Routinely used statistical hypothesis tests are often inadequate for longitudinal flow cytometry data, necessitating more sophisticated modeling approaches. Analyzing dynamic biomarker expression across hematopoietic maturation presents several analytical challenges, notably nonlinearity, serial correlation between subsequent stages, and varying total cell counts. This study addresses these issues by comparing traditional stage-by-stage hypothesis tests with two marginal modeling approaches: Generalized least squares (GLS) and a generalized linear model fitted with generalized estimating equations (GEE-GLM). We analyzed cell fractions expressing the dynamic biomarkers Bcl-2 (anti-apoptotic marker) and Ki-67 (proliferation marker), as well as the Bcl-2:Ki-67 ratio, across 20 stages of erythropoietic maturation in bone marrow aspirates from 25 patients with myelodysplastic syndromes (MDS), 25 patients with acute myeloid leukemia (AML), and 50 nonmalignant controls. The GLS modeling approach accounts for nonlinearity and correlation between subsequent maturation stages, while GEE-GLM additionally accounts for varying total cell counts by assigning greater weight to observations with larger cell counts. Instead of repeated stage-by-stage testing, GLS and GEE-GLM model the average maturation profile across different sub-populations, enabling visualization and robust population-level statistical inference. We demonstrate that variations in the total number of cells influence the estimated population mean profiles and their confidence intervals. This effect is particularly pronounced during early maturation stages, where low Ki-67+ cell counts can lead to substantially different estimates and conclusions when using the GEE-GLM approach. Conversely, the GLS approach offers the flexibility to model the unbounded Bcl-2:Ki-67 ratio, revealing differences between AML and nonmalignant patients across all 20 stages. The computational workflow and scripts are provided to facilitate reproducibility and application to similar time-dependent flow cytometry datasets.

Although routinely detected in human blood and tissue samples, immune cell doublets are typically discarded during data acquisition or analysis, often dismissed as technical artifacts arising from ex vivo cell handling. However, emerging evidence suggests that these immune cell doublets reflect biologically meaningful interactions, offering critical insights into immune communication during health and disease. In this review, we evaluate the current evidence supporting the existence of biological immune cell doublets across various human tissues, highlighting their diverse phenotypes and functions that distinguish them from technical noise. We explore how existing single-cell datasets and analytical frameworks can be repurposed to study doublets, and we examine the primary challenge in the field, which is the lack of definitive markers to distinguish biological doublets from artifacts. Finally, we examine how novel technologies, namely high-throughput image-enabled sorting, are facilitating the next generation of research on this topic.

Modern flow cytometry experiments routinely measure 18 or more fluorescent markers across many samples, patients, and tissues. As these experiments' complexity increases, manual gating becomes unacceptably inefficient and can introduce operator-to-operator variation. Computational cytometry algorithms such as unsupervised clustering, automated gating, and dimensionality reduction can increase the speed and reliability of these analyses, but using them requires time and expertise that many biomedical scientists lack. Cytoflow was built to bridge this gap. Cytoflow is open source, user-friendly point-and-click software written in Python that allows non-programmers to apply modern computational flow cytometry methods to their data sets. Additionally, Cytoflow's modules can be used directly in a Python script or a JupyterLab notebook. Finally, extending Cytoflow with new modules that support future applications is straightforward, and feedback from an active user community continues to guide ongoing development. As a result, Cytoflow can save an experimenter time and improve the reliability and reproducibility of their analysis. Cytoflow is available to download at https://cytoflow.github.io. Source code is hosted at https://github.com/cytoflow/cytoflow, and documentation is available at https://cytoflow.readthedocs.io.

