Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

Single-cell profiling of myeloid cells in glioblastoma across species and disease stage reveals macrophage competition and specialization

Nature Neuroscience volume 24, pages 595–610 (2021)Cite this article

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: scRNA-seq of the human and mouse GBM immune compartment.
Fig. 2: Single-cell analysis of tumor DCs and identification of distinct cDC2 subsets that are conserved in humans and mice.
Fig. 3: GBM TAMs exhibit a mixed ontogeny that is altered depending on human disease stage.
Fig. 4: Fate mapping and spatial analysis reveals the distribution of Mo-TAMs and Mg-TAMs in mouse and human tumors.
Fig. 5: scRNA-seq reveals the heterogeneity of Mo-TAMs and identifies subsets that are conserved across species.
Fig. 6: CITE-seq analysis validates TAM and DC subsets at the protein level and reveals subset-specific cell surface protein markers.
Fig. 7: Functional profiling of Mo-TAMs and Mg-TAMs reveals their T-cell suppressive potential.
Fig. 8: CCR2 deficiency results in a switch in TAM ontogeny and boosts Mo-TAM proliferation, while CSF1R blockade depletes TAMs.

Similar content being viewed by others

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.

References

  1. Van Hove, H. et al. A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Nat. Neurosci. 22, 1021–1035 (2019).

    Article  PubMed  CAS  Google Scholar 

  2. Mrdjen, D. et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 380–395.e6 (2018) ; erratum 48, 599 (2018).

    Article  CAS  PubMed  Google Scholar 

  3. Jordao, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Louveau, A. et al. CNS lymphatic drainage and neuroinflammation are regulated by meningeal lymphatic vasculature. Nat. Neurosci. 21, 1380–1391 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Omuro, A. & DeAngelis, L. M. Glioblastoma and other malignant gliomas: a clinical review. JAMA 310, 1842–1850 (2013).

    Article  CAS  PubMed  Google Scholar 

  6. Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835–849.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wang, Q. et al. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 32, 42–56.e6 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Pombo Antunes, A. R. et al. Understanding the glioblastoma immune microenvironment as basis for the development of new immunotherapeutic strategies. eLife 9, e52176 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Quail, D. F. & Joyce, J. A. The microenvironmental landscape of brain tumors. Cancer Cell 31, 326–341 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Mundt, S. et al. Conventional DCs sample and present myelin antigens in the healthy CNS and allow parenchymal T cell entry to initiate neuroinflammation. Sci. Immunol. 4, eaau8380 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019).

    Article  CAS  PubMed  Google Scholar 

  12. Kierdorf, K., Masuda, T., Jordao, M. J. C. & Prinz, M. Macrophages at CNS interfaces: ontogeny and function in health and disease. Nat. Rev. Neurosci. 20, 547–562 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Goldmann, T. et al. Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat. Immunol. 17, 797–805 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Bowman, R. L. et al. Macrophage ontogeny underlies differences in tumor-specific education in brain malignancies. Cell Rep. 17, 2445–2459 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Muller, S. et al. Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment. Genome Biol. 18, 234 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Darmanis, S. et al. Single-cell RNA-seq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma. Cell Rep. 21, 1399–1410 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sankowski, R. et al. Mapping microglia states in the human brain through the integration of high-dimensional techniques. Nat. Neurosci. 22, 2098–2110 (2019).

    Article  CAS  PubMed  Google Scholar 

  18. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Serbina, N. V. & Pamer, E. G. Monocyte emigration from bone marrow during bacterial infection requires signals mediated by chemokine receptor CCR2. Nat. Immunol. 7, 311–317 (2006).

    Article  CAS  PubMed  Google Scholar 

  21. Günther, P.C. et al. A rule-based data-informed cellular consensus map of the human mononuclear phagocyte cell space. Preprint at bioRxiv https://doi.org/10.1101/658179 (2019).

  22. Miller, J. C. et al. Deciphering the transcriptional network of the dendritic cell lineage. Nat. Immunol. 13, 888–899 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. See, P. et al. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356, eaag3009 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Dutertre, C. A. et al. Single-cell analysis of human mononuclear phagocytes reveals subset-defining markers and identifies circulating inflammatory dendritic cells. Immunity 51, 573–589.e8 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Brown, C. C. et al. Transcriptional basis of mouse and human dendritic cell heterogeneity. Cell 179, 846–863.e24 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Bennett, F. C. et al. A combination of ontogeny and CNS environment establishes microglial identity. Neuron 98, 1170–1183.e8 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Cronk, J. C. et al. Peripherally derived macrophages can engraft the brain independent of irradiation and maintain an identity distinct from microglia. J. Exp. Med 215, 1627–1647 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Shemer, A. et al. Engrafted parenchymal brain macrophages differ from microglia in transcriptome, chromatin landscape and response to challenge. Nat. Commun. 9, 5206 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Goldmann, T. et al. A new type of microglia gene targeting shows TAK1 to be pivotal in CNS autoimmune inflammation. Nat. Neurosci. 16, 1618–1626 (2013).

