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Defining the emergence of myeloid-derived suppressor cells in breast cancer using single-cell transcriptomics

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Science Immunology  21 Feb 2020:
Vol. 5, Issue 44, eaay6017
DOI: 10.1126/sciimmunol.aay6017
  • Fig. 1 Identifying MDSC-specific gene expression signatures using scRNAseq.

    (A) Approach overview for single-cell analysis of and (SYTOX Blue–negative) CD45+CD11b+Gr1+ cells were sorted from the spleen of control WT and tumor-bearing PyMT mice by FACS after droplet-enabled scRNAseq. (B and C) Combined Seurat analysis of a total of 14,646 cells from control and PyMT mice shown in tSNE projection results in various distinct clusters of splenic CD11b+Gr1+ cells. Main cell types (T cells, B cells, neutrophils, and monocytes) are outlined on the basis of hallmark gene expression. (C) Feature plots of characteristic markers of the four main cell types showing expression levels with low expression in gray to high expression in purple. (D) G-MDSCs were identified in cluster C1 by expression marker genes (Arg2 and Il1β) from the PyMT sample. (E) Subset analysis of monocyte cluster identified M-MDSCs. Three clusters were found; cluster M2 was identified as M-MDSCs (positive for Arg2 and Il1β). (F) Heat map displaying the scaled expression patterns of top marker genes within each G-MDSC and M-MDSC cluster compared with normal neutrophil and monocyte clusters from WT mice, respectively. Yellow, high expression; purple, low expression. (G) Venn diagram showing the number of statistically significant marker genes and overlap between G-MDSC and M-MDSC. (H) GO term analysis using Enrichr of curated MDSC signature. (I) Validation using qPCR of selected up-regulated MDSC genes and statistical analysis using unpaired t test (means ± SEM of n = 3), *P < 0.05.

  • Fig. 2 Comparative analysis using MDSC signature in myeloid cells from human patients with breast cancer.

    (A) Seurat analysis of previously published scRNAseq dataset comprising various immune cell populations in primary human breast tumor samples (36) projected in UMAP with cell type labels as indicated in different colors. pDC, plasmacytoid dendritic cell; mDC, myeloid dendritic cell. (B) Violin plot showing relative MDSC score of all cells in this dataset ordered by cell type showing the highest scores in neutrophils and monocytes. (C) Separate unbiased Seurat clustering analysis of neutrophil alone projected in UMAP yielded four distinct clusters of neutrophils in this dataset. (D) Heat map showing top 10 marker genes for each neutrophil cluster. (E) Violin plots showing relative MDSC score ordered by neutrophil subcluster, showing that cluster 0 exhibits the highest expression of MDSC gene signature. (F) Subset monocyte-specific Seurat clustering analysis projected in UMAP yielded three distinct clusters of monocytes in this dataset. (G) Heat map showing top 10 marker genes for each monocyte cluster. (H) Violin plots showing relative MDSC score ordered by monocyte subclusters.

  • Fig. 3 Identification of cell surface markers for MDSCs in breast cancer models.

    (A) CD84 expression profiling in WT and tumor-bearing PyMT showing that only spleen and primary tumor (TM) from PyMT exhibit significant expression. SSC-A, side scatter area. (B) Combined results and statistical analysis using unpaired t test (means ± SEM of n = 10), *P < 0.05. (D) Profiling JAML expression in WT and PyMT showing only spleen and tumor from PyMT exhibit significant expression. (E) Combined results and statistical analysis unpaired t test (means ± SEM of n = 3), *P < 0.05. (C and F) Concatenate multiple flow samples to visualize CD84 and JAML expression in one feature plot across all samples including FMO, bone marrow (BM), lung, spleen, MFP, and tumor from WT and PyMT; significant expression was only observed in spleen and tumor from PyMT. (G) Overview of PBMC collection, culture condition, and FACS approach. (H) Concatenate multiple flow samples to visualize CD84 expression in G- and M-MDSCs in one feature plot across all samples including PBMC control and treated. (I and J) Statistical analysis using unpaired t test (means ± SEM of n = 3), *P < 0.05.

  • Fig. 4 CD11b+Gr1+CD84hi cells exhibit potent capacity for T cell suppression and increased ROS production.

    (A) Overview of FACS approach using two different tissues (spleen and primary tumor) from WT and PyMT were subjected to T cell activation, ROS formation, and qPCR assays. (B and C) Splenic CD11b+Gr1+CD84hi cells from tumor-bearing mice suppress T cell proliferation. Histogram overlay (B) and quantitative bar charts (C) showing CD4/CD8 T cell proliferation measured by FACS in control samples with T cells only (black), T cells activated by CD3/CD28 (blue), activated T cells plus CD11b+Gr1+ cells from control spleens (SPLN) (orange), activated T cells plus CD11b+Gr1+CD84−/lo cells (purple), and activated T cells plus CD11b+Gr1+CD84hi (red) from spleen of tumor-bearing mice. (C) Statistical analysis using unpaired t test (means ± SEM of n = 3) *P < 0.05. (D and E) T cell suppression analysis using CD11b+Gr1+CD84hi and CD84−/lo cells isolated from primary tumors. Histogram overlay (D) and quantitative bar charts (E) showing CD4/CD8 T cell proliferation measured by FACS in control sample T cells (black), T cells activated by CD3/CD28 (blue), activated T cells plus CD11b+Gr1+CD84−/lo cells (purple), and activated T cells plus CD11b+Gr1+CD84hi (red) from tumor of tumor-bearing mice. (E) Statistical analysis using unpaired t test (means ± SEM of n = 3) *P < 0.05. (F and G) CD11b+Gr1+CD84hi cells from tumor-bearing mice show increased ROS formation compared with CD11b+Gr1+CD84−/lo; PMA-treated cells were used as positive control. ROS was measured by FACS using H2DCFDA. (G) Statistical analysis of ROS assay using unpaired t test (means ± SEM of n = 3) *P < 0.05. DCF, 2′,7′-dichlorofluorescein diacetate. ns, not significant.

