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.

Negative feedback control of neuronal activity by microglia

Abstract

Microglia, the brain’s resident macrophages, help to regulate brain function by removing dying neurons, pruning non-functional synapses, and producing ligands that support neuronal survival1. Here we show that microglia are also critical modulators of neuronal activity and associated behavioural responses in mice. Microglia respond to neuronal activation by suppressing neuronal activity, and ablation of microglia amplifies and synchronizes the activity of neurons, leading to seizures. Suppression of neuronal activation by microglia occurs in a highly region-specific fashion and depends on the ability of microglia to sense and catabolize extracellular ATP, which is released upon neuronal activation by neurons and astrocytes. ATP triggers the recruitment of microglial protrusions and is converted by the microglial ATP/ADP hydrolysing ectoenzyme CD39 into AMP; AMP is then converted into adenosine by CD73, which is expressed on microglia as well as other brain cells. Microglial sensing of ATP, the ensuing microglia-dependent production of adenosine, and the adenosine-mediated suppression of neuronal responses via the adenosine receptor A1R are essential for the regulation of neuronal activity and animal behaviour. Our findings suggest that this microglia-driven negative feedback mechanism operates similarly to inhibitory neurons and is essential for protecting the brain from excessive activation in health and disease.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Microglia respond to neuronal activation and prevent excessive neurostimulation.
Fig. 2: Spatial control of neuronal activity by microglia.
Fig. 3: Microglia control synchrony and firing frequency of striatal neurons in vivo.
Fig. 4: Microglia suppress neuronal activation via ATP–AMP–ADO–A1R- dependent feedback.

Data availability

The gene expression data related to this study are available at the NCBI Gene Expression Omnibus (GEO) under accession number GSE149897Source data are provided with this paper.

Code availability

The code used for analysis of calcium transience in neurons to analyse event rates, magnitude, spatial correlation and synchrony can be found at https://github.com/GradinaruLab/striatum2P.

References

  1. 1.

    Werneburg, S., Feinberg, P. A., Johnson, K. M. & Schafer, D. P. A microglia-cytokine axis to modulate synaptic connectivity and function. Curr. Opin. Neurobiol. 47, 138–145 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Li, Y., Du, X. F., Liu, C. S., Wen, Z. L. & Du, J. L. Reciprocal regulation between resting microglial dynamics and neuronal activity in vivo. Dev. Cell 23, 1189–1202 (2012).

    CAS  Google Scholar 

  3. 3.

    Eyo, U. B. et al. Neuronal hyperactivity recruits microglial processes via neuronal NMDA receptors and microglial P2Y12 receptors after status epilepticus. J. Neurosci. 34, 10528–10540 (2014).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Akiyoshi, R. et al. Microglia enhance synapse activity to promote local network synchronization. eNeuro 5, ENEURO.0088-18.2018 (2018).

    PubMed  PubMed Central  Google Scholar 

  5. 5.

    Kato, G. et al. Microglial contact prevents excess depolarization and rescues neurons from excitotoxicity. eNeuro 3, ENEURO.0004-16.2016 (2016).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Wake, H., Moorhouse, A. J., Jinno, S., Kohsaka, S. & Nabekura, J. Resting microglia directly monitor the functional state of synapses in vivo and determine the fate of ischemic terminals. J. Neurosci. 29, 3974–3980 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Peng, J. et al. Microglial P2Y12 receptor regulates ventral hippocampal CA1 neuronal excitability and innate fear in mice. Mol. Brain 12, 71 (2019).

    PubMed  PubMed Central  Google Scholar 

  8. 8.

    Cserép, C. et al. Microglia monitor and protect neuronal function through specialized somatic purinergic junctions. Science 367, 528–537 (2020).

    ADS  Google Scholar 

  9. 9.

    Bernier, L. P. et al. Nanoscale surveillance of the brain by microglia via cAMP-regulated filopodia. Cell Rep. 27, 2895–2908.e4 (2019).

    CAS  Google Scholar 

  10. 10.

    Madry, C. et al. Microglial ramification, surveillance, and interleukin-1β release are regulated by the two-pore domain K+ channel THIK-1. Neuron 97, 299–312.e6 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Liu, Y. U. et al. Neuronal network activity controls microglial process surveillance in awake mice via norepinephrine signaling. Nat. Neurosci. 22, 1771–1781 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Stowell, R. D. et al. Noradrenergic signaling in the wakeful state inhibits microglial surveillance and synaptic plasticity in the mouse visual cortex. Nat. Neurosci. 22, 1782–1792 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Bozzi, Y. & Borrelli, E. The role of dopamine signaling in epileptogenesis. Front. Cell. Neurosci. 7, 157 (2013).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Chitu, V., Gokhan, Ş., Nandi, S., Mehler, M. F. & Stanley, E. R. Emerging roles for CSF-1 receptor and its ligands in the nervous system. Trends Neurosci. 39, 378–393 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Kana, V. et al. CSF-1 controls cerebellar microglia and is required for motor function and social interaction. J. Exp. Med. 216, 2265–2281 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Easley-Neal, C., Foreman, O., Sharma, N., Zarrin, A. A. & Weimer, R. M. CSF1R ligands IL-34 and CSF1 are differentially required for microglia development and maintenance in white and gray matter brain regions. Front. Immunol. 10, 2199 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Saunders, A. et al. Molecular diversity and specializations among the cells of the adult mouse brain. Cell 174, 1015–1030.e16 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Wenzel, M., Hamm, J. P., Peterka, D. S. & Yuste, R. Acute focal seizures start as local synchronizations of neuronal ensembles. J. Neurosci. 39, 8562–8575 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Pankratov, Y., Lalo, U., Verkhratsky, A. & North, R. A. Vesicular release of ATP at central synapses. Pflugers Arch. 452, 589–597 (2006).

    CAS  Google Scholar 

  21. 21.

    Pascual, O. et al. Neurobiology: astrocytic purinergic signaling coordinates synaptic networks. Science 310, 113–116 (2005).

    CAS  ADS  Google Scholar 

  22. 22.

    Corkrum, M. et al. Dopamine-evoked synaptic regulation in the nucleus accumbens requires astrocyte activity. Neuron 105, 1036–1047.e5 (2020).

    CAS  Google Scholar 

  23. 23.

    Beamer, E., Conte, G. & Engel, T. ATP release during seizures—a critical evaluation of the evidence. Brain Res. Bull. 151, 65–73 (2019).

    CAS  Google Scholar 

  24. 24.

    Haynes, S. E. et al. The P2Y12 receptor regulates microglial activation by extracellular nucleotides. Nat. Neurosci. 9, 1512–1519 (2006).

    CAS  Google Scholar 

  25. 25.

    Ayata, P. et al. Epigenetic regulation of brain region-specific microglia clearance activity. Nat. Neurosci. 21, 1049–1060 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Madry, C. et al. Effects of the ecto-ATPase apyrase on microglial ramification and surveillance reflect cell depolarization, not ATP depletion. Proc. Natl Acad. Sci. USA 115, E1608–E1617 (2018).

    CAS  Google Scholar 

  27. 27.

    Dissing-Olesen, L. et al. Activation of neuronal NMDA receptors triggers transient ATP-mediated microglial process outgrowth. J. Neurosci. 34, 10511–10527 (2014).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Robson, S. C., Sévigny, J. & Zimmermann, H. The E-NTPDase family of ectonucleotidases: structure function relationships and pathophysiological significance. Purinergic Signal. 2, 409–430 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Lanser, A. J. et al. Disruption of the ATP/adenosine balance in CD39−/− mice is associated with handling-induced seizures. Immunology 152, 589–601 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Dunwiddie, T. V. & Masino, S. A. The role and regulation of adenosine in the central nervous system. Annu. Rev. Neurosci. 24, 31–55 (2001).