    Article  CAS  PubMed  Google Scholar 

  31. Hanna, R. N. et al. The transcription factor NR4A1 (Nur77) controls bone marrow differentiation and the survival of Ly6C− monocytes. Nat. Immunol. 12, 778–785 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Jaitin, D. A. et al. Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner. Cell 178, 686–698.e14 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Xue, J. et al. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity 40, 274–288 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).

    Article  CAS  PubMed  Google Scholar 

  35. Gentles, A. J. et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 21, 938–945 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Pyonteck, S. M. et al. CSF-1R inhibition alters macrophage polarization and blocks glioma progression. Nat. Med. 19, 1264–1272 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Elmore, M. R. et al. Colony-stimulating factor 1 receptor signaling is necessary for microglia viability, unmasking a microglia progenitor cell in the adult brain. Neuron 82, 380–397 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Binnewies, M. et al. Unleashing type-2 dendritic cells to drive protective antitumor CD4+ T cell immunity. Cell 177, 556–571.e16 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Laoui, D. et al. The tumour microenvironment harbours ontogenically distinct dendritic cell populations with opposing effects on tumour immunity. Nat. Commun. 7, 13720 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Salmon, H. et al. Expansion and activation of CD103+ dendritic cell progenitors at the tumor site enhances tumor responses to therapeutic PD-L1 and BRAF inhibition. Immunity 44, 924–938 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Michea, P. et al. Adjustment of dendritic cells to the breast-cancer microenvironment is subset specific. Nat. Immunol. 19, 885–897 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. Cabeza-Cabrerizo, M. et al. Tissue clonality of dendritic cell subsets and emergency DCpoiesis revealed by multicolor fate mapping of DC progenitors. Sci. Immunol. 4, eaw1941 (2019).

    Article  Google Scholar 

  43. Friebel, E. et al. Single-cell mapping of human brain cancer reveals tumor-specific instruction of tissue-invading leukocytes. Cell 181, 1626–1642.e20 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Klemm, F. et al. Interrogation of the microenvironmental landscape in brain tumors reveals disease-specific alterations of immune cells. Cell 181, 1643–1660.e17 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kioi, M. et al. Inhibition of vasculogenesis, but not angiogenesis, prevents the recurrence of glioblastoma after irradiation in mice. J. Clin. Invest. 120, 694–705 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Akkari, L. et al. Dynamic changes in glioma macrophage populations after radiotherapy reveal CSF-1R inhibition as a strategy to overcome resistance. Sci. Transl Med. 12, eaaw7843 (2020).

    Article  CAS  PubMed  Google Scholar 

  47. Goswami, S. et al. Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. Nat. Med. 26, 39–46 (2020).

    Article  CAS  PubMed  Google Scholar 

  48. Zilionis, R. et al. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity 50, 1317–1334.e10 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Boring, L. et al. Impaired monocyte migration and reduced type 1 (Th1) cytokine responses in C-C chemokine receptor 2 knockout mice. J. Clin. Invest. 100, 2552–2561 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Jung, S. et al. Analysis of fractalkine receptor CX3CR1 function by targeted deletion and green fluorescent protein reporter gene insertion. Mol. Cell. Biol. 20, 4106–4114 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Srinivas, S. et al. Cre reporter strains produced by targeted insertion of EYFP and ECFP into the ROSA26 locus. BMC Dev. Biol. 1, 4 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lee, S. L. et al. Unimpaired thymic and peripheral T cell death in mice lacking the nuclear receptor NGFI-B (Nur77). Science 269, 532–535 (1995).

    Article  CAS  PubMed  Google Scholar 

  53. Ausman, J. I., Shapiro, W. R. & Rall, D. P. Studies on the chemotherapy of experimental brain tumors: development of an experimental model. Cancer Res. 30, 2394–2400 (1970).

    CAS  PubMed  Google Scholar 

  54. Baumann, B.C., Dorsey, J. F., Benci, J. L., Joh, D. Y. & Kao, G. D. Stereotactic intracranial implantation and in vivo bioluminescent imaging of tumor xenografts in a mouse model system of glioblastoma multiforme. J. Vis. Exp. https://doi.org/10.3791/4089 (2012).

  55. Lun, A. T., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with bioconductor. F1000Res. 5, 2122 (2016).

    PubMed  PubMed Central  Google Scholar 

  56. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Huber, W., von Heydebreck, A., Sultmann, H., Poustka, A. & Vingron, M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18, S96–S104 (2002).

    Article  PubMed  Google Scholar 

  59. Cannoodt, R. et al. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. Preprint at bioRxiv https://doi.org/10.1101/079509v2 (2016).

  60. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Movahedi, K. et al. Different tumor microenvironments contain functionally distinct subsets of macrophages derived from Ly6Chigh monocytes. Cancer Res. 70, 5728–5739 (2010).

    Article  CAS  PubMed  Google Scholar 

  62. Bolognesi, M. M. et al. Multiplex staining by sequential immunostaining and antibody removal on routine tissue sections. J. Histochem. Cytochem. 65, 431–444 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Bosisio, F. M. et al. Functional heterogeneity of lymphocytic patterns in primary melanoma dissected through single-cell multiplexing. eLife 9, e53008 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Cattoretti, G., Bosisio, F.M., Marcelis, L. & Bolognesi, M.M. Multiple iteractive labeling by antibody neodeposition (MILAN). Preprint at Protocol Exchange https://doi.org/10.1038/protex.2018.106 (2018).

Download references

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.

Author information

Author notes

  1. These authors contributed equally: Ana Rita Pombo Antunes, Isabelle Scheyltjens.

  2. These authors jointly supervised this work: Jo A. Van Ginderachter, Kiavash Movahedi.

Authors and Affiliations

  1. Myeloid Cell Immunology Lab, VIB Center for Inflammation Research, Brussels, Belgium

    Ana Rita Pombo Antunes, Isabelle Scheyltjens, Karen De Vlaminck, Hannah Van Hove, Signe Schmidt Kjølner Hansen, Jo A. Van Ginderachter & Kiavash Movahedi

  2. Laboratory of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium

    Ana Rita Pombo Antunes, Isabelle Scheyltjens, Karen De Vlaminck, Hannah Van Hove, Signe Schmidt Kjølner Hansen, Jo A. Van Ginderachter & Kiavash Movahedi

  3. Laboratory of Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium

    Francesca Lodi & Diether Lambrechts

  4. VIB–KU Leuven Center for Cancer Biology, VIB, Leuven, Belgium

    Francesca Lodi & Diether Lambrechts

  5. Laboratory for Precision Cancer Medicine, Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium

    Julie Messiaen, Asier Antoranz & Frederik De Smet

  6. Department of Neurosurgery, UZ Brussels, Brussels, Belgium

    Johnny Duerinck

  7. Janssen Pharmaceuticals, Beerse, Belgium

    Daliya Kancheva & Koen Van der Borght

  8. Laboratory of Myeloid Cell Ontogeny and Functional Specialization, VIB Center for Inflammation Research, Ghent, Belgium

    Liesbet Martens & Martin Guilliams

  9. Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium

    Liesbet Martens & Martin Guilliams

  10. Laboratory of Myeloid Cell Heterogeneity and Function, VIB Center for Inflammation Research, Ghent, Belgium

    Liesbet Martens

  11. Translational Cell and Tissue Research Unit, Department of Imaging and Pathology, KU Leuven, Leuven, Belgium

    Francesca Maria Bosisio & Raf Sciot

  12. Department of Neurosurgery, UZ Leuven, Leuven, Belgium

    Steven De Vleeschouwer

  13. Laboratory for Experimental Neurosurgery and Neuroanatomy, Department of Neurosciences and Leuven Brain Institute (LBI), KU Leuven, Leuven, Belgium

    Steven De Vleeschouwer

  14. Cell Differentiation Lab, Vrije Universiteit Brussel, Brussels, Belgium

    Luc Bouwens

  15. Department of Neurosurgery, Europe Hospitals Saint Elisabeth, Ukkel, Belgium

    Michiel Verfaillie

  16. Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium

    Niels Vandamme & Yvan Saeys

  17. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Niels Vandamme & Yvan Saeys

  18. VIB Center for Inflammation Research, Gent, Belgium

    Roosmarijn E. Vandenbroucke

  19. Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium

    Roosmarijn E. Vandenbroucke

  20. Cancer Research Institute Ghent (CRIG), Ghent University, Ghent, Belgium

    Olivier De Wever

  21. Laboratory for Experimental Cancer Research, Ghent University, Ghent, Belgium

    Olivier De Wever

  22. Clinical and Experimental Endocrinology (CEE), KU Leuven, Leuven, Belgium

    Conny Gysemans

  23. Department of Medical Oncology, UZ Brussels, Brussels, Belgium

    Bart Neyns

Authors
  1. Ana Rita Pombo Antunes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Isabelle Scheyltjens
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francesca Lodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Julie Messiaen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Asier Antoranz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Johnny Duerinck
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daliya Kancheva
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Liesbet Martens
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Karen De Vlaminck
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hannah Van Hove
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Signe Schmidt Kjølner Hansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Francesca Maria Bosisio
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Koen Van der Borght
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Steven De Vleeschouwer
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Raf Sciot
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Luc Bouwens
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Michiel Verfaillie
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Niels Vandamme
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Roosmarijn E. Vandenbroucke
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Olivier De Wever
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Yvan Saeys
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Martin Guilliams
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Conny Gysemans
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Bart Neyns
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Frederik De Smet
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Diether Lambrechts
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Jo A. Van Ginderachter
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Kiavash Movahedi
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Corresponding author

Correspondence to Kiavash Movahedi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Itay Tirosh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Reporting Summary

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.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-020-00789-y

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer