Single-cell transcriptome sequencing reveals the mechanism of Realgar improvement on erythropoiesis in mice with myelodysplastic syndrome

Abstract

Myelodysplastic syndrome (MDS) is a malignant hematologic disorder with limited curative options, primarily reliant on hematopoietic stem cell transplantation. Anemia, a prevalent symptom of MDS, has few effective treatment strategies. Realgar, though known for its therapeutic effects on MDS, remains poorly understood in terms of its mechanism of action. In this study, both in vivo and in vitro experiments were conducted using Realgar and its primary active component, As₂S₂, to examine their impact on mouse erythroblasts at the single-cell level. Realgar treatment significantly altered the transcriptional profiles and cellular composition of bone marrow in mice, both in vivo and in vitro. Differentially expressed genes in erythroblasts regulated by Realgar were identified, unveiling potential regulatory functions and signaling pathways, such as heme biosynthesis, hemoglobin production, oxygen binding, IL-17 signaling, and MAPK pathways. These findings suggest that Realgar enhances the differentiation of erythroblasts in mouse bone marrow and improves overall blood cell counts. This work offers preliminary insights into Realgar's mechanisms, expands the understanding of this mineral medicine, and may inform strategies to optimize its therapeutic potential in hematologic diseases.

Keywords: Erythropoiesis; MDS; Myelodysplastic syndrome; Realgar.

Fig. 1

Therapeutic efficacy of Realgar in the murine MDS model (NUP98-HOXD13). (A) Complete blood count (CBC) analysis of peripheral blood from wild-type, NHD13, and Realgar-treated mice (n = 5). WBC, white blood cells; RBC, red blood cells; HB, hemoglobin; PLT, platelets. (B) Histopathological examination of liver, spleen, heart, and kidney tissues from each group. Sections were stained with H&E. Scale bar, 200 μm. (C) Body weight changes in WT, murine MDS model, and Realgar-treated mice during the treatment period. Data are presented as mean ± SD (n = 5). (D) Quantification of colony formation in cultures of nucleated BM or spleen cells isolated from WT, NHD13, or Realgar-treated groups, cultured in BFU-E medium for 12 days or CFU-E medium for 2 days. Data are presented as mean ± SEM (n = 3). (E) (Left) Flow cytometric analysis of erythroblasts in BM from the three groups. Erythroblasts were classified into four subpopulations based on surface staining for CD71 and Ter-119: Ter-119^medCD71^high proerythroblasts (R1), Ter-119^highCD71^high basophilic erythroblasts (R2), Ter-119^highCD71^med late basophilic and polychromatophilic erythroblasts (R3), and Ter- 119^highCD71.^low orthochromatophilic erythroblasts (R4). (F) (Right) Quantification of the percentages of R1–R4 populations shown in the left panel. Data are presented as mean ± SEM (n = 5). (*, P < 0.05; **, P < 0.01; ***, P < 0.001; n.s., not significant)

Fig. 2

Single-cell atlas of bone marrow cells across three groups (In vivo). (A) Flowchart illustrating the research process for single-cell RNA sequencing. (B) Thirty-four bone marrow cell clusters. UMAP representation of unbiased clustering of all bone marrow cells from the three groups (combined bone and bone marrow fractions, n = 5 mice). (C) UMAP representation of unbiased clustering and cell annotation for bone marrow cells from the three groups. A total of 12 cell types were identified. (D) Cluster signature genes are highlighted on the left. Average expression of the top differentially expressed genes (rows) across the cells (columns), with warmer colors indicating higher expression. (E) UMAP plot displaying the distribution of representative markers for each cell type. Color represents the relative expression level of each gene. (F) Proportions of each cell type

Fig. 3

Bone marrow erythroblasts cell types defined by scRNA-seq. (A) UMAP plot displaying eleven subclusters of bone marrow erythroblasts. (B) Heatmap illustrating discriminative gene sets for each cluster, with a cutoff threshold of FDR < 0.01 and |logFC|> 0.25. (C) Cell differentiation trajectory reconstructed using Monocle. Each dot represents a single cell, with colors indicating different cell clusters. (D) UMAP plot colored by expression levels of Mki67 (Left) and Bpgm (Right). (E) Cell cycle phases identified using Seurat. Phases are depicted in different colors: G1 in green, S in blue, and G2/M in red. (F) UMAP plot showing the distribution of erythroblast differentiation stages. ProE, BasoE, PolyE, and OthoE are represented in distinct colors. (G) Enriched GO terms and P-values for the four stages of cells. (H) Proportions of the four cell types in WT, NHD13, and Realgar groups

