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.

Preformed and de novo antibodies against donor human leukocyte antigen (HLA) antigens remain a major cause of antibody-mediated rejection and graft loss after organ transplantation. Although solid-phase assays and virtual crossmatching have reshaped pre-transplant risk assessment, physical crossmatching, which assesses whether recipient antibodies react with donor cells, remains widely used as the final compatibility assessment before transplantation. These workflows include both complement-dependent cytotoxicity (CDC) and flow cytometry crossmatch (FCXM) assays, but their interpretation remains partly manual, operator-dependent, and, for microscopic CDC readout, semi-quantitative. Here, we present AlloViewer, a web-based software platform for automated and traceable interpretation of image-based and flow-cytometry-based HLA antibody diagnostics. For CDC microscopy, AlloViewer employs a simulation-trained deep learning (UNet) workflow that combines automated lymphocyte segmentation with experiment-specific fluorescence classification and well-level cytotoxicity scoring. To deliberately capture the technical variability encountered in routine diagnostics, we generated simulated CDC-like training images spanning differences in image resolution, acquisition conditions, staining quality, cell density, cell distribution, clustering, and background fluorescence, among others. The resulting simulation-trained model enabled robust lymphocyte detection across heterogeneous imaging conditions and outperformed a conventional rule-based image analysis pipeline under variable acquisition conditions. Automated CDC scoring achieved performance within the range of human inter-annotator variability and approached the practical reproducibility limit defined by human disagreement. To cover all modalities of pre-transplant physical crossmatching, AlloViewer further supports automated FCXM interpretation through cell population identification and population-specific immunoglobulin G (IgG) readouts. The platform integrates these workflows in an assay-specific web interface and provides application programming interface (API) access for programmatic submission of assay data and retrieval of processed results. Together, AlloViewer establishes an integrated computational framework for standardized and traceable interpretation of CDC crossmatch assays, CDC-based HLA antibody identification testing using commercial test-cell panels, and FCXM workflows. More broadly, this work demonstrates how simulation-trained artificial intelligence (AI) can facilitate robust computational analysis across technically heterogeneous laboratory environments, providing a framework for standardizing traditionally operator-dependent diagnostic workflows.

Inflammatory activation remodels the Golgi apparatus to meet increased secretory demand, yet whether these structural changes carry computationally accessible information about immune cell state has not been explored. Here we show that machine learning applied to imaging flow cytometry images of the Golgi can distinguish lipopolysaccharide (LPS) treated from control cells. We trained models on Golgi images from human THP-1 monocytes before and 16 h after LPS stimulation in vitro, and from circulating myeloid cells of mice 24 h after intratracheal LPS or saline in vivo. In each dataset, the best-performing model was a deep learning model trained directly on images rather than a classifier using predefined morphometric features. Cross-domain performance varied with the model, transfer direction and use of labelled target data: several deep learning models trained on mouse images classified THP-1 cells above chance without changing model parameters. Grad-CAM highlighted regions within or adjacent to the GM130 fluorescence pattern. These findings indicate that GM130 images carry information associated with LPS treatment that supports classification within and across the two datasets. To our knowledge, this is the first application of supervised deep learning to imaging flow cytometry images of a subcellular organelle for inflammatory phenotyping, establishing organelle level image analysis as a potential complement to molecular assays for immune cell classification.

