Cytometry Part A

IntroductionThe advent of full spectral flow cytometry has enabled the development of complex panels with over 35 colors, with the latest panels reaching 50 colors (1). This capability is made possible by cytometers equipped with numerous detectors beyond those in traditional cytometers and an expanded range of fluorochromes with emission peaks across the visible spectrum. However, our observations reveal significant challenges in the current unmixing, spread prediction, and panel design methodologies. Existing tools and guidelines, largely optimized for panels with up to 20+ colors, are limited in their ability to navigate this new ultra-high-color landscape. Without improvements in unmixing algorithms, predictive tools for spread, and design strategies, researchers risk creating suboptimal panels and obtaining inaccurate results. This article aims to highlight a range of emerging challenges associated with ultra-high parameter flow cytometry, particularly for practitioners accustomed to conventional panel design and analysis. As the field advances toward increasingly complex multiparameter experiments, novel issues have surfaced--many of which were previously unrecognized. Although this work does not provide comprehensive solutions to all of these observations, it underscores the need for continued methodological development. We anticipate that ongoing research by experts in the field will yield robust frameworks to address these challenges and advance best practices in high-dimensional cytometric analysis. Brief descriptionO_LIDifferent unmixing/compensation algorithms can result in different biological interpretations from the same raw dataset. C_LIO_LIA method to identify the optimal unmixing algorithm for accurate analysis is discussed. C_LIO_LIBoth panel size and specific fluorochrome combinations significantly impact population spread. C_LIO_LISSM (Spillover Spreading Matrix) values are influenced by panel size and fluorochrome combinations, which, if not carefully evaluated, may lead to misleading conclusions during panel design. C_LI

Single-cell methods such as flow cytometry, Mass cytometry and single-cell mRNA sequencing collect high-dimensional data on thousands to millions of individual cells. An important aim during the analysis of such data is to classify cells into known categories and cell types. One commonly used approach towards this is clustering of cells with similar features followed by manual annotation of clusters in relation to known biology. A second approach, commonly used for cytometry data relies on manual sorting or "gating" of cells, often based on pairwise combinations of measurements used in a stepwise and very tedious process of cell annotation. Both of these approaches require manual inspection and annotation of every new dataset generated, a process that is not only time consuming but also subjective and surely influential for the conclusions drawn. The manual annotation is also difficult to reproduce by other researchers with a different perception of features that signify their cells of interest. Here we propose an alternative strategy based on machine learning of known phenotypes from manually curated, high-dimensional data and thereby enabling rapid classification of subsequent datasets in a more reproducible manner. This simple approach increases both throughput, reproducibility and simplicity of cell classification in single-cell biology.

As the dimensionality, throughput, and complexity of cytometry data increases, so does the demand for user-friendly, interactive analysis tools that leverage high-performance machine learning frameworks. Here we introduce FlowAtlas.jl: an interactive web application that bridges the user-friendly environment of FlowJo and computational tools in Julia developed by the scientific machine learning community. We demonstrate the capabilities of FlowAtlas using a novel human multi-tissue, multi-donor immune cell dataset, highlighting key immunological findings.

Although spectral flow cytometry has become a ubiquitous tool for cell analysis, the use of spectral cytometry on cell sorters requires additional considerations arising from the unique requirements of sorting workflows. Here, we show that care should be taken when ascertaining the purity of a sort on a spectral cell sorter, as the mismatch of buffers used for initial sample suspension and the buffers used for sort collection can affect the unmixing of the data, potentially giving rise to erroneous purity check results.

Single-cell flow cytometry stands as a pivotal instrument in both biomedical research and clinical practice, not only offering invaluable insights into cellular phenotypes and functions but also significantly advancing our understanding of various patient states. However, its potential is often constrained by factors such as technical limitations, noise interference, and batch effects, which complicate comparison between flow cytometry experiments and compromise its overall impact. Recent advances in deep representation learning have demonstrated promise in overcoming similar challenges in related fields, particularly in the context of single-cell transcriptomic sequencing data analysis. Here, we propose flowVI, a multimodal deep generative model, tailored for integrative analysis of multiple massively parallel cytometry datasets from diverse sources. By effectively modeling noise variances, technical biases, and batch-specific heterogeneity using probabilistic data representation, we demonstrate that flowVI not only excels in the imputation of missing protein markers but also seamlessly integrates data from distinct cytometry panels. FlowVI thus emerges as a potent tool for constructing comprehensive flow cytometry atlases and enhancing the precision of flow cytometry data analyses. The source code for replicating these findings is hosted on GitHub, theislab/flowVI

