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. 2024 Nov 21:12:e18521.
doi: 10.7717/peerj.18521. eCollection 2024.

Single-cell data revealed the function of natural killer cells and macrophage cells in chemotherapy tolerance in acute myeloid leukemia

Jing Gao  1 , Xueqian Yan  1 , Dan Fan  1 , Yuanchun Li  1
Affiliations

Single-cell data revealed the function of natural killer cells and macrophage cells in chemotherapy tolerance in acute myeloid leukemia

Jing Gao et al. PeerJ. .

Abstract

Background: Acute myeloid leukemia (AML) is highly prevalent and heterogeneous among adult acute leukemias. Current chemotherapeutic approaches for AML often face the challenge of drug resistance, and AML immune cells play an important role in the regulation of AML drug resistance. Thus, it is of key significance to explore the regulatory mechanisms of immune cells in AML to alleviate chemotherapy resistance in AML.

Methods: Based on AML single-cell transcriptomic data, this study revealed the differences in the expression of immune cell subpopulations and marker genes in AML patients in the complete remission group (CR) compared to AML patients in the non-complete remission group (non-CR) after chemotherapy. Functional enrichment by clusterprofiler revealed the regulatory functions of differentially expressed genes (DEGs) in AML. AUCell enrichment scores were used to assess the immunoregulatory functions of immune cells. Pseudotime analysis was used to construct immune cell differentiation trajectories. CellChat was used for cellular communication analysis to elucidate the interactions between immune cells. Survival analysis with the R package "survival" revealed the role of immune cell marker genes on AML prognosis. Finally, the wound healing and trans-well assay were performed.

Results: Single-cell clustering analysis revealed that NK/T cells and macrophage cells subpopulations were significantly higher in non-CR AML patients than in CR AML. AUCell enrichment analysis revealed that FCAR+ and FCGR3A+ macrophages were significantly more active in the non-CR group and correlated with processes regulating cellular energy metabolism and immune cell activity. Differentially expressed NK cell marker genes between CR and non-CR groups mainly included HBA1, S100A8, and S100A9, which were associated with cancer drug resistance regulation, these marker genes of (FCAR, FCGR3A, PREX1, S100A8 and S100A9) were upregulated in human chronic myeloid leukemia cells (HAP1) and silencing of S100A8 affected migration and invasion of HAP1 cells. In particular, the differentiation pathways of macrophages and NK cells in non-CR differed from those of patients in the CR group. Cellular communication analyses showed that ligand-receptor pairs between NK cells and macrophage cells mainly included HLA-E-KLRK1, HLA-E-KLRC1, HLA-E-CD94:NKG2A, CLEC2B-KLRB1. In addition, LGALS9-CD45, CCL3L1- CCR1, CCL3-CCR1 between these two immune cells mainly regulate secreted signaling to mediate AML progression. Marker genes in NK/T cells and macrophage cells were significantly associated with AML prognosis.

Conclusion: This study reveals the potential role of NK cells and macrophages in AML chemoresistance through the analysis of single-cell RNA sequencing data. This provides new ideas and insights into the key mechanisms of immune cells in AML treatment.

