Abstract
Glioblastomas are aggressive primary brain cancers that recur as therapy-resistant tumors. Myeloid cells control glioblastoma malignancy, but their dynamics during disease progression remain poorly understood. Here, we employed single-cell RNA sequencing and CITE-seq to map the glioblastoma immune landscape in mouse tumors and in patients with newly diagnosed disease or recurrence. This revealed a large and diverse myeloid compartment, with dendritic cell and macrophage populations that were conserved across species and dynamic across disease stages. Tumor-associated macrophages (TAMs) consisted of microglia- or monocyte-derived populations, with both exhibiting additional heterogeneity, including subsets with conserved lipid and hypoxic signatures. Microglia- and monocyte-derived TAMs were self-renewing populations that competed for space and could be depleted via CSF1R blockade. Microglia-derived TAMs were predominant in newly diagnosed tumors, but were outnumbered by monocyte-derived TAMs following recurrence, especially in hypoxic tumor environments. Our results unravel the glioblastoma myeloid landscape and provide a framework for future therapeutic interventions.
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Data availability
All human and mouse scRNA-seq and CITE-seq datasets described in this article can be accessed via our interactive webserver at www.brainimmuneatlas.org, where all gene–cell count and cell annotation matrices can also be downloaded. Through this tool, it is possible to evaluate the expression of genes or proteins via CITE-seq antibody staining in our datasets and to download DE gene lists. Additionally, all mouse scRNA-seq and CITE-seq raw data, mouse gene–cell count matrices and human gene–cell count matrices are deposited at GEO (NCBI) under accession number GSE163120. Raw sequencing reads of the human scRNA-seq and CITE-seq experiments have been deposited in the controlled access public repository European Genome-phenome Archive (EGA), under study accession number EGAS00001004871. Other data that support the findings of this study are available from the corresponding author upon request.
Code availability
The R codes that were used for scRNA-seq and CITE-seq analyses can be found at Github: https://github.com/Movahedilab/Glioblastoma.
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Acknowledgements
This work was supported by Innoviris (Attract grant, BB2B 2015-2) to K.M.; VLAIO (ImmCyte HBC.2016.0889) to K.M. and J.A.V.G.; KU Leuven C-funding (grant C14/17/084) to F.D.S.; the Research Foundation Flanders (FWO, grant G0I1118N to F.D.S. and the fellowships of J.M. and I.S.); and the Leuven Kankerinstituut and VIB. F.L. is supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 766069 (‘GLIOTRAIN’). We thank Y. Elkrim, A. Murgaski, G. van Isterdael, the VIB Flow Core, the VIB Nucleomics Core and the VIB BioImaging Core for technical assistance, J. Mathys for advice regarding statistical analysis, VIB Tech Watch for support regarding scRNA-seq technologies, L. Solie and M. Derweduwe for the collection and processing of fresh GBM tumor samples, and the KU Leuven Histology Core for its support. We thank the VIB Single-Cell Accelerator Program, V. Mylka, B. Vanneste, and C. Scott for help with setting up CITE-seq at VIB.
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Contributions
A.R.P.A., I.S., F.L., D.L., J.A.V.G. and K.M. designed the experiments. A.R.P.A., I.S., F.L., J.M., K.D.V., H.V.H., S.S.K.H., N.V., R.E.V., O.D.W., C.G., F.D.S. and K.M. performed the experiments. A.R.P.A., I.S., F.D.S. and K.M. carried out data analyses. I.S., F.L., A.A., D.K., L.M., K.V.d.B., N.V., Y.S., D.L. and K.M. performed or helped with bioinformatics analyses. J.D., S.D.V., R.S., M.V. and B.N. provided clinical samples. F.M.B., L.B. and F.D.S. helped with imaging and its analyses. J.D., S.D.V., L.B., O.D.W., Y.S., M.G., B.N., F.D.S., D.L. and J.A.V.G. provided advice on experimental design, data analyses and interpretation. A.R.P.A., I.S., J.A.V.G. and K.M. wrote the manuscript. A.R.P.A., I.S., J.A.V.G. and K.M. revised the manuscript. K.M. conceived the study, and J.A.V.G. and K.M. supervised the study.
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Peer review information Nature Neuroscience thanks Itay Tirosh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Harmony efficiently corrects for patient-, batch-, and platform-induced gene expression noise without obscuring relevant heterogeneity.
a, UMAP-projections without (left) and with (right) Harmony-correction of the entire CD45+ immune compartment of human recurrent GBM tumors (top) and of a subsetted and re-clustered dataset of monocytes and macrophages only (bottom). Clusters are colored based on patient identity (R1–4), illustrating a clear patient-specific segregation without Harmony-correction, but the absence of this segregation after Harmony-correction. b, Harmony-corrected UMAP-projections of patients R1 to R4 represented separately on each plot, indicating that each cluster carries cells from every patient. The UMAP clusters are colored based on cluster annotations in Fig. 1c. Only cells belonging to one individual patient are colored per panel, cells belonging to the other patients, are colored in grey. n.c. = necrotic core. c, Percentage of the indicated populations within the CD45+ compartment of human ND and recurrent tumors, as measured via scRNA-Seq. Each datapoint stems from an individual patient: ND1-7 and R1-4. Bar graphs represent mean ± s.e.m. Significance was evaluated using the Mann-Whitney test (two-tailed). d, The percentage of cluster TAM 1 within (TAM 1 + TAM 2) in human ND and recurrent tumors. Each datapoint stems from an individual patient: ND1-7 and R1-4. Bar graphs represent mean ± s.e.m. Significance was evaluated using the Mann-Whitney test (two-tailed). e, Percentage of the indicated populations within the CD45+ compartment of WT and CCR2-KO GL261 tumors, as measured via scRNA-Seq. Each datapoint stems from an individual scRNA-Seq sample: WT 1-3 and CCR2-KO 1–3. Bar graphs represent mean ± s.e.m. Significance was evaluated using an unpaired two-tailed t-test with Welch’s correction. f, The percentage of cluster TAM 1 within (TAM 1 + TAM 2) in WT and CCR2-KO GL261 tumors. Each datapoint stems from an individual scRNA-Seq sample: WT 1–3 and CCR2-KO 1–3. Bar graphs represent mean ± s.e.m. Significance was evaluated using an unpaired two-tailed t-test with Welch’s correction. ns P > 0.05; * P < 0.05; ** P < 0.01; *** P < 0.001. Exact P values can be found in Supplementary Table 19.
Extended Data Fig. 2 DC heterogeneity in human and mouse GBM tumors.
a, UMAP feature plots based on the Harmony-corrected UMAP-projection of DCs from human ND and recurrent GBM tumors of Fig. 2a, showing the expression levels of the indicated genes. b, Pie charts visualizing the percentage of the indicated DC subsets in ND and recurrent GBM tumors, represented per patient. The relative abundance of the various DC subsets was calculated for the individual patients for which sufficient numbers of DCs were captured (ND7 and R1–4). Colors match the cluster colors of Fig. 2a. c, UMAP feature plots based on the Harmony-corrected UMAP-projection of DCs from mouse GL261 tumors in Fig. 2e, showing the expression levels of the indicated genes. d, Scatter plot comparing the log10(adjusted P) value of the most significantly up-or downregulated genes in mouse MigDC versus other DC populations (x-axis) and in human MigDC versus other DC populations (y-axis). The sign of the log10(adjusted P) corresponds to up- or downregulation of genes. Common up- and downregulated genes are represented in red and blue, respectively. Genes that are only up- or downregulated within 1 species (human or mouse), but are not differential in the other species, are represented in black. Genes that are upregulated in mouse and downregulated in human, or vice versa, are represented in pink. P value adjustment was performed using Bonferroni correction. (e), Similar to (d) but for pDCs. f, Bar graphs illustrating the percentages of DCs belonging to the distinct DC subpopulations for WT (WT 1–3) and CCR2-KO (KO 1–3) samples. Bar graphs represent mean ± s.e.m. Significance was evaluated using an unpaired two-tailed t-test with Welch’s correction. ns P > 0.05; * P < 0.05. Exact P values can be found in Supplementary Table 19. g, Representative flow cytometry plots depicting the gating strategy for selecting cDC1 and cDC2 subsets in GL261 tumors. Cells were pre-gated as live and single cells (top). Plots show BrdU staining in cDC1s or cDC2s from control (non-injected) or BrdU injected mice (bottom). Representative plots of 2 independent experiments with n = 7 mice.