High-dimensional cytometry technologies such as flow cytometry (FCM) and mass cytometry (CyTOF) are central to immunophenotyping in research and clinical practice. While manual gating remains the standard for cell population annotation, it is time-consuming, difficult to scale, and subject to inter-operator variability. Supervised annotation methods have emerged as a way of scaling manual annotation work, yet independent benchmarks for comparing these tools remain limited and quickly become outdated. This study presents a reproducible and extensible benchmark of supervised cytometry annotation tools implemented within the OmniBenchmark framework. Five supervised annotation methods were evaluated, spanning linear models, nearest-neighbor approaches, tree-based classifiers, mixture-rule systems, and deep learning, across eight publicly available datasets carefully selected to cover technologies, tissues, panel designs, and healthy and disease contexts. Using a sample-centric cross-validation design that reflects common reference-mapping scenarios, overall and per-population F1 scores, performance on rare populations, runtime, and robustness to reduced training set sizes was tested. Performance varied substantially across datasets and was not fully explained by dataset size or dimensionality, highlighting both operator dependence in annotation and the importance of biological context, cohort heterogeneity, and population imbalance. Less prevalent populations (<1%) remained a key challenge for most methods. Downsampling analyses showed that moderate reference sizes were often sufficient to achieve near-maximum performance. Rather than ranking methods, this benchmark provides a standardized and transparent framework for evaluating annotation tools under realistic deployment conditions. As a living resource, the OmniBenchmark implementation supports continuous integration of new datasets, tools, and metrics for both tool developers and end users annotating datasets. This enables ongoing, reproducible method comparison and informed tool selection for diverse cytometry applications.

MotivationHigh-parameter cytometry is widely used in longitudinal studies, but technical variation across batches can confound biological signals. However, tools that objectively identify problematic batches and markers are limited. ResultsWe introduce cytoFlagR, a comprehensive tool 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 Movers Distance (EMD) of signal intensity distributions. Additionally, cytoFlagR identifies cell type specific batch problems via unsupervised clustering and is suitable for mass and spectral cytometry datasets where it objectively detects distinct types of batch issues. We demonstrated cytoFlagRs utility for assessing datasets that include or lack reference controls. Thus, cytoFlagR improves quality control by objective identification of technical variations that may impact downstream analysis. Availability and ImplementationcytoFlagR is freely available as R scripts with documentation and an example at https://github.com/AndorfLab/cytoFlagR. Contactandorfsa@ucmail.uc.edu

AI-based image recognition has significantly advanced the analysis of tissues and individual cells both in the context of translational studies and diagnostics. To date, recognition is primarily based on the identification of certain cell characteristics (e.g. by staining). The morphological assessment of unstained cells holds additional potential, as it allows for virtually real-time assessment without the need to manipulate the cells. This facilitates longitudinal observations, as required for drug testing, and forms a basis for autonomous experimental execution. A semi-automated cell culture system (AICE3, LabMaite) was used to culture myeloid leukemic cell lines (K562, HL-60, Kasumi-1). K562 cells were treated with hemin and PMA to induce erythroid and megakaryocytic differentiation, respectively. Cell images were acquired using automated bright field microscopy. Images were used to train an AI model using an NVIDIA DGX A100 GPU with Ultralytics YOLOv8. Morphologic features were extracted using RedTell. The model reliably distinguished K562 cells from HL-60 and Kasumi-1 using >400 images per class (average >15 cells/image). Bounding boxes were generated correctly (mAP@.5 >98%); precision and sensitivity exceeded 97%. Validation on an external K562 dataset confirmed these results. Classification of all three cell lines achieved >97% sensitivity/specificity and 94.6% precision. To test drug response, we used YOLOv8-s to distinguish untreated K562 cells from those undergoing erythroid or megakaryocytic differentiation (n >3,000 annotations). Precision, sensitivity, and specificity were >95%. RedTell identified 3 of 74 morphological traits contributing significantly to class separation. We demonstrate accurate, near real-time detection of unstained cells, enabling future AI-based drug testing.

