Cytometry Part B: Clinical Cytometry最新文献

X-linked inhibitor of apoptosis (XIAP) deficiency is an infrequent inborn error of immunity caused by mutations in XIAP gene. Most cases present with absence of XIAP protein which can be detected by flow cytometry (FC), representing a rapid diagnostic method. However, since some genetic defects may not preclude protein expression, it is important to include a complementary functional test in the laboratory workup of these patients. L-selectin (CD62-L) is a molecule that is cleaved from the surface membrane of leukocytes upon stimulation of different receptors such as toll like receptors (TLRs) and nucleotide-binding oligomerization domain-like receptors (NLRs), including NOD2. Considering that XIAP deficiency impairs NOD2 signaling, we decided to assess CD62-L down-regulation by FC post-stimulation of neutrophils and monocytes with L18-muramyl Di-Peptide (L18-MDP), a NOD2 specific agonist, in order to develop a novel assay for the functional evaluation of patients with suspicion of XIAP defects. Whole blood samples from 20 healthy controls (HC) and four patients with confirmed molecular diagnosis of XIAP deficiency were stimulated with 200 ng/mL of L18-MDP for 2 h. Stimulation with 100 ng/mL of lipopolysaccharide (LPS) was carried out in parallel as a positive control of CD62-L shedding. CD62-L expression was evaluated by FC using an anti CD62-L- antibody and down-regulation was assessed by calculating the difference in CD62-L expression before and after stimulation, both in terms of percentage of CD62-L expressing cells (Δ%CD62-L) and median fluorescence intensity (ΔMFI%). Neutrophils and monocytes from XIAP deficient patients displayed a significantly diminished response to L18-MDP stimulation compared with HC (p < 0.0001), indicating a severely altered mechanism of CD62-L down-regulation following activation of NOD2-XIAP axis. On the other hand, the response to LPS stimulation was comparable between patients and heathy controls, suggesting preserved CD62-L shedding with a different stimulus. FC detection of CD62-L down-regulation in monocytes and neutrophils after whole blood stimulation with L18-MDP results in an effective and rapid functional test for the identification of XIAP deficient patients.

Peripheral blood lymphocyte phenotyping panels typically include CD45 for discrimination of the lymphocyte population, and fluorophore-conjugated monoclonal antibodies to identify T, B, and Natural Killer (NK) cells. While CD45 combined with side scatter is generally sufficient to clearly distinguish lymphocytes from monocytes in the majority of peripheral blood samples, it is challenging to accurately gate lymphocytes in samples from patients with monocytosis or significant lymphopenia, or from very young infants. Addition of a monocyte marker to lymphocyte phenotyping panels for monocyte exclusion has previously been evaluated for improved discrimination of lymphocytes, albeit largely in healthy donor adult samples. Here we evaluate the effect of the addition of CD14 to a standard lymphocyte phenotyping panel on total lymphocyte, T, B, and NK cell percentages in a predominantly pediatric population of patients under evaluation chiefly for immunodeficiency, immune-depletion, or immune reconstitution. Addition of CD14 to the standard lymphocyte phenotyping improved discrimination of lymphocytes from monocytes, resulted in decreased NK cell percentages, likely because CD16+ and/or CD56+ monocytes were included in the CD56+CD16+ NK cell gate with conventional gating, and although less significant, resulted in an increased percentage of B cells, since relatively larger B cells were likely gated out by more restrictive light scatter gating used with the conventional gating approach. The change in NK and B cell percentages were more pronounced in samples from patients below a year of age, and in patients who were relatively lymphopenic. These data suggest that addition of CD14 to conventional lymphocyte phenotyping panels that utilize CD45 versus side scatter gating results in significant improvement in the accuracy of lymphocyte gating, and accurate quantification of NK and B cells particularly in samples from infants and lymphopenic individuals.

Multiparameter flow cytometry data is visually inspected by expert personnel as part of standard clinical disease diagnosis practice. This is a demanding and costly process, and recent research has demonstrated that it is possible to utilize artificial intelligence (AI) algorithms to assist in the interpretive process. Here we report our examination of three previously published machine learning methods for classification of flow cytometry data and apply these to a B-cell neoplasm dataset to obtain predicted disease subtypes. Each of the examined methods classifies samples according to specific disease categories using ungated flow cytometry data. We compare and contrast the three algorithms with respect to their architectures, and we report the multiclass classification accuracies and relative required computation times. Despite different architectures, two of the methods, flowCat and EnsembleCNN, had similarly good accuracies with relatively fast computational times. We note a speed advantage for EnsembleCNN, particularly in the case of addition of training data and retraining of the classifier.

Meningeal infiltration in children with B acute lymphoblastic leukemia is one of the most serious complications. Timely diagnosis not only significantly enhances treatment efficacy but also leads to improve patient outcome and reduce risk of relapse. This is particularly crucial in low to middle income countries facing health constraints, where optimizing resources is essential. Conventional cytology (CC) study of cerebrospinal fluid (CSF) is considered in different countries to be the Gold-standard despite its low sensitivity (< 50%). The study of CSF by multiparametric flow cytometry (MFC) appears to be an alternative. The aim of our study was to assess MFC analytical performance compared with CC. Our cross sectional study was conducted over a six-month period in the biological hematology department. CSF samples underwent analysis for the presence of blasts using both CC and MFC. Cytological slides of the CSF were prepared by cytocentrifugation in a Shandon Cytospin 4™. Flow cytometric analysis was performed on the BD FACSLyric™ flow cytometer. All statistical analyses were performed using SPSS version 21.0 (SPSS Inc.). Agreement between the two methods was made using the Kappa index and χ² test. This study was approved by the local ethics committee. Sixty CSF samples from 39 children with B acute lymphoblastic leukemia were analyzed. Meningeal infiltration was detected respectively in 20% of cases by MFC and 5% of cases by CC, with a significant difference p = 0.006. Comparing the two methods, the Kappa coefficient was 0.35, indicating weak agreement between the two methods. Moreover, MFC positivity was higher even for hypocellular samples. Of the 51 hypocellular samples, eight were positive by MFC while they were negative by CC. MFC shows better sensitivity while retaining good specificity for the detection of meningeal involvement. MFC could therefore be a complementary method to CC for detecting blast cells in the central nervous system.

The assessment of T-cell clonality by flow cytometry has long been suboptimal, relying on aberrant marker expression and/or intensity. The introduction of TRBC1 shows much promise for improving the diagnosis of T-cell neoplasms in the clinical flow laboratory. Most laboratories considering this marker already have existing panels designed for T-cell workups and will be determining how best to incorporate TRBC1. We present this comprehensive summary of TRBC1 and supplemental case examples to familiarize the flow cytometry community with its potential for routine application, provide examples of how to incorporate it into T-cell panels, and signal caution in interpreting the results in certain diagnostic scenarios where appropriate.

CD20⁺ T cells constitute a small subset of T cells. These are found among CD4⁺, CD8⁺, CD4⁺CD8⁺, CD4⁻CD8⁻ T, and TCRγδ⁺ T cells, and have been poorly characterized. The aim of this study was to characterize peripheral blood (PB) CD20⁺ T cells and compare them to their PB CD20⁻ T cell counterparts. PB from 17 healthy individuals was collected. The distribution of CD20⁺ T cells among maturation-associated T cells compartments (naïve, central memory, transitional memory, effector memory, and effector T cells), their polarization, activation status, and expression of immune-regulatory proteins were evaluated by flow cytometry. Their function was also assessed, by measuring IFN-γ, TNF-α, and IL-17 production. Compared with CD20⁻ T cells, CD20⁺ T cells represent a higher proportion of transitional memory cells. Furthermore, CD20⁺ T cells display a proinflammatory phenotype, characterized by the expansion of Th1, Th1/17, and Tc1 cell subsets , associated to a high expression of activation (CD25) and exhaustion (PD-1) markers. In addition, the simultaneous production of the proinflammatory cytokines IFN-γ, TNF-α, and IL-17 was also detected in CD4⁺CD20⁺ T cells. Our results show that CD20⁺ T cells are phenotypically and functionally different from CD20⁻ T cells, suggesting that these cells are a distinct subset of T cells.

The monocyte subset partitioning by flow cytometry, known as “monocyte assay,” is now integrated into the new classifications as a supporting criterion for CMML diagnosis, if a relative accumulation of classical monocytes above 94% of total circulating monocytes is observed. Here we provide clinical flow cytometry laboratories with technical support adapted for the most commonly used cytometers. Step-by-step explanations of the gating strategy developed on whole peripheral blood are presented while underlining the most common difficulties. In a second part, interpretation recommendations of circulating monocyte partitioning from the dedicated French working group “CytHem-LMMC” are shared as well as the main pitfalls, including false positive and false negative cases. The particular flow-defined inflammatory profile is described and the usefulness of the nonclassical monocyte specific marker, namely slan, highlighted. Examples of reporting to the physician with frequent situations encountered when using the monocyte assay are also presented.

Thrombocythemia (ET), polycythemia vera (PV), primary myelofibrosis (PMF), prefibrotic/early (pre-PMF), and overt fibrotic PMF (overt PMF) are classical Philadelphia-Negative (Ph-negative) myeloproliferative neoplasms (MPNs). Differentiating between these types based on morphology and molecular markers is challenging. This study aims to clarify the application of flow cytometry in the diagnosis and differential diagnosis of classical MPNs. This study retrospectively analyzed the immunophenotypes, clinical characteristics, and laboratory findings of 211 Ph-negative MPN patients, including ET, PV, pre-PMF, overt PMF, and 47 controls. Compared to ET and PV, PMF differed in white blood cells, hemoglobin, blast cells in the peripheral blood, abnormal karyotype, and WT1 gene expression. PMF also differed from controls in CD34⁺ cells, granulocyte phenotype, monocyte phenotype, percentage of plasma cells, and dendritic cells. Notably, the PMF group had a significantly lower plasma cell percentage compared with other groups. A lasso and random forest model select five variables (CD34⁺CD19⁺cells and CD34⁺CD38⁻ cells on CD34⁺cells, CD13^dim+CD11b⁻ cells in granulocytes, CD38^str+CD19^+/−plasma, and CD123⁺HLA-DR⁻basophils), which identify PMF with a sensitivity and specificity of 90%. Simultaneously, a classification and regression tree model was constructed using the percentage of CD34⁺CD38⁻ on CD34⁺ cells and platelet counts to distinguish between ET and pre-PMF, with accuracies of 94.3% and 83.9%, respectively. Flow immunophenotyping aids in diagnosing PMF and differentiating between ET and PV. It also helps distinguish pre-PMF from ET and guides treatment decisions.