Cytometry Part B: Clinical Cytometry最新文献

Flow cytometry is a high-throughput, high-dimensional technique that generates large sets of single-cell data. Prior to analyzing this data, it is common to exclude any events that contain two or more cells, multiplets, to ensure downstream analysis and quantification is of single-cell events, singlets, only. The process of singlet discrimination is critical yet fundamentally subjective and time-consuming; it is performed manually by the user, where the proper exclusion of multiplets depends on the user's expertise and often varies from experiment to experiment. To address this problem, we have developed an algorithm to automatically discriminate singlets from other unwanted events such as multiplets and debris. Using parameters derived from imaging, the algorithm first identifies high-density clusters of events using a density-based clustering algorithm, and then classifies the clusters based on their properties. Multiplets are discarded in the first step, while singlets are distinguished from debris in the second step. The algorithm can use different strategies on imaging feature selection-based user's preferences and imaging features available. In addition, the relative importance of singlets precision vs. sensitivity can be further tweaked via a density coefficient adjustment. Twenty-two datasets from various sites and of various cell types acquired on the BD FACSDiscover™ S8 Cell Sorter with CellView™ Image Technology were used to develop and validate the algorithm across multiple imaging feature sets. A consistent singlets precision >97% with a solid >88% sensitivity has been demonstrated with a LightLoss feature set and the default density coefficient. This work yields a high-precision, high-sensitivity algorithm capable of objective and automated singlet discrimination across multiple cell types using various imaging-derived parameters. A free FlowJo™ Software plugin implementation is available for simple and reproducible singlet discrimination for use at the beginning of any user's workflow.

The FDA-approved ClearLLab 10C Reagents Panel (Beckman Coulter) simplified the diagnosis of leukemias and lymphomas by flow cytometry. However, the requirement of using 3 × 10⁶ cells/mL cannot be met for paucicellular samples. Therefore, we tested whether this 10-color panel can be reliably employed to analyze specimens with low cell concentrations. Serial dilutions of 16 samples (5 normal, 11 abnormal), yielding concentrations ranging from 3.0 × 10⁶ to 0.0469 × 10⁶ cells/mL (64-fold difference), were stained using the B-cell and T-cell panels of the ClearLLab 10C system, and mean fluorescence intensity (MFI) was measured for each antibody. For each cell dilution, the deviation from the value obtained with the FDA-approved concentration of 3.0 × 10⁶ cells/mL was calculated. The agreement between the highest and lowest cell concentration data was evaluated by the Bland and Altman method, Pearson's and Spearman's correlation analyses, and linear regression. In all patients, the antigen expression pattern was similar at all cell concentrations tested, and the mean deviation of the MFI from the value obtained using 3.0 × 10⁶ cells/mL never exceeded 10% for any of the antibodies. The Bland-Altman method demonstrated the similarity between results obtained with the FDA-approved cell concentration and a 64-fold diluted cell suspension, and a high positive correlation was found between MFI acquired under these two conditions. The tests utilizing the lowest density of cells yielded the same patterns of antigen expression in all patients as those performed with the FDA-approved concentration, documenting a 100% concordance between these two protocols. The ClearLLab 10C panel can reliably determine the expression of markers of leukemias and lymphomas in paucicellular samples containing as little as 0.0469 × 10⁶ cells/mL (64-fold lower than the FDA-approved concentration). This finding markedly expands the applicability of the ClearLLab 10C platform in a clinical setting.

Cervical cancer (CC) is the fourth most common malignant tumor in women worldwide. Detecting different biomarkers together on single cells by novel method mass cytometry could contribute to more precise screening. Liquid-based cytology (LBC) cervical samples were collected (N = 53) from women categorized as normal and precancerous lesions. Human papillomavirus was genotyped by polymerase chain reaction, while simultaneous examination of the expression of 29 proteins was done by mass cytometry (CyTOF). Differences in cluster abundances were assessed with Spearman's rank correlation as well as high dimensional data analysis (t-SNE, FlowSOM). Cytokeratin (ITGA6, Ck5, Ck10/13, Ck14, Ck7) expression patterns allowed determining the presence of different cells in the cervical epithelium. FlowSOM analysis enabled to phenotype cervical cells in five different metaclusters and find new markers that could be important in CC screening. The markers Ck18, Ck18, and CD63 (Metacluster 3) showed significantly increasing associated with severity of the precancerous lesions (Spearman rank correlation rho 0.304, p = 0.0271), while CD71, KLF4, LRIG1, E-cadherin, Nanog and p53 (Metacluster 1) decreased with severity of the precancerous lesions (Spearman rank correlation rho −0.401, p = 0.0029). Other metaclusters did not show significant correlation, but metacluster 2 (Ck17, MCM, MMP7, CD29, E-cadherin, Nanog, p53) showed higher abundance in low- and high-grade intraepithelial lesion cases. CyTOF appears feasible and should be considered when examining novel biomarkers on cervical LBC samples. This study enabled us to characterize different cells in the cervical epithelium and find markers and populations that could distinguish precancerous lesions.

