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A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment

Nature Neuroscience volume 22, pages 1021–1035 (2019)Cite this article

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Abstract

While the roles of parenchymal microglia in brain homeostasis and disease are fairly clear, other brain-resident myeloid cells remain less well understood. By dissecting border regions and combining single-cell RNA-sequencing with high-dimensional cytometry, bulk RNA-sequencing, fate-mapping and microscopy, we reveal the diversity of non-parenchymal brain macrophages. Border-associated macrophages (BAMs) residing in the dura mater, subdural meninges and choroid plexus consisted of distinct subsets with tissue-specific transcriptional signatures, and their cellular composition changed during postnatal development. BAMs exhibited a mixed ontogeny, and subsets displayed distinct self-renewal capacity following depletion and repopulation. Single-cell and fate-mapping analysis both suggested that there is a unique microglial subset residing on the apical surface of the choroid plexus epithelium. Finally, gene network analysis and conditional deletion revealed IRF8 as a master regulator that drives the maturation and diversity of brain macrophages. Our results provide a framework for understanding host–macrophage interactions in both the healthy and diseased brain.

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Fig. 1: scRNA-seq of discrete brain compartments reveals regional immune cell heterogeneity.
Fig. 2: Border-associated macrophages are heterogeneous and exhibit tissue-specific transcriptional signatures.
Fig. 3: Identification of BAM subsets using high-dimensional fluorescence cytometry.
Fig. 4: Bulk RNA-seq of BAM subsets suggests tissue-specific functional adaptations.
Fig. 5: Sall1+ CPepi BAMs reside on the apical surface of the choroid plexus epithelium.
Fig. 6: CPepi-BAMs share a common transcriptional profile with DAM found in Alzheimer’s disease models.
Fig. 7: BAM subsets exhibit a mixed ontogeny and display varying self-renewal capacity.
Fig. 8: Conditional deletion of Irf8 reveals its pivotal role in regulating the transcriptional program of microglia and CP-BAMs.

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Data availability

To facilitate the use of our single-cell datasets, an online tool was generated for evaluation of gene expression at single-cell resolution: www.brainimmuneatlas.org. All gene–cell count matrices can also be downloaded via this link. In addition, all scRNA-seq and bulk RNA-seq data are also deposited at GEO (NCBI) with accession code GSE128855. Other data that support the findings of this study are available from the corresponding author upon request. There are no restrictions on data availability.

Code availability

The R codes that were used for scRNA-seq and bulk RNA-seq data analysis can be found at Github: https://github.com/saeyslab/brainimmuneatlas/

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Acknowledgements

This work was supported by Innoviris (Attract grant No. BB2B 2015-2) and Fonds Wetenschappelijk Onderzoek (grant No. 1506316 N) to K.M. and a VLAIO grant (No. ImmCyte HBC.2016.0889) to K.M, J.A.V.G., J.A. and D.M. H.V.H. is supported by an FWO predoctoral fellowship (No. 1163218 N). Y.S. is an ISAC Marylou Ingram Scholar. We thank Y. Elkrim for technical assistance, M. Kiss for helpful discussions, VIB BioImaging Core for support regarding microscopy and VIB Tech Watch for support and funding with regard to scRNA-seq technologies.

Author information

Author notes

  1. These authors contributed equally: Jo A. Van Ginderachter, Yvan Saeys.

Authors and Affiliations

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

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

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

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

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

    Liesbet Martens, Niels Vandamme & Yvan Saeys

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

    Liesbet Martens, Sofie De Prijck, Niels Vandamme, Gert Van Isterdael, Charlotte L. Scott, Roosmarijn E. Vandenbroucke & Martin Guilliams

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

    Sofie De Prijck, Charlotte L. Scott & Martin Guilliams

  6. Cancer Research Institute Ghent, Ghent, Belgium

    Niels Vandamme & Geert Berx

  7. Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders, Center of Intestinal Neuro-immune Interactions, KU Leuven, Leuven, Belgium

    Sebastiaan De Schepper & Guy E. Boeckxstaens

  8. VIB Flow Core, VIB Center for Inflammation Research, Ghent, Belgium

    Gert Van Isterdael

  9. Institute of Infection, Immunity and Inflammation, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK

    Charlotte L. Scott

  10. Department of Neuroscience, Janssen Research & Development (a division of Janssen Pharmaceutica NV), Beerse, Belgium

    Jeroen Aerts & Diederik Moechars

  11. Molecular and Cellular Oncology Laboratory, Department of Biomedical Molecular Biology, Ghent University, Ghent, Belgium