Keywords: Acute myeloid leukemia; Chemotherapy resistance; Macrophage cells; Natural killer cells; Single cell RNA-seq.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Single-cell mapping of AML.
(A) UMAP downscaling plot after AML clustering annotation. (B) Bubble plot of cell subpopulation marker gene expression. (C) Violin plot of cell subpopulation marker gene expression. (D) Proportion of cell subpopulations within AML samples. (E) Difference in cell subpopulation infiltration in CR-AML samples compared to non-CR-AML samples.
Figure 2
Figure 2. Macrophage cells single cell atlas.
(A) UMAP on the infiltration levels of subpopulations of macrophage cells. (B) Bubble plots of the relative expression levels of marker genes in each subpopulations of macrophage cells. (C) Violin plots of the relative expression levels of marker genes in each subpopulation of macrophage cells. (D) Differences in the infiltration ratios of each subpopulation of macrophage cells. (E) Functional enrichment analysis of marker genes in each subpopulation of macrophage cells.
Figure 3
Figure 3. Pseudotemporal analysis of Macrophage cells.
(A) Macrophage pseudotime scatter plot. (B) Macrophage pseudotime analysis heatmap. (C) Macrophage pseudotime analysis branching heatmap. (D) Expression of key genes in cell branching with pseudotime.
Figure 4
Figure 4. Single-cell mapping of NK/T cells.
(A) UMAP plots of NK/T cells. (B) Bubble plots of marker genes highly expressed in each subpopulation of NK/T cells cells. (C) Violin plots of marker genes highly expressed in each subpopulation of NK/T cells cells. (D) Relative infiltration ratios of each cell subpopulation of NK/T cells in non-CR-AML and CR-AML.
Figure 5
Figure 5. Pseudotemporal analysis of NK/T cells.
(A) Biofunctional enrichment analysis of highly expressed marker genes in NK/T cells. (B) NK/T cell pseudo-temporal analysis of branching scatter plots. (C) NK/T cell pseudo-temporal analysis of branching heatmaps. (D) Difference in expression of key marker genes in NK/T cells in non-CR-AML and CR-AML.
Figure 6
Figure 6. Cellular communication analysis.
(A) Pairing-receptor pairs that mediate contact between macrophages and natural killer cells. (B) Pairing-receptor pairs that mediate secreted signaling between macrophages and natural killer cells.
Figure 7
Figure 7. NK/T cells and macrophage cells activity were analyzed.
(A) AUC cell analysis of NK/T cells cell subsets in nature killer cells activation. (B) AUC cell analysis of NK/T cell subsets in nature killer cells proliferation. (C) AUC cell analysis of macrophage cell subsets in macrophage activation. (D) AUC cell analysis of macrophage cell subsets in macrophage activation.
Figure 8
Figure 8. Survival analysis of AML by key genes mediating AML drug resistance.
(A) Survival analysis of HBA1 in AML. (B) Survival analysis of PREX1 in AML. (C) Survival analysis of S100A8 in AML. (D) Survival analysis of S100A9 in AML.
Figure 9
Figure 9. Functional verification of marker genes in vitro.
(A) qPCR for the gene expression in cells. (B–C) Wound healing assay for cell migration. (D–E) Trans-well assay for cell invasion.
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References

    1. Baragano Raneros A, Martin-Palanco V, Fernandez AF, Rodriguez RM, Fraga MF, Lopez-Larrea C, Suarez-Alvarez B. Methylation of NKG2D ligands contributes to immune system evasion in acute myeloid leukemia. Genes & Immunity. 2015;16(1):71–82. doi: 10.1038/gene.2014.58. - DOI - PubMed
    1. Bottcher M, Panagiotidis K, Bruns H, Stumpf M, Volkl S, Geyh S, Dietel B, Schroeder T, Mackensen A, Mougiakakos D. Bone marrow stroma cells promote induction of a chemoresistant and prognostic unfavorable S100A8/A9high AML cell subset. Blood Advances. 2022;6(21):5685–5697. doi: 10.1182/bloodadvances.2021005938. - DOI - PMC - PubMed
    1. Bruchard M, Mignot G, Derangere V, Chalmin F, Chevriaux A, Vegran F, Boireau W, Simon B, Ryffel B, Connat JL, Kanellopoulos J, Martin F, Rebe C, Apetoh L, Ghiringhelli F. Chemotherapy-triggered cathepsin B release in myeloid-derived suppressor cells activates the Nlrp3 inflammasome and promotes tumor growth. Nature Medicine. 2013;19(1):57–64. doi: 10.1038/nm.2999. - DOI - PubMed
    1. Chen Z, Zhao M, Li M, Sui Q, Bian Y, Liang J, Hu Z, Zheng Y, Lu T, Huang Y, Zhan C, Jiang W, Wang Q, Tan L. Identification of differentially expressed genes in lung adenocarcinoma cells using single-cell RNA sequencing not detected using traditional RNA sequencing and microarray. Laboratory Investigation; a Journal of Technical Methods and Pathology. 2020;100(10):1318–1329. doi: 10.1038/s41374-020-0428-1. - DOI - PubMed
    1. Clay DE, Fox DT. DNA damage responses during the cell cycle: insights from model organisms and beyond. Genes. 2021;12(12):1882. doi: 10.3390/genes12121882. - DOI - PMC - PubMed

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