PS1235 RED BLOOD CELL GENERATION FROM HUMAN INDUCED PLURIPOTENT STEM CELLS

Abstract

Background: Multiparameter flow cytometry (MFC) is an essential tool for the diagnosis and follow-up of hematological malignancies. Numerous panels have been devised that best allow to achieve these goals. However, analysis of MFC graphs still heavily relies on the expertise of biologists and on specifically well adapted cascade-gating strategies. Because of the development of mass cytometry, which generates data in a multidimensional environment of up to 50 dimensions, new strategies have been developed, that could be applied to classical MFC. Aims: In this study, the capacities of a rapid new tool for multidimensional analysis, FlowSOM (self-organizing maps) have been combined to a classical MFC analysis tool, Kaluza®, for the identification of normal and diseased human bone marrow (BM) cell subpopulations. Methods: Normal BM samples collected during bone surgery were analyzed in MFC with four antibody panels adapted to the exploration of acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) respectively. The listmode files obtained were merged and submitted to FlowSOM analysis, using R packages, which yielded trees of 100 nodes defining normal BM cell subsets. FlowSOM parameters were then transferred to Kaluza® for a bidimensional representation of the trees within which selected navigation could be performed with Kaluza® tools. In a second part of the study, the same stainings were performed on myelodysplastic or leukemic (AML or ALL) samples (diagnosis or follow-up), which were submitted to FlowSOM together with the reference merged BM. Results: Node-by-node selection allowed for a precise definition of each of the unsupervised subsets generated by the software. This disclosed a selective segregation of delicate subsets such as non-classical monocytes, hematogones or stem cells. Direct simultaneous comparison of diseased samples and normal BM allowed for a precise definition of abnormal cells (blasts) at diagnosis, and their immediate recognition as minimal residual disease in follow-up samples. Summary/Conclusion: Flow SOM appears as a new and efficient tool for a rapid identification of BM cell subsets, highlighting differences from normal BM in dysplastic of leukemic samples. The node-by-node approach facilitates the definition of abnormal cells and their quantification, supervision intervening only after the generation of subsets delineated by artificial intelligence.