Fig. 1: HMA treatment induces DNA methylation heterogeneity in AML cells. | Leukemia

Fig. 1: HMA treatment induces DNA methylation heterogeneity in AML cells.

From: Hypomethylating agents induce epigenetic and transcriptional heterogeneity with implications for acute myeloid leukemia cell self-renewal

Fig. 1

A Schematic of experiment design. HL-60, MOLM-13 and MV-4–11 cells were labelled with CellTrace and treated with decitabine (DAC; 100 nM) or azacytidine (AZA; HL-60: 2000 nM, MOLM-13 and MV-4-11: 500 nM) every 24 h for 72 h. Single cells collected by indexed FACS on experiment day 3 were subjected to scNMT-seq (HL-60) or scTEM-seq (MOLM-13, MV-4-11). Created in BioRender. Lee, H. (2025) https://BioRender.com/1y2phwxB Violin plots of DNA methylation levels in single HL-60 (left), MOLM-13 (middle) and MV-4-11 (right) cells. Superimposed points show single-cell values from untreated (UNT, orange), DAC (cyan) and AZA (purple) groups. Dashed boxes surround DAC and AZA cells with methylation levels within the range of UNT samples. Data are shown for 185-222 cells from 2–3 replicate experiments in each cell line (UNT, n = 27-38; DAC n = 63-93; AZA n = 68-91). Statistical analysis was performed using ordinary one-way ANOVA with Dunnett’s (multiple comparisons test: ** p < 0.0005 vs. UNT). C Scatter plots comparing CellTrace fluorescence and DNA methylation in single cells, with linear regressions, F-test p-values, and Pearson correlation coefficients (r). D sPLS projection of HMA treated (AZA and DAC only) HL-60 cells based on transcript features coloured by cell group 1–4 (from E). E Heatmap of transcript features selected by sPLS displaying all samples (treated and untreated) as columns, are split by k-means clustering and grouped by treatment. Individual gene and TE expression levels (rows) are z-score normalised and split by k-means clustering with internal hierarchical clustering. F and G sPLS projections coloured by F LINE:L2:L2a expression, and G global methylation level. H and I Simplified tree plots of the gene ontology analysis for gene expression clusters 3 and 1. J Pearson correlations were computed between gene expression and DNA methylation (left) or accessibility (right) of associated promoters. Bar graphs show the percentage of correlations (p < 0.05) with negative and positive coefficients for all genes and filtered by gene expression cluster (identified in E).

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