    CAS  Google Scholar 

  31. 31.

    Zimmermann, H., Zebisch, M. & Sträter, N. Cellular function and molecular structure of ecto-nucleotidases. Purinergic Signal. 8, 437–502 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Flagmeyer, I., Haas, H. L. & Stevens, D. R. Adenosine A1 receptor-mediated depression of corticostriatal and thalamostriatal glutamatergic synaptic potentials in vitro. Brain Res. 778, 178–185 (1997).

    CAS  Google Scholar 

  33. 33.

    Yabuuchi, K. et al. Role of adenosine A1 receptors in the modulation of dopamine D1 and adenosine A2A receptor signaling in the neostriatum. Neuroscience 141, 19–25 (2006).

    CAS  Google Scholar 

  34. 34.

    Trusel, M. et al. Coordinated regulation of synaptic plasticity at striatopallidal and striatonigral neurons orchestrates motor control. Cell Rep. 13, 1353–1365 (2015).

    CAS  Google Scholar 

  35. 35.

    Zhou, S. et al. Pro-inflammatory effect of downregulated CD73 expression in EAE astrocytes. Front. Cell. Neurosci. 13, 233 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Bateup, H. S. et al. Cell type-specific regulation of DARPP-32 phosphorylation by psychostimulant and antipsychotic drugs. Nat. Neurosci. 11, 932–939 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Wendeln, A. C. et al. Innate immune memory in the brain shapes neurological disease hallmarks. Nature 556, 332–338 (2018).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Süß, P. et al. Chronic peripheral inflammation causes a region-specific myeloid response in the central nervous system. Cell Rep. 30, 4082–4095.e6 (2020).

    Google Scholar 

  39. 39.

    Krasemann, S. et al. The TREM2–APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581.e9 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Mildner, A., Huang, H., Radke, J., Stenzel, W. & Priller, J. P2Y12 receptor is expressed on human microglia under physiological conditions throughout development and is sensitive to neuroinflammatory diseases. Glia 65, 375–387 (2017).

    Google Scholar 

  41. 41.

    Palop, J. J. et al. Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer’s disease. Neuron 55, 697–711 (2007).

    CAS  Google Scholar 

  42. 42.

    Lam, A. D. et al. Silent hippocampal seizures and spikes identified by foramen ovale electrodes in Alzheimer’s disease. Nat. Med. 23, 678–680 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Wohleb, E. S., Franklin, T., Iwata, M. & Duman, R. S. Integrating neuroimmune systems in the neurobiology of depression. Nat. Rev. Neurosci. 17, 497–511 (2016).

    CAS  Google Scholar 

  44. 44.

    Spangenberg, E. E. et al. Eliminating microglia in Alzheimer’s mice prevents neuronal loss without modulating amyloid-β pathology. Brain 139, 1265–1281 (2016).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Bejar, R., Yasuda, R., Krugers, H., Hood, K. & Mayford, M. Transgenic calmodulin-dependent protein kinase II activation: dose-dependent effects on synaptic plasticity, learning, and memory. J. Neurosci. 22, 5719–5726 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Alexander, G. M. et al. Remote control of neuronal activity in transgenic mice expressing evolved G protein-coupled receptors. Neuron 63, 27–39 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Stanley, S. et al. Profiling of glucose-sensing neurons reveals that ghrh neurons are activated by hypoglycemia. Cell Metab. 18, 596–607 (2013).

    CAS  Google Scholar 

  48. 48.

    Parkhurst, C. N. et al. Microglia promote learning-dependent synapse formation through brain-derived neurotrophic factor. Cell 155, 1596–1609 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Wang, Y. et al. IL-34 is a tissue-restricted ligand of CSF1R required for the development of Langerhans cells and microglia. Nat. Immunol. 13, 753–760 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Tronche, F. et al. Disruption of the glucocorticoid receptor gene in the nervous system results in reduced anxiety. Nat. Genet. 23, 99–103 (1999).

    CAS  Google Scholar 

  51. 51.

    Harris, S. E. et al. Meox2Cre-mediated disruption of CSF-1 leads to osteopetrosis and osteocyte defects. Bone 50, 42–53 (2012).

    CAS  Google Scholar 

  52. 52.

    Rothweiler, S. et al. Selective deletion of ENTPD1/CD39 in macrophages exacerbates biliary fibrosis in a mouse model of sclerosing cholangitis. Purinergic Signal. 15, 375–385 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Yona, S. et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity 38, 79–91 (2013).

    CAS  Google Scholar 

  54. 54.

    Scammell, T. E. et al. Focal deletion of the adenosine A1 receptor in adult mice using an adeno-associated viral vector. J. Neurosci. 23, 5762–5770 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Thompson, L. F. et al. Crucial role for ecto-5′-nucleotidase (CD73) in vascular leakage during hypoxia. J. Exp. Med. 200, 1395–1405 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    André, P. et al. P2Y12 regulates platelet adhesion/activation, thrombus growth, and thrombus stability in injured arteries. J. Clin. Invest. 112, 398–406 (2003).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Oakley, H. et al. Intraneuronal β-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial Alzheimer’s disease mutations: potential factors in amyloid plaque formation. J. Neurosci. 26, 10129–10140 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Casanova, E. et al. A CamKIIα iCre BAC allows brain-specific gene inactivation. Genesis 31, 37–42 (2001).

    CAS  Google Scholar 

  59. 59.

    Doyle, J. P. et al. Application of a translational profiling approach for the comparative analysis of CNS cell types. Cell 135, 749–762 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Heiman, M. et al. A translational profiling approach for the molecular characterization of CNS cell types. Cell 135, 738–748 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    von Schimmelmann, M. et al. Polycomb repressive complex 2 (PRC2) silences genes responsible for neurodegeneration. Nat. Neurosci. 19, 1321–1330 (2016).

    Google Scholar 

  62. 62.

    Kim, D. & Salzberg, S. L. TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol. 12, R72 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    CAS  PubMed  Google Scholar 

  64. 64.

    Purushothaman, I. & Shen, L. SPEctRA: a scalable pipeline for RNA -seq ana lysis. https://zenodo.org/record/60547#.X1khQDNKjIU (2016).

  65. 65.

    Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Hickman, S. E. et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. 16, 1896–1905 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Howe, E. A., Sinha, R., Schlauch, D. & Quackenbush, J. RNA-seq analysis in MeV. Bioinformatics 27, 3209–3210 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69.

    Chen, E. Y. et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Kuleshov, M. V. et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 44 (W1), W90−W97 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Gokce, O. et al. Cellular taxonomy of the mouse striatum as revealed by single-cell RNA-seq. Cell Rep. 16, 1126–1137 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Bohlen, C. J., Bennett, F. C. & Bennett, M. L. Isolation and culture of microglia. Curr. Protoc. Immunol. 125, e70 (2019).

    Google Scholar 

  73. 73.

    Butovsky, O. et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat. Neurosci. 17, 131–143 (2014).

    CAS  Google Scholar 

  74. 74.

    Gabriel, L. R., Wu, S. & Melikian, H. E. Brain slice biotinylation: an ex vivo approach to measure region-specific plasma membrane protein trafficking in adult neurons. J. Vis. Exp. 86, e51240 (2014).

