In flow cytometry, raw detector values do not directly represent fluorochrome abundances and must be mathematically unmixed using coefficients derived from single-stained reference controls. Inaccuracies in these controls, referred to as reference errors, can distort the estimated fluorochrome abundances, leading to skewed population distributions. However, as panel complexity and dataset sizes increase, manually inspecting all marker combinations for such artifacts becomes impractical. To address this challenge, we developed CompensAID, an open-source R-based tool that automatically flags marker combinations potentially affected by reference errors, thereby supporting quality control workflows in flow cytometry. Preprocessed data from both conventional and spectral flow cytometry were used to develop and validate the tool. CompensAID applies a density-based cutoff detection to automatically gate the negative and positive populations. The positive population is then divided into equally sized segments, after which the Secondary Stain Index (SSI) is computed for each segment. Marker combinations are flagged if the last segment yields an SSI value below -1. The tool's performance was evaluated against the consensus of five flow cytometry specialists. For the conventional dataset, 1761 out of 2240 marker combinations (79%) were unanimously classified as free of reference errors, while 24 marker combinations received a consensus score of three or higher, indicating that most flow cytometry specialists suspected reference errors. CompensAID successfully identified 23 out of these 24 marker combinations, achieving a sensitivity of 0.96. The single missed case involved a spillover spread error. In the spectral dataset, 1505 out of 1656 marker combinations (90%) were deemed free of reference errors. Of the 28 marker combinations suspected of reference errors, CompensAID flagged 21 marker combinations (sensitivity, 0.74). Five out of the seven missed marker combinations had SSI values close to the threshold. In both datasets, false positive findings were observed, often attributable to suboptimal gating or low event counts within the segments contributing to negative SSI values. CompensAID offers a robust approach for detecting marker combinations exhibiting signs of reference errors. While some false positives occur, the tool significantly reduces the burden of manual inspection. Its integration into quality control pipelines can enhance the reliability of flow cytometry data analysis. The R package is available at https://github.com/Olsman/CompensAID and will be made available through the Bioconductor platform.
Recovery is a key performance parameter in cell sorters, a metric that assesses the match between the number of particles reported as sorted by the instrument and the actual number of particles gathered in the collection vessels. Sorting relies on the precise timing of the charging of a droplet containing the particle of interest in a critical measurement called drop delay (DD). DD timings are typically reliant on manufacturer recommended fluorescent bead reagents. Cuvette-based cell sorters in particular depend upon these fixed-sized QC beads for an automated approach to the DD calculation using an image-based camera system. Previous literature has highlighted the mismatch between these DD values and the settings best accommodating actual samples. Here, we present a new method for DD calculation-the Three-Puddle Method (3PM), based on procedures originally described for jet-in-air cell sorters; it optimizes DD values according to the target particle to be sorted, increasing sort recoveries for a range of cell sizes and particle types. With regard to recovery, 3PM-calculated DD values correlate with those achieved via optimum DD, defined using Rmax protocol, a robust metric for recovery. The advantages of the 3PM then are that it is a simple-to-implement protocol which has limited cell expenditure, essential in the handling of precious rare samples and the success of single cell and bulk sorting and the downstream applications relying on it.
For vaccination, the mechanisms of action of antigens and of immunostimulatory pathogen-associated molecular pattern (PAMP) adjuvants are better understood than the roles of the depot, carrier, and other formulation-related influences. Here, we carried out cytometric studies of vaccine formulations and the effect of adjuvant formulations on B-cell responses. In particular, the AddaVax squalene oil-in-water nanoemulsion formulated with MPLA and CpG, termed IVAX-1, which is particularly effective in eliciting influenza hemagglutinin (HA)-specific mono-reactive or dual-reactive antibodies, was used as the model adjuvant to explore the requirements for generation of these B-cell antibody responses in vitro and in vivo. Using an in vitro inducible germinal center (GC) reaction, we found that AddaVax-mediated pre-assembly of B-cell stimuli for antigen, TLR agonists, CD40 ligand, and cytokines enhanced antibody class switching compared to aqueous buffer solutions. Covalent HA labeling was used to provide high-fidelity detection of mono- and dual-reactive B cells. In immunized mice, MPLA and CpG together enhanced the numbers of total and HA-specific plasma cells relative to each alone. Interestingly, titration of green fluorescent protein (GFP) and fluorescent HAs delivered in AddaVax nanoemulsion revealed that > 50% of input antigens are adsorbed onto the surface of these nanoparticles at the level of ~50-200 protein antigens per nanoparticle. Likewise, several protein antigens, including HAs, a bacterial outer membrane protein, CBU1910 from Coxiella burnetii , and GFP, were all found to associate with AddaVax emulsion nanoparticles in flow cytometry assays. Finally, the AddaVax carrier was more potent in inducing total and dual-reactive antigen-reactive B cells compared to a liposomal carrier, the Immunosome-NTA(Ni). These data cumulatively indicate that concentrated oil-in-water nanoemulsions are effective carriers for molecular adjuvants and antigen in extemporaneous formulations of research-grade protein subunit vaccines.