Platelet-leukocyte aggregates (PLAs) are a proposed interface between coagulation and immune regulation, but their distribution across leukocyte subsets beyond monocytes and neutrophils, and their behavior in acute disorders of immune regulation, such as sepsis and severe trauma, remain largely undefined. The study of thromboimmune mechanisms in these acute conditions has relied heavily on murine sepsis models, whose species differences in platelet and leukocyte biology limit translation to human disease. Direct measurement in human whole blood is needed yet reported PLA frequencies vary widely because quantification is highly sensitive to sample handling. Here we present a robust, minimally manipulative 27-color spectral flow cytometry protocol that allows simultaneous deep immunophenotyping and classification of PLAs across >40 leukocyte subpopulations. In healthy donors, PLA frequency ranged from 1.7% on memory CD4 T cells to over 50% on monocyte subsets, with monocytes contributing 25% of all PLAs. We also found novel platelet-binding leukocyte populations, including Th9 and Th22 CD4+ T cells. Optimization identified anticoagulant choice, centrifugation conditions, dilution, and sample processing time as key determinants of reproducible measurement; PLA quantification was validated by imaging flow cytometry. In a small validation cohort of sepsis and trauma patients, the assay recapitulated established immunophenotypic features of critical illness, including neutrophilia and myeloid-derived suppressor cells (MDSC) expansion, and quantified PLAs within each subset. This protocol provides a reference framework for studying thromboinflammation and other dysregulated immune response diseases directly in human blood, and a foundation for further studies, defining the mechanisms and clinical relevance of platelet-leukocyte interactions in disease.

Imaging Mass Cytometry (IMC) enables the simultaneous quantification of 40+ protein markers at single cell resolution in tissue, however biologically faithful phenotyping at scale remains a critical bottleneck. Unsupervised clustering fragments coherent populations or conversely merges biologically incoherent ones into a single cluster, supervised classifiers impose a closed vocabulary, and the presence of rare subsets (encoding clinically relevant biology) in conjunction with abundant subsets may be detrimental to detection performances. We present AltraFlowSOM, a semi-supervised extension of FlowSOM that embeds partial expert annotations directly into self-organizing map training via a two-layer SuperSOM architecture, balancing label-guided topology anchoring with unsupervised discovery. By anchoring the map to biologically labelled reference points, AltraFlowSOM circumvents the canonical dependency between batch correction and clustering. Evaluated under Leave-one-out cross validation on two independent IMC cohorts, Lupus Nephritis (n=22 ROIs) and Sjogren syndrome (n=10 ROIs), AltraFlowSOM outperformed all unsupervised and supervised baseline on Adjusted Rand Index, F1 scores (macro and weighted), weighted purity and in the identification of rare populations. The median Treg cell recovery exceeded that of all comparator methods. AltraFlowSOM resolves the scalability-alignment-discovery trilemma, by establishing a semi-supervised SOM as a generalizable method for high dimensional IMC phenotyping.

Background/Objectives: Many myeloid malignancies are defined by recurrent structural variants (SVs) and copy-number alterations (CNAs) under the current World Health Organization (WHO) and International Consensus Classification (ICC) frameworks. Detecting these abnormalities typically requires several standard-of-care (SOC) assays, each with its own blind spots, and then laboratories and clinicians are left to piece the results together. Genome mapping has emerged as a mainstream technology with the potential to serve as a high-resolution alternative to several standard-of-care cytogenetic assays such as karyotyping (KT), fluorescence in situ hybridization (FISH) or chromosomal microarray (CMA). Our laboratory at Augusta University has published numerous publications on the analytical and clinical utility of optical genome mapping (OGM) as a first-line assay for hematological malignancies. Electronic genome mapping (EGM) pursues the same goal with an electronic rather than optical readout. The objective of this study was to demonstrate the performance and utility of EGM as a valuable tool to detect clinically relevant SVs and CNAs found by SOC testing. Cytogenomic characterization of hematologic malignancies commonly requires multiple complementary assays, including karyotyping, FISH, and chromosomal microarray, each with distinct analytical limitations. Electronic genome mapping (EGM) provides genome-wide interrogation of structural variants and copy-number alterations using electronic single-molecule detection. We evaluated the feasibility and analytical performance of EGM in previously characterized myeloid malignancies. Methods: EGM was performed on the Nabsys OhmX platform using five previously characterized bone marrow aspirates from cases with acute myeloid leukemia (AML) or chronic myeloid leukemia (CML). EGM calls were compared with findings from karyotyping (KT), fluorescence in situ hybridization (FISH), and OGM. Results: All five samples passed quality control, with contig N50 values ranging from 1.8 to 3.8 Mb and more than 90% of reference labels represented in the assembled contigs. EGM detected every SOC-reported abnormality including two t(9;22)(q34.12;q11.23)/BCR::ABL1 rearrangements, trisomy 4, trisomy 21, and monosomy 7. In the most complex case, EGM resolved a del(20q) reported by KT and FISH into an intrachromosomal fusion, fus(20;20)(q11.21;q13.12), showing not only what was lost but where the chromosome rejoined. Like OGM, EGM identified an approximately 1.8 Mb loss at 7q22.1 involving 45 of genes of which only CUX1 is associated with cancer. This 7q loss was not detected by KT or FISH. Interestingly, EGM also detected the KMT2A-PTD variant previously detected by OGM. Collectively, EGM demonstrated 100% concordance (6/6 SOC-reported abnormalities; 9/9 OGM calls). Conclusions: In this pilot study on five hematological malignancy specimens, EGM matched SOC and OGM across translocations, aneuploidies, insertions and deletions, including BCR::ABL1 rearrangements in both AML and CML, and added high-resolution breakpoint detail beyond karyotype resolution. These results support larger studies across more subtypes to establish EGM as a first-line genome-wide cytogenomic tool for hematological malignancies. Keywords: Genome mapping, Electronic genome mapping, hematological malignancies, structural variants, copy number variants