    Google Scholar 

  75. 75.

    Crupi, M. J. F., Richardson, D. S. & Mulligan, L. M. Cell surface biotinylation of receptor tyrosine kinases to investigate intracellular trafficking. Methods Mol. Biol. 1233, 91–102 (2015).

    Google Scholar 

  76. 76.

    Sullivan, J. M. et al. Autism-like syndrome is induced by pharmacological suppression of BET proteins in young mice. J. Exp. Med. 212, 1771–1781 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Pnevmatikakis, E. A. & Giovannucci, A. NoRMCorre: an online algorithm for piecewise rigid motion correction of calcium imaging data. J. Neurosci. Methods 291, 83–94 (2017).

    CAS  Google Scholar 

  78. 78.

    Pachitariu, M. et al. Suite2p: beyond 10,000 neurons with standard two-photon microscopy. Preprint at https://www.biorxiv.org/content/10.1101/061507v2 (2016).

  79. 79.

    Yoder, N. C. peakfinder(x0, sel, thresh, extrema, includeEndpoints, interpolate). https://www.mathworks.com/matlabcentral/fileexchange/25500-peakfinder-x0-sel-thresh-extrema-includeendpoints-interpolate (Matlab Central File Exchange, 2016).

  80. 80.

    Klaus, A. et al. The spatiotemporal organization of the striatum encodes action space. Neuron 95, 1171–1180.e7 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. 81.

    Barbera, G. et al. Spatially compact neural clusters in the dorsal striatum encode locomotion relevant information. Neuron 92, 202–213 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Kato, D. et al. in Microglia. Methods in Molecular Biology (eds. Garaschuk, O. & Verkhratsky A.) (Humana, 2019).

  83. 83.

    Thévenaz, P., Ruttimann, U. E. & Unser, M. A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7, 27–41 (1998).

    ADS  Google Scholar 

  84. 84.

    Ting, J. T. et al. Preparation of acute brain slices using an optimized N-methyl-d-glucamine protective recovery method. J. Vis. Exp. 132, e53825 (2018).

    Google Scholar 

  85. 85.

    Fieblinger, T. et al. Cell type-specific plasticity of striatal projection neurons in parkinsonism and L-DOPA-induced dyskinesia. Nat. Commun. 5, 5316 (2014).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  86. 86.

    Graves, S. M. & Surmeier, D. J. Delayed spine pruning of direct pathway spiny projection neurons in a mouse model of parkinson’s disease. Front. Cell. Neurosci. 13, 32 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Wong, J. M. T. et al. Benzoyl chloride derivatization with liquid chromatography-mass spectrometry for targeted metabolomics of neurochemicals in biological samples. J. Chromatogr. A 1446, 78–90 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88.

    Gangarossa, G. et al. Convulsant doses of a dopamine D1 receptor agonist result in Erk-dependent increases in Zif268 and Arc/Arg3.1 expression in mouse dentate gyrus. PLoS One 6, e19415 (2011).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Bunch, L. & Krogsgaard-Larsen, P. Subtype selective kainic acid receptor agonists: discovery and approaches to rational design. Med. Res. Rev. 29, 3–28 (2009).

    CAS  Google Scholar 

  90. 90.

    Willoughby, J. O., Mackenzie, L., Medvedev, A. & Hiscock, J. J. Distribution of Fos-positive neurons in cortical and subcortical structures after picrotoxin-induced convulsions varies with seizure type. Brain Res. 683, 73–87 (1995).

    CAS  Google Scholar 

  91. 91.

    Sipe, G. O. et al. Microglial P2Y12 is necessary for synaptic plasticity in mouse visual cortex. Nat. Commun. 7, 10905 (2016).

    CAS  ADS  PubMed  PubMed Central  Google Scholar 

  92. 92.

    Racine, R. J. Modification of seizure activity by electrical stimulation. II. Motor seizure. Electroencephalogr. Clin. Neurophysiol. 32, 281–294 (1972).

    CAS  Google Scholar 

  93. 93.

    Silverman, J. L., Yang, M., Lord, C. & Crawley, J. N. Behavioural phenotyping assays for mouse models of autism. Nat. Rev. Neurosci. 11, 490–502 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Langfelder, P. et al. Integrated genomics and proteomics define huntingtin CAG length-dependent networks in mice. Nat. Neurosci. 19, 623–633 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Wang, Y. et al. TREM2 lipid sensing sustains the microglial response in an Alzheimer’s disease model. Cell 160, 1061–1071 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

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

    CAS  Google Scholar 

  97. 97.

    Sousa, C. et al. Single-cell transcriptomics reveals distinct inflammation-induced microglia signatures. EMBO Rep. 19, e46171 (2018).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank P. Greengard and A. Nairn for sharing the DARPP32 antibodies; J. J. Badimon for the Ticagrelor and Clopidogrel; R. Greene for the Adora1fl/fl mice; M. Merad and F. Desland for the Csf1fl/fl; NestinCre mice, the MSSM FACS facility and J. Ochando, C. Bare, and G. Viavattene for assistance with flow cytometry analysis; A. Lopez and A. Watters for assistance with microdialysis experiments; G. Milne and the Vanderbilt University Neurochemistry Core for LC–MS analysis; D. Wagenaar and CalTech Neurotechnology Laboratory for help with construction of the two-photon system; and all Schaefer laboratory members and A. Tarakhovsky for discussions and critical comments on the manuscript. This work was supported by the National Institutes of Health (NIH) Director New Innovator Award DP2 MH100012-01 (A.S.), NIH grants R01NS091574 (A.S.), R01MH118329 (A.S.), DA047233 (A.S.), R01NS106721 (A.S.) and U01AG058635 (A.S.), a Robin Chemers Neustein Award (P.A.), NIH grant RO1AG045040 (J.X.J.), Welch Foundation Grant AQ-1507 (J.X.J.), NARSAD Young Investigator Award no. 25065 (P.A.), NIH grants T32AG049688 (A.B.), T32AI078892 (A.T.C.), 1K99NS114111 (M.A.W.), T32CA207201 (M.A.W.), R01NS102807 (F.J.Q.), R01AI126880 (F.J.Q.), and R01ES025530 (F.J.Q.), a TCCI Chen Graduate Fellowship (X.C.), an A*STAR National Science Scholarship (A.N.), the CZI Neurodegeneration Challenge Network (V.G.), NIH BRAIN grant RF1MH117069 (V.G.), NIH grants HL107152 (S.C.R.), HL094400 (S.C.R.), AI066331 (S.C.R.), GM-136429 (W.G.J.), GM-51477 (W.G.J.), GM-116162 (W.G.J.), HD-098363 (W.G.J.), DA042111 (E.S.C.), DA048931 (E.S.C.), funds from a VUMC Faculty Research Scholar Award (M.G.K.), the Brain and Behavior Research Foundation (M.G.K. and E.S.C), the Whitehall Foundation (E.S.C.), and the Edward Mallinckrodt Jr. Foundation (E.S.C.). The Vanderbilt University Neurochemistry Core is supported by the Vanderbilt Brain Institute and the Vanderbilt Kennedy Center (EKS NICHD of NIH Award U54HD083211).