Multiparametric flow cytometry (MFC) is widely used to detect measurable residual disease (MRD) in acute myeloid leukemia (AML). However, conventional flow assays require multiple tubes, with an additional tube for leukemia stem cell (LSC) analysis and lack hemodilution evaluation. Spectral flow cytometry (SFC) can overcome the limitation of flow channels and has the potential for multifunctional design using a single tube. We developed a 29-color single-tube assay that adheres to the recommendations of the European Leukemia Network Flow-MRD Working Party and incorporates the simultaneous evaluation of MRD, LSC, and hemodilution. The Complexity Index of the assay was calculated at 9.08. Through limit dilution experiments using the KG-1α AML cell line, we determined the limit of blank (LOB), limit of detection (LOD), and limit of quantification (LOQ) for four leukemia-associated immunophenotypes (LAIP). The assay easily achieved the minimum sensitivity requirement for MRD detection ≤ 0.1% with minimal intra- and interassay variations. Background signals for 24 LAIPs and 10 LSC immunophenotypes were evaluated in eight healthy bone marrow (BM) samples. The single-tube SFC assay was compared with the five-tube conventional assay by analyzing 20 AML BM samples, demonstrating high concordance. To assess hemodilution, markers to detect established parameters, including immature granulocytes, neutrophils, mast cells, and plasma cells, were included. In summary, we provide a versatile single-tube 29-color SFC-based MRD assay that minimizes cell requirements, integrates LSC evaluation, and assesses hemodilution. This assay has the potential to improve the reliability and simplicity of MRD detection.
This manuscript is the first in a series that develops and realizes core ideas from metrology and uncertainty quantification (UQ) as applied to flow cytometry. The work herein is motivated by the problem of estimating the detection efficiency (Q) and background (B) of cytometers. Despite more than 30 years of study, canonical solutions to this problem make approximations that both ignore and amplify various sources of noise, thereby leading to unstable estimators of Q and negative values of B . Moreover, it is not always clear how to compare instruments on the basis of such properties. To address these issues, we propose a global data analysis strategy that combines measurements taken with different gains while simultaneously accounting for gain-independent background effects, which are typically ignored but often dominant. Of note, this technique yields stable estimates of Q and B while also quantifying the relative impacts of other noise sources. Conceptually, our analysis also unifies and explains the shortcomings of existing data analysis methods. Most importantly, however, this work allows us to rigorously define concepts such as limits of detection and quantification associated with instrument performance alone and in a way that removes effects associated with sample preparation, operator effects, and so forth. Importantly, this allows for direct comparison of cytometers on the basis of sample-independent uncertainty metrics and yields information for optimizing cytometer performance in terms of instrument-induced uncertainties. Results are experimentally verified using both commercial instruments and a NIST-developed serial cytometer, with extensions considered in companion manuscripts of this series.
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.
Flow cytometers are powerful tools for bioanalytical applications, yet new systems that promise better measurements are continuously being introduced as sensors and other technologies advance. One such advancement by NIST was the recently demonstrated a serial microcytometer that enables unique capabilities for uncertainty quantification on a per-object basis. In an effort to benchmark and improve the measurement capabilities of the serial microcytometer, we found limitations to the quantitative comparison of instruments using conventional metrics and methods. To address these shortcomings, we recently developed an improved model that builds upon conventional models to improve comparability (Patrone et al. "Uncertainty Quantification of Fluorescence Signals in Flow Cytometry Part I: An Analytical Perspective Beyond Q and B" submitted in conjunction with this manuscript). In Part I, and continued here, our aim was to develop metrics that enable comparisons based on upper limit of linearity, limit of background, limit of detection, noise-to-signal ratio, and uncertainty decomposition thereof. We found that the NIST serial microcytometer has similar performance capabilities to a conventional analytical flow cytometer. This manuscript continues the development of uncertainty quantification (UQ) for flow cytometry by demonstrating how a serial microcytometer facilitates separation of the instrument-and population-dependent contributions to UQ. Component-level contributions to UQ can also be analyzed. Ultimately, these methods establish robust metrics for instrument performance and introduce per-object uncertainty as a mechanism facilitating better classification and utilization of cytometry data in research and clinical use.
