance-based colocalisation analysis methods. We also introduce PyBioProx, an extensible open-source Python module and graphical user interface that produces PDmean measurements.

Spatial transcriptomics technologies face a fundamental trade-off between transcriptomic breadth and spatial resolution, with widely-used platforms like 10x Visium capturing multiple cells per spot, limiting single-cell insights. Current computational deconvolution methods attempt to address this limitation but uniformly suffer from reference dependency, platform effects, and complete neglect of tissue morphology. Here we present SpatialCell AI, a computational framework that achieves true single-cell resolution from spot-based spatial transcriptomics through morphology-guided computational enhancement. Unlike existing methods that rely solely on expression similarity, SpatialCell AI integrates AI-powered cell segmentation from histological images with spatial gene expression, eliminating reference requirements while leveraging tissue architecture. We rigorously validated our approach using publicly available matched colorectal cancer samples analyzed across Visium (55m), Visium HD (8m, 16m), and Xenium (single-cell ground truth). SpatialCell AI achieved strong accuracy with expression correlation of r=0.791, 7.82-fold improvement in expression accuracy, and 5.30-fold enhancement in gene detection compared to spot-based measurements. Comprehensive benchmarking against 28+ existing methods revealed that all computational approaches share identical limitations that SpatialCell AI uniquely overcomes through its morphology-first design. The framework converts standard Visium outputs from spot-level to true single-cell resolution (Cell_1, Cell_2, Cell_3...), enabling precise cellular interaction mapping and rare cell type identification previously impossible with spot-based technologies. By bridging the resolution gap between affordable spot-based platforms and expensive single-cell technologies, this approach enables broader access to single-cell resolution analysis for research and clinical applications.

Metastatic prostate cancer is a highly heterogeneous and dynamic disease and practicable tools for patient stratification and resistance monitoring are urgently needed. Liquid biopsy analysis of circulating tumor DNA and circulating tumor cells (CTCs) are promising, but due to the diversity of resistance mechanisms, comprehensive testing is essential. Previously, we demonstrated that CTCs can be characterized by mRNA-based in situ padlock probe hybridization. Now, we have developed a novel combinatorial dual-color (CoDuCo) approach with increased multiplex capacity of up to 15 distinct markers, complemented by semi-automated image analysis and machine learning-assisted CTC classification. Here, we present three exemplary cases of patient samples in which the CoDuCo assay visualized diverse resistance mechanisms (AR-V7, neuroendocrine differentiation (SYP, CHGA, NCAM1)), as well as druggable targets and predictive markers (PSMA, DLL3, SLFN11). The combination of high multiplex capacity and microscopy-based single-cell analysis is a unique and powerful feature of the CoDuCo in situ assay. This synergy enables the identification and characterization of CTCs with epithelial, epithelial-mesenchymal, and neuroendocrine phenotypes, the detection of CTC clusters, and the visualization of CTC heterogeneity. In conclusion, the assay is a promising tool for monitoring the dynamic molecular changes associated with drug response and resistance in prostate cancer. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/592946v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@46a186org.highwire.dtl.DTLVardef@116824dorg.highwire.dtl.DTLVardef@c4c842org.highwire.dtl.DTLVardef@1378b67_HPS_FORMAT_FIGEXP M_FIG C_FIG

The need for protein production has been growing over the years in various industries. We here present a high-throughput screening strategy to isolate high producer budding yeast clones from a mutagenized cell population using gel microdrop (GMD) technology. We use a microfluidic water-in-oil (W/O) emulsion method to produce monodisperse GMDs and a microfluidic cell sorter for damage-free sorting of GMDs by fluorescently quantifying secreted proteins. As a result, this high-throughput GMD screening method effectively selects high producer clones and improves protein production up to five-fold. We speculate that this screening strategy can be applied, in principle, to select any types of high producer cells (bacterial, fungal, mammalian, etc.) which produce arbitrary target protein as it does not depend on enzymes to be produced.

Human induced pluripotent stem cells (hiPSCs) are the most accessible source material for derivation of stem-cell-based therapies at scale. However, a disconnect exists between quality characteristics of phenotype in the pluripotent state, and downstream metrics for efficacy and safety. Bridging this gap is a major challenge. Given hiPSC plasticity, environmental conditioning plays a crucial role in guiding phenotype. This work presents a parallelizable scale-down approach, acquiring real-time data to inform hiPSC phenotype throughout biomanufacturing. We developed an optoelectronic instrumentation suite capable of measuring pH, dissolved oxygen, and cell density as important surrogates for phenotype in a scale-down expansion bioprocess. We were successful in obtaining continuous, integrated parametric data throughout cultivation and estimating metabolic characteristics of hiPSC phenotype. This system functions as a proof-of-concept tool for development of predictive models and monitoring strategies around the elucidation of phenotypic dynamics within hiPSC biomanufacturing. We have demonstrated a feasible open-source multivariate continuous monitoring approach at research scale that combines common process parameters with a scattering measurement against aggregate density. The combination of these parameters enables surrogate measurement of a metric for metabolic phenotype. This contribution emphasizes monitoring how the bioprocess influences variables important in the context of cell state, in broader pursuit of better understanding the link to downstream functionality and global optima in hiPSC biomanufacturing for regenerative medicine.

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.