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.
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.
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.
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.
Tumors that develop in the bone exhibit poor responses to most treatment options, including advanced immunotherapies. Despite its immune-rich composition, the bone marrow microenvironment often fails to suppress tumor progression and instead promotes tumor growth and immune evasion, although the underlying mechanisms remain poorly understood. To investigate tumor-induced remodeling of the bone marrow immune environment and characterize tumor-infiltrating immune populations, we developed a high-dimensional 22-marker spectral flow cytometry panel for use in preclinical models of bone and bone marrow cancers. The panel enables profiling of major immune populations including CD4+ T and CD8+ T cells, B cells, Natural Killer cells, dendritic cells, monocytes, macrophages and neutrophils, and incorporates activation, exhaustion and immune checkpoint markers. Panel optimization was performed using spleen, freshly isolated bone marrow and tibial osteosarcoma tissue from adult BALB/c mice. While optimized for murine osteosarcoma and bone marrow tissue, this panel provides a robust and adaptable platform for high-dimensional immune profiling across solid tumors and hematological malignancies.
Increasing evidence suggests connection between mitochondrial dysfunction and platelet-related disorders, necessitating developing of a high throughput method for assessing the functioning of mitochondria in platelets. Here we develop an approach to monitor the membrane potential of mitochondria simultaneously with intracellular calcium signaling in platelets using continuous flow cytometry. Platelets were loaded with Fura Red and DiOC6(3), which did not affect intracellular calcium signaling and the realization of platelet functional responses. The values were normalized using the negative controls with CCCP in order to make the signal independent of the platelet size and the number of mitochondria. Stimulation with ADP and TRAP-6 produced transient mitochondrial hyperpolarization, whose amplitude was directly proportional to the calcium mobilization. Platelets from patients with Wiskott-Aldrich syndrome (WAS) demonstrated reduced membrane potential of mitochondria in resting platelets in comparison to healthy donors, but their ability to increase the activity of mitochondria upon stimulation was not impaired.
Bispecific T cell engagers bring together T cells with malignant B cell targets to enable synapse formation and disease eradication of hematological malignancies. The proportion of T cells and their ability to bind to their targets varies between patients and may be a biomarker of response. In this study, we developed a method to detect T and B cell conjugates, as measured by conventional flow cytometry, and confirmed using imaging flow cytometry. After bispecific antibody engagement, multiple T cells were bound to each target cell in complexes comprising CD4+, CD8+, and CD4+ and CD8+ T cells. Imaging flow cytometry confirmed that the number and size of multimers increased significantly in the presence of bispecific antibodies. Conventional cytometry misidentified the memory phenotype of T cells bound to target cells due to augmented co-expression of CD45RA and CCR7 cell surface memory markers. Imaging flow cytometry verified conventional flow cytometry data showing increased T:B synapse and multimer formation after incubation with bispecific T cell engagers. Therefore, both flow cytometry platforms are suitable for identification of T cell: target cell conjugates.
High-dimensional cytometry, such as mass cytometry (CyTOF), measures protein expression in single cells. When paired with AI-enhanced data analytics, it facilitates the discovery of immune biomarkers that can assist in diagnosing and treating immune-related diseases. However, fluctuating instrument readouts, known as batch effects, can obscure biological patterns. To enhance our previously reported EPIC immune atlas platform, we developed the ImmuneMapBuilder app to integrate, annotate, and cluster CyTOF data. To evaluate biological stratification and batch effects in clustering results, we created a two-step visualization method, called Group Similarity Analysis (GSA). First, multidimensional immune profiles are projected into two-dimensional embeddings to reveal similarities between samples. Second, silhouette analysis of embedding coordinates quantifies cohesion within technical and biological groups. We illustrate the scope and effectiveness of GSA using six batch normalization methods across three datasets, demonstrating that batch-effect correction enhances the separation of age groups and tissue types. The goals of batch normalization are increased biological and decreased technical GSA scores, both of which correlate with decreased Earth Mover's Distance (EMD), an alignment indicator of signal distributions. To dissect the contributions of individual marker misalignments to batch effects, we introduce a mix-and-match strategy that combines normalized and raw channels. GSA also helps to compare meta-clustering outputs. In a case study, we confirmed the significance of CD62L expression in T cells for age-based stratification. Overall, GSA is a versatile method to evaluate clustering results from cytometry data.
Flow cytometry-based TBNK immunophenotyping is widely used to assess immune status in research, clinical diagnostics, and cell therapy development. Biological control materials such as PBMCs often serve as physiologically relevant controls, but suffer from considerable variability due to donor differences, limited stability, and fluctuations in antigen expression levels between lots. These factors make it challenging to achieve consistent assay performance, especially in longitudinal or multi-site environments. TBNK Cell Mimic (synthetic controls in PhenoCyte product line, developed by Slingshot Biosciences; henceforth referred to as TBNK Cell Mimic in this manuscript) are polymer-based cell mimics engineered to provide defined scatter properties, controlled antigen density, and stable subset ratios. Their scatter profiles are designed to be biologically relevant and comparable to those of native leukocyte populations. In this study, we performed a comprehensive analytical validation of a TBNK immunophenotyping assay using these TBNK Cell Mimics as standardized reference controls. Validation parameters included repeatability, intermediate precision, accuracy, linearity, specificity, robustness, stability, and carryover. The TBNK Cell Mimic met all predefined acceptance criteria, demonstrating ≤ 5% CV for intra- and inter-assay precision and R2 values > 0.998 across the linearity range. Accelerated stability studies performed at 25°C and 37°C showed < 5% variation in population frequencies, supporting the material's suitability for extended quality monitoring. These results indicate that TBNK Cell Mimic provides a consistent and reproducible reference material that can support assay validation and routine performance assessment. While not a replacement for biological samples when evaluating donor-specific or viability-dependent biology, their stability and lot-to-lot consistency offer a practical tool for reducing technical variability and improving harmonization across instruments, operators, and testing sites.
