Multiparameter flow cytometry (MFC) is a powerful and versatile tool to accurately analyze cell subsets, notably to explore normal and pathological hematopoiesis. Yet, mostly supervised subjective strategies are used to identify cell subsets in this complex tissue. In the past few years, the implementation of mass cytometry and the big data generated have led to a blossoming of new software solutions. Their application to classical MFC in hematology is however still seldom reported. Here, we show how one of these new tools, the FlowSOM R solution, can be applied, together with the Kaluza® software, to a new delineation of hematopoietic subsets in normal human bone marrow (BM). We thus combined the unsupervised discrimination of cell subsets provided by FlowSOM and their expert-driven node-by-node assignment to known or new hematopoietic subsets. We also show how this new tool could modify the MFC exploration of hematological malignancies both at diagnosis (Dg) and follow-up (FU). This can be achieved by direct comparison of merged listmodes of reference normal BM, Dg, and FU samples of a representative acute myeloblastic case tested with the same immunophenotyping panel. This provides an immediate unsupervised evaluation of minimal residual disease. © 2019 International Society for Advancement of Cytometry.

中文翻译：

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R衍生的FlowSOM流程，用于分析正常和恶性人类骨髓经典流式细胞仪数据的无监督聚类。

多参数流式细胞术（MFC）是一种功能强大且用途广泛的工具，可以准确地分析细胞亚群，尤其是探索正常和病理性造血功能。然而，大多数受监督的主观策略被用来识别这种复杂组织中的细胞亚群。在过去的几年中，大规模细胞计数法的实施和生成的大数据导致了新软件解决方案的兴起。然而，仍然很少报道它们在血液学中的经典MFC中的应用。在这里，我们展示了如何将这些新工具之一FlowSOM R解决方案与Kaluza®软件一起应用于正常人骨髓（BM）中造血亚群的新描述。因此，我们将FlowSOM提供的细胞子集的无监督区分及其专家驱动的逐个节点分配给已知或新造血子集进行了组合。我们还展示了该新工具如何在诊断（Dg）和随访（FU）方面修改MFC对血液系统恶性肿瘤的探索。这可以通过直接比较参考急性BM病例的参考正常BM，Dg和FU样本的合并列表模式来实现，该样本用同一免疫表型专家组进行了测试。这提供了对最小残留疾病的即时无监督评估。©2019国际细胞计数学会。这提供了对最小残留疾病的即时无监督评估。©2019国际细胞计数学会。这提供了对最小残留疾病的即时无监督评估。©2019国际细胞计数学会。