Extended Data Fig. 3 Adoptive transfer of classical and non-classical monocytes in C57BL/6 or NR4A1-KO GL261 tumor-bearing mice.
a, Representative flow cytometry plots showing GFP+ cells in GL261 tumors from C57BL/6 mice, 5 days after the adoptive transfer of classical (c. Mono) or non-classical (nc. Mono) monocytes that were purified from Cx3cr1GFP/+ mice. Plots are gated on live single CD45+CD11b+ cells. GFP+ cells were not detected in tumors of mice that did not receive an adoptive transfer of monocytes (Control). b, In mice that had received an adoptive transfer of classical monocytes, GFP+CD11b+CD45+ cells (red) were overlaid on GFP−CD11b+CD45+ cells (grey). The representative plots show how the transferred cells (red) relate to the host cells (grey) in terms of scatter profile and expression of the indicated markers. c, In mice that had received an adoptive transfer of non-classical monocytes, GFP+CD11b+CD45+ cells (blue) were overlaid on GFP−CD11b+CD45+ cells (grey). The representative plots show how the transferred cells (blue) relate to the host cells (grey) in terms of scatter profile and expression of the indicated markers. d, Overlay of GFP+ cells from (b) and (c). The results show that 5 days post adoptive transfer, classical monocytes (red) exhibited an enlarged scatter profile and upregulated CX3CR1, F4/80 and MHCII expression, while this was not seen for adoptively transferred non-classical monocytes (blue). e–h, Similar to a-d except that adoptive transfers were performed in tumor-bearing NR4A1-KO mice. Plots are representative of two independent experiments n = 2 to 4 mice. c. Mono = classical monocytes; nc. Mono = non-classical monocytes.
Extended Data Fig. 4 Delineating the gene signatures of human Mo-TAMs.
a, Volcano plot showing DE genes (-log10(adjusted P) > 20, log2(FC) > 0.5) between SEPP1high Mo-TAMs (cluster 5 in Fig. 5a) and SEPP1low Mo-TAMs (cluster 4 in Fig. 5a). P value adjustment was performed using Bonferroni correction. SEPP1high TAM enriched genes are marked in green, if their expression was reported to be affected by a panel of cytokines/stimuli in the study of Xue et al., 201433. b, For the SEPP1high TAM enriched genes marked in green in (a), a heatmap was made using the data from Xue et al., 2014, visualizing the normalized gene expression levels of human macrophages treated with the indicated stimuli. c, Volcano plot showing DE genes (in red: -log10(adjusted P) > 20, log2(FC) > 0.5) between IFN sign. Mo-TAMs (cluster 6 in Fig. 5a) and SEPP1low Mo-TAMs (cluster 4 in Fig. 5a). P value adjustment was performed using Bonferroni correction. We marked IFN sign. enriched genes in pink, if their expression was reported to be affected by a panel of cytokines/stimuli in the study of Xue et al., 2014. d, For the IFN sign. enriched genes marked in pink in (c), a heatmap was made using the data from Xue et al., 2014, visualizing the normalized gene expression levels of human macrophages treated with the indicated stimuli. GC = glucocorticoid, HDL = high-density lipoprotein, upLPS = ultrapure lipopolysaccharide, LA = lauric acid, LiA = linoleic acid, OA = oleic acid, P3C = Pam3CSK4, PA = palmitic acid, PGE2 = prostaglandine E2, SA = stearic acid, sLPS = standard lipopolysaccharide, TPP = TNF + PGE2 + P3C.