BackgroundImaging flow cytometry (IFC) provides a high quantity of single-cell morphological data, yet the field lacks open access tools for designing interpretable, bespoke parameters. In particular, rare and atypical cell populations where well annotated data is limited, are negatively affected. ResultsWe present Flow cytometry Feature Importance (FlowFI), an open-source graphical software for bespoke image parameter design and analysis. FlowFI provides a suite of image parameter options combining data across multiple channels and markers, tailored digital noise reduction (reducing noise resulting from common flow cytometry ultra-high image acquisition modalities), and a scalable, unsupervised feature selection pipeline that allows experimentalists to refine image-derived parameters iteratively, with a novel ensemble subsampling approach that provides robust feature importance scoring. We validated FlowFI using data from a rare and heterogenous bone marrow cell type, megakaryocytes, demonstrating that the tool can successfully identify novel, discriminatory morphological features to improve the purity of selected cell populations and gating strategy. ConclusionFlowFIs core functionalities are interacted with through an intuitive user interface for researchers with options to export data directly to common image and flow cytometry software formats. With this in mind, FlowFI offers a scalable way to both feature design, and feature refinement using a range of approaches to manifold learning, augmented by a data efficient bootstrap subsampling approach for unsupervised parameter recommendations in the big data regime. The software also introduces a new feature selection measures based on common manifold learning methods in the space inspired by the Uniform Manifold Approximation and Projection (UMAP) algorithm and finds performance comparable to existing methods. FlowFI provides a versatile testing ground for future developments in broad and dynamically developing areas of research including single cell analysis, label-free sorting and intra- and inter-cellular interaction analysis, while ensuring interoperability with current research workflows. Desktop installation options as well as detailed documentation can be found at https://github.com/EarlhamInst/FlowFI

We report the development of an optimized 50-color spectral flow cytometry panel designed for the in-depth analysis of the immune system in human blood and tissues, with the goal of maximizing the amount of information that can be collected using currently available flow cytometry platforms. We established and tested this panel using peripheral blood mononuclear cells (PBMCs), but included CD45 to enable its use for the analysis of human tissue samples. The panel contains lineage markers for all major immune cell subsets, and an extensive set of phenotyping markers focused on the activation and differentiation status of the T cell and dendritic cell (DC) compartment. We outline the biological insight that can be gained from the simultaneous measurement of such a large number of proteins and propose that this approach provides a unique opportunity for the comprehensive exploration of the immune status in tissue biopsies and other human samples with a limited number of cells. Of note, we tested the panel to be compatible with cell sorting for further downstream applications. Furthermore, to facilitate the wide-spread implementation of such a panel across different cohorts and samples, we established a trimmed-down 45-color version which can be used with different spectral cytometry platforms. Finally, to generate this panel, we utilized not only existing panel design guidelines, but also developed new metrics to systematically identify the optimal combination of 50 fluorochromes and evaluate fluorochrome-specific resolution in the context of a 50-color unmixing matrix.

Innovation in cytometry propelled it to an almost "omic" dimension technique during the last decade. The application fields concomitantly enlarged, resulting in generation of high-dimensional high-content data sets which have to be adequately designed, handled and analyzed. Experimental solutions and detailed data processing pipelines were developed to reduce both the staining conditions variability between samples and the number of tubes to handle. However, an unavoidable variability appears between samples, barcodes, series and instruments (in multicenter studies) contributing to "batch effects" that must be properly controlled. Computer aid to this aim is necessary, and several methods have been published so far, but configuring and carrying out batch normalization remains unintuitive for scientists with "pure" academic backgrounds in biology. To address this challenge, we developed an R package called CytoBatchNorm that offers an intuitive and user-friendly graphical interface. Although the processing is based on the script by Schuyler et al., the graphical interface revolutionizes its use. CytoBatchNorm enables users to define a specific correction for each marker in a single run. It provides a graph that guides you through quickly setting the correction for each marker. It allows corrections to be previewed and inter-marker effects to be checked as the settings are made. CytoBatchNorm will help the cytometry community to adequately scale data between batches, reliably reducing batch effects and improving subsequent dimension reduction and clustering. VISUAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/596492v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@600a1eorg.highwire.dtl.DTLVardef@13860cborg.highwire.dtl.DTLVardef@5ad915org.highwire.dtl.DTLVardef@6218dc_HPS_FORMAT_FIGEXP M_FIG C_FIG

Full-spectrum flow cytometry has demonstrated clear advantages over conventional approach. Cyteks Aurora platform has played a pivotal role in popularizing this technology, even though SONY introduced a full-spectrum flow cytometer earlier. Full spectral cytometers can identify the autofluorescence and unmix the autofluorescence much better than conventional cytometers. The default gain setting for Aurora, known as the Cytek Assay Setting (CAS), is effective for its intended purpose. However, there is no clear evidence to confirm that CAS provides optimal data resolution. This technical note outlines a methodology for enhancing gain settings to achieve a two-to four-fold improvement in resolution on average.