  • Fig. 5 G-MDSCs emerge through aberrant differentiation trajectory during cancer.

    (A) Neutrophil-specific Monocle analysis on subset of Ly6g+ neutrophil clusters resulted in branched trajectory with five distinct Monocle states (color code for each state is indicated), which are named on the basis of respective gene expression profile. (B) Pseudotime plot illustrating expression of selected marker genes over pseudotime with the branch ending in state 1, shown with the dotted line, and the branch ending with state 3, highlighted by the solid line. Neutrophil progenitors are characterized by high levels of Elane, Mpo, and Prtn3 (state 4), which bifurcate into mature neutrophils (state 3; Camp, Ltf, and Lcn2) on the one branch and MDSCs (state 1; e.g., CD84) on the other branch. (C) Early G-MDSC transition was marked by high expression of Asprv1, Plscr1, and Pirb. (D) Summary schematic indicates that G-MDSCs emerge from neutrophil progenitor cells via an aberrant form of neutrophil differentiation rather than from mature neutrophils that are reprogrammed into immunosuppressive cells.

  • Fig. 6 Proposed model of aberrant neutrophil differentiation in the spleen during cancer.

    Myeloid cells differentiate in bone marrow from hematopoietic stem cells through common myeloid progenitors. Common granulocyte/monocyte progenitors expand in the bone marrow and migrate to spleen as a marginated pool, where they give rise to normal neutrophil maturation and, in cancer, aberrant neutrophil differentiation into G-MDSCs. Our findings indicate that MDSC-specific gene signature is largely shared between G- and M-MDSCs but differs from their normal myeloid counterparts. This MDSC signature includes numerous chemokine receptors, which likely guide their migration toward primary tumor or metastatic sites (indicated by arrows), where they may shield tumor cells from antitumor immunity. G-CSF, granulocyte colony-stimulating factor.

Supplementary Materials

  • immunology.sciencemag.org/cgi/content/full/5/44/eaay6017/DC1

    Fig. S1. Expansion of CD11b+Gr1+ cells during tumor progression in PyMT mice.

    Fig. S2. MDSCs emerge predominantly in spleen of tumor-bearing mice.

    Fig. S3. Sample labels and marker genes from scRNAseq analysis.

    Fig. S4. CD84 is a generalizable MDSC marker in different breast cancer models.

    Fig. S5. CD84 and JAML are up-regulated by in vitro–generated mouse and human MDSCs.

    Fig. S6. Characterization and validation of myeloid cell subsets for CD84 and JAML expression.

    Fig. S7. Reconstruction of MDSC differentiation trajectory in neutrophils and monocytes.

    Table S1. Marker genes from combined Seurat analysis.

    Table S2. Marker genes from Seurat analysis of monocytes only.

    Table S3. Gene signature from G-MDSCs-versus-neutrophils comparison.

    Table S4. Gene signature from M-MDSCs-versus-monocytes comparison.

    Table S5. Combined MDSC signature gene list.

    Table S6. GO terms (Biological Process 2018) MDSC gene signature.

    Table S7. qPCR primer sequences.

    Table S8. Marker genes from Seurat analysis of neutrophils only.

    Table S9. Neutrophil-specific Monocle state marker genes.

    Table S10. Monocyte-specific Monocle state marker genes.

  • Supplementary Materials

    The PDF file includes:

    • Fig. S1. Expansion of CD11b+Gr1+ cells during tumor progression in PyMT mice.
    • Fig. S2. MDSCs emerge predominantly in spleen of tumor-bearing mice.
    • Fig. S3. Sample labels and marker genes from scRNAseq analysis.
    • Fig. S4. CD84 is a generalizable MDSC marker in different breast cancer models.
    • Fig. S5. CD84 and JAML are up-regulated by in vitro–generated mouse and human MDSCs.
    • Fig. S6. Characterization and validation of myeloid cell subsets for CD84 and JAML expression.
    • Fig. S7. Reconstruction of MDSC differentiation trajectory in neutrophils and monocytes.
    • Legends for tables S1 to S10

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    Other Supplementary Material for this manuscript includes the following:

    • Table S1 (Microsoft Excel format). Marker genes from combined Seurat analysis.
    • Table S2 (Microsoft Excel format). Marker genes from Seurat analysis of monocytes only.
    • Table S3 (Microsoft Excel format). Gene signature from G-MDSCs-versus-neutrophils comparison.
    • Table S4 (Microsoft Excel format). Gene signature from M-MDSCs-versus-monocytes comparison.
    • Table S5 (Microsoft Excel format). Combined MDSC signature gene list.
    • Table S6 (Microsoft Excel format). GO terms (Biological Process 2018) MDSC gene signature.
    • Table S7 (Microsoft Excel format). qPCR primer sequences.
    • Table S8 (Microsoft Excel format). Marker genes from Seurat analysis of neutrophils only.
    • Table S9 (Microsoft Excel format). Neutrophil-specific Monocle state marker genes.
    • Table S10 (Microsoft Excel format). Monocyte-specific Monocle state marker genes.

    Files in this Data Supplement:

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