Extended Data Fig. 5 Mo-TAM heterogeneity in GBM tumors.
a, Overview of Mo-TAM subsets in human ND GBM tumors, with 5 subclusters that were discerned: (1) Transitory Mo-TAM (green), (2) Phagocytic/lipid signature Mo-TAM (purple), (3) Hypoxic Mo-TAM (blue), (5) SEPP1+ Mo-TAM (pale orange), and (6) IFN signature Mo-TAM (pink). b, UMAP plots showing the expression of key signature genes that are differential between the various subsets. Dashed lines encircle the subsets as annotated in panel a. c, d, DE analysis in human ND Mo-TAMs. c, Volcano plot showing genes that are DE between Hypoxic Mo-TAMs (cluster 3) and SEPP1+ Mo-TAMs (cluster 5). d, Volcano plot showing genes that are DE between Phagocytic/lipid Mo-TAMs (cluster 2) and SEPP1+ Mo-TAMs (cluster 5). In red are shown genes with -log10(adjusted P) > 20, log2(FC) > 0.5; P value adjustment was performed using Bonferroni correction. e, f, Pie charts showing the percentage of Mo-TAM subsets in human ND tumors (e), and human recurrent tumors (f), per individual patient sample. g, Bar graphs showing the mean normalized expression of selected genes in ND SEPP1+ Mo-TAMs (ND cluster 5), Recurrent SEPP1low Mo-TAMs (R cluster 4) and Recurrent SEPP1high Mo-TAMs (R cluster 5), for the individual patient samples. Bar graphs represent mean ± s.e.m. Significance was evaluated using an ordinary One-way ANOVA and Tukey’s multiple comparisons tests. ns P > 0.05; * P < 0.05; ** P < 0.01. Exact P values can be found in Supplementary Table 19. h, Pie charts showing the percentage of Mo-TAM subsets in mouse GL261 tumors per individual mouse sample.
Extended Data Fig. 6 Human GBM Mo-TAM heterogeneity is recapitulated in mouse GL261 tumors with conserved gene signatures between species.
a–c, DE analysis in mouse GL261 Mo-TAMs. a, Volcano plot showing genes that are DE between mouse GL261 SEPP1+ Mo-TAMs and Transitory Mo-TAMs. b, Volcano plot showing genes that are DE between mouse GL261 Phagocytic/lipid Mo-TAMs and Transitory Mo-TAMs. c, Volcano plot showing genes that are DE between Hypoxic Mo-TAMs and other Mo-TAMs. In red are shown genes with -log10(adjusted P) > 20, log2(FC) > 0.5; P value adjustment was performed using Bonferroni correction. d, Scatter plot comparing the log10(adjusted P) value of the most significantly up- or downregulated genes in mouse GL261 Lipid Mo-TAM versus other Mo-TAM populations (x-axis) and in human recurrent GBM Lipid Mo-TAM versus other Mo-TAM populations (y-axis). The sign of the log10(adjusted P) corresponds to up- or downregulation of genes. Common up- and downregulated genes are represented in red and blue, respectively. Genes that are only up- or downregulated within 1 species (human or mouse), but are not differential in the other species, are represented in black. Genes that are upregulated in mouse and downregulated in human, or vice versa, are represented in pink. P value adjustment was performed using Bonferroni correction. e, Similar to (d) but for mouse and human IFN Mo-TAM f, Similar to (d) but for mouse and human Hypoxic Mo-TAM. g, Similar to (d) but for mouse Sepp1+ Mo-TAM and human SEPP1high Mo-TAM. h, UMAP plot showing the expression of Arg1, which was enriched in mouse hypoxic Mo-TAMs. i, GL261 tumor-bearing mice were injected with pimonidazole 2 hours prior to sacrifice. Coronal cryosections of GL261 brain tumor tissue, were stained for ARG1 (red), F4/80 (green) and pimonidazole (blue) to visualize hypoxia. ARG1 specifically colocalizes with F4/80+ macrophages (yellow) and is enriched in PIMO+ hypoxic tumor regions. Image is representative for n = 3 mice. Scalebar = 50 μm.
Extended Data Fig. 7 Mg-TAM heterogeneity in human and mouse GBM.
a, SCORPIUS trajectory inference was run on a dataset containing Mg-TAMs and microglia that were isolated from the surrounding brain tissue of GL261 tumor-bearing WT mice (WT5 w.b.). Cells were automatically ordered along a linear trajectory, where green cells represent brain microglia and blue cells Mg-TAMs. The top genes were clustered into three gene modules (normalized expression) that are down- or upregulated as brain microglia transition towards Mg-TAMs. b, Volcano plot showing genes that are DE (in red: -log10(adjusted P) > 20, log2(FC) > 0.5) between human ND GBM Mg-TAM cluster 3 (Phagocytic/lipid Mg-TAM) and Mg-TAM cluster 1. P value adjustment was performed using Bonferroni correction. c, d, UMAP plots of signature genes for phagocytic/lipid, IFN and hypoxic subsets in human recurrent (c) and mouse GL261 tumors (d) are shown. e-f, Volcano plots showing genes that are DE (in red: -log10(adjusted P) > 20, log2(FC) > 0.5) in hypoxic Mg-TAMs in human recurrent (e) and mouse GL261 (f) tumors. P value adjustment was performed using Bonferroni correction.
Extended Data Fig. 8 Altered transcriptional states in TAM subsets from human ND versus recurrent tumors.
a, b, Bar plots showing the mean normalized expression levels of a selection of genes that are enriched in human recurrent versus ND tumors (a), or that are enriched in human ND versus recurrent tumors (b), in Mg-TAM (left), SEPP1+ Mo-TAM (middle) and Hypoxic Mo-TAM (right), for each patient sample. Each data point corresponds to the individual human scRNA-seq sample, n = 7 for ND and n = 5 for recurrent. Bar graphs represent mean ± s.e.m. Significance was evaluated using unpaired two-tailed t test with Welch’s correction for Mg-TAM and hypoxic Mo-TAM and Ordinary One-way ANOVA for SEPP1+ Mo-TAM. ns P > 0.05; * P < 0.05; ** P < 0.01; *** P < 0.001; **** P < 0.0001. Exact P values can be found in Supplementary Table 19. c, d, Gene ontology analysis on DE genes between human recurrent GBM SEPP1+ Mo-TAMs and ND GBM SEPP1+ Mo-TAMs showing the top 20 enriched GO terms in recurrent versus ND GBM TAMs (c) and enriched GO terms in ND versus recurrent GBM TAMs (d) (adjusted P-value cutoff of 10−10, log2FC cutoff of 0.379). e, Bar graphs showing the mean normalized expression values for a selection of genes that exhibited a high variation in expression across individual ND and recurrent patients. Meta-z-score values for these genes were obtained via PRECOG35 which represents the association of the genes with GBM patient survival based on public datasets. The values are colored according to the color-scale at the bottom.
Extended Data Fig. 9 CITE-Seq protein validation for human and mouse GBM TAMs.
