Craig Issue Highlight 2015

The articles in this issue of the Journal address many aspects of clinical flow cytometric test interpretation, with examples related to quantitative, quasi-quantitative and qualitative assays. How good are you at gating? Unfortunately, even experts vary in their subjective gating decisions. In this issue of the Journal Linda Wong and colleagues use a probability state modelling (PSM) approach, rather than gating, to automatically determine neutrophil CD64 expression in the quantitative flow cytometric assessment for sepsis (1). Using the PSM approach, each event is assigned to a cell type using the probability that it belongs, and then antigen expression of each cell type is quantitated. Neutrophilic expression of CD64 has demonstrated utility in the assessment for sepsis, either alone or as part of a multiparametric approach (2). For this, and other quantitative cytometric assays, it’s important to minimize sources of pre-analytical, analytical and post-analytical variation. The Leuko64 assay used in this article is a commercial IVD assay for the evaluation of neutrophil CD64 expression that has a standardized protocol for sample preparation, instrument set-up and data acquisition, and takes into account lot-to-lot variability of reagents. The addition of automated PSM interpretation would address variability in data interpretation, and might facilitate development of an assay that could be offered around the clock. Can you describe the phenotype of hematogones? These normal bone marrow B-cell precursors have a very characteristic phenotype, with coordinated gain and loss of antigens during maturation. This knowledge is important for distinguishing B lymphoblastic leukemia from hematogones. As recently illustrated by Lukasz Sedek and colleagues, the blasts of B lymphoblastic leukemia have phenotypic similarities to hematogones, but demonstrate identifiable differences in antigen expression(3). In this issue of the Journal Bruce Bagwell and colleagues demonstrate how PSM can be applied to investigate the finer details, and timing, of these coordinated changes (4). Computational studies like this are an important step towards developing a highly sensitive, automated quasiquantitative assay for minimal residual disease detection. Is it appropriate to use numerical fluorescence cutoffs to identify cells with a paroxysmal nocturnal hemoglobinuria (PNH)-like phenotype? PNH assays are high sensitivity quasi-quantitative flow cytometric tests, which have been the topic of recent articles about assay design, validation and standardization (5,6), and are particularly useful in the evaluation of patients with MDS and aplastic anemia(7). In this issue of the Journal, Michael Liew and colleagues investigate the performance of their high sensitivity PNH RBC assay (8). As reported by others, their assay identifies rare PNH-like cells even in normal subjects (6,8,9). By further analyzing many millions of cells from normal specimens, they report that a fluorescence threshold of 2.5% of the CD59 positive peak fluorescence can be used to separate CD59 positive and CD59 negative events, even when fewer events are acquired. This approach is controversial and, as the authors recommend, requires further investigation. Is it valid to interpret flow cytometric data without seeing a population of events? As shown in the previous articles, the answer is definitely yes, but perhaps only if there’s sufficient evidence on which to base a confident interpretation. Is there a place for numerical reference ranges in clinical flow cytometric immunophenotyping? It depends on the goal of the assay. Quantitative assays require a set of values obtained from individuals without disease for the purpose of interpreting results, whereas qualitative assays, such as leukemia and lymphoma immunophenotyping, do not. Quantitative immune monitoring is an Perspectives from Fiona E. Craig, M.D.