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Free keywords:
Antigens, CD/analysis; Bone Marrow Cells/*classification; Cell Differentiation/physiology; Cell Separation/*methods; Cluster Analysis; Flow Cytometry/*methods; Granulocytes/classification; Hematopoiesis/*physiology; Humans; Monocytes/classification; Tumor Cells, Cultured
Abstract:
The study of normal or malignant haematopoiesis requires the analysis of heterogeneous cell populations using multiple morphological and molecular criteria. Flow cytometry has the capacity to acquire multi-parameter information of large haematopoietic cell populations, utilizing various combinations of >200 molecular markers (clusters of differentiation, CD). However, current flow cytometry analyses are based on serial gating of two-parametric scatter plots--a process that is inherently incapable to discriminate all subgroups of cells in the data. Here we studied the cellular diversity of normal bone marrows (BM) using multi-dimensional cluster analysis of six-parametric flow cytometry data (four CD, forward scatter and side scatter), focusing mainly on the myeloid lineage. Twenty-three subclasses of cells were resolved, many of them inseparable even when examined in all possible two-parametric scatter plots. The multi-dimensional analysis could distinguish the haematopoietic progenitors according to International Society of Haematotherapy and Graft Engineering criteria from other types of immature cells. Based on the defined clusters, we designed a classifier that assigns BM cells in samples to subclasses based on robust six-dimensional position and extended shape. The analysis presented here can manage successfully both the increasing numbers of haematopoietic cellular markers and sample heterogeneity. This should enhance the ability to study normal haematopoiesis, and to identify and monitor haematopoietic disorders.