Full spectrum flow cytometry is a powerful tool for immune monitoring on a single-cell level and with currently available machines, panels of 40 or more markers per sample are possible. However, with an increased panel size, spectral unmixing issues arise, and appropriate single stain reference controls are required for accurate experimental results and to avoid unmixing errors. In contrast to conventional flow cytometry, full spectrum flow cytometry takes into account even minor differences in spectral signatures and requires the full spectrum of each fluorochrome to be identical in the reference control and the fully stained sample to ensure accurate and reliable results. In general, using the cells of interest is considered optimal, but certain markers may not be expressed at sufficient levels to generate a reliable positive control. In this case, compensation beads show some significant advantages as they bind a consistent amount of antibody independent of its specificity. In this study, we evaluated two types of manufactured compensation beads for use as reference controls for full spectrum cytometry and compared them to human and murine primary leukocytes. While most fluorochromes show the same spectral profile on beads and cells, we demonstrate that specific fluorochromes show a significantly different spectral profile depending on which type of compensation beads is used, and some fluorochromes should be used on cells exclusively. Finally, we provide a list of appropriate reference controls for 30 of the most commonly used and commercially available fluorochromes.

In the past decade, high-dimensional single cell technologies have revolutionized basic and translational immunology research and are now a key element of the toolbox used by scientists to study the immune system. However, analysis of the data generated by these approaches often requires clustering algorithms and dimensionality reduction representation which are computationally intense and difficult to evaluate and optimize. Here we present Cyclone, an analysis pipeline integrating dimensionality reduction, clustering, evaluation and optimization of clustering resolution, and downstream visualization tools facilitating the analysis of a wide range of cytometry data. We benchmarked and validated Cyclone on mass cytometry (CyTOF), full spectrum fluorescence-based cytometry, and multiplexed immunofluorescence (IF) in a variety of biological contexts, including infectious diseases and cancer. In each instance, Cyclone not only recapitulates gold standard immune cell identification, but also enables the unsupervised identification of lymphocytes and mononuclear phagocytes subsets that are associated with distinct biological features. Altogether, the Cyclone pipeline is a versatile and accessible pipeline for performing, optimizing, and evaluating clustering on variety of cytometry datasets which will further power immunology research and provide a scaffold for biological discovery.

Unraveling biological complexity, whether it be immune subset distribution in infectious disease(s), autoimmunity or tumor heterogeneity, requires technologies capable of single-cell proteomic analysis, such as flow cytometry. Surface immunophenotyping alone is often insufficient, as interrogating functional capacity is required to determine cellular mechanisms and effectively inform diagnostic biomarker discovery, therapeutics and vaccine development. However, large panels with intracellular markers are subject to numerous challenges, including spectral overlap and background cellular autofluorescence, reducing resolving power for rare subsets or populations defined by low-abundance expression. We posited that mass cytometry may overcome such limitations; to address this, three small (11-12-plex) clone-matched antibody panels were evaluated by spectral flow and mass cytometry. Panels were comprised of surface and intracellular targets (phospho-epitopes, transcription factors or cytokines) and designed to minimize fluorescence spectral overlap. CyTOF technology offered superior signal resolution across the range of intracellular targets. Improved signal-to-noise provided better resolution of phospho-events and transcription factor expression, in particular TOX and T-bet. Most strikingly, stimulation-specific IL-10+ and IL-13+ cells were only detected by CyTOF. Superior resolution of these cytokines enabled accurate population clustering, permitting more unique immune cell signatures to be found, including Tr1 and Tc2 populations, thus providing a more comprehensive picture of the immuno-diversity present. Our findings indicate that CyTOF technology could catalyze seminal discoveries in functional immune profiling, driving therapeutic design and diagnostics.

The advent of spectral flow cytometry has seen a rapid rise in the complexity of flow cytometry experiments, allowing the construction of assays with at least 50 fluorescent parameters. To correctly determine the contributions of each fluorophores signal to the high parameter data an accurate unmixing matrix needs to be generated. Even with single-stained controls, however, these matrixes include errors such as spillover spread, which compounds with each additional parameter, functionally limiting panel design. An additional source of errors is heterogeneity of cellular autofluorescence, which can affect both the unmixing matrix and misalign signals when the matrix is applied to individual cells in complex cell mixtures. Here we developed AutoSpectral, a statistical approach to automate the production of minimal-residual error unmixing matrixes and pair multiple distinct multifluorescent spectra to individual cells within a mixed sample, via an R-based software tool. AutoSpectral improves unmixing accuracy, improving incorrectly assigned cell positions by up to 9000-fold, reduces spread, particularly in samples with variable autofluorescence, and allows multi-lineage analysis of mixed populations, providing superior data for spectral flow cytometry experiments.

Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level, widely used in basic research and routine clinical diagnostics. Traditionally, data analysis is carried out using manual gating, in which cut-offs are defined manually for each marker. Recent technical advances, including the introduction of mass cytometry, have increased the number of proteins that can be simultaneously assessed in each cell. To tackle the resulting escalation in data complexity, numerous new analysis algorithms have been developed. However, many of these show limitations in terms of providing statistical testing, data sharing, cross-experiment comparability integration with clinical data. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of clinical meta data. MetaGate allows manual gating to take place in traditional cytometry analysis software, while providing a combinatorial gating system for simple and transparent definition of biologically relevant cell populations. We demonstrate the utility of MetaGate through a comprehensive analysis of peripheral blood immune cells from 28 patients with diffuse large B-cell lymphoma (DLBCL) along with 17 age- and sex-matched healthy controls using two mass cytometry panels made of a total of 55 phenotypic markers. In a two-step process, raw data from 143 FCS files is first condensed through a data reduction algorithm and combined with information from manual gates, user-defined cellular populations and clinical meta data. This results in one single small project file containing all relevant information to allow rapid statistical calculation and visualization of any desired comparison, including box plots, heatmaps and volcano plots. Our detailed characterization of the peripheral blood immune cell repertoire in patients with DLBCL corroborate previous reports showing expansion of monocytic myeloid-derived suppressor cells, as well as an inverse correlation between NK cell numbers and disease progression.

Morphology-based classification of cells in the bone marrow aspirate (BMA) is a key step in the diagnosis and management of hematologic malignancies. However, it is time-intensive and must be performed by expert hematopathologists and laboratory professionals. We curated a large, high-quality dataset of 41,595 hematopathologist consensus-annotated single-cell images extracted from BMA whole slide images (WSIs) containing 23 morphologic classes from the clinical archives of the University of California, San Francisco. We trained a convolutional neural network, DeepHeme, to classify images in this dataset, achieving a mean area under the curve (AUC) of 0.99. DeepHeme was then externally validated on WSIs from Memorial Sloan Kettering Cancer Center, with a similar AUC of 0.98, demonstrating robust generalization. When compared to individual hematopathologists from three different top academic medical centers, the algorithm outperformed all three. Finally, DeepHeme reliably identified cell states such as mitosis, paving the way for image-based quantification of mitotic index in a cell-specific manner, which may have important clinical applications.

With the recent growth in spectral flow cytometry many laboratories are investing in new spectral flow cytometers in order to maximise the information gathered about every cell. This study hypothesised that traditional cytometers already within many laboratories may be used as spectral cytometers and have shown using a range of different cytometers that data acquired may be unmixed after acquisition.

Selecting fluorochromes for polychromatic panels for flow cytometry is complex and time-consuming. Poorly designed panels can result in overlap between the emission spectra of the different fluorochromes, making their signals difficult to separate. While assessing all possible panels is simple to do programmatically, the combinatorial complexity of most real world problems renders brute force computation impractical. Here we present a novel complexity optimization algorithm for fast design of minimal spectral overlap fluorochrome combinations. To aid researchers in designing fluorochrome panels we implemented the algorithm in a web server, Spectracular, that also considers instrument laser configuration and allows users to define various constraints related to antibody-fluorochrome availability and marker co-expression.