Cytometry Part A最新文献

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.

Flow and mass cytometry experiments are essential for profiling immune cells at single-cell resolution. Better understanding of human immunology increasingly involves analyzing studies at the scale of hundreds or thousands of samples, with data analysis a significant bottleneck. This trend increases the demand for automated analysis methods. In particular, a common preprocessing step in cytometry data analysis is distinguishing single cells from doublets (or multiplets), events in which two (or more) cells pass simultaneously through the detector. Typically, doublets are identified on two-dimensional density plots, using their high measured values for DNA intercalators (mass cytometry) or scattering channels (flow cytometry). Despite its popularity, this bivariate gating method is sometimes imprecise: for example, we show that bivariate gating of mass cytometry data can mistake single eosinophils for doublets, due to their high DNA content. Taking inspiration from methods already used in single-cell transcriptomics, but not in the cytometry community, we propose an alternative approach. Our method, called Cleanet, first simulates doublet events, then identifies true events with protein expression similar to the simulated doublets. This simple method is completely automated and detects both homotypic and heterotypic doublets. We validate it in datasets acquired with mass and flow cytometry; moreover, we verify with imaging flow cytometry data from ImageStream and Discover A8 instruments that most events predicted to be doublets truly consist of multiple cells. Cleanet can also classify doublets based on their component cell types, which potentially enables the study of cell–cell interactions, mining extra information out of doublet events that would otherwise be discarded. As a proof of concept, we demonstrate that Cleanet can detect a treatment-specific increase in interactions between two cell lines. By automating doublet detection and classification, we aim to streamline the data analysis in large cytometry studies and provide a more accurate picture of both immune cell populations and cell–cell interactions.

We report the optimization of a 38-parameter spectral flow cytometry panel to identify, phenotype, and assess lineage plasticity of human regulatory T cells (Tregs). Tregs are an indispensable T cell lineage with pleiotropic tolerogenic functions whose activities contribute critically to numerous disease settings including cancer, autoimmunity, and infectious disease, among others. Phenotypic and functional heterogeneity within the Treg lineage has been appreciated, but the etiology and impact of Treg plasticity across disease states remain incompletely understood. Better tools are thus needed to deeply characterize human Treg phenotypic heterogeneity at the single cell level to advance discovery of Treg-based biomarkers in disease and to assess the effects of Treg-directed therapeutics. To this end, our 38-parameter panel consists of a 13-marker PBMC backbone module to broadly phenotype PBMCs while specifically discriminating Tregs, and a 25-marker Treg phenotyping module to determine Treg differentiation state, activation profile, and lineage subtype. In contrast to many high-parameter OMIPs that rely on surface staining only, we incorporated several intracellular targets in this panel. This afforded the opportunity to thoroughly evaluate binding characteristics of all antibodies in both pre-fixation and post-fixation and permeabilization settings; we identify several antibodies eligible for overnight post-fixation staining that require substantially reduced titers as compared to traditional pre-fixation staining, resulting in significant cost savings. Dimensionality reduction and semi-supervised clustering on healthy donor PBMC-derived Tregs profiled by our panel reveal up to 14 discrete Treg phenotypes. In sum, this panel enables deep phenotypic characterization of human Treg heterogeneity in peripheral blood specimens by spectral flow cytometry.

The analysis of immune cell compartments in cancer patients is crucial to predict treatment efficacy and relapse. We introduce a robust 40-parameter, 37-channel spectral cytometry panel designed to profile human lymphoid subsets and CAR-T cell expansion, with the capability to assess exhaustion status by profiling immune checkpoints and activating receptors in cancer patients. Developed for the 5-laser Cytek Aurora, the panel optimizes fluorophore selection and uses three pairs of mutually exclusive markers assigned to a single fluorescent parameter to simplify setup and ensure robust data, adopting a conservative design choice to keep similarity indices below 0.85; though higher overlaps can still yield high-quality data when best practices are applied. The panel enables detailed analysis of well-defined lymphoid subsets using a conventional gating strategy, as well as detection of unconventional subsets with variable expression patterns by unsupervised algorithm-based analysis. The effectiveness of the panel is demonstrated through a dataset simulating the progression of multiple myeloma, from pre-malignant disease to a highly aggressive stage.

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.

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.