Multiplex cytokine bead assays (MCBA) have previously been carried out on conventional (aka classical, or bandwidth) flow cytometers, and issues involving unmixing distortions were encountered when using the manufacturer's set-up beads for unmixing full-spectrum data. After resolving such issues, we introduce and characterize the first full-spectrum flow cytometry implementation of MCBAs. In more detail, the set-up beads were originally produced for conventional cytometers, and a temporary work-around was issued for the semi-spectral mode only: spectral acquisition but bandwidth data display and analysis. We proceeded to make our own reference fluorophore controls (RFCs), starting with polystyrene beads derivatized with carboxyl functional groups that were first conjugated with streptavidin and then titrated with PE-biotin. The autofluorescence profile of the in-house conjugated PE beads matched that of the kit beads, and proper use of PE and "APC" RFCs enabled normal spectral unmixing without any distortions. Comparison of the three modes, namely spectral, semi-spectral, and bandwidth, illustrated the advantages of the spectral mode, including precision and resolution. In addition to enabling proper spectral implementation of the MCBA, a full-spectrum approach opens the door for various types of more complex multi-analyte bead assays yielding richer information, which have previously remained out of reach in conventional cytometry.

Flow cytometers, based on an optical measurement principle, are widely used for cell characterization in a variety of medical applications. Especially in hematology, high counting accuracy is crucial. Therefore, it is essential to develop accurate but also fast coincidence correction methods. We developed a procedure for post-processing the time-dependent signals (pulses) captured by the detectors during the transition of particles or cells through the flow cytometer's laser beam. These pulses are characterized by their area, maximum intensity, standard deviation, and skewness. By using standard deviation versus area, we are able to detect significantly more coincidences in monodisperse suspensions than by using the common method of area or width versus height. This demonstrates the superior sensitivity and accuracy of our approach for coincidence detection. In polydisperse suspensions and whole blood, we successfully identify and distinguish coincidences between differently sized particles (e.g., 1 and 5 μm) and particles of the same size, as well as between thrombocytes and erythrocytes based on pulse skewness and standard deviation. Unlike the current state-of-the-art method for coincidence correction in cell concentration measurements, which relies on statistical evaluation through dilution series, the developed procedure identifies a specific number of coincidences for individual measurements.

Studies evaluating cellular phenotypes by cytometry techniques are increasingly facing analytical challenges due to the multitudes of samples and parameters that are evaluated concurrently. Spurious technical effects resulting from a lack of standardization can affect marker distributions and further complicate multi-sample analyses. User-friendly tools for exploratory data analysis to identify such technical effects in large datasets are lacking. To fill this gap, we present a novel R package, CytoScan, that evaluates inter-measurement variation in cytometry datasets and allows for detecting anomalous measurements after data acquisition. CytoScan can detect two types of anomalies: files with limited similarity to others within a dataset (outliers) and files with limited similarity to previously acquired high-quality reference data (novelties). Using simulations of skewed marker distributions and real-life technical effects, we demonstrate that CytoScan can accurately detect such anomalies. CytoScan can be applied to large cytometry datasets on consumer-grade hardware with informative visualizations, providing accessible quality control for more reliable analyses.

High-parameter flow cytometry (hpFCM) enables powerful immune profiling and biomarker discovery in clinical trials. However, its clinical adoption is hindered by assay variability, logistical challenges, and technical complexity. Ensuring data reproducibility and robustness requires stringent quality controls and standardized validation. We introduce a new concept for implementing hpFCM in drug development, a modular hpFCM approach. It features a 12-marker core panel with 21 additional markers distributed in five interchangeable modules, enabling flexible assay customization throughout clinical studies. This innovative design allows for dynamic modifications during clinical development, facilitating evolving immunophenotyping of T and NK cells and comprehensive biomarker analysis. Validation in accordance with CLSI H62 guidelines confirmed the assay's reliability across exploratory and secondary endpoints, demonstrating high accuracy, selectivity, repeatability, and reproducibility independent of the applied assay configuration. Key secondary endpoints, including CD8+ cytotoxic and regulatory T cells, achieved precision, with repeatability and inter-operator variability under 20% coefficient of variation (CV), while specimen stability was maintained over 4 days of ambient storage. For exploratory endpoints, 90% of analytes met repeatability criteria, with 55% remaining stable over 4 days. The modular approach enhances end-to-end solutions and assay adaptability, supporting both targeted and exploratory strategies for clinical biomarker analysis. Importantly, robust performance and data comparability among different assay configurations were confirmed in both whole blood and frozen PBMC samples. By addressing key limitations in current hpFCM workflows, this modular approach provides a scalable, agile, and cost-effective framework designed to support biomarker strategies during drug development. This framework was conceptualized to increase operational efficiency and enable reverse translational approaches by allowing the rapid integration of emerging markers. Consequently, this design facilitates dynamic immunophenotyping and biomarker discovery, streamlining the path from exploratory research to clinical implementation. Importantly, while the assay is designed for biomarker discovery and exploratory immune monitoring in clinical trials, its clinical utility, therapeutic-response performance, and feasibility in multi-site real-world trial logistics remain to be established in ongoing studies involving specific disease cohorts.

Cell-in-cell (CIC) structures are among the most intriguing cellular phenomena occasionally observed in human cancer specimens. Once regarded as incidental findings, accumulating evidence has linked specific CIC subtypes, particularly entosis, to tumor progression and patient prognosis. Despite growing interest, systematic investigation of entosis remains limited due to the labor-intensive nature of manual identification and analysis. Computational approaches are, therefore, needed to enable scalable and reproducible entosis detection. In this study, we developed and evaluated five morphology-driven deformable segmentation models alongside a YOLOv8 deep learning-based detection framework for automated entotic cell identification in BxPC3 (pancreatic) and MCF7 (breast) cancer cell lines. The deformable models were designed to capture complementary morphological characteristics, including entropy, spatial proximity, contour topology, and circularity. Comparative evaluation showed that deformable Models A and C achieved the highest sensitivity, with recall values ranging from 0.91 to 0.94 and F1-scores between 0.81 and 0.83, demonstrating robust performance across heterogeneous entotic morphologies. YOLOv8 achieved high overall accuracy (0.97) and specificity (0.98), indicating strong background discrimination, but exhibited lower recall (0.65) and F1-score (0.59), reflecting a conservative detection profile under extreme class imbalance, where entotic events comprised approximately 1% of all observed cells. While deformable models provided higher sensitivity and detailed morphological segmentation, YOLOv8 offered advantages in computational efficiency and rapid inference. Together, these findings highlight the complementary strengths of morphology-driven segmentation and deep learning-based detection, and support the future development of scalable hybrid frameworks for automated entosis analysis.

Archived patient samples (e.g., formalin-fixed paraffin embedded (FFPE) tissue blocks, cryoembedded tissue blocks, and frozen whole tissue samples) are a source of immune information for which corresponding patient data is often collected. These precious resources present a unique opportunity to garner clinically relevant immunological insights of disease. We optimized a technique to immunophenotype T cells from archived patient samples through spectral flow cytometry. We demonstrate that frozen whole tissue samples were feasible to immunophenotype using surface markers analyzed on a spectral flow cytometer. Similar to previous findings acquired from freshly collected tissue in our laboratory, in frozen tissue samples there was a higher frequency of inhibitory receptors: CTLA4, PD1, LAG3, and TIM3, in colorectal cancer (CRC) tumors compared to adjacent non-tumor bowel from the same patients. Developing the technique further would allow us to better understand how T cell heterogeneity affects CRC patient survival and prognosis.

Conventional bright-field (BF) cytology of thyroid fine-needle aspiration biopsy (FNAB) suffers from staining variability and limited subcellular contrast. Here, we present a refractive index-correlated pseudocoloring (RICP) framework that integrates quantitative refractive index (RI) maps obtained by holotomography (HT) with color BF images to enhance subcellular visualization and image interpretability. A digital micromirror device (DMD)-based HT system combined with an RGB LED illumination module enabled sequential acquisition of RI tomograms and BF images from Papanicolaou (PAP)-stained thyroid samples over the same field of view. The RICP algorithm adaptively embeds RI-derived structural information into a complementary hue channel while preserving the overall BF appearance. Applied to benign and malignant thyroid clusters, RICP highlighted subcellular features such as nucleoli, lipid droplets, and nuclear irregularities. Hue-saturation analysis revealed differences between benign and malignant clusters, while complementary image-fusion metrics and delentropy analysis both indicated systematic changes in structural image content after fusion. An ablation analysis further showed that the RI-derived input contributes structural content beyond that obtained when the RI map is replaced by an inverted Value channel of the BF image in HSV space within the same pseudocoloring framework. These results indicate that RICP provides a perceptually grounded approach for correlative multimodal visualization that bridges conventional color cytology and quantitative optical imaging.

In biological dosimetry a radiation dose is estimated using the average number of chromosomal aberrations per peripheral blood lymphocytes. This analysis is still manually performed on 2D metaphase images depicting the 23 pairs of chromosomes because the false discovery rate of current automated detection systems is too high and variable because of sensitivity to small variations in image quality (chromosome spread, illumination variations …). Therefore, the current systems are only used to assist human experts. Designing more performant automatic and reliable chromosomal aberration detection systems has become of paramount importance to improve diagnosis speed and reduce human expertise time. Here, we propose a novel deep-learning method for automatic rare chromosomal aberration detection and uncertainty quantification. We formulate the problem as a unique regression problem requiring the minimization of a sparsity-promoting loss to reduce the false alarm rate. Furthermore, we select checkpoints at the end of each epoch during training to form a model ensemble. The resulting artificial experts are further analyzed to derive a consensus voting, similar to an agreement of human annotator rating, to provide trustworthy aberration detections and confidence intervals. A radiation dose curve is finally derived from deep learning-assisted counting of dicentrics and fragments in metaphase images, in high agreement with the reference hand-crafted curve in biological dosimetry with a promising dose estimation validation.

Dogs are a critical non-rodent species used in preclinical safety studies, particularly, in the pharmaceutical field, due to their physiological, metabolic, and immunological similarities to humans. As such, immunophenotyping of canine peripheral blood mononuclear cells (PBMCs) plays a crucial role in translational research, immune monitoring, and safety evaluations in drug development. However, the limited availability of canine-specific antibodies restricts detailed and accurate immune profiling, which is essential for advancing safety evaluations in drug development. To address this challenge, we developed a 15-marker panel for comprehensive mass cytometry-based immunophenotyping of cryopreserved canine PBMCs. This panel encompasses major leukocyte subsets, including B cells, CD4+ T helper cells, regulatory T cells, CD8+ cytotoxic T cells, memory T cell subsets, natural killer T cells, natural killer cells, dendritic cells, CD4+ monocytes, classical monocytes, and neutrophils. We utilized both extracellular and intracellular markers to facilitate in-depth immune profiling, despite the limited availability of canine-specific antibodies. The panel was thoroughly optimized in terms of marker selection, antibody clone validation, and metal isotope pairing. Additionally, by the use of mass cytometry, several channels remain unoccupied, providing flexibility for future panel expansion.

Advances in spectral cytometry instrumentation and fluorescent reagents have led to the possibility of ultra-high-parameter panels exceeding 50 colors. However, panel size is limited in practice by unmixing-dependent spreading (UDS), a phenomenon which leads to a progressive deterioration of unmixed signal-to-noise ratios in panels that contain fluorochrome combinations with significant spectral overlap. So far, choosing spectrally compatible sets of fluorochromes that avoid UDS has been a complex and labor-intensive task involving substantial trial-and-error experimentation. Here, we provide a detailed explanation of UDS and practical strategies for handling UDS in large spectral panels. We describe the empirical hallmarks of UDS, demonstrate how to quantify its impact, and dissect its underlying mathematical cause in terms of spectral collinearity. We present practical tools derived from the regression literature that can be used to select optimal combinations of fluorochromes in a platform-agnostic fashion based on publicly available reference data, providing a general tool for spectral panel design.