Cytometry enables simultaneous assessment of individual cellular characteristics, offering vital insights for diagnosis, prognosis, and monitoring various human diseases. Despite its significance, the process of manual cell clustering, or gating, remains labor-intensive, tedious, and highly subjective, which restricts its broader application in both research and clinical settings. Although automated clustering solutions have been developed, manual gating continues to be the clinical gold standard, possibly due to the suboptimal performance of automated solutions. We hypothesize that their performance can be improved via an appropriate representation of data from the clustering point of view. To this end, this work presents a novel unsupervised deep learning (DL) architecture wherein an efficient cytometry data representation is learned that helps discover cluster assignments. Specifically, we propose MuSARCyto, a multi-head self-attention-based representation learning network (RN) for the unsupervised clustering of cytometry data, utilizing a fully-connected representation network backbone. To benchmark MuSARCyto against the state-of-the-art cytometry clustering methods, we propose a cluster evaluation metric adjudicator score ( Ad n ), which is an ensemble of prevalent cluster evaluation metrics. Extensive experimentation demonstrates the superior performance of MuSARCyto against the existing state-of-the-art cytometry clustering methods across six publicly available mass and flow cytometry datasets. The proposed DL achitectures are small and easily deployable for clinical settings. This work further suggests using DL methods for identifying meaningful clusters, particularly in the context of critical immunology applications.
Multiple sclerosis (MS) is a chronic, immune-mediated autoimmune disease characterized by the infiltration of autoreactive T cells and other inflammatory immune cells from the periphery into the central nervous system. Currently, there is no cure for MS, and treatment consists of disease-modifying therapies (DMTs), most of which modify or delete specific immune cell populations. These populations express crucial MS treatment-associated receptors, which may be differentially expressed in each patient, and thus each drug may affect individuals differently. Here, we developed the first comprehensive 24-parameter flow cytometry immunophenotyping panel to evaluate treatment-associated receptor expression on the major MS-associated immune cells in whole blood. Analyzing whole blood samples from treatment-naïve individuals with MS using this panel, we demonstrated that expression levels of these receptors vary between individuals. Response to the chosen DMT treatment also differed across participants. When monitoring the receptor expression during the course of treatment, we detected an increased response to treatment when receptor expression was elevated at the start of treatment. This panel reliably detects these receptors in MS treatment-naïve participants and enables monitoring of their expression throughout treatment. This tool will enable deep interrogation of the immune receptors targeted by MS therapies and highlights that treatment-associated receptor expression levels might be used to predict or correlate with treatment response.
The use of flow cytometry to investigate phytoplankton functional groups is rapidly expanding worldwide, using lab- or ship-based instruments or autonomous environmental monitoring platforms. Automation, coupled with greater autonomy, allows for higher spatial and temporal resolution of phytoplankton groups, enhancing understanding of their dynamics and patterns, generating large datasets. The level of resolution is determined by both instrumental capabilities and optimization of its acquisition settings. Sharing these datasets with the scientific community, whether to improve global phytoplankton distribution resolution or facilitate the intercomparison of environmental indicators among monitoring laboratories, strongly relies on quality-controlled instruments and standardized data acquisition and analysis. This article focuses on CytoSense-type (CytoBuoy, NL) flow cytometers, which operate by recording the optical pulse shapes of particles as they pass through a laser beam. Different configurations such as laser wavelength and power, sheath fluid management, sample inlet design, and dataset output format were not considered, in order to focus on optimization and protocol standardization to resolve the whole phytoplankton size spectrum, from the smallest autofluorescing prokaryotes to colonies and chain-forming species. In this study, coincidence, PMT voltage, trigger threshold optimization, and regular quality control procedures are considered and discussed, using datasets from three types of instruments and two contrasted marine coastal waters as case studies. The primary goal of this study is to establish a framework to guide and support the exploration and application of this type of flow cytometer, ultimately achieving a reliable and optimal resolution for sample acquisition of natural waters.