Author information

Affiliations

Authors

Contributions

A.S. and A.B. conceived and designed the study. A.B. did molecular, behavioural, FACS and imaging experiments. H.J.S. did primary neuronal culture, microglia isolation, microglia culture, FACS and Axion microelectrode array experiments. P.A. did in vivo TRAP experiments. A.B., X.C., A.N., V.G. and A.S. designed two-photon imaging experiments, which were performed by X.C. and A.N. A.K. built the customized two-photon system. A.B., A.I., H.W. and A.S. designed the two-photon imaging of microglial protrusions, which was performed by A.I. A.T.C. and R.S. performed single-nucleus 10X sequencing. Y.-C.W. analysed single-nucleus 10X sequencing data. Y.-H.E.L. analysed bulk RNA-seq data from TRAP experiments. A.S., D.J.S. and S.M.G. designed experiments to measure neuronal excitability that were conducted by S.M.G. A.B., M.I., P.J.K. and A.S. designed experiments to measure sEPSCs that were conducted by M.I. A.S. and A.B. designed and P.H. performed molecular and imaging experiments. C.L. and W.G.J. conducted the HPLC analysis. M.G.K. and E.S.C. conducted the microdialysis experiments. A.B., J.O.U. and U.B.E. conducted seizure susceptibility experiments on P2ry12/ mice. S.C.R. generated Cd39fl/fl mice. J.X.J. generated Csf1fl/fl mice. M.C. generated Il34fl/fl mice. M.A.W. and F.J.Q. generated Cd39fl/flCx3cr1CreErt2/+(Jung) mice. A.B., M.A.W., F.J.Q. and A.S. designed behavioural experiments. A.S. and A.B. wrote the manuscript. All authors discussed results, and provided input and edits on the manuscript.

Corresponding author

Correspondence to Anne Schaefer.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature thanks Ania Majewska 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 figures and tables

Extended Data Fig. 1 DREADD-based mouse models to study microglia responses to neuronal activation and inhibition reveals distinct microglia responses.

a, b, Neuron-specific activation (a) and inhibition (b) has been achieved by the expression of the Gq-coupled (activating) hM3Dq or Gi-coupled (inhibiting) hM4Di in CaMKII+ forebrain neurons. The CaMKII-tTa mice were bred to either tetO-CHRM3 or tetO-CHRM4 mice to generate CaMKII-tTa; tetO-CHRM3 or CaMKII-tTa; tetO-CHRM4 mice. hM3Dq or hM4Di were activated by i.p. injection of clozapine-N-oxide (CNO) to activate (0.25 mg kg–1) or inhibit (1 mg kg–1) CaMKII+ neuronal activity, respectively. c-e, Validation of CNO-mediated neuronal activation and inhibition: c, Heatmap (left) and violin plot (right) show RNA expression levels of 18 immediate early genes in total striatum 2 h after CNO-mediated neuronal inhibition (orange) or neuronal activation (blue) as compared with controls (n = 2 CaMKII-tTa; tetO-CHRM4, n = 5 control, and n = 3 CaMKII-tTa; tetO-CHRM3 mice) (right, P = 0.0001, One-way ANOVA (Kruskal–Wallis test) with Dunn’s multiple comparison test). d, Dot plot showing quantification of the average number of cFOS+ cells in the dorsal striatum of CaMKII-tTa; tetO-CHRM4 (orange, n = 4 mice), control (black, n = 6 mice), and CaMKII-tTa; tetO-CHRM3 (blue, n = 4 mice) mice one hour after treatment with CNO (P = 0.0004, One-way ANOVA with Tukey’s post hoc test). e, Representative images showing cFOS+ cells (green) in the striatum of CaMKII-tTa; tetO-CHRM4 (top), control (middle), and CaMKII-tTa; tetO-CHRM3 (bottom) mice in response to CNO, DAPI (blue) (image are representative of two independent cohorts of mice). f, To allow for the microglia-specific analysis of changes in ribosome-associated RNA levels following neuron inhibition, the CaMKII-tTa; tetO-CHRM4 mice were bred to Cx3cr1CreErt2/+(Litt); Eef1a1LSL.eGFPL10a/+ mice followed by tamoxifen-induced Cre-mediated L10a-eGFP expression in microglia. g, Changes in ribosome-bound mRNA levels in striatal microglia were determined using the TRAP-sequencing approach. The heatmap shows the variation in the expression levels of 135 upregulated and 220 downregulated genes (z-scored log2(RPKM) at 2 h following CNO-mediated neuronal inhibition. h, Selected gene ontology (using GO) annotations for upregulated genes (using DESeq2) in striatal microglia in response to neuronal inhibition, GO analysis was performed using ENRICHR analysis69,70 (dotted line, P = 0.05). i, Venn diagrams comparing microglial genes up- and downregulated following CaMKII+ neuronal activation and inhibition reveals highly differential microglia response. j, qPCR confirmation of increased mRNA expression (lower ΔCT, normalized to Gapdh) in microglia upon neuronal activation (Ccl3, left, n = 3 mice, P = 0.059, unpaired two-tailed t-test) and neuronal inhibition (Cd74, right, n = 2 mice). k, Dot plots show lack of expression changes in selected genes in the striatum of wild type mice 2 h after saline, 0.25 mg kg–1 CNO injection, or 1 mg kg–1 CNO injection (n = 3, 3, and 4 mice; Kdm6b: P = 0.70 Adrb1: P = 0.22, Ccl24: P = 0.54, Ccl3: P = 0.43, Kcnk13: P = 0.37, Ikbkb, P = 0.62, One-way ANOVA with Tukey’s post hoc test). RPKM: reads per kilobase of transcript per million mapped reads, TRAP: translating ribosome affinity purification; Data shown as mean ± s.e.m.

Source data

Extended Data Fig. 2 Microglia deficient mice show normal baseline behaviours but exaggerated responses to neurostimulants.

a, Dot plots show the average number of microglia per mm2 in cortex, striatum, cerebellum and hippocampus in control and microglia deficient mice (n = 3 and 4 mice, cortex: P < 0.0001, striatum: P = 0.0003, cerebellum: P = 0.0001, DG: P < 0.0001, CA3: P < 0.0001, CA1: P < 0.0001, unpaired two-tailed t-test). b-e, Behavioural characteristics of microglia deficient mice. b, Anxiety-like behaviour was measured by the ratio of time spent in the open arms/closed arms in the elevated plus maze (n = 10 mice, P = 0.65, unpaired two-tailed t-test). c, Motor coordination was measured by latency to fall from the accelerating rotarod (n = 8 and 12 mice, interaction: P = 0.89, time: P = 0.13, treatment: P = 0.36; subjects: P < 0.0001, Two-way repeated measures ANOVA). d, Olfactory behaviour was measured by the sniff test (n = 21 and 13 mice, P = 0.09, unpaired two-tailed t-test). e, Social behaviour was measured by using the classic three-chamber sociability task (Social preference: mouse preference for sniffing another mouse over object, Control: n = 7 mice; P = 0.0002, microglia deficient: n = 9 mice, P < 0.0001; Social Memory: mouse preference for sniffing novel mouse over familiar mouse, Control n = 7 mice, P = 0.0023, microglia deficient: n = 7 mice, P = 0.0009; paired two-tailed t-test). f, Representative images show brain-wide gene expression patterns of receptors targeted by kainic acid (kainate and AMPA receptor), picrotoxin (GABAA receptor), and SKF81297 (D1 receptor) (Allen Institute). g, Number of stage IV-V seizures (Racine scale92) per mouse visually recorded within one hour in response to kainic acid (18 mg kg–1, i.p.) are shown as a dot plot (n = 9 and 10 mice, P = 0.0008, unpaired two-tailed t-test). h, Dot plot showing distance travelled in response to D1 agonist in one hour in the open field (SKF81297, 3 mg kg–1, i.p.)(n = 14 and 8 mice, P = 0.025, unpaired two-tailed t-test). i, Representative cortical EEG traces during a tonic-clonic seizure event in response to D1 agonist treatment (SKF81297, 5 mg kg–1 i.p.) in control (top) and microglia deficient (bottom) mice showing high amplitude and rhythmic discharges followed by EEG depression. DG: dentate gyrus; Data shown as mean ± s.e.m.

