Department of Clinical Sciences, Oncology and Pathology, Lund University, Faculty of Medicine, Lund, Sweden.
Department of Clinical Genetics and Pathology, Skåne University Hospital, Lund, Sweden.
Cytometry B Clin Cytom. 2022 Mar;102(2):134-142. doi: 10.1002/cyto.b.22059. Epub 2022 Feb 12.
The Flow-Self Organizing Maps (FlowSOM) artificial intelligence (AI) program, available within the Bioconductor open-source R-project, allows for an unsupervised visualization and interpretation of multiparameter flow cytometry (MFC) data.
Applied to a reference merged file from 11 normal bone marrows (BM) analyzed with an MFC panel targeting erythropoiesis, FlowSOM allowed to identify six subpopulations of erythropoietic precursors (EPs). In order to find out how this program would help in the characterization of abnormalities in erythropoiesis, MFC data from list-mode files of 16 patients (5 with non-clonal anemia and 11 with myelodysplastic syndrome [MDS] at diagnosis) were analyzed.
Unsupervised FlowSOM analysis identified 18 additional subsets of EPs not present in the merged normal BM samples. Most of them involved subtle unexpected and previously unreported modifications in CD36 and/or CD71 antigen expression and in side scatter characteristics. Three patterns were observed in MDS patient samples: i) EPs with decreased proliferation and abnormal proliferating precursors, ii) EPs with a normal proliferating fraction and maturation defects in late precursors, and iii) EPs with a reduced erythropoietic fraction but mostly normal patterns suggesting that erythropoiesis was less affected. Additionally, analysis of sequential samples from an MDS patient under treatment showed a decrease of abnormal subsets after azacytidine treatment and near normalization after allogeneic hematopoietic stem-cell transplantation.
Unsupervised clustering analysis of MFC data discloses subtle alterations in erythropoiesis not detectable by cytology nor FCM supervised analysis. This novel AI analytical approach sheds some new light on the pathophysiology of these conditions.
Flow-Self Organizing Maps（FlowSOM）人工智能程序可在 Bioconductor 开源 R 项目中使用，可实现对多参数流式细胞术（MFC）数据的无监督可视化和解释。
应用于针对红细胞生成靶向的 MFC 面板分析的 11 个正常骨髓（BM）参考合并文件，FlowSOM 允许鉴定出六个红细胞生成前体（EP）亚群。为了了解该程序如何帮助鉴定红细胞生成异常，对 16 名患者（5 名非克隆性贫血和 11 名 MDS 初诊患者）的列表模式文件的 MFC 数据进行了分析。
无监督 FlowSOM 分析鉴定出 18 个在合并正常 BM 样本中不存在的 EP 亚群。它们大多数涉及 CD36 和/或 CD71 抗原表达和侧向散射特征的细微、意外和以前未报道的改变。在 MDS 患者样本中观察到三种模式：i）增殖减少和异常增殖前体的 EP，ii）具有正常增殖分数但晚期前体成熟缺陷的 EP，iii）红细胞生成分数减少但主要为正常模式的 EP，表明红细胞生成受到的影响较小。此外，对一名 MDS 患者治疗过程中的连续样本进行分析显示，阿扎胞苷治疗后异常亚群减少，异基因造血干细胞移植后接近正常。
MFC 数据的无监督聚类分析揭示了细胞学和 FCM 监督分析无法检测到的红细胞生成中的细微改变。这种新的人工智能分析方法为这些疾病的病理生理学提供了一些新的见解。