Rapid, quantitative analysis of single-cell proteins with arbitrary spatial distributions carries pivotal significance across fundamental biological research and clinical applications. However, as the golden method, flow cytometry can only quantify cell surface proteins due to non-uniform excitation illumination and the lack of calibration microbeads with known numbers of internal fluorescent molecules. This study reported a quantitative microfluidic flow cytometer utilizing aligned microfabricated metal apertures to selectively extract the central portions of Gaussian beams, forming a spaced uniform optical field. Based on this spaced uniform optical field, multicolor fluorescence signals originating from antibody-binding events with arbitrary distributions on single microbeads were converted into the number of proteins. The geometries of the microfluidic cuvette and the uniformity of fluorescence intensities across the excitation fields were quantitatively validated. Using this methodology, calibration curves with high linearities for all nine fluorescence channels were established, and a mixture of rainbow calibration microbeads was analyzed, where five types of fluorescent probes were quantitatively measured, enabling the classification of the rainbow mixture into five distinct subpopulations. The presented flow cytometer enables quantitative analysis of single-cell proteins with arbitrary spatial distributions and thus demonstrates strong potential for fundamental biological research and clinical applications at the single-cell level.
Quantification of T-cells, B-cells, and NK-cells assay is crucial for diagnosing and monitoring immune diseases and evaluating lymphodepleting therapies. To standardize and validate an optimized workflow of the Beckman Coulter DuraClone IM Phenotyping Basic Kit for quantification of TBNK subsets in peripheral blood. Procedural changes included the use of an alternative lysis buffer, the addition of counting beads, the elimination of centrifugation steps, and an increase in acquisition volume. Validation followed CLSI H42-A2 and H62 guidelines, assessing accuracy, precision, linearity, and limit of quantification (LLOQ). Accuracy was evaluated by comparison with Immuno-Trol controls and by Bland-Altman analysis against standard immunophenotyping methods. External proficiency was assessed through participation in the College of American Pathologists (CAP) TBNK program in 2024. Procedural steps were reduced by 50%, and processing time by 38.6%. The modified method demonstrated high accuracy (-3 < bias < 35; cells/μL), a low bias based on Immuno-Trol targets, and strong agreement in the Bland-Altman analysis. The method successfully passed three CAP external proficiency tests in 2024, confirming interlaboratory reliability. Coefficients of variation for precision were below 10% for all subsets. Linearity exceeded R 2 > 0.99 across clinically relevant ranges. Most subsets demonstrated an LLOQ below 10-50 cells/μL, which is suitable for clinical applications. The proposed modifications to the DuraClone IM kit protocol improved workflow efficiency and analytical performance without compromising accuracy or reproducibility. The validated method provides a standardized, reliable, and time-efficient alternative for lymphocyte subset quantification.
Extracellular vesicles (EVs) are potential disease biomarkers released by cells into body fluids. Flow cytometers measure EV concentrations within their size detection range. These size ranges can be quantified by relating the arbitrary units of measured light scattering signals to the optical diameter of EVs with Mie theory. However, Mie theory requires input parameters, including the refractive index (RI) of the suspension liquid surrounding the measured particles, which is not accurately known. Here we measure traceable RIs of suspension liquids, including Dulbecco's phosphate buffered saline (DPBS) and human blood plasma dilutions with an uncertainty of 1.4 × 10 - 5 for relevant wavelengths between 405 and 644 nm. We show that differences between traceably measured and currently used RIs for suspension liquids lead to differences in applied optical diameter gates and reported EV concentrations. The currently assumed RI of DPBS in Rosetta Calibration leads to a difference of 9% to 35% between reported and actual EV concentrations in diluted plasma, depending on the lower detection limit of the flow cytometer. A traceably measured RI of the suspension liquid should be used to calibrate light scattering signals with Mie theory to improve accuracy of optical diameter and concentration measurements by EV flow cytometry.
Cytotoxic CD8+ T cells eliminate virus-infected or cancer cells, thus playing a pivotal role in anti-viral and anti-cancer immunity. Tetramer reagents, which consist of fluorochrome-labeled streptavidin coupled with peptide-loaded MHC I molecules, enable the detection of antigen-specific CD8+ T cells using flow cytometry. The development of tetramer reagents has been instrumental for our understanding of antigen-specific CD8+ T cells and their roles in immune responses. More recently, combinatorial tetramer staining protocols have enabled the simultaneous detection and monitoring of multiple specificities and concomitant pathogen-dependent CD8+ T cell dynamics. However, these methods are either based on mass cytometry, preventing the isolation of antigen-specific CD8+ T cells for downstream investigation, or have provided a less comprehensive picture of the phenotypic characteristics of antigen-specific CD8+ T cells when based on flow cytometry. Here we describe the development of a combinatorial tetramer staining protocol in combination with high-dimensional CD8+ T cell immunophenotyping in the context of virus-specific CD8+ T cells leveraging spectral flow cytometry. Our assay enables the simultaneous measurement of 15 different CD8+ T cell specificities and includes an additional 18 markers to define the phenotypic and functional characteristics of antigen-specific CD8+ T cells. We describe our assay optimization strategies, with the goal of improving marker and tetramer resolution while eliminating sources of background noise. Finally, we apply this method to reveal the phenotypic heterogeneity of virus-specific CD8+ T cells against common viral pathogens in healthy individuals.