    Geert Berx

  12. Barriers in Inflammation lab, VIB Center for Inflammation Research, Ghent, Belgium

    Roosmarijn E. Vandenbroucke

  13. Ghent Gut Inflammation Group, Ghent University, Ghent, Belgium

    Roosmarijn E. Vandenbroucke & Lars Vereecke

  14. Host-Microbiota Interaction lab, VIB Center for Inflammation Research, Ghent, Belgium

    Lars Vereecke

  15. Department of Rheumatology, University Hospital Ghent, Ghent, Belgium

    Lars Vereecke

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

    Yvan Saeys

Authors
  1. Hannah Van Hove
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Contributions

H.V.H. and K.M. conceived the study and designed experiments. H.V.H., I.S., K.D.V., A.R.P.A., S.D.P., N.V., S.D.S., G.V.I., C.L.S., J.A., R.E.V., L.V. and K.M. performed experiments. L.M. developed analysis algorithms and the online tool. H.V.H., L.M., I.S., Y.S. and K.M. carried out analyses. C.L.S., G.E.B., L.V., D.M. and M.G. provided important reagents. G.B., G.E.B., R.E.V., D.M., M.G., J.A.vG. and Y.S. provided important advice on experimental design, data analysis and interpretation. H.V.H and K.M. wrote the manuscript. I.S., D.M., M.G., J.A.vG. and Y.S. revised the manuscript. K.M. directed the study.

Corresponding author

Correspondence to Kiavash Movahedi.

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The authors declare no competing interests.

Additional information

Journal peer review information: Nature Neuroscience thanks Jonathan Kipnis and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Integrated supplementary information

Supplementary Figure 1 Heterogeneity in CP BAMs at the level of IEGs.

(a) tSNE plot of 3940 cells from the choroid plexus. This shows two main macrophage clusters: CP-BAM A1 and CP-BAM A2, in addition to the smaller macrophage cluster CP-BAM B. (b) Expression of prototypical macrophages genes such as Csf1r, Adgre1 and Fcgr1 confirms that CP-BAM A1, CP-BAM A2 and CP-BAM B are macrophages. (c) Volcano plot showing the genes that are DE between CP-BAM A1 vs. CP-BAM A2 in red. P-value adjustment was performed using bonferroni correction. (d) tSNE plot following regress out of IEGs. This shows that CP BAM-A1 and CP-BAM A2 now cluster as a single population. (e) tSNE maps showing the expression of H2-Aa, Mrc1, Gas6 and Ccr2 before and after IEG regress out. BAM heterogeneity at the level of these genes is more evident after regressing out EGs, showing that IEG induction can overshadow potentially more interesting biological heterogeneity.

Supplementary Figure 2 IEGs are induced during tissue processing and can be blocked via ActD treatment.

(a) Schematic representation of the standard and Actinomycin D (ActD) protocols for obtaining single-cell suspensions. In the standard protocol, CP and dura were digested at 37 °C. In the ActD protocol, CP and dura were digested at 11 °C and decreasing concentrations of ActD were added in several steps of the protocol to block transcription during processing. (b) tSNE plot of 7.949 CD45+ cells pooled from dural tissue that was processed with the standard (red) or ActD (blue) protocol. The left plot shows the various clusters that were identified, while the right plot indicates whether individual cells originate from the standard (red) or ActD (blue) dissociation protocol. This information was then used to encircle the various clusters on the left (red = standard, blue = ActD) to show the segregation between the standard and ActD protocol for each cell type. (c) tSNE maps showing the expression of immediate early genes, showing a lower expression in cells obtained via the ActD protocol. (d) Volcano plot showing the genes that are DE between D-BAMs obtained via the standard or the ActD protocol. P-value adjustment was performed using bonferroni correction. (e) left: tSNE plot of 3.667 CD45+ cells pooled from choroid plexus tissue that was processed with the standard (red) or ActD (blue) protocol. The left plot shows the various clusters that were identified, while the right plot indicates whether individual cells originate from the standard (red) or ActD (blue) dissociation protocol. This information was then used to encircle the various clusters on the left (red = standard, blue = ActD) to show the segregation between the standard and ActD protocol for each cell type. (f) tSNE maps showing the expression of immediate early genes, showing a lower of expression in cells obtained via the ActD protocol. (g) Volcano plot showing the genes that are DE between CP-BAMs obtained via the standard or the ActD protocol. P-value adjustment was performed using bonferroni correction.

Supplementary Figure 3 Gating strategy for flow cytometric analysis of brain myeloid cells.

(a-c) cells were stained with the general panel (see Fig. 3a) (a) tSNE plots from the whole brain, enriched SDM, dura and CP showing expression of the indicated markers. (b) Depiction of the manual gating strategy used for identifying microglia and BAM subsets in the whole brain or dissected border regions. Microglia and CPepi-BAMs were gated as CD11b+CD11aloCX3CR1hiF4/80intMMRloMHCIIlo cells. For other BAM subsets, we first selected CD11b+CD11aloCX3CR1hiF4/80hi cells, followed by gating of MMRhiMHCIIlo and MMRintMHCIIhi BAMs. (c) Gating strategy for dendritic cells (cDC1 and cDC2), neutrophils, monocytes (classical and non-classical) and MdCs shown for whole brain samples. (a-c) cells were stained with the general panel indicated in Fig. 3 (d) Gating strategy for BAMs and microglia based on 7 markers. All brain macrophages were CD45+ and CD11bhi. Doublets and dead cells were excluded. All BAMs were gated as CX3CR1hi CD45int cells and microglia were gated as CX3CR1hi CD45lo cells. CP-BAMs were further subdivided in MHCIIloCLEC12Alo CPepi-BAMs (green) and CLEC12A+ CPlo-BAMs or CPhi-BAMs (orange). D-BAMs were further subdivided into Dlo-BAMs (MMRhi MHCIIlo, green) and Dhi-BAMs (MMRlo MHCIIhi, orange). SD-BAMs were gated as MMRhi MHCIIlo (green). The enriched SDM also contained MMR− MHCII− microglia (orange). The different BAM gates were overlaid on the tSNE plots of single cells. Blue represents ungated cells. Brains and border regions were dissected from 20W old C57BL/6 mice.

Supplementary Figure 4 Correlation between mRNA and protein expression for surface markers and transcription factors in macrophage subsets.

(a) Legend showing the 6 BAM subsets and microglia. (b) histograms showing the isotype controls used in panel c (c) Comparison between gene and protein expression for the indicated markers. Gene expression levels (top) are expressed as mean normalized count. Protein levels are expressed as delta median fluorescence intensity or ΔMFI (=MFI marker—MFI isotype) (middle) or as histograms (bottom). For TFs intracellular staining was performed. Colors and numbers correspond to the legend in (a). * Proteins that were used in the macrophage Symphony panel as shown in Fig. 3. Tissue from 3 mice was pooled for the flow cytometric analysis of each marker.

Supplementary Figure 5 Transcriptional signatures of brain macrophages as revealed by bulk RNA-seq.

(a) Gene expression heatmap showing genes that are high in brain macrophages and low in the examined peripheral macrophages. Genes are grouped into cell surface (yellow), intracellular (blue), secreted protein (SP) (green) and transcription factor (TF) (magenta) genes. (b) Heatmap showing microglial signature genes. The genes are ordered based on the expression levels in CPhi-BAMs. (a-b) n = 4 (or 3 for microglia) independent sorts per macrophage subset. (c) Gene Ontology (GO) Network based on genes that are DE between BAM subsets and microglia. The Network is based on DE genes shared by all BAMs and enriched in microglia with a log2FC < -1.

Supplementary Figure 6 CPepi-BAMs express APOE and CLEC7A.

(a) Legend corresponding to (b-c) showing the in situ-location of the different BAM subsets. (b-c) Three-color IHC performed on coronal cryo-sections of brains from tamoxifen-injected Sall1CreER:R26-YFP mice. (b) Sections containing the lateral ventricle CP (top) or parenchyma (bottom) were stained with anti-IBA1 (red), anti-APOE (blue) and anti-GFP/YFP (green). Left panel shows all three channels, middle and right panels show green and blue channel, respectively. (c) Section containing the lateral ventricle CP (top) or parenchyma (bottom) was stained with anti-IBA1 (red), anti-CLEC7A (blue) and anti-GFP/YFP (green). Left panel shows all three channels, middle and right panels show green and blue channel, respectively. Representative images of n = 3 mice. full arrows: CPepi-BAM dashed arrows: microglia.

Supplementary Figure 7 DAM increase as APP/PS1 mice age and are absent in non-transgenic littermates.

(a) Schematic representation showing the sorting of CD45+ cells from whole brains of 16-month-old APP/PS1 transgenic mice and their age-matched non-transgenic littermates. CD45+ cells were used for scRNA-seq. (b) tSNE plot of 6.095 CD45+ cells pooled from the whole brains of 16-month-old APP/PS1 transgenic mice and age-matched non-transgenic littermates. (c) similar to (b) with colors matching the tissue origin of the cells. blue: WT littermates; red: APP/PS1 transgenic mice. (d) tSNE plots showing the exclusive expression of DAM genes in DAM population of APP/PS1 mice.

Supplementary Figure 8 Abundance of BAMs and microglia after PLX3397-mediated depletion and after repopulation.

(a) Cell counts of BAMs and microglia in mice that received a PLX3397 (red) or control (black) diet for 21 days. Relates to Fig. 7i. Bars represent mean ± SEM of n = 6 mice. The significance was evaluated using an unpaired two-tailed t-test. Statistics can be found in Supplementary Table 2. (b-d) Flow cytometric analysis of BAM and microglia repopulation (see Fig. 7j) in 12-14W-old M/F CX3CR1CreER:R26-YFP mice. (b-c) The abundance of BAMs and microglia is represented as the % of CD45+ cells (b) or as cell counts (c). Bars represent mean ± SEM of n = 8 mice (n = 7 in dura). The significance was evaluated using an unpaired two-tailed t-test. Statistics can be found in Supplementary Table 2. (d) % of the indicated BAM subsets within the YFP+ fraction of the dura (left) and CP (right) in control (black) or repopulated (red) mice. Bars represent mean ± SEM of n = 7 (dura) or n = 8 mice (CP). The significance was evaluated using an unpaired two-tailed t-test. Statistics can be found in Supplementary Table 2. (e-f) Flow cytometric analysis of BAM and microglia repopulation (see Fig. 7k) in 11-14W-old M/F Sall1CreER:R26-YFP mice. The abundance of BAMs and microglia is represented as the % of CD45+ cells (e) or as cell counts (f). Bars represent mean ± SEM of n = 5 (repopulation) or n = 3 (control) mice. The significance was evaluated using an unpaired two-tailed t-test. Statistics can be found in Supplementary Table 2. ns p-value > 0.05, * p-value < 0.05, ** p-value < 0.01, *** p-value < 0.001.

Supplementary Figure 9 SCENIC reveals the gene regulatory networks and master regulators that drive the transcriptional program of BAMs.

(a-c) SCENIC was performed on 10.947 brain macrophages pooled from the whole brain and border regions (see Fig. 2a). SCENIC yields AUC values per cell and per regulon which serves as a measure of the activity of a regulon in a cell (a) Binary activity matrix that predicts whether the indicated regulons are active (based on a regulon-specific AUC cutoff value) in individual cells. Black and white designate active and inactive regulons, respectively. Cells were clustered based on their regulon activity. The colors of the individual cells (shown at the top of the activity matrix) match the color of the clusters that were identified based on gene expression using Seurat (b top). Important regulons are shown for the various macrophage subsets: Microglia (orange), general BAMs (green), MHCIIlo BAMs (blue), MHCIIhi BAMs (red), CPepi-BAM (pink) (b) tSNE plot based on gene expression (top) and tSNE plot based on the binary regulon activity matrix (bottom). Cells are colored based on their designation to the clusters in the expression-based tSNE. (c) AUC activity matrix that shows regulon activity using continuous AUC values from low (blue) to high (red). Cells are ordered per cell type on top with the colors matching the tSNE plot based on gene expression. Regulons are clustered based on their activity across cell types.

Supplementary Figure 10 Selected regulons from SCENIC.

SCENIC regulons with the regulator (green) and a selection of the regulated genes (white circle) visualized with iRegulon in cytoscape. The regulons of the indicated TFs are shown, together with the corresponding motif and the number of genes within the regulon.

Supplementary Figure 11 Microglial heterogeneity in IRF8-KO mice.

(a) tSNE plot of 12.078 CD45+ cells pooled from the whole brains of Fcgr1Cre/+:Irf8fl/fl and Fcgr1+/+:Irf8fl/fl mice. Microglia from Cre+(KO) or Cre−(WT) mice are circled in green or red respectively. Cre+ microglia are further subdivided in four clusters based on unsupervised clustering. (b) Volcano plot showing the genes that are DE between cluster 3 and 1 of IRF8 KO microglia. P-value adjustment was performed using bonferroni correction. (c) tSNE maps showing the expression of selected genes that are differentially expressed in clusters 1-3 of IRF8 KO microglia. (d) Volcano plot showing the genes that are DE between cluster 4 and the three other IRF8 KO microglia, showing the expression of Interferon-induced genes that are specifically upregulated in cluster 4. P-value adjustment was performed using bonferroni correction. (e) Number of genes that are DE between IRF8 KO and WT Microglia or IRF8 KO and WT Stromal CP-BAMs. Venn diagram shows the shared and unique DE genes.

Supplementary information

Supplementary Information

Supplementary Figs. 1–11 and Supplementary Tables 1 and 4.

Reporting Summary

Supplementary Table 2

Statistical parameters.

Supplementary Table 3

List of dissociation-induced genes in CP and dural BAMs.

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Van Hove, H., Martens, L., Scheyltjens, I. 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). https://doi.org/10.1038/s41593-019-0393-4

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