Single-cell mass cytometry reveals intracellular survival/proliferative signaling in FLT3-ITD-mutated AML stem/progenitor cells

Abstract

Understanding the unique phenotypes and complex signaling pathways of leukemia stem cells (LSCs) will provide insights and druggable targets that can be used to eradicate acute myeloid leukemia (AML). Current work on AML LSCs is limited by the number of parameters that conventional flow cytometry (FCM) can analyze because of cell autofluorescence and fluorescent dye spectral overlap. Single-cell mass cytometry (CyTOF) substitutes rare earth elements for fluorophores to label antibodies, which allows measurements of up to 120 parameters in single cells without correction for spectral overlap. The aim of this study was the evaluation of intracellular signaling in antigen-defined stem/progenitor cell subsets in primary AML. CyTOF and conventional FCM yielded comparable results on LSC phenotypes defined by CD45, CD34, CD38, CD123, and CD99. Intracellular phosphoprotein responses to ex vivo cell signaling inhibitors and cytokine stimulation were assessed in myeloid leukemia cell lines and one primary AML sample. CyTOF and conventional FCM results were confirmed by western blotting. In the primary AML sample, we investigated the cell responses to ex vivo stimulation with stem cell factor and BEZ235-induced inhibition of PI3K and identified activation patterns in multiple PI3K downstream signaling pathways including p-4EBP1, p-AKT, and p-S6, particularly in CD34(+) subsets. We evaluated multiple signaling pathways in antigen-defined subpopulations in primary AML cells with FLT3-ITD mutations. The data demonstrated the heterogeneity of cell phenotype distribution and distinct patterns of signaling activation across AML samples and between AML and normal samples. The mTOR targets p-4EBP1 and p-S6 were exclusively found in FLT3-ITD stem/progenitor cells, but not in their normal counterparts, suggesting both as novel targets in FLT3 mutated AML. Our data suggest that CyTOF can identify functional signaling pathways in antigen-defined subpopulations in primary AML, which may provide a rationale for designing therapeutics targeting LSC-enriched cell populations.

Keywords: Key terms: acute myeloid leukemia; flow cytometry; leukemia stem cells; mass cytometry; western blotting.

Figure 1

LSC phenotypes were defined by CyTOF and FCM in primary AML samples. (A) Surface Ag staining and definitions of cell populations for CyTOF (upper panel) and FCM (Flow; lower panel) for one representative AML sample are shown. Replicate analysis of two primary AML samples and one normal bone marrow sample is shown in Supplemental Figures 1 and 2. (B) Pearson correlation of frequencies of each defined population across samples as determined by FCM and CyTOF is shown.

Figure 2

CyTOF, FCM, and western blotting yield comparable information on intracellular signaling pathway markers in leukemia cell lines. Serum-deprived TF-1 cells were stimulated with SCF or GM-CSF (GM) (A) or treated with ruxolitinib (Ruxo; B) for 1 hour before stimulation with IL-6. (C) Serum-starved Mo7e cells were treated with ruxolitinib, CI-1040, or BEZ235 for 1 hour. P-STAT5, pERK, and pAKT were detected by CyTOF, FCM (Flow), and western blotting. Pearson correlation of the magnitude of response of each intracellular protein to stimulation and inhibition across samples as determined by FCM, CyTOF, and western blotting is shown in Supplemental Figure 3. (D) CyTOF, FCM, and western blotting yielded comparable information on intracellular signaling pathways markers in primary AML samples.

Figure 3

CyTOF detected activation of signal transduction pathways in response to ex vivo perturbations. Cells from AML patient #9 were treated with BEZ235 and/or stimulated with SCF. (A) Tree plots were generated from patient 1using five surface markers (CD34, CD123, CD45, CD99, and CD38) with the annotations shown in Table S3. (B) The heatmap shows expression of surface markers in all annotations A1 through A9. (C) For each annotated phenotype, median intensity was computed for each phosphoprotein marker for each annotation, and the results are visualized in heatmaps to illustrate differences in phosphoprotein expression of each annotated phenotype under different conditions.

Figure 4

CyTOF detects variability in phenotypes and activation of signal transduction pathways in stem/progenitor cell subsets in primary AML samples harboring FLT3-ITD mutations compared to normal bone marrow samples. (A) The SPADE tree plots were generated using five surface markers (CD34, CD123, CD45, CD99, and CD38) from seven patients and three normal bone marrow (NBM) samples together and was colored by the median intensity of CD45 and CD34. Phosphoprotein markers were not used to construct the tree. Different cell populations were manually annotated as A1 to A11 (Table S4). (B) For each annotated phenotype, median intensity of the marker expression was computed for each phosphoprotein marker and for each sample and results were visualized as heatmaps to illustrate differences in phosphoprotein expression of each annotated phenotype across different patients. (C) Differences between samples were compared using the earth mover distance metric (EMD). For each individual sample, the percentages of cells in that sample belonging to each node of the SPADE tree were computed. The pairwise EMD distances were organized by hierarchical clustering and displayed.