Over the past 6 decades, cytometry has evolved from a niche experimental method into a cornerstone of modern biomedical research. The development of the first cell sorter by Mack Fulwyler laid the groundwork for technologies that now define single-cell analysis. From the early challenges of instrument operation and laser alignment to today's era of automated, high-parameter systems, the field has undergone continual transformation. We now enter what may be termed the "diamond age" of cytometry, an era of exceptional sensitivity, resolution, and analytical depth, driven by innovations such as spectral flow cytometry. Here, we reflect on the historical milestones that shaped the discipline, the cultural and educational shifts within the community, and the future challenges of standardization and quantitative rigor.
Unraveling biological complexity, such as immune subset distribution in infectious disease(s), autoimmunity, or tumor heterogeneity, requires technologies capable of single-cell proteomic analysis such as flow cytometry. Surface phenotyping alone is often insufficient, as interrogating functional capacity is required to determine cellular mechanisms and effectively inform diagnostic biomarker discovery as well as development of novel therapeutics and vaccines. However, large flow 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 use of mass cytometry may overcome such limitations; to address this, three small (10-12-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. Overall, CyTOF technology demonstrated superior signal resolution compared with fluorescent counterparts for all three types of the intracellular targets that were compared. There was clear stimulation-specific resolution of IL-10 and IL-13 cytokine-producing cells from using mass cytometry that was not seen in the fluorescent panel and is not routinely detectable using that platform. Thus, accurate detection of immune cells with distinct functional signatures was enabled, permitting more unique and understudied populations to be quantified, including T regulatory 1 (Tr1) and cytotoxic type 2 (Tc2) T cell subsets. These results indicate that CyTOF technology has a unique ability to provide a high-resolution, comprehensive picture of the immuno-diversity present in a sample. Therefore, we posit that a new focus on use of mass cytometry for intracellular readouts could catalyze seminal discoveries in functional immune profiling, driving therapeutic design and diagnostics.
Insight into disease detection and treatment through comprehensive immune phenotyping relies on the generation of high-quality data. However, the execution of robust immune monitoring in clinical trials by flow cytometry is complex due to logistical challenges in sample preparation and reagent stability. These challenges are exacerbated when considering remote and rural communities in Australia, which are burdened by both an increased prevalence and a worse prognosis of many chronic and infectious pathologies. This is not unique to the Australian context as globally remote and rural communities are underrepresented in biomedical research studies, contributing to persistent health inequities. To address these challenges, we have harnessed the unique benefits of mass cytometry to develop and test a workflow that enables inclusion of remote communities in high-parameter immune phenotyping studies. The stability of heavy-metal conjugated antibodies and the lack of any signal derived from cellular autofluorescence allows samples stained for mass cytometry to be cryopreserved and stored long-term. In this approach, whole blood (WB) samples are collected and stained fresh with dry-format antibodies specific for 38 surface targets, then cryopreserved without washing. By removing the need for centrifugation, we enable staining and preservation of fresh samples in resource-limited settings without access to specialized equipment. Samples are delivered to the central site, where neutrophils are removed and samples are barcoded with a 6-channel cocktail, pooled and stained for an additional 8 intracellular targets, achieving 52-plex coverage. In a pilot to demonstrate workflow feasibility, we characterized blood immune and myeloid cell populations from cancer patients recruited at 3 sites across Australia, including 26 patients recruited from Dubbo Base Hospital which is located approximately 400 km from the state capital city, Sydney. We find consistency in immune phenotype between WB samples prepared with our simplified WB approach and matched PBMC samples prepared in parallel. This is the first time, to our knowledge, that a multi-centre high-parameter immune phenotyping study has included cancer patients from regional Australia. In summary, we show here that the preparation of samples for high-dimensional mass cytometry can be simplified to be compatible with resource-limited environments while conserving data quality.
Systemic mastocytosis (SM) is a neoplastic disease characterized by abnormal mast cell (MC) activation and proliferation. Accurate diagnosis often relies on flow cytometry to detect aberrant CD25, CD2, and CD30 expression on MCs in bone marrow (BM). However, the frequently low abundance of MCs in BM, lack of completely specific antigens, and strong and highly variable autofluorescence can cause misinterpretation and lead to diagnostic misclassifications. We investigated the potentially interfering cell populations in flow cytometric analysis of MCs based on literature and expert insights, focusing on CD117, CD45, CD203c, and FcεR1. Additionally, we determined the most appropriate approach to quantify aberrant CD25, CD2, and CD30 expression. Apoptotic granulocytes frequently cause misinterpretation by mimicking strong CD117 and aberrant CD25, CD2, and CD30 expression, and must be distinguished from MCs with a viability dye like DRAQ7. CD117-positive myeloblasts and promyelocytes overlap with CD117-reduced immature MCs in advanced SM disease and can be differentiated using CD203c. Quantifying CD25, CD2, and CD30 expression is skewed on log-transformed scales due to the strong and highly heterogeneous autofluorescence of MCs. Linear calculation of net expression levels of CD25, CD2, and CD30 yields the highest accuracies in predicting SM with a Youden index of 0.96, 0.93, and 0.88, respectively. Incorporating a viability dye like DRAQ7 and CD203c into the flow cytometric analysis for MC identification, along with the linear quantification of aberrant expression, significantly enhances the correct identification of MCs and increases the diagnostic accuracy of aberrant CD25, CD2, and CD30 expression for SM.
Antibody titration is an important step in every cytometric workflow, with the goal being to determine antibody concentrations that ensure highly reproducible results. When aiming to compare antigen expression between samples using mean or median fluorescence intensity (MFI), reagents should be used at a saturating concentration so that unavoidable variations in staining conditions do not affect the fluorescence signal. The recommended concentrations of commercially available fluorophore-labeled monoclonal antibodies (mAbs) may not achieve plateau staining, and their saturating concentration may be too high to be experimentally useful. To address these common concerns, we present a novel method to achieve saturation of fluorophore-conjugated mAbs, by 'spiking-in' unlabelled antibody of the same clone. Here, we demonstrate the application of this workflow to human anti-CD3 (clone OKT3, mouse IgG2a) and anti-TCRαβ (clone IP26, mouse IgG1), two mAbs that do not achieve saturation at 2-fold above their commercially recommended concentrations. First, the saturating concentration of unlabelled (purified) OKT3 and IP26 was determined by detection with a fluorophore-labeled anti-mouse IgG (H + L) secondary antibody. Titration curves of unlabelled and labeled mAbs were compared for each clone to determine whether labeling had resulted in any loss in binding activity. Unlabelled antibody was then 'spiked' into the labeled antibody at varying ratios, and those that achieved saturation while maintaining an adequate fluorescence signal were identified. We demonstrate that antibody saturation can be achieved with an optimized mixture of labeled and unlabelled antibody, while maintaining a clear signal from the fluorophore. While this workflow has only been applied to OKT3 and IP26, it has potential applicability for any antibody clone for which both labeled and unlabelled preparations are available. This method has significance for robust comparison of biomarker expression when fluorophore labeled reagents do not reach saturation under standard staining conditions.
Cell lineage detection refers to the inference of differentiation pathways from immature cells to distinct mature cell types. We developed TimeFlow 2, a new method for lineage inference in large flow cytometry datasets. It uses a single static snapshot of unordered cells and does not require prior knowledge of the number of pathways, cell types or temporal labels. TimeFlow 2 uses the cell orderings from TimeFlow and defines coarse cell states along pseudotime segments. By connecting these states, it constructs paths at the cell state level. To approximate the trajectory structure, it further groups the paths based on an optimal transport-based cost function. We used TimeFlow 2 on three healthy bone marrow samples and accurately assigned monocytes, neutrophils, erythrocytes and B-cells of different maturation stages to four distinct pathways. Marker dynamics across the inferred pathways showed highly correlated patterns for the corresponding lineages in all three patients. We compared the performance of TimeFlow 2 and three other established methods using standard classification and correlation metrics. TimeFlow 2 outperformed the others on flow cytometry datasets and remained competitive on the challenging mass cytometry datasets. Overall, TimeFlow 2 detects biologically informative pathways, allowing bioinformaticians to model and compare marker dynamics across cell lineages in a data-driven way. Source code in Python and tutorials are available at https://github.com/MargaritaLiarou1/TimeFlow2.