A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment.
Journal
Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671
Informations de publication
Date de publication:
06 2019
06 2019
Historique:
received:
09
07
2018
accepted:
21
03
2019
pubmed:
8
5
2019
medline:
6
7
2019
entrez:
8
5
2019
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31061494
doi: 10.1038/s41593-019-0393-4
pii: 10.1038/s41593-019-0393-4
doi:
Substances chimiques
Interferon Regulatory Factors
0
interferon regulatory factor-8
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1021-1035Commentaires et corrections
Type : CommentIn
Références
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).
doi: 10.1038/nri3391
Engelhardt, B., Vajkoczy, P. & Weller, R. O. The movers and shapers in immune privilege of the CNS. Nat. Immunol. 18, 123–131 (2017).
doi: 10.1038/ni.3666
Louveau, A. et al. Structural and functional features of central nervous system lymphatic vessels. Nature 523, 337–341 (2015).
doi: 10.1038/nature14432
Aspelund, A. et al. A dural lymphatic vascular system that drains brain interstitial fluid and macromolecules. J. Exp. Med. 212, 991–999 (2015).
doi: 10.1084/jem.20142290
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).
doi: 10.1002/cne.901640202
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).
doi: 10.1002/cne.902510209
Wolburg, H. & Paulus, W. Choroid plexus: biology and pathology. Acta Neuropathol. 119, 75–88 (2010).
doi: 10.1007/s00401-009-0627-8
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).
doi: 10.1016/j.immuni.2018.01.011
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).
doi: 10.1038/s41593-018-0100-x
Korin, B. et al. High-dimensional, single-cell characterization of the brain’s immune compartment. Nat. Neurosci. 20, 1300–1309 (2017).
doi: 10.1038/nn.4610
Okabe, Y. & Medzhitov, R. Tissue biology perspective on macrophages. Nat. Immunol. 17, 9–17 (2016).
doi: 10.1038/ni.3320
Prinz, M. & Priller, J. Microglia and brain macrophages in the molecular age: from origin to neuropsychiatric disease. Nat. Rev. Neurosci. 15, 300–312 (2014).
doi: 10.1038/nrn3722
Goldmann, T. et al. Origin, fate and dynamics of macrophages at central nervous system interfaces. Nat. Immunol. 17, 797–805 (2016).
doi: 10.1038/ni.3423
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).
doi: 10.1002/(SICI)1097-0029(19980401)41:1<43::AID-JEMT5>3.0.CO;2-V
Guilliams, M. et al. Unsupervised high-dimensional analysis aligns dendritic cells across tissues and species. Immunity 45, 669–684 (2016).
doi: 10.1016/j.immuni.2016.08.015
Miller, J. C. et al. Deciphering the transcriptional network of the dendritic cell lineage. Nat. Immunol. 13, 888–899 (2012).
doi: 10.1038/ni.2370
Mildner, A. et al. Genomic characterization of murine monocytes reveals C/EBPβ transcription factor dependence of Ly6C
doi: 10.1016/j.immuni.2017.04.018
van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).
doi: 10.1038/nmeth.4437
Haimon, Z. et al. Re-evaluating microglia expression profiles using RiboTag and cell isolation strategies. Nat. Immunol. 19, 636–644 (2018).
doi: 10.1038/s41590-018-0110-6
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).
doi: 10.1016/j.neuron.2017.09.026
Hickman, S. E. et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. 16, 1896–1905 (2013).
doi: 10.1038/nn.3554
Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2013).
doi: 10.1038/nn.3599
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).
doi: 10.1038/nn.3531
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).
doi: 10.1016/j.cell.2017.05.018
Krasemann, S. et al. The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581.e9 (2017).
doi: 10.1016/j.immuni.2017.08.008
Gomez Perdiguero, E. et al. Tissue-resident macrophages originate from yolk-sac-derived erythro-myeloid progenitors. Nature 518, 547–551 (2014).
doi: 10.1038/nature13989
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).
doi: 10.1016/j.neuron.2014.02.040
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
doi: 10.1038/nmeth.4463
Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).
doi: 10.1016/j.cell.2014.11.018
Matcovitch-Natan, O. et al. Microglia development follows a stepwise program to regulate brain homeostasis. Science 353, aad8670 (2016).
doi: 10.1126/science.aad8670
Buttgereit, A. et al. Sall1 is a transcriptional regulator defining microglia identity and function. Nat. Immunol. 17, 1397–1406 (2016).
doi: 10.1038/ni.3585
Kierdorf, K. et al. Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways. Nat. Neurosci. 16, 273–280 (2013).
doi: 10.1038/nn.3318
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).
doi: 10.1016/j.immuni.2018.07.004
Ginhoux, F. & Guilliams, M. Tissue-resident macrophage ontogeny and homeostasis. Immunity 44, 439–449 (2016).
doi: 10.1016/j.immuni.2016.02.024
Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, 1248–1259 (2017).
doi: 10.1126/science.aal3222
Jordão, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).
doi: 10.1126/science.aat7554
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).
doi: 10.1084/jem.20180247
Deczkowska, A. et al. Disease-associated microglia: a universal immune sensor of neurodegeneration. Cell 173, 1073–1081 (2018).
doi: 10.1016/j.cell.2018.05.003
Butovsky, O. et al. Targeting miR-155 restores abnormal microglia and attenuates disease in SOD1 mice. Ann. Neurol. 77, 75–99 (2015).
doi: 10.1002/ana.24304
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).
doi: 10.1016/j.jalz.2016.07.004
Wang, Y. et al. TREM2 lipid sensing sustains the microglial response in an Alzheimer’s disease model. Cell 160, 1061–1071 (2015).
doi: 10.1016/j.cell.2015.01.049
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).
doi: 10.1016/j.neuron.2016.06.015
Tamura, T., Kurotaki, D. & Koizumi, S. Regulation of myelopoiesis by the transcription factor IRF8. Int. J. Hematol. 101, 342–351 (2015).
doi: 10.1007/s12185-015-1761-9
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).
doi: 10.1016/j.immuni.2016.08.013
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).
doi: 10.15252/embj.201693801
Masuda, T. et al. IRF8 Is a critical transcription factor for transforming microglia into a reactive phenotype. Cell Rep. 1, 334–340 (2012).
doi: 10.1016/j.celrep.2012.02.014
Masuda, T. et al. IRF8 is a transcriptional determinant for microglial motility. Purinergic Signal. 10, 515–521 (2014).
doi: 10.1007/s11302-014-9413-8
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).
doi: 10.1016/j.immuni.2018.11.004
Radde, R. et al. Aβ42-driven cerebral amyloidosis in transgenic mice reveals early and robust pathology. EMBO Rep. 7, 940–946 (2006).
doi: 10.1038/sj.embor.7400784
Yona, S. et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38, 79–91 (2013).
doi: 10.1016/j.immuni.2012.12.001
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).
doi: 10.1084/jem.20072168
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).
pubmed: 20127799
Madisen, L. et al. A robust and high-throughput Cre reporting and characterization. Nat. Neurosci. 13, 133–140 (2010).
doi: 10.1038/nn.2467
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).
pubmed: 27909575
pmcid: 5112579
Xu, C. & Su, Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974–1980 (2015).
doi: 10.1093/bioinformatics/btv088
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
doi: 10.1016/j.cell.2015.05.047
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).
doi: 10.1038/nbt.3192
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
doi: 10.1038/nbt.3519
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).
doi: 10.1371/journal.pcbi.1003731
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
doi: 10.1101/gr.1239303
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).
doi: 10.2202/1544-6115.1027
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).
doi: 10.1093/bioinformatics/bti551
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).
doi: 10.1371/journal.pone.0013984
Kucera, M., Isserlin, R., Arkhangorodsky, A. & Bader, G. D. AutoAnnotate: a Cytoscape app for summarizing networks with semantic annotations. F1000Res. 5, 1717 (2016).
doi: 10.12688/f1000research.9090.1
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
doi: 10.1038/nmeth.2019