a, b, RNA-based UMAP plots showing expression of signature genes for Mg-TAM, Monocytes, Transitory Mo-TAMs, Phagocytic/lipid Mo-TAMs, Hypoxic Mo-TAMs, SEPP1+ Mo-TAMs and IFN Mo-TAMs in human (a) and mouse (b) tumors. c, d, Protein-based UMAP plots in which the TAM clusters identified based on RNA-expression are highlighted in human (c) and mouse (d) tumors. e–g, Scatter plots comparing the log10(adjusted P) value of the most significantly up- or downregulated proteins (ADT values) (x-axis) and genes (y-axis) in human GBM Mg-TAMs versus other TAM subsets (e), in human GBM SEPP1high TAMs versus other TAM subsets (f) and in mouse GL261 Mg-TAMs versus other TAM subsets (g). The sign of the log10(adjusted P) corresponds to up- or downregulation of protein/gene combinations. Common up- and downregulated protein/gene combinations are represented in red and blue, respectively. Protein/gene combinations that are only up- or downregulated on the protein level but not on the gene expression level, are represented in green. Genes that are upregulated on the protein level and downregulated on the gene expression level, or vice versa, are represented in pink. P value adjustment was performed using Bonferroni correction. h, Mouse RNA-based UMAP plots showing the expression of the indicated genes and their corresponding cell surface protein markers.
Extended Data Fig. 10 CITE-Seq analysis reveals the persistence of cDC1 or cDC2-related proteins in a fraction of MigDCs in human and mouse GBM.
a, Protein-based UMAP plot of human DCs, in which MigDCs are highlighted in purple (left). The rectangle delineates the insets shown in the right panels, where the expression of cDC1- or cDC2-related marker genes and their corresponding cell surface proteins are plotted. b, RNA-based UMAP plot of mouse DCs, in which MigDCs are highlighted in purple (left). The rectangle delineates the insets shown in the right panels, where the expression of cDC1- or cDC2-related marker genes and their corresponding cell surface proteins are plotted. c, RNA-based UMAP plots of mouse DCs showing the expression level of H2-Aa in the top panel and the corresponding protein IA-IE in the bottom panel. d, RNA-based UMAP plots of mouse DCs showing showing the expression of the indicated genes and their corresponding cell surface protein markers.
Supplementary information
Supplementary Information
Supplementary Figs. 1–5 and Supplementary Tables 20 and 21.
Supplementary Table 1
Patient data.
Supplementary Table 2
Single-cell differential gene expression analysis of the human ND GBM immune cell populations.
Supplementary Table 3
Single-cell differential gene expression analysis of the human recurrent GBM immune cell populations.
Supplementary Table 4
Single-cell differential gene expression analysis of the mouse GL261 immune cell populations.
Supplementary Table 5
Single-cell differential gene expression analysis of the human GBM DC subsets.
Supplementary Table 6
Single-cell differential gene expression analysis of the mouse GL261 DC subsets.
Supplementary Table 7
Human–mouse comparisons of the DE genes in GBM DCs.
Supplementary Table 8
Single-cell differential gene expression analysis of the human recurrent GBM TAM subsets.
Supplementary Table 9
GO analysis data.
Supplementary Table 10
Single-cell differential gene expression analysis of the human ND GBM TAM subsets.
Supplementary Table 11
Single-cell differential gene expression analysis of the mouse GBM TAM subsets.
Supplementary Table 12
Human–mouse comparisons of the DE genes in TAM subsets.
Supplementary Table 13
Single-cell differential gene expression analysis of TAM subsets in ND versus recurrent tumors.
Supplementary Table 14
CITE-seq antibody list (mouse and human).
Supplementary Table 15
Single-cell differential cell surface protein expression (ADT) analysis of the human GBM monocyte, TAM and DC subsets.
Supplementary Table 16
Single-cell differential cell surface protein expression (ADT) analysis of the mouse GL261 monocyte, TAM and DC subsets.
Supplementary Table 17
Single-cell differential cell surface protein expression (ADT) analysis of the human GBM DC subsets.
Supplementary Table 18
Single-cell differential cell surface protein expression (ADT) analysis of the mouse GL261 DC subsets.
Supplementary Table 19
Statistics.
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Pombo Antunes, A.R., Scheyltjens, I., Lodi, F. et al. Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization. Nat Neurosci 24, 595–610 (2021). https://doi.org/10.1038/s41593-020-00789-y
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DOI: https://doi.org/10.1038/s41593-020-00789-y