Source data

Extended Data Fig. 3 Generation and characterization of Il34-deficient and Csf1-deficient mice.

a, Violin plots show the expression levels of cell-type specific representative marker genes across the 10 identified cell types from striatum snRNA-seq data analysis. Black dots indicate mean expression of selected gene per cell type. b, In situ hybridization for Il34 (left) and Csf1 (right) mRNAs show differential, region-specific expression in cortex, striatum, CA1, dentate gyrus (DG), CA3, corpus callosum (CC), and cerebellum of wild-type mice (WM: white matter, GM: grey matter, ML: molecular layer, GCL: granule cell layer, scale bar = 100μm). c, h, The striatal grey matter-specific or white matter-specific microglia depletion was achieved by breeding NestinCre/+ mice to Il34fl/fl mice or Csf1fl/fl mice, respectively, to generate Il34fl/fl; NestinCre/+ (purple, c) and Csf1fl/fl; NestinCre/+ mice (blue, h). d, i, Dot plots showing relative expression levels of Il34 and Csf1 mRNA normalized to Gapdh in the striatum of Il34fl/fl; NestinCre/+ mice (d) or Csf1fl/fl; NestinCre/+ mice (i) compared with littermate controls (d, n = 4 mice each, Il34 P < 0.0001, Csf1 P = 0.69; i, n = 3 and 5 mice, Il34 P = 0.07, Csf1 P < 0.0001, unpaired two-tailed t-test). e, Dot plots show the average microglia density per mm2 per mouse in cortex, striatum, cerebellum (cortex: n = 9, 12, and 10 mice, P < 0.0001, striatum: n = 9, 13, and 10 mice, P < 0.0001, cerebellum: n = 7, 7, and 8 mice, P = 0.34, One-way ANOVA with Tukey’s post hoc test). f, left, Dot plot shows levels of IL34 protein as determined by western blot analysis of striatal protein lysate from Il34fl/fl, Il34fl/+; NestinCre/+ or Il34fl/fl; NestinCre/+ mice normalized to DARPP32 expression (n = 3 mice, P = 0.0077, One-way ANOVA with Tukey’s post hoc test). g, j, Bar graphs show the average percentage of white matter regions in striatal images (0.5mm × 0.5mm) used to count WM and GM microglia in control and mutant mice for the data shown in Fig. 2c and e. (g, P = 0.99, n = 4 and 3 mice, unpaired two-tailed t-test; j, n = 4 and 2 mice). For gel source data, see Supplementary Fig. 1. Data shown as mean ± s.e.m.

Source data

Extended Data Fig. 4 Generation of mice with striatum-specific microglia depletion.

a, b, (left), The striatum-specific microglia depletion was achieved by breeding Il34fl/fl mice to Drd1aCre/+ or Drd2Cre/+ mice to generate Il34fl/fl; Drd1aCre/+ (a, green) and Il34fl/fl; Drd2Cre/+ mice (b, grey). Right, dot plots show relative expression of Il34 mRNA in the striatum normalized to Gapdh (a, n = 6 and 7 mice, P < 0.0001; b, n = 4 mice, P = 0.0004, unpaired two-tailed t-test). c, Representative striatal images of sagittal brain slices from Il34fl/fl, Il34fl/fl; Drd1aCre/+ and Il34fl/fl; Drd2Cre/+ mice following immunofluorescent staining for P2RY12 (microglia, green) and DAPI (nuclei, blue) (scale bar = 50μm). d, e, Dot plots show the average microglia density per mm2 per mouse per specific region in the hippocampus of Il34fl/fl; Drd1aCre/+ (d) and Il34fl/fl; Drd2Cre/+ mice (e) compared to littermate controls (d, n = 3 mice, DG: P = 0.88, CA3: P = 0.85, CA1: P = 0.1; e, n = 3 mice, DG: P = 0.69, CA3: P = 0.56, CA1: P = 0.72; unpaired two-tailed t-test). f, g, Dot plots showing total distance travelled in response to D1 agonist (SKF81297, 3 mg kg–1, i.p.) in one hour in the open field for Il34fl/fl; Drd1aCre/+ (f) and Il34fl/fl; Drd2Cre/+ mice (g) compared with littermate controls (f: n = 8 and 9 mice, P = 0.034 g: n = 8 mice, P = 0.0087, unpaired two-tailed t-test). h, Percentage of mice seizing 30 min after administration of picrotoxin (1 mg kg–1, i.p.) shown as a bar graph (n = 21, 9, and 8 mice; P = 0.80, Chi-squared test). DG: dentate gyrus. i, Microglia-neuron ratio defines the threshold of D1 neuron activation by D1 agonist. Bar graph shows the percentage of mice with stage IV-V seizures in response to D1 agonist (4 mg kg–1, i.p.) in control, Il34fl/fl; Drd1aCre/+, and microglia deficient mice (n = 11, 13, and 9 mice; right, P = 0.0005, Chi-squared test). While all mice display an increased seizure response to 5 mg kg–1D1 agonist treatment, only microglia deficient (99% reduction of microglia), but not Il34fl/fl; Drd1aCre/+ (60% reduction of microglia in the striatum) display an increased seizure response at 4 mg kg–1D1 agonist treatment. Data shown as mean ± s.e.m.

Source data

Extended Data Fig. 5 Striatum-specific microglia reduction has no overall effects on striatal cellular composition, D1/D2 neuronal morphology, D1/D2 MSN characteristic electrophysiological and molecular phenotypes, and glial phenotypes.

