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Ever since hematopoietic cells became “events” enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large array of solutions was then developed to identify at best the numerous cell subsets that can be delineated, notably among hematopoietic cells. As instruments became more and more stable and robust, the focus moved to analytic software. Almost concomitantly, the capacity increased to use large panels (both with mass and classical cytometry) and to apply artificial intelligence/machine learning for their analysis. The combination of these concepts raised new analytical possibilities, opening an unprecedented field of subtle exploration for many conditions, including hematopoiesis and hematological disorders. In this review, the general concepts and progress achieved in the development of new analytical approaches for exploring high- dimensional data sets at the single- cell level will be described as they appeared over the past few years. A larger and more practical part will detail the various steps that need to be mastered, both in data acquisition and in the preanalytical check of data files. Finally, a step- by- step explanation of the solution in development to combine the Bioconductor clustering algorithm FlowSOM and the popular and widely used software Kaluza® (Beckman Coulter) will be presented. The aim of this review was to point out that the day when these progresses will reach routine hematology laboratories does not seem so far away. [ABSTRACT FROM AUTHOR]

Ever since hematopoietic cells became "events" enumerated and characterized in suspension by cell counters or flow cytometers, researchers and engineers have strived to refine the acquisition and display of the electronic signals generated. A large array of solutions was then developed to identify at best the numerous cell subsets that can be delineated, notably among hematopoietic cells. As instruments became more and more stable and robust, the focus moved to analytic software. Almost concomitantly, the capacity increased to use large panels (both with mass and classical cytometry) and to apply artificial intelligence/machine learning for their analysis. The combination of these concepts raised new analytical possibilities, opening an unprecedented field of subtle exploration for many conditions, including hematopoiesis and hematological disorders. In this review, the general concepts and progress achieved in the development of new analytical approaches for exploring high‐dimensional data sets at the single‐cell level will be described as they appeared over the past few years. A larger and more practical part will detail the various steps that need to be mastered, both in data acquisition and in the preanalytical check of data files. Finally, a step‐by‐step explanation of the solution in development to combine the Bioconductor clustering algorithm FlowSOM and the popular and widely used software Kaluza® (Beckman Coulter) will be presented. The aim of this review was to point out that the day when these progresses will reach routine hematology laboratories does not seem so far away. [ABSTRACT FROM AUTHOR]

Monoclonal antibodies have become daily partners of both biologists and clinicians, as reagents and therapeutic agents. Behind their odd names and incredible diversity lies an amazing story of inventiveness and daring. This review tries to retrace the major steps of this saga, initiated by the search for anti‐rabbit red blood cell antibodies and currently culminating in amazing molecular constructions saving lives. After some historical and basic reminders, the fields of reagents and drugs will be addressed. This invaluable contribution of immunology to the understanding of both physiology and treatment clearly deserves to be fully recognized. [ABSTRACT FROM AUTHOR]

The article presents a case study of 39-year-old woman with no history of previous disease presented with asthenia, anorexia, and headache and her complete blood count showed a normocytic anemia. It mentions a diagnosis of myelodysplastic syndrome with excess blasts-2 (MDS EB-2); and also mentions need to be treated by intensive chemotherapy.

B‐lineage lymphoproliferative disorders (LPD) are rather frequent diseases, associated with specific clinical or biological features but also sometimes of fortuitous discovery. Multiparameter flow cytometry plays a major role for a rapid diagnostic indication, on peripheral blood or bone marrow samples in most instances, guiding complementary analyses and allowing for the proper therapeutic management of patients. After describing the important pre‐analytical precautions required for an adequate assessment, the immunophenotypic features of small‐cell and large‐cell lymphomas are described in this review. The ubiquitous expression of CD19 is a first mandatory gating step. A possible clonal proliferation is then suspected by the demonstration of surface immunoglobulin light chain restriction. The aberrant presence of CD5 allows to segregate chronic lymphocytic leukemia and mantle cell lymphoma in most cases. Other LPD exhibit specific immunophenotypic features. A table of useful markers and a decision tree are provided. Of note, immunophenotypic data should as much as possible be interpreted in an integrated manner, involving the patient's clinical and other biological features, and be completed by further chromosomal and/or molecular investigations. [ABSTRACT FROM AUTHOR]