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.

AO_SCPLOWBSTRACTC_SCPLOWHigh-dimensional flow cytometry measures immune cells at single-cell resolution, enabling systematic characterization of cell populations at scale. But harnessing this potential requires seamless interoperability between the interactive gating tools used by biologists and the statistical environments used for detailed downstream analysis. FlowJo, one of the most widely used commercial cytometry analysis software packages, now stores workspaces in a format that existing R tools cannot read, leaving researchers unable to import their gating strategies into R, or to return R-based results to FlowJo for visual review or collaborative sharing, without manual reconstruction. We present CyFj11, an R package that closes this gap, enabling import of FlowJo v11 gating hierarchies into R and export of R-defined gates back to FlowJo (throughout this paper, "import" refers to bringing a FlowJo v11 workspace into R, and "export" to writing an R-derived GatingSet back out to FlowJo). Using a combination of synthetic test scenarios and a real-world immunophenotyping dataset, we show that population counts in FlowJo 10 and 11 matched R-derived values with Pearson correlation coefficients exceeding 0.99. CyFj11 is platform-independent, requires no additional software infrastructure, and is freely available at https://github.com/C3BI-pasteur-fr/CyFj11.

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.

Chimeric antigen receptor (CAR) T cells are patient-derived T cells engineered to recognize specific antigens on malignant cells, representing a major breakthrough in personalized cancer immunotherapy. While CAR-T cell therapy is an established treatment for relapsed or refractory hematologic malignancies, understanding the rationale and methodology behind its development is essential for training the next generation of oncology researchers. Here, we describe a novel, hands-on small-enrollment laboratory course designed to teach upper-level undergraduate and graduate students about immunotherapies and CAR-T cell technology by paralleling a typical bench-scale development process. Students isolated T cells from porcine peripheral blood mononuclear cells (PBMCs) via immunomagnetic selection, followed by flow cytometry to assess purity, viability, and yield. Students then used pre-engineered B7H3 CAR-T cells to perform co-culture assays with U87 glioblastoma cells to evaluate tumor cell killing efficacy. Cytotoxicity was qualitatively evaluated using immunofluorescence microscopy, while IFN-gamma release was quantified using Enzyme-linked Immunosorbent Assay (ELISA). Student performance was evaluated through electronic laboratory notebooks, discussion questions, a capstone project, and student-generated data. Our results demonstrate students high academic performance across assignments and the successful execution of complex laboratory workflows. Collectively, these findings establish the feasibility and implementation of a research-integrated curriculum providing foundational exposure to immuno-oncology research tools and CAR-T cell production techniques.

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.