a, Dot plots show average number of D1 neurons (left, dark green, GFP+, DARPP32+) and D2 neurons (right, light green, GFP-, DARPP32+) per mouse in the striatum of Il34fl/flDrd1aeGFPL10a and Il34fl/flDrd1aeGFPL10aDrd1aCre/+ mice. Mice expressing eGFP-tagged ribosomal subunit L10a under the Drd1a promoter were used to identify GFP+ D1 neurons and GFP- D2 neurons in control Il34fl/flDrd1aeGFPL10a and mutant Il34fl/flDrd1aeGFPL10aDrd1aCre/+ (n = 2 mice). b, c, D1 or D2 neuron cell morphology was determined by the number of primary dendrites (b), total dendritic length (c, left), and sholl analysis (c, right) (b, D1 neurons: n = 11 and 15 D1 neurons, P = 0.33; D2 neurons: n = 15 and 11 D2 neurons, P = 0.59; unpaired two-tailed t-test; c, D1 neurons, n = 11 and 15 D1 neurons, dendritic length: P = 0.83, unpaired two-tailed t-test; sholl, interaction: P = 0.99; genotype: P = 0.069; distance: P < 0.0001, two-way ANOVA; D2 neurons, n = 15 and 10 D2 neurons, dendritic length: P = 0.80, unpaired two-tailed t-test; sholl: interaction: P = 0.051; genotype: P = 0.67; distance: P < 0.0001, two-way ANOVA). d, Intrinsic excitability of D1 neurons (left) and D2 neurons (right) in ex vivo slices as measured by current-evoked action potentials (AP, left) and equilibrium potentials as voltage-current (VC) plots (right) (D1: n = 11 and 15 D1 neurons, AP: interaction: P = 1.0; genotype: P = 0.98; pA: P < 0.0001, subjects: P < 0.0001; VC: interaction: P = 1.0; genotype: P = 0.48; distance: P < 0.0001, subjects: P < 0.0001; D2: n = 16 and 10 D2 neurons; AP: interaction: P = 1.0; genotype: P = 0.5; distance: P < 0.0001, subjects: P < 0.0001; VC: interaction: P = 0.99; genotype: P = 0.7; distance: P < 0.0001, subjects: P < 0.0001; two-way ANOVA). e, Dendritic excitability of D1 neurons (left) and D2 neurons (right) in ex vivo slices as determined by back-propagating action potentials as measured by Ca2+-sensitive fluorescence (D1: n = 12 and 15 D1 neurons, dendrites: P = 0.90, spines: P = 0.85; D2: n = 16 and 10 D2 neurons, dendrites, P = 0.27, spines, P = 0.61; two-way ANOVA). f, Frequency (Hz) and amplitude (pA) of sEPSPs in D1 neurons from ex vivo slices shown as box and whisker plots (Frequency: n = 19 cells from 5 mice and 16 cells from 5 mice, P = 0.23, unpaired two-tailed t-test; amplitude: n = 19 cells from 5 mice and 16 cells from 5 mice, P = 0.796, unpaired two-tailed t-test with Welch’s correction). g, Membrane bound DRD1 protein expression normalized to total DRD1 expression as determined by ex vivo brain slice biotinylation assay shown as a dot plot (n = 6 mice, P = 0.21). h, Generation of Il34fl/flDrd1aCre/+Drd1eGFPL10a for D1 neuron specific TRAP sequencing analysis. i, Volcano plot shows lack of any major gene expression changes in D1 neurons in 3 month old Il34fl/flDrd1aCre/+Drd1eGFPL10a mice and littermate controls as determined by differential expression analysis (DESeq2, n = 3 mice each, P < 0.05, fold change >1.5, red: upregulated, blue: downregulated). j-k, Total striatal RNA expression analysis from control and Il34fl/flDrd1aCre/+ mice reveals unperturbed striatum cell-type specific gene expression pattern except the expected ~50% reduction in the expression of microglia-enriched genes. j, RPKM, normalized to controls, showing pan-medium spiny neuron (MSN), D1 neuron (D1), D2 neuron (D2), interneuron (IN), astrocyte (astro), oligodendrocyte (oligo), and microglia specific genes in Il34fl/flDrd1aeGFPL10a and Il34fl/flDrd1aCre/+Drd1aeGFPL10a mice (n = 4 mice each, P2ry12: P = 0.003, Siglech: P = 0.001, Cx3cr1: P = 0.01, Csf1r: P = 0.007, Tmem119: P = 0.005, Fcrls: P = 0.03, unpaired two-tailed t-test). k, RPKM, normalized to controls, showing unperturbed expression of astrocyte-specific activation markers66 (n = 4 mice each, unpaired two-tailed t-test). l, Microglia show wild-type like expression of selected microglia sensome genes67, RPKMs of selected genes have been normalized to Hexb RPKM, (n = 4 mice each, unpaired two-tailed t-test). The experiments shown in h-k have been independently repeated in a second cohort (n = 3 mice) with identical results. For gel source data, see Supplementary Fig. 1. Box and whisker plots in b, c, e, and f are shown with arithmetic median (middle line), box shows upper and lower quartile, whiskers show min-max range. Data shown as mean ± s.e.m.

Source data

Extended Data Fig. 6 Microglia regulate striatal neuron synchrony and responses to D1 agonist treatment in an ADO/A1R dependent fashion.

a, Representative tile scan of coronal brain slice showing implantation of GRIN lens and AAV9.hSyn.GCaMP6 s expression in the dorsal striatum. b, Increased synchrony in the dorsal medial striatum of microglia deficient mice (n = 9 mice) at baseline compared with controls (n = 7 mice) (treatment: P < 0.0001, distance: P < 0.0001, interaction: P < 0.0001; Two-way ANOVA with Sidak’s multiple comparisons test). c, Bar graphs show magnitude of Ca2+ events (ΔF/F) recorded in control (black) and microglia deficient mice (grey) at baseline (left) and in response to D1 agonist (SKF81297, 3 mg kg–1, right) (baseline: control, n = 824 cells from 7 mice; microglia deficient, n = 775 cells from 9 mice, P = 0.87; D1 agonist: control, n = 995 cells from 7 mice; microglia deficient, n = 1021 cells from 9 mice; P = 0.89, unpaired two-tailed t-test). d, e, Co-administration of A1R agonist (CPA, 0.1 mg kg–1) with D1 agonist (SKF81297, 3 mg kg–1) normalizes increased neuronal activity in microglia deficient mice. Bar graphs show wild type-like frequency (per mouse, d) and magnitude (ΔF/F, e) of Ca2+ events per neuron per minute in control (black) and microglia deficient (grey) (d, control, n = 7 mice; microglia deficient, n = 9 mice, P = 0.82, unpaired two-tailed t-test; e, control, n = 387 cells from 7 mice; microglia deficient, n = 305 cells from 9 mice; P = 0.69, unpaired two-tailed t-test). f, Spatiotemporal coding of neuronal activity (baseline shown in Fig. 3c) is disrupted by D1 agonist administration (dotted line) and largely normalized by co-administration with an A1R agonist (blue line) in control (top, n = 7 mice) and microglia deficient mice (left, n = 9 mice). For better visualization, the distance axis was logarithmically scaled. (Control, n = 7 mice: interaction: P = 0.0012, distance: P < 0.0001, treatment: P < 0.0001; Microglia deficient, n = 9 mice: interaction: P = 0.0014, distance: P < 0.0001, treatment: P < 0.0001; Two-way ANOVA with Sidak’s multiple comparisons test). g, Bar graphs show the frequency of Ca2+ events per neuron per minute in control (left) and microglia deficient (right) mice at baseline, in response to D1 agonist (SKF81297, 3 mg kg–1, i.p.) alone, or in response to D1 agonist and A1R agonist treatment (CPA, 0.1 mg kg–1, i.p.) treatment (Control: n = 332-995 cells from 7 mice, P < 0.0001; Microglia deficient: n = 243-1021 cells from 9 mice, P < 0.0001; One-way ANOVA with Bonferroni post hoc test). h, Confirmation of CNO-mediated neuronal activation for data shown in Fig. 3h. The neuron-specific expression of GCaMP6 s and hM3Dq was achieved by injecting the indicated viruses. Virally labelled thalamocortical projection neurons were identified (mCherry expression) and calcium transients were recorded at baseline, after saline injection, and after CNO injection. i, Representative traces (left) and quantification of the area under the curve (AUC) (right) of calcium transients per mouse in virally labelled neurons pre-injection, after saline injection, and after CNO injection (n = 3 mice, P = 0.0009, One-way ANOVA with Tukey’s post hoc test). j, Microglia baseline process velocity (left) and contact with synaptic boutons (right) is not affected by either the expression of the DREADD virus (red bars) or by CNO injection (5 mg kg–1, black bars) alone (n = 3 mice, left: P = 0.96, right, P = 0.25, unpaired two-tailed t-test). The experiments shown in a-g are data combined from two independent imaging cohorts of mice. Box and whisker plots in c, e, and g are shown with arithmetic median (middle line), box shows upper and lower quartile, whiskers show 1.5x interquartile range. CNO: clozapine-N-oxide; Data shown as mean ± s.e.m.

Source data

Extended Data Fig. 7 Microglial expression of Entpd1/CD39 and Nt5e/CD73 in vitro and in vivo.

a, Dot plots show normalized, ribosome-associated mRNA levels (RPKM) for Entpd1 (left) and Nt5e (right) in astrocytes, neurons, and microglia from distinct brain regions of adult mice using cell-type specific TRAP sequencing (n = 2, 2, 3, 6, 4, 5, 19, and 15 mice). b, CD39 surface protein expression on ex vivo isolated forebrain cells of Cx3cr1CreErt2/+(Litt) mice (mice express cytosolic YFP in Cx3cr1+ microglia). Percoll-purified cells were incubated with anti-CD39-AlexaFluor700 followed by FACS analysis. The histogram shows expression levels of CD39, which is almost exclusively restricted to YFP+ microglia (red) and is not found on YFP- non-microglia cells (grey) as shown previously73 (data are representative of three independent experiments). c, Scheme shows ex vivo isolation procedure of CD11b+ microglia following neonatal mouse forebrain tissue dissociation and Percoll enrichment for live cells. d, e, Ex vivo CD11b+ microglia isolation procedure from neonatal pups yields highly pure microglia population. d, Microglia were positively selected for by using CD11b+ magnetic bead purification and were incubated with anti-CD39-AlexaFluor700 followed by FACS analysis to assess the purity of the population. The numbers show the percentage of live (DAPI) cells with distinct pattern of CD39 expression levels (>98% CD39+; data are representative of two independent experiments). e, Immunofluorescent analysis of purity of CD11b+ microglia isolation. Left, cells were plated on cover slips and stained for cell-type specific protein expression using antibodies specific for IBA1 (microglia), GFAP (astrocyte), OLIG2 (oligodendrocytes) or NEUN (neurons) to identify and quantify different cells within the populations in order to assess microglia purity (n = 6 GFAP/IBA1 images and 6 OLIG2/NEUN/IBA1 images). Right, representative image of cover slip containing 99% pure microglia following CD11b+ isolation procedure is shown (IBA1, green; DAPI, blue). f, left, Cell lysates of increasing numbers of CD11b+ bead-purified microglia cells have been analysed for CD39, CD73, P2RY12, and IBA1 protein expression by Western Blot analysis as indicated, 5ng of total striatal lysate from control or Nt5e/ (CD73-deficient) mice have been used to verify CD73 antibody specificity, H3 protein expression has been used as a loading control (k = thousand, M = million; SuperSignal ECL substrate was used to visualize CD73 expression, regular ECL was used for all other proteins) Right, Whole striatal tissue lysates of control and Nt5e/ (CD73-deficient) striatal tissue were loaded at low (5ng) and high (30ng) concentrations and analysed for microglia-specific protein expression (CD39, CD73, P2RY12, and IBA1) by Western Blot analysis as indicated. Whole striatal tissue lysates of control and microglia deficient mice have been used to verify antibody specificity. H3 protein expression has been used as a loading control. (SuperSignal ECL substrate was used to visualize P2RY12 expression, regular ECL was used for all other proteins). Blots are representative from two independent experiments. For gel source data, see Supplementary Fig. 1. Data shown as mean ± s.e.m.

Source data

Extended Data Fig. 8 Microglia suppress neuronal activation via an ATP/AMP/ADO/A1R- dependent feedback mechanism.

a, Scheme for generation of mice with microglia-specific CD39 depletion by breeding Cd39fl/fl mice to Cd39fl/fl; Cx3cr1CreErt2/+(Jung) mice followed by tamoxifen-mediated Cre induction at 4-6 weeks of age. b, Dot plots show relative expression of Entpd1, Il34, and Csf1 mRNA in the striatum of Cd39fl/fl; Cx3cr1CreErt2/+ mice and littermate controls normalized to Gapdh (n = 5 and 6 mice, Entpd1: P = 0.0012, Il34: P = 0.38, Csf1: P = 0.22, unpaired two-tailed t-test). c, left, Representative images of striatal sections from Cd39fl/fl and Cd39fl/fl; Cx3cr1CreErt2/+ mice stained for IBA1 (microglia, green) and DAPI (nuclei, blue) (scale bar:100μm); right, dot plots show the average number of microglia per mm2 per mouse in the striatum of Cd39fl/fl and Cd39fl/fl; Cx3cr1CreErt2/+ mice (n = 4 mice, P = 0.33, unpaired two-tailed t-test with Welch’s correction for variance). d, Microglia-specific CD39 ablation leads to increased levels of neuronal PKA activity in the striatum as measured by phosphorylation levels of GLUR1 at Ser845 in striatal protein lysate from Cd39fl/fl; Cx3cr1CreErt2/+ and littermate controls, pGLUR1 levels have been normalized to total GLUR1 in each sample, (n = 8 and 6 mice, P = 0.029, two-tailed Mann–Whitney Test). e, f, Increased seizure response in Cd39fl/fl; Cx3cr1CreErt2/+: e, Dot plot shows number of stage IV-V seizures recorded within one hour in response to D1 agonist (SKF81297, 5 mg kg–1) (n = 11 mice each, P = 0.0004; unpaired two-tailed t-test). f, Bar graph showing percentage of mice (left) and dot plot showing number (right) of stage IV-V seizures in response to kainic acid (15 mg kg–1) in Cd39fl/fl; Cx3cr1CreErt2/+ mice as compared to littermate controls (n = 5 and 8 mice; left, P = 0.17, Fisher’s exact test with Yates correction, right, P = 0.032, unpaired two-tailed t-test). g, left, Scheme for the generation of mice with a D1 neuron-specific Adora1 depletion by breeding Adora1fl/fl mice to Drd1aCre/+ mice; right, dot plots show relative expression of Adora1 mRNA in the striatum of Adora1fl/fl; Drd1aCre/+ mice and littermate controls normalized to Gapdh (n = 5 and 4 mice, P = 0.002, unpaired two-tailed t-test). h, Co-administration of A1R agonist (CPA, 0.1 mg kg–1) and D1 agonist (SKF81297, 5 mg kg–1) does not prevent the increased seizure susceptibility in Adora1fl/fl; Drd1aCre/+ mice (n = 12 and 6 mice, P = 0.009, Fisher’s exact test with Yates correction). i, Bar graph shows percentage of microglia deficient mice with seizures in response to D1 agonist alone (SKF81297, 5 mg kg–1, i.p.) or co-administered with an A2AR agonist (CGS21680, 0.1 mg kg–1, i.p.) or an A1R agonist (CPA, 0.1 mg kg–1, i.p.) (n = 9-10 mice, P = 0.005, Chi-squared test with Bonferroni post hoc adjustment). j, A1R agonist administration (CPA, 0.1 mg kg–1) normalizes increased PKA activity in Il34fl/fl; Drd1aCre/+ mice but does not affect PKA activity in control Il34fl/fl mice as measure by phosphorylation levels of GLUR1 at Ser845 in striatal protein lysate, pGLUR1 levels have been normalized to total GLUR1 expression in each sample (Il34fl/fl mice, n = 5 mice, P = 0.62, Il34fl/fl; Drd1aCre/+ mice, n = 5 mice, P = 0.06, unpaired two-tailed t-test). All statistical tests are two-tailed; Data shown as mean ± s.e.m.

