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- Béné, Marie C., et al.
(author)
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Unsupervised flow cytometry analysis in hematological malignancies : A new paradigm
- 2021
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In: International Journal of Laboratory Hematology. - : Wiley. - 1751-5521 .- 1751-553X. ; 43:S1, s. 54-64
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Research review (peer-reviewed)abstract
- 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.
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- Porwit, Anna, et al.
(author)
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Unsupervised cluster analysis and subset characterization of abnormal erythropoiesis using the bioinformatic Flow-Self Organizing Maps algorithm
- 2022
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In: Cytometry Part B - Clinical Cytometry. - : Wiley. - 1552-4949 .- 1552-4957. ; 102:2, s. 134-142
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Journal article (peer-reviewed)abstract
- BACKGROUND: 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.METHODS: 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.RESULTS: 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.CONCLUSION: 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.
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