Image-activated cell sorting (IACS) enables high-throughput cell classification by linking cellular morphology to physiology. While integrating advanced artificial intelligence (AI) can enhance the capture of subtle morphological heterogeneity, AI models inevitably introduce greater computational complexity when addressing complex problems, leading to increased analysis latency and latency instability. High latency implies longer chip lengths, while latency instability leads to incorrect sorting timing. To address this challenge, IACS can utilize field-programmable gate array (FPGA) as a stable, low-latency image analysis tool. Here, we developed a two-stage FPGA processing system for cellular data acquisition and real-time AI inference, utilizing the high-level synthesis (HLS) framework. By deploying a customized U-Net model on the AMD-Xilinx accelerator card and integrating hardware acceleration modules including activation function approximation and pixel-level convolution acceleration, we achieved a stable segmentation latency of 3.06 milliseconds (ms) at a 272 MHz clock frequency and delivered a processing throughput of up to 16,601 frames per second (fps). Using the morphological parameters obtained after segmentation, we successfully separated deformed HeLa cells from normal cells and distinguished colorectal cells, red blood cells, HeLa cells, and microspheres. This work provides IACS with a stable and low-latency image processing solution.
We completed an international, multicenter, randomized, open-label Phase I/IIb trial assessing the safety and preliminary efficacy of transendocardial injection of autologous expanded CD34+ cells (ProtheraCytes) in patients after acute myocardial infarction (AMI; NCT02669810). A multicenter, randomized, controlled Phase III study is now being initiated. To support release of ProtheraCytes clinical batches, we validated two flow cytometry methods for accurate quantification of CD34/CD45+ cells (stem cell enumeration-SCE method) and characterization of accessory leukocyte subsets (monocytes, granulocytes, and B, T, NK lymphocytes-accessory populations immunophenotyping method). All the recovery rates for both methods, with calculations derived from QC materials specifications, met the acceptance criteria, based on precision assessment according to ICH Q2(R2), European Pharmacopeia (Ph. Eur. 2.7.23 and Ph. Eur. 2.7.24), and ISHAGE guidelines. In addition, the precision results (repeatability and intermediate precision) were lower than 28.3% (≤ 30% for accessory populations immunophenotyping method) and lower than 13.5% (≤ 25% for SCE method). Finally, a perfect linearity was demonstrated for SCE method across 1.7-2622.5 cells/μL with coefficient of determination (R2) of linear regression above 0.99 and matrix effects nearly negligible for both methods. The specificity, precision and accuracy of these methods were proven in the analysis of six determinations per operator in three different series. Altogether, these results indicate a good accuracy and precision of the proposed methods determining absolute counts, viability, and proportions of live CD34/CD45+ cells and accessory populations. This validated flow cytometry assay will be implemented for release testing in the forthcoming Phase III clinical trial of ProtheraCytes in post-AMI patients.
The introduction of full spectral technology in flow cytometry has facilitated access to an increasing number of markers to define cell subsets with higher precision. Cell sorting has a unique advantage to combine high throughput single cell analysis and recovery of rare live single cells for further downstream multi-omics analysis. Many studies have focused on advancing high dimensional single cell analysis; however, strategies to maximize cell sorting recovery in the context of deep immunophenotyping remain poorly defined. In this study, we evaluated sort performance in a six-way simultaneous cell sort setup. We modified a protocol using counting beads to assess absolute count in different sort decision criteria or modes. We demonstrate that the number of events collected can vary as much as 20% from the values indicated by the sort counter and is dependent on sort mode. Using the absolute count assay, we confirmed optimal conditions for six-way sorting of diverse human peripheral blood cell subsets defined by a 35-color panel and delineated pitfalls that can ultimately lead to suboptimal yield. Together, these findings provide novel insights into optimization of sort performance for advanced sorting and introduce a new approach for refining strategies for the simultaneous isolation of complex or rare cell subsets.
Rapid advancements in mass and flow cytometry technologies have allowed researchers to generate and analyze high-dimensional single cell datasets, often utilizing upwards of 40 protein markers. Such high-parameter cytometry is increasingly used in longitudinal immunological studies, but technical variations across experimental batch runs can confound biological signals. To mitigate the impact on downstream analyses, many studies include reference control samples in every run, and several approaches exist to adjust for batch effects. However, tools that objectively identify problematic batches and markers present within a dataset are limited. We introduce CytoBatchFlagR, a comprehensive and interpretable tool designed to flag batch-related problems at the marker and cell cluster level based on robust statistical evaluations. Batch and marker variations are assessed based on median signal intensities of negative and positive cell populations and positive cell frequencies, along with Earth Mover's Distance (EMD) of signal intensity distributions. Additionally, CytoBatchFlagR identifies cell type specific batch problems via unsupervised clustering. The tool is suitable for mass and flow cytometry datasets where it objectively detects distinct types of batch issues. We developed and tested CytoBatchFlagR using three cytometry datasets to demonstrate its utility and performance. We also demonstrated CytoBatchFlagR's effectiveness in assessing datasets that include or lack reference controls. CytoBatchFlagR improves quality control by enabling objective identification of technical variations that may impact downstream analysis in high-parameter cytometry data. The tool uses a series of complementary metrics to identify potential batch-related problems at the marker and cell population level and presents the results through interpretable visualizations. This allows users to make informed decisions about whether to apply batch correction or exclude specific batches or markers from downstream analyses. CytoBatchFlagR is freely available as R scripts, with documentation and a tutorial to help users get started.