Lenalidomide (Revlimid), bortezomib (Velcade), and dexamethasone, are used alone or in combination (RVd therapy) as first-line therapies for hematologic malignancies including the plasma cell dyscrasia, Multiple Myeloma (MM). The effects of RVd treatment on tumor cells have been documented, but little is known about their impacts on "healthy" immune cells. New therapeutics, including chimeric antigen receptor T cell (CAR-T) and bispecific T-cell engagers (BiTEs) depend on robust T cell function but these are often harvested for cellular therapies after or in tandem with RVd therapy. Understanding the molecular effects of these drugs on healthy T cells is therefore of particular importance. Since the molecular effects of each drug includes regulation of key transcription factors, we used a tri-modal, single-cell assay (TEA-seq), simultaneously profiling mRNA transcripts, cell surface proteins, and chromatin accessibility in primary human peripheral blood T cells treated for 4, 24, or 72 hours in vitro. Synergies and conflicts potentially arising from combinatorial therapy were identified and validated by flow cytometry. Doublet and triplet drug combinations were used to assess effects on cell-surface marker expression and on T cell activation. Our results suggest that T cell function can be optimized by the administration of these drugs individually and that sequential administration may offer an opportunity to enhance T cell function by affecting localization and altering T cell states. This work provides those interested in immunology, oncology, and cell therapies an opportunity to explore molecular pathways that could be manipulated by these commonly used drugs to modify T cell function.

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

Flow cytometry is central to clinical hematology, generating extensive archives comprising billions of single-cell profiles linked to expert-derived diagnostic summaries. While these records represent a largely untapped repository of collective expert knowledge, the underlying single-cell annotations required for supervised machine learning are typically not recorded in a standardized, accessible format. We present IMPGAT (implicit gating), a weakly supervised multiple-instance learning framework that reconstructs single-cell gating strategies directly from existing sample-level cell-type frequency distributions. The model utilizes an induced set attention mechanism to map single-cell marker profiles to specific cell populations, requiring neither explicit cell-level labels nor unsupervised clustering during training. We evaluate the framework on acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) cohorts, totaling 711 measurements from 268 individuals and 240 million single cells. In ALL, the model's quantification of cell-type abundances shows no statistically significant difference from human inter-expert variability. In AML, the framework achieves blast quantification on par with a recently established supervised state-of-the-art model that relies on explicit single-cell annotations for training. Error analysis shows that mismatches between model predictions and expert gating primarily involve expert-labeled non-cellular events, reflecting the non-incorporation of any explicit physical prior knowledge (such as the geometric relationships traditionally used to identify doublets and debris in scatter cytograms) in model training. Conversely, model certainty correlates with phenotypic borderline cases where manual bisection is inherently difficult. Our approach enables the systematic utilization of retrospective clinical records for the development of automated, standardized gating logic and is applicable to broader high-throughput contexts, such as large-scale immune monitoring or phenotypic drug screening.

BackgroundStrategies to define human monocytes by flow cytometry vary considerably across studies. Recently, toll-like receptor 2 (TLR2) has been proposed as a marker to identify "all monocytes" in human peripheral blood. However, the TLR2-defined monocytes also contained a previously ignored TLR2posCD14dim/negCD16neg population, which we termed the unclassified subset (UCS). MethodsPeripheral blood mononuclear cells (PBMCs) from healthy donors and patients with multiple myeloma (MM) or monoclonal gammopathy of undetermined significance (MGUS) were analyzed by multiparameter flow cytometry using TLR2pos gating. PBMCs from additional healthy donors were analyzed to characterize the UCS population, including the impact of using either TLR2pos or a negative selection-based gating strategy. ResultsThe TLR2pos CD14dim/neg CD16neg UCS population was present in healthy controls, MGUS, and MM patients. The UCS expressed the monocyte-macrophage scavenger receptor CD163 and was significantly reduced in MM patients compared to healthy donors (P<0.002). Further phenotypic characterization in healthy blood donors revealed that approximately 80% of UCS cells expressed CD163 at levels comparable to classical monocytes, yet phenotypically resembled CD163pos dendritic cells (DCs). Importantly, gating strategies influenced the composition of the UCS: negative selection-based gating captured all DC subsets, whereas TLR2pos gating primarily included CD1cpos DCs that were highly CD163pos. ConclusionsThese findings demonstrate that circulating CD163pos CD1cpos DCs are included in the TLR2pos cell population previously described as exclusively monocytes, highlighting the impact of gating strategy on monocyte subset identification. Further, the lower level of TLR2pos CD14dim/neg CD16neg CD163pos cells in MM patients may represent decreased levels of circulating DCs that may contribute to the immune dysregulation in this disease.

Natural killer (NK) cells display striking variability in their cytotoxic responses, but whether this variation reflects random events, stable differences between cells, or effects of previous encounters remains unclear. We developed a Bayesian framework that uses single-cell interaction histories to disentangle these sources of variability. The framework models target encounters and killing decisions to quantify population heterogeneity, determine the sample sizes needed to distinguish competing mechanisms, and separate stable cell-to-cell differences from history-dependent behaviour. Synthetic data established when these mechanisms can be reliably distinguished in practice. We tested the framework using time-lapse imaging of NK cells exposed to rituximab or CC-96673, an antibody co-targeting CD20 and CD47. Despite similar overall killing, the framework identified distinct underlying responses: rituximab mainly increased mean killing rate, whereas CC-96673 reduced cell-to-cell variation. Event-count analysis supported continuous population heterogeneity, while ordered interaction histories revealed that both stable differences in killing propensity and previous encounters shape cytotoxic decisions. To make the framework directly usable, we introduce BARRACUDA, an open-source web platform and Python package implementing these analyses, including donor-aware extensions, for single-cell cytotoxicity datasets.

The field of microglial research has evolved throughout the years. Microglia, the immune cells of the central nervous system, are recognized as highly heterogeneous and dynamic cells, known for modifying their structure and function based on the local context. Investigating microglial density, spatial distribution, and morphological states is important for uncovering their distinct functional states in health and pathology. However, quantifying these features accurately is often a methodological challenge. Fully automated computational approaches often fail to capture subtle biological nuances and complex structural variations. Also, the high diversity of image sets makes it difficult to maintain consistent reliability. In contrast, entirely manual quantification is labor-intensive and prone to observer bias. To bridge this gap, we propose a semi-automated framework using the open-source software CellProfiler. Our workflow is divided into two distinct pipelines designed to combine automation of batch analysis with targeted user oversight, allowing for manual intervention when necessary to ensure maximum accuracy. Both pipelines are capable of recognizing microglial soma and tracing their processes. In the density workflow, it automatically calculates cell density and provides spatial distribution measurements, such as closest-neighbor distance and spacing index. For morphological profiling, it yields extensive structural data, including area, perimeter, convex area, form factor, and various shape descriptors. Furthermore, we demonstrate how researchers can optimize the pipeline settings to accommodate varied image datasets and experimental conditions. We hope this open-source framework can standardize microglial density, distribution, and morphological analysis and reduce systematic bias, providing researchers with a robust tool to better characterize their heterogeneity.

Background: The clinical course of acute leukemias and related hematologic neoplasms is defined by aggressive clonal proliferation, severe hematopoietic disruption, and recurrent molecular alterations. Although serial bone marrow biopsies, multiparameter flow cytometry, and next-generation sequencing remain standard surveillance modalities, procedural invasiveness, high costs, and operational turnaround times limit their frequency. Minimally invasive, scalable biomarkers integrating routine hematologic indices, targeted gene expression, circulating oncoprotein levels, and patient compliance metrics may provide an accessible framework for dynamic monitoring. Methods: In this longitudinal cohort study of 66 patients across 272 serial encounters (discrete follow-up intervals: days 0, 14, 41, 71, and 78), we assessed the prognostic value of peripheral complete blood counts (white blood cell count [WBC], hemoglobin [Hb]), molecular indicators (FLT3, NPM1, and DNMT3A transcript levels; targeted soluble FLT3 quantification), and medication adherence. The primary clinical endpoint was longitudinal progression to critical deterioration or disease-related mortality. Generalized linear mixed-effects models (GLMM) with patient-specific random intercepts were constructed to account for intra-subject repeated measures. Predictors were standardized per 1-SD increment. Model performance was assessed via the Akaike Information Criterion (AIC) and the Concordance index (C-index). Results: Leukocyte expansion correlated inversely with hemoglobin concentration (r=-0.877, p<0.001), reflecting bone marrow infiltration and secondary erythroid suppression. Multivariable mixed-effects modeling confirmed that elevated WBC count (adjusted odds ratio [aOR] = 1.52 per 1-SD increase; 95% CI: 1.24-1.86; p<0.001) and reduced medication adherence (aOR = 0.58; 95% CI: 0.44-0.76; p<0.001) independently predicted adverse clinical transition. Integrating targeted molecular markers (FLT3 expression and soluble protein levels) with routine hematologic indices and adherence demonstrated superior calibration and discrimination over single-domain models ({Delta}AIC[&ge;]116; Harrells C-index = 0.81). Conclusions: Readily accessible peripheral hematologic indices, targeted molecular quantification, and medication adherence provide complementary prognostic information regarding disease activity. Integrating these low-burden parameters into clinical workflows offers an efficient, scalable paradigm for early risk stratification in hematologic malignancies.

Pairing label-free microscopy with virtual staining could reduce the cost and experimental burden of fluorescence microscopy, but its impact is conditional on generalizable inference. Most virtual staining studies assess performance using image quality assessment (IQA) metrics developed for natural images, yet how well these metrics translate to microscopy remains unknown. Here, we examined the behavior of seven commonly-used full-reference training objectives and metrics, MAE, PSNR, SSIM, foreground PSNR and SSIM, LPIPS, and DISTS, under controlled image degradation and realistic out-of-distribution virtual staining. We applied graded intensity, textural, and morphological transformations to Cell Painting images spanning 18 cell lines, seeding densities, and fluorescence channels. Channel, cell line identity and seeding density explained substantial metric variation after controlling for degradation magnitude. DISTS and foreground metrics showed more favorable balance between degradation sensitivity and biological invariance, although no metric reported performance independent of biological context. Incrementally degrading images and evaluating concomitant metric degradation further revealed that most metrics used only a small fraction of their nominal numerical ranges and frequently plateaued while image degradation visibly continued. We next trained three popular virtual staining model architectures (UNet, WGAN-GP, UNeXt) on five U2-OS seeding densities separately, and computed metrics on model predictions across 17 unseen cell lines. We observed that architecture and training U2-OS seeding density together explain less than 2% of metric variation. Visual inspection suggested comparable scores across cell lines correspond to qualitatively distinct errors, such as differences in cell morphology and marker intensity. These findings show that conventional IQA metrics do not effectively translate to virtual staining applications. Selection or optimization of virtual staining models against real application such as in label-free high content drug screening should instead be approached in an application-oriented fashion.

Circulating cell-cell complexes (doublets) provide critical insights into in vivo immune interactions. Here, we leverage high-dimensional imaging spectral cytometry to systematically map the landscape of immune doublets in human peripheral blood. By integrating morphometric imaging features with a 22-color spectral panel, we established a gating workflow that cleanly segregates genuine, physically interacting complexes from coincidental transits and platelet contaminants, and resolved 12 highly pure homotypic and heterotypic immune doublet configurations in peripheral blood mononuclear cells (PBMC). At steady state, each doublet population displayed unique abundance, affinity and surface phenotypic profiles relative to circulating singlets. Principal component analysis (PCA) of quantitative imaging features further revealed lineage-specific topologies distinguishing singlet cells, homotypic doublets, and heterotypic pairs. Finally, comparative cohort analysis demonstrated that while doublet frequencies, affinities and surface phenotypes remain comparable between healthy donors and patients with tuberculosis (TB) disease, imaging-derived features uncovered disease-specific alterations in doublets morphology. Specifically, high-dimensional clustering using imaging parameters showed that TB disease significantly enriches for a tightly synapsed T cell-monocyte cluster characterized by high CD3-CD14 pixel correlation. Collectively, this study establishes a robust framework for mapping circulating cell-cell networks and demonstrates the superior sensitivity of imaging features over conventional fluorescence for capturing functional immune interactions in health and disease.