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.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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/
References
Shechter, R., London, A. & Schwartz, M. Orchestrated leukocyte recruitment to immune-privileged sites: absolute barriers versus educational gates. Nat. Rev. Immunol. 13, 206–218 (2013).
Engelhardt, B., Vajkoczy, P. & Weller, R. O. The movers and shapers in immune privilege of the CNS. Nat. Immunol. 18, 123–131 (2017).
Louveau, A. et al. Structural and functional features of central nervous system lymphatic vessels. Nature 523, 337–341 (2015).
Aspelund, A. et al. A dural lymphatic vascular system that drains brain interstitial fluid and macromolecules. J. Exp. Med. 212, 991–999 (2015).
Nabeshima, S., Reese, T. S., Landis, D. M. D. & Brightman, M. W. Junctions in the meninges and marginal glia. J. Comp. Neurol. 164, 127–169 (1975).
Balin, B. J., Broadwell, R. D., Salcman, M. & El‐Kalliny, M. Avenues for entry of peripherally administered protein to the central nervous system in mouse, rat, and squirrel monkey. J. Comp. Neurol. 251, 260–280 (1986).
Wolburg, H. & Paulus, W. Choroid plexus: biology and pathology. Acta Neuropathol. 119, 75–88 (2010).
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 (2018).
Ajami, B. et al. Single-cell mass cytometry reveals distinct populations of brain myeloid cells in mouse neuroinflammation and neurodegeneration models. Nat. Neurosci. 21, 541–551 (2018).
Korin, B. et al. High-dimensional, single-cell characterization of the brain’s immune compartment. Nat. Neurosci. 20, 1300–1309 (2017).
Okabe, Y. & Medzhitov, R. Tissue biology perspective on macrophages. Nat. Immunol. 17, 9–17 (2016).
Prinz, M. & Priller, J. Microglia and brain macrophages in the molecular age: from origin to neuropsychiatric disease. Nat. Rev. Neurosci. 15, 300–312 (2014).
Goldmann, T. et al. Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat. Immunol. 17, 797–805 (2016).
Ling, E.-A., Kaur, C. & Jia, L. Origin, nature, and some functional considerationsof intraventricular macrophages, with special reference to the epiplexus cells. Microsc. Res. Tech. 41, 43–56 (1998).
Guilliams, M. et al. Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species. Immunity 45, 669–684 (2016).
Miller, J. C. et al. Deciphering the transcriptional network of the dendritic cell lineage. Nat. Immunol. 13, 888–899 (2012).
Mildner, A. et al. Genomic characterization of murine monocytes reveals C/EBPβ transcription factor dependence of Ly6C− cells. Immunity 46, 849–862.e7 (2017).
van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).
Haimon, Z. et al. Re-evaluating microglia expression profiles using RiboTag and cell isolation strategies. Nat. Immunol. 19, 636–644 (2018).
Wu, Y. E., Pan, L., Zuo, Y., Li, X. & Hong, W. Detecting activated cell populations using single-cell RNA-Seq. Neuron 96, 313–329.e6 (2017).
Hickman, S. E. et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. 16, 1896–1905 (2013).
Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2013).
Cannoodt, R., et al. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/079509v2 (2016).
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).
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).
Krasemann, S. et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581.e9 (2017).
Gomez Perdiguero, E. et al. Tissue-resident macrophages originate from yolk-sac-derived erythro-myeloid progenitors. Nature 518, 547–551 (2014).
Elmore, M. R. P. 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).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).
Matcovitch-Natan, O. et al. Microglia development follows a stepwise program to regulate brain homeostasis. Science 353, aad8670 (2016).
Buttgereit, A. et al. Sall1 is a transcriptional regulator defining microglia identity and function. Nat. Immunol. 17, 1397–1406 (2016).
Kierdorf, K. et al. Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways. Nat. Neurosci. 16, 273–280 (2013).
Scott, C. L. et al. The transcription factor ZEB2 is required to maintain the tissue-specific identities of macrophages. Immunity 49, 312–325.e5 (2018).
Ginhoux, F. & Guilliams, M. Tissue-resident macrophage ontogeny and homeostasis. Immunity 44, 439–449 (2016).
Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, 1248–1259 (2017).
Jordão, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).
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).
Deczkowska, A. et al. Disease-associated microglia: a universal immune sensor of neurodegeneration. Cell 173, 1073–1081 (2018).
Butovsky, O. et al. Targeting miR-155 restores abnormal microglia and attenuates disease in SOD1 mice. Ann. Neurol. 77, 75–99 (2015).
Song, W. et al. Alzheimer’s disease-associated TREM2 variants exhibit either decreased or increased ligand-dependent activation. Alzheimer’s Dement. 13, 381–387 (2017).
Wang, Y. et al. TREM2 lipid sensing sustains the microglial response in an Alzheimer’s disease model. Cell 160, 1061–1071 (2015).
Yeh, F. L., Wang, Y., Tom, I., Gonzalez, L. C. & Sheng, M. TREM2 binds to apolipoproteins, including APOE and CLU/APOJ, and thereby facilitates uptake of amyloid-beta by microglia. Neuron 91, 328–340 (2016).
Tamura, T., Kurotaki, D. & Koizumi, S. Regulation of myelopoiesis by the transcription factor IRF8. Int. J. Hematol. 101, 342–351 (2015).
Sichien, D. et al. IRF8 transcription factor controls survival and function of terminally differentiated conventional and plasmacytoid dendritic cells, respectively. Immunity 45, 626–640 (2016).
Hagemeyer, N. et al. Transcriptome‐based profiling of yolk sac‐derived macrophages reveals a role for Irf8 in macrophage maturation. EMBO J. 35, 1730–1744 (2016).
Masuda, T. et al. IRF8 Is a critical transcription factor for transforming microglia into a reactive phenotype. Cell Rep. 1, 334–340 (2012).
Masuda, T. et al. IRF8 is a transcriptional determinant for microglial motility. Purinergic Signal. 10, 515–521 (2014).
Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271.e6 (2019).
Radde, R. et al. Aβ42-driven cerebral amyloidosis in transgenic mice reveals early and robust pathology. EMBO Rep. 7, 940–946 (2006).
Yona, S. et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38, 79–91 (2013).
Benz, C., Martins, V. C., Radtke, F. & Bleul, C. C. The stream of precursors that colonizes the thymus proceeds selectively through the early T lineage precursor stage of T cell development. J. Exp. Med. 205, 1187–1199 (2008).
Inoue, S., Inoue, M., Fujimura, S. & Nishinakamura, R. A mouse line expressing Sall1-driven inducible cre recombinase in the kidney mesenchyme. Genesis 48, 207–212 (2010).
Madisen, L. et al. A robust and high-throughput Cre reporting and characterization. Nat. Neurosci. 13, 133–140 (2010).
Lun, A. T. L., 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).
Xu, C. & Su, Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974–1980 (2015).
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Janky, R. et al. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput. Biol. 10, e1003731 (2014).
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
Smyth, G. K. limma: Linear Models for Microarray Data. in Bioinformatics and Computational Biology Solutions Using R and Bioconductor Ch.23 (eds. Gentleman, R., Carey, V., Huber, W., Irizarry, R. & Dudoit, S.) 397–420 (Springer-Verlag, 2005).
Smyth, G. K. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 1–25 (2004).
Maere, S., Heymans, K. & Kuiper, M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448–3449 (2005).
Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G. D. Enrichment Map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5, e13984 (2010).
Kucera, M., Isserlin, R., Arkhangorodsky, A. & Bader, G. D. AutoAnnotate: a Cytoscape app for summarizing networks with semantic annotations. F1000Res. 5, 1717 (2016).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
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
Authors and Affiliations
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
Ethics declarations
Competing interests
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.
Supplementary Table 2
Statistical parameters.
Supplementary Table 3
List of dissociation-induced genes in CP and dural BAMs.
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41593-019-0393-4
This article is cited by
-
Single-cell analysis of mesenchymal cells in permeable neural vasculature reveals novel diverse subpopulations of fibroblasts
Fluids and Barriers of the CNS (2024)
-
The role of CD56bright NK cells in neurodegenerative disorders
Journal of Neuroinflammation (2024)
-
Border-associated macrophages in the central nervous system
Journal of Neuroinflammation (2024)
-
Understanding immune microenvironment alterations in the brain to improve the diagnosis and treatment of diverse brain diseases
Cell Communication and Signaling (2024)
-
Blood–brain borders: a proposal to address limitations of historical blood–brain barrier terminology
Fluids and Barriers of the CNS (2024)