Automated analysis of flow cytometry data can improve objectivity and reduce analysis time but has generally required work by software and algorithm experts. Here, we investigated the performance of BD ElastiGate™ Software (hereafter ElastiGate), which allows users to automate gating by selecting gated training files, then uses elastic image registration to gate new files. Three assays of increasing complexity were examined: TBNK, stem cell enumeration (SCE), and lymphoid screening tube (LST). For TBNK analysis, 60 peripheral blood (PB) samples from normal, HIV+, and controls were tested with ground truth analysis by an existing automated method. For SCE, 128 samples including bone marrow (BM), cord blood (CB), and apheresis were tested with analysis by multiple manual analysts. For LST, 80 PB and 28 BM samples were tested with manual analysis. For ElastiGate, a minimal number of training files was selected. Results were compared by Bland-Altman or F1 score analysis. For TBNK, ElastiGate using three training files (1 control, 1 normal, 1 HIV+) showed mean %bias across all reported populations between -1.48% and 7.13% (average 2.08%). For SCE, ElastiGate using three BM and two CB training files showed median F1 scores >0.93 in comparison to >0.94 and >0.92 for two other manual analysts. For LST, ElastiGate using four training files for each of PB and BM showed median F1 scores >0.945 for 13 of 14 PB populations and 10 of 14 BM populations, with generally similar or better performance for normal samples compared to abnormal; populations with lower scores were often associated with lower agreement between manual analysts. Based on analysis of three assays with four sample types of increasing complexity, ElastiGate with minimal training files may perform as an automated gating assistant. The results reported here are for research use only, not for use in diagnostic or therapeutic procedures.

High-grade B-cell lymphomas (HGBCL) represent a heterogeneous group of very rare mature B-cell lymphomas. The 4th revised edition of the WHO Classification of Tumors of Hematopoietic and Lymphoid Tissues (WHO-HAEM) previously defined two categories of HGBCL: the so-called double-hit (DHL) and triple-hit (THL) lymphomas, which were related to forms harboring MYC and BCL2 and/or BCL6 rearrangements, and HGBCL, NOS (not otherwise specified), corresponding to entities with intermediate characteristics between diffuse large B-cell lymphoma (DLBCL) and Burkitt lymphoma (BL), without rearrangement of the MYC and BCL2, and/or BCL6 genes. In the 5th edition of the WHO-HAEM, DHL with MYC and BCL2 rearrangements or THL were reassigned as DLBCL/HGBCL with MYC and BCL2 rearrangements (DLBCL/HGBL-MYC/BCL2), whereas the category HGBCL, NOS remains unchanged. Characterized by an aggressive clinical presentation and a poor prognosis, HGBCL is often diagnosed at an advanced, widespread stage, leading to potential disseminated forms with a leukemic presentation, or spreading to the bone marrow (BM) or other biological fluids. Flow cytometric immunophenotypic study of these disseminated cells can provide a rapid method to identify HGBCL. However, due to the scarcity of cases, only limited data about the immunophenotypic features of HGBCL by multiparametric flow cytometry are available. In addition, identification of HGBCL cells by this technique may be challenging due to clinical, pathological, and biological features that can overlap with other distinct lymphoid malignancies, including Burkitt lymphoma (BL), diffuse large B-cell lymphoma (DLBCL), and even B acute lymphoblastic leukemia (B-ALL). In this study, we aimed to characterize the detailed immunophenotypic portrait of HGBCL, evaluating by multiparametric flow cytometry (MFC) the expression of 26 markers on biological samples obtained from a cohort of 10 newly-diagnosed cases and comparing their level of expression with normal peripheral blood (PB) B lymphocytes (n = 10 samples), tumoral cells from patients diagnosed with B-ALL (n = 30), BL (n = 13), or DLBCL (n = 22). We then proposed a new and simple approach to rapidly distinguish disseminated forms of HGBCL, BL, and DLBCL, using the combination of MFC data for CD38, BCL2, and CD39, the three most discriminative markers explored in this study. We finally confirmed the utility of the scoring system previously proposed by Khanlari to distinguish HGBCL cells from B lymphoblasts of B-ALL. In conclusion, we described a distinct immunophenotypic portrait of HGBCL cells and proposed a strategy to differentiate these cells from other aggressive B lymphoma entities in biological samples.

HLA-B27 is a major histocompatibility complex (MHC) class I antigen which exhibits strong association (90%) with ankylosing spondylitis. HLA-B27 detection in patients by flow cytometry is a widely used clinical test, performed on many different flow cytometer models. We sought to develop and validate a test conversion protocol for the HLA-B27 test performed on the BD FACSCanto to BD's newer FACSLyric flow cytometers. The development and validation experiments were performed using anti-HLA-B27*FITC/CD3*PE antibody-stained whole blood patient specimens. The anti-HLA-B27*FITC logarithmic median fluorescence (LMF) results on the BD FACSCanto were converted to median fluorescence intensity (MFI) values on the BD FACSLyric. Clustering of the HLA-B27 positive and negative values, using a 3rd order polynomial equation, resulted in a conversion of the BD FACSCanto cutoff values, negative (<150 LMF) and positive (≥160 LMF), to negative (<4530 MFI) and positive (≥6950 MFI) on the BD FACSLyric. Accuracy was assessed by comparing the flow results obtained on the BD FACSCanto and BD FACSLyric to a molecular PCR based assay. Additional validation parameters (compensation verification, intra- and inter-assay precision, and instrument comparison) were performed per the recommendations outlined in the Clinical and Laboratory Standards Institute (CLSI) H62 guidelines for validation of flow cytometry assays.