CD43 and CD49d from the B‐Cell Chronic Lymphoproliferative Disorders Diagnostic Panel Are Useful to Detect Erythroid Dysplasia

The role of CD20+ T cells: Insights in human peripheral blood

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.

Diagnosis of erythroid dysplasia by flow cytometry: a review

INTRODUCTION

The diagnosis of myelodysplastic syndrome (MDS) is complex. Flow cytometric analysis of the myelomonocytic compartment can be helpful, but it is highly subjective and reproducibility by non-specialized groups is unclear. Analysis of the erythroid lineage by flow cytometry is emerging as potentially more reproducible and easier to conduct, while keeping a high diagnostic performance.

AREAS COVERED

We review the evidence in this area, including 1) the use of well-established markers - CD71 and CD36 - and other less well-established markers and parameters; 2) the use of flow cytometric scores for the erythroid lineage; and 3) additional aspects, including the emergence of computational tools and the roles of flow cytometry beyond diagnosis. Finally, we discuss the limitations with the current evidence, including 1) the impact of the sample processing protocol and reagents on the results, 2) the lack of a standard gating strategy, and 3) conceptualization and design issues in the available publications.

EXPERT OPINION

We end by offering our recommendations for the current use - and our personal take on the value - of the analysis of erythroid lineage by flow cytometry.

Immunophenotypical profiling of myeloid neoplasms with erythroid predominance using mass cytometry (CyTOF)

Acute erythroid leukemia (AEL) is a disease continuum between Myelodysplastic syndrome (MDS) and Acute myeloid leukemia (AML) with the cellular hallmark of uncontrolled proliferation and impaired differentiation of erythroid progenitor cells. First described by Giovanni di Guglielmo in 1917 AEL accounts for less than 5% of all de novo AML cases. There have been efforts to characterize AEL at a molecular level, describing recurrent alterations in TP53, NPM1 and FLT3 genes. A genomic analysis of AEL cases confirmed its complexity. Despite these advances, the biology underlying erythroid proliferations remains unclear and the prognosis is dismal with a median survival of only 3 months for pure erythroid leukemia (PEL). Marker combinations suitable for the identification and characterization of leukemic stem cell (LSC) candidates, monitoring measurable residual disease (MRD) during chemotherapy treatment and the development of innovative targeted therapies are missing. Here, we developed a mass cytometry panel for an in-depth characterization of erythroid and myeloid blast cell populations from human AEL bone marrow samples in comparison to other AML subtypes and healthy counterparts. A total of 8 AEL samples were analyzed and compared to 28 AML samples from different molecular subtypes, healthy bone marrow counterparts (n = 5) and umbilical cord blood (n = 6) using high-dimensional mass cytometry. Identification of erythroid and myeloid blast populations in high-dimensional mass cytometry data enabled a refined view on erythroblast differentiation stages present in AEL erythroid blasts and revealed immunophenotypical profiles specific to AEL. Profiling of phenotypic LSCs revealed aberrant erythroid marker expression in the CD34⁺ CD38^- stem cell compartment. In addition, the identification of novel candidate surface marker combinations and aberrancies might enhance clinical diagnostics of AEL. We present a high-parameter mass cytometry approach feasible for immunophenotypical analysis of blast and stem cell populations in myeloid neoplasms with erythroid predominance laying the foundation for more precise experimental approaches in the future.

Multiparameter flow cytometry in the evaluation of myelodysplasia: Analytical issues: Recommendations from the European LeukemiaNet/International Myelodysplastic Syndrome Flow Cytometry Working Group

Multiparameter flow cytometry (MFC) is one of the essential ancillary methods in bone marrow (BM) investigation of patients with cytopenia and suspected myelodysplastic syndrome (MDS). MFC can also be applied in the follow-up of MDS patients undergoing treatment. This document summarizes recommendations from the International/European Leukemia Net Working Group for Flow Cytometry in Myelodysplastic Syndromes (ELN iMDS Flow) on the analytical issues in MFC for the diagnostic work-up of MDS. Recommendations for the analysis of several BM cell subsets such as myeloid precursors, maturing granulocytic and monocytic components and erythropoiesis are given. A core set of 17 markers identified as independently related to a cytomorphologic diagnosis of myelodysplasia is suggested as mandatory for MFC evaluation of BM in a patient with cytopenia. A myeloid precursor cell (CD34⁺ CD19^- ) count >3% should be considered immunophenotypically indicative of myelodysplasia. However, MFC results should always be evaluated as part of an integrated hematopathology work-up. Looking forward, several machine-learning-based analytical tools of interest should be applied in parallel to conventional analytical methods to investigate their usefulness in integrated diagnostics, risk stratification, and potentially even in the evaluation of response to therapy, based on MFC data. In addition, compiling large uniform datasets is desirable, as most of the machine-learning-based methods tend to perform better with larger numbers of investigated samples, especially in such a heterogeneous disease as MDS.