Fig. 4

Arsenic disulfide promotes erythroid differentiation of mouse bone marrow cells in vitro. (A) Flowchart outlining the research process to investigate the effect of arsenic disulfide. (B) Mouse bone marrow cells were cultured for 48 h or 10 days (as indicated) with 625 nM As₂S₂. Expression of CD71 and Ter119 was assessed by flow cytometry. (C) Quantification of the percentages of CD71⁺Ter119⁺ populations shown in the left panel. Data are presented as mean ± SEM (n = 3). (D) K562 cells were cultured for 48 h (as indicated) with 30 μM hemin or 625 nM As₂S₂. Expression of CD71 and CD235a was analyzed by flow cytometry. (E) Quantification of the percentages of CD71⁺CD235a.⁺ populations shown in the left panel. Data are presented as mean ± SEM (n = 3)

Fig. 5

Single-cell atlas of bone marrow cells in control and As₂S₂ groups (in vitro). (A) t-SNE plot showing unbiased clustering of all bone marrow cells in the control and As₂S₂ groups, revealing 18 distinct cell clusters. (B) t-SNE plot of unbiased clustering and cell annotation of bone marrow cells in control and As₂S₂ groups. A total of 10 cell types were identified. (C) Bubble chart displaying the top 3 markers for each cell type. Dot size corresponds to the percentage of cells in which the gene is detected, and color indicates the average expression level of the gene in each cell type. (D) t-SNE plot showing the distribution of representative markers for each cell type. Color represents the relative expression level of each gene. (E) Proportions of each cell type

Fig. 6

Enrichment analysis of differentially expressed genes in erythroblasts from in vivo experiments. (A) Volcano plot showing DEGs, with red dots indicating significantly upregulated genes (P ≤ 0.05, |Log2 FC|≥ 0.25) and blue dots representing significantly downregulated genes (P ≤ 0.05, |Log2 FC|≥ 0.25). The top 10 DEGs are labeled. (B) Venn diagram illustrating the overlap of upregulated DEGs in erythroblasts between the WT vs. NHD13 and Realgar vs. NHD13 comparisons, revealing 17 overlapping genes. (C) GO enrichment analysis of upregulated DEGs in the Realgar vs. NHD13 comparison. (D) Enrichment plot from Gene Set Enrichment Analysis (GSEA) comparing differentially regulated genes in the Realgar group versus the NHD13 group. GSEA was conducted using GSEA software (v4.1.0) and MSigDB, with one-sided statistical tests and adjustments for multiple comparisons. Black bars represent individual genes, and enrichment is indicated in green. Normalized enrichment score (NES) is displayed. (E) KEGG enrichment analysis of upregulated DEGs in the Realgar vs. NHD13 comparison

Fig. 7

Enrichment analysis of differentially expressed genes in erythroblasts in vitro and analysis of the intersection of in vivo and in vitro differentially expressed genes. (A) Volcano plot displaying DEGs, with red dots representing significantly upregulated genes (P ≤ 0.05, |Log2 FC|≥ 0.25) and blue dots indicating significantly downregulated genes (P ≤ 0.05, |Log2 FC|≥ 0.25). The top 10 DEGs are labeled. (B) KEGG enrichment analysis of upregulated DEGs in the As₂S₂ vs. Control comparison. (C) Venn diagram showing the overlap of upregulated DEGs in erythroblasts between Realgar vs. NHD13 (in vivo) and As₂S₂ vs. Control (in vitro) comparisons, with 17 overlapping genes identified. (D) GO enrichment analysis of the overlapping upregulated genes. (E) KEGG enrichment analysis of the overlapping upregulated genes. (F) Reactome enrichment analysis of the overlapping upregulated genes

Fig. 8

Preliminary validation of bioinformatics results. (A) Violin plot depicting the expression levels of S100a8 and S100a9 in erythroblasts. Gene expression in the WT group is shown in blue, in the NHD13 group in red, and in the Realgar group in green. Statistical significance is indicated above each gene. (B) Western blot analysis of S100a8 and S100a9 protein expression in Ter119⁺ bone marrow cells. (C) Quantification of the gray values of the protein bands in panel B. Data are expressed as mean ± SEM (n = 3). (D) Western blot analysis of ERK1/2 and pERK1/2 protein expression in mouse bone marrow cells after in vitro culture. (E) Quantification of the gray values of the protein bands in panel D. Data are expressed as mean ± SEM (n = 3). (F) Flow cytometric analysis of CD71 and CD235a expression in erythroblasts. (G) Quantification of the percentage of CD71⁺CD235a⁺ populations from the flow cytometry analysis in panel F. Data are expressed as mean ± SEM (n = 3)