Source data

Extended Data Fig. 9 Microglia can suppress glutamate-induced cortical neuron activation in a CD39/ADO/A1R-dependent fashion in vitro.

a-d, Experimental approaches for the assessment of adenosine-mediated regulation of cortical neuron activity in vitro. Embryonic cortical neurons were cultured on Axion microelectrode array (MEA) plates which allow for continuous electrical field recordings. a, A1Rs modulate cortical neuronal activity at baseline and in response to glutamate. On day in vitro (DIV) 14, neuronal cultures were treated with vehicle, glutamate (10μM), A1R agonist (CPA, 100nM), A1R antagonist (DCPCX, 100nM), glutamate and A1R agonist, or glutamate and A1R antagonist. Dot plot shows the percentage change in mean firing rate of neurons 1 h after treatment compared to their baseline before drug treatment. (n = 7 wells, P < 0.0001, One-way ANOVA with Tukey’s post hoc test). b, Adenosine suppresses neuronal activity via A1R activation. On DIV14, cultures were treated with vehicle, adenosine (10μM), A1R antagonist (DCPCX, 100nM), or co-treated with adenosine and A1R antagonist. Dot plot shows percentage change in mean firing rate of neurons 1 h after treatment compared to their baseline before drug treatment. (n = 8 wells, P < 0.0001, One-way ANOVA with Tukey’s post hoc test). c, Microglia suppress neuronal activity in response to glutamate-induced activation in an A1R-dependent manner. Microglia were isolated from neonatal pups, plated onto the neuronal culture on DIV 14, and allowed to settle for 48 h. Mixed cultures were treated with vehicle and/or glutamate (10μM) and/or A1R antagonist (100nM) on DIV 16. Dot plot shows percentage change in mean firing rate of neurons 1 h after treatment compared to their baseline before drug treatment. (left, n = 12 wells, P < 0.0001, right, n = 4, 6, 9, and 7 wells, P = 0.001, One-way ANOVA with Tukey’s post hoc test). d, Microglia suppress neuronal activity in a CD39-dependent manner in response to glutamate-induced activation. Microglia were isolated from neonatal pups, plated onto the neuronal culture on DIV 14, and allowed to settle for 48 h. Mixed cultures were pretreated with CD39 inhibitor (ARL67156, 200μM) or vehicle (30 min) and then treated with glutamate (10μM). Dot plot shows percentage change in mean firing rate of neurons 1 h after treatment compared to the corresponding baseline neuronal activity levels before their baseline before drug treatment. (n = 12, 12, 11, and 11 wells, P = 0.0045, One-way ANOVA with Tukey’s post hoc test). Data shown as mean ± s.e.m. and representative of 2-3 independent experiments.

Source data

Extended Data Fig. 10 Reactive microglia in different neuroinflammatory and neurodegenerative conditions show a reduction in Entpd1 and P2ry12 expression that is associated with an A1R-dependent increase in D1 neuron responses.

a-g, Changes in Entpd1 and P2ry12 gene expression are shown in: a, RNA extracted from whole striatum of 6-month old control mice and Q175 (Huntington’s disease) mice94 (Entpd1: P = 0.0001; P2ry12: P = 0.004; n = 8 mice, fold change and P-value provided in publication). b, RNA from FACS-sorted CD11b+/F4/80+ cortical and hippocampal microglia from 8.5-month old control and 5xfAD mouse model of Alzheimer’s Disease95 (Entpd1: P = 0.009; P2ry12: P = 0.0035; n = 5 mice, fold change and P-value provided in publication). c, RNA from FACS-sorted forebrain microglia from 10-month old control and APP/PS1 Alzheimer’s disease mouse model39 (n = 3 mice, Entpd1: P = 0.038; P2ry12: P = 0.023, unpaired two-tailed t-test). d, RNA from FACS-sorted FCRLS+ phagocytic and non-phagocytic microglia isolated after stereotaxic injection of apoptotic neurons39 (n = 4 mice, Entpd1: P < 0.0001; P2ry12: P < 0.0001, unpaired two-tailed t-test). e, FACS-sorted FCRLS+ microglia in 24-month old control mice or APP/PS1 Alzheimer’s disease mouse model. Plaque associated microglia were identified and sorted based on CLEC7A expression39 (n = 6 mice, Entpd1: P = 0.01; P2ry12: P < 0.0001, One-way ANOVA with Tukey’s post hoc test). f, Massively parallel single-cell RNA-seq (MARS-seq) from isolated homeostatic microglia and disease associated microglia (DAM) in 5xfAD mice96 (Entpd1: P < 0.0001; P2ry12: P < 0.0001; n = 893 single microglia, fold change and P-value provided in publication). g, FACS-sorted CD11b+CD45int single microglia in control and LPS-injected mice (4 mg kg–1)97 (Entpd1: P < 0.0001; P2ry12: P < 0.0001; n = 477 microglia from saline injected mice and 770 microglia from LPS injected mice, fold change and P-value provided in publication). h, i, Bar graphs show increased seizure susceptibility to D1 agonist administration (SKF81297, 5 mg kg–1, i.p.) in LPS-injected (indicated doses, i.p.) (h) and 6-8-month old 5xfAD Alzheimer’s mice (i) that is prevented by co-administration of an A1R agonist (CPA, 0.1 mg kg–1, i.p.) (h, n = 10-22 male mice, P = 0.032, Chi-squared test; i, n = 5-10 mice per genotype, left, P = 0.031, Fisher’s exact test with Yates correction; right, P = 0.49, Fisher’s exact test with Yates correction). j, Scheme illustrating the model of microglia-mediated adenosine-controlled regulation of D1 neuron responses in the healthy striatum (left) and its potential dysfunction upon microglia activation during inflammatory and/or neurodegenerative diseases (right). All statistical tests are two-tailed; Data shown as mean ± s.e.m.

Source data

Supplementary information

Supplementary Information

This file contains Supplementary Figs 1-5 and legends for Supplementary Tables 1-3 and Supplementary Videos 1-2.

Reporting Summary

Supplementary Table 1

Genes enriched in striatal microglia upon neuronal activation (DESeq2, n=3 mice per group; P value < 0.05, fold change> 1.2) over unbound fraction (DESeq2, n=3/TRAP and unbound; P value < 0.05, fold > 2).

Supplementary Table 2

Genes enriched in striatal microglia upon neuronal inhibition (DESeq2, n=2 mice per group; P value < 0.05, fold change> 1.2) over unbound fraction (DESeq2, n=2/TRAP and unbound; P value < 0.05, fold > 2).

Supplementary Table 3

Genes enriched in D1 neurons in Il34fl/flDrd1Cre/+Drd1aTRAP mice over cre-negative littermate controls (DESeq2, n=3 mice per group; p value < 0.05, fold > 1.5) over unbound fraction (DESeq2, n=3 TRAP and 4 unbound; p value < 0.05, fold > 2).

Video 1

Representative field of view for live imaging of calcium transients in striatal neurons for data shown in Figure 3a-f and Extended Data Figure 6a-g.

Video 2

Representative field of view for live imaging of microglia (green) contact with neuronal terminals (red) for data shown in Figure 3g-h and Extended Data Figure 6h-j. Scale bar =20μM.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Badimon, A., Strasburger, H.J., Ayata, P. et al. Negative feedback control of neuronal activity by microglia. Nature 586, 417–423 (2020). https://doi.org/10.1038/s41586-020-2777-8

Download citation

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing