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Age‐related changes of healthy bone marrow cell signaling in response to growth factors provide insight into low risk MDS
Author(s) -
Kornblau Steven M.,
Cohen Aileen C.,
Soper David,
Huang YingWen,
Cesano Alessandra
Publication year - 2014
Publication title -
cytometry part b: clinical cytometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.646
H-Index - 61
eISSN - 1552-4957
pISSN - 1552-4949
DOI - 10.1002/cyto.b.21125
Subject(s) - haematopoiesis , bone marrow , myeloid , stem cell , signal transduction , cd34 , biology , immunology , hematopoietic stem cell , flow cytometry , granulocyte colony stimulating factor , erythropoietin , erythropoiesis , cancer research , medicine , microbiology and biotechnology , endocrinology , genetics , anemia , chemotherapy
Background Single Cell Network Profiling (SCNP) is a multiparametric flow cytometry‐based assay that quantifiably and simultaneously measures changes in intracellular signaling proteins in response to in vitro extracellular modulators at the single cell level. Myelodysplastic syndrome (MDS) is a heterogeneous clonal disorder of hematopoietic stem cells that occurs in elderly subjects and is characterized by dysplasia and ineffective hematopoiesis. The functional responsiveness of MDS bone marrow (BM) hematopoietic cells, including functionally distinct myeloid and erythroid precursor subsets, to hematopoietic growth factors (HGF) and the relationship of modulated signaling to disease characteristics is poorly understood. Methods SCNP was used first to examine the effects of age on erythropoietin (EPO) and granulocyte colony stimulating factor (GCSF)‐induced signaling in myeloid, nucleated red blood cells (nRBC), and CD34 expressing cell subsets in healthy BM ( n = 15). SCNP was then used to map functional signaling profiles in low risk (LR) MDS ( n = 7) for comparison to signaling in samples from healthy donors and to probe signaling associations within clinically defined subgroups. Results In healthy BM samples, signaling responses to HGF were quite homogeneous (i.e., tightly regulated) with age‐dependent effects observed in response to EPO but not to GCSF. Despite the relatively small number of samples assayed in the study, LR MDS could be classified into distinct subgroups based on both cell subset frequency and signaling profiles. Conclusions As a correlate of underlying genetic abnormalities, signal transduction analyses may provide a functional and potentially clinically relevant classification of MDS. Further evaluation in a larger cohort is warranted. © 2013 International Clinical Cytometry Society