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APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer’s disease

Abstract

Beta-amyloid deposition is a defining feature of Alzheimer’s disease (AD). How genetic risk factors, like APOE and TREM2, intersect with cellular responses to beta-amyloid in human tissues is not fully understood. Using single-nucleus RNA sequencing of postmortem human brain with varied APOE and TREM2 genotypes and neuropathology, we identified distinct microglia subpopulations, including a subpopulation of CD163-positive amyloid-responsive microglia (ARM) that are depleted in cases with APOE and TREM2 risk variants. We validated our single-nucleus RNA sequencing findings in an expanded cohort of AD cases, demonstrating that APOE and TREM2 risk variants are associated with a significant reduction in CD163-positive amyloid-responsive microglia. Our results showcase the diverse microglial response in AD and underscore how genetic risk factors influence cellular responses to underlying pathologies.

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All data are available in the main text or the supplementary materials, or are available upon request.

References

  1. Disease GBD, Injury I, Prevalence C (2016) Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet (Lond, Engl) 388(10053):1545–1602

    Article  Google Scholar 

  2. Glenner GG, Wong CW (1984) Alzheimer's disease: initial report of the purification and characterization of a novel cerebrovascular amyloid protein. Biochem Biophys Res Commun 120(3):885–890

    CAS  PubMed  Article  Google Scholar 

  3. Scheff SW, Price DA (1993) Synapse loss in the temporal lobe in Alzheimer's disease. Ann Neurol 33(2):190–199

    CAS  PubMed  Article  Google Scholar 

  4. Terry RD et al (1991) Physical basis of cognitive alterations in Alzheimer's disease: synapse loss is the major correlate of cognitive impairment. Ann Neurol 30(4):572–580

    CAS  PubMed  Article  Google Scholar 

  5. Thal DR et al (2002) Phases of Aβ-deposition in the human brain and its relevance for the development of AD. Neurology 58(12):1791–1800

    Article  PubMed  Google Scholar 

  6. Efthymiou AG, Goate AM (2017) Late onset Alzheimer’s disease genetics implicates microglial pathways in disease risk. Mol Neurodegener 12(1):43

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  7. Guerreiro R et al (2012) TREM2 variants in Alzheimer's disease. N Engl J Med 368(2):117–127

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  8. Jonsson T et al (2012) Variant of TREM2 associated with the risk of Alzheimer's disease. N Engl J Med 368(2):107–116

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  9. Kunkle BW et al (2019) Genetic meta-analysis of diagnosed Alzheimer's disease identifies new risk loci and implicates Abeta, tau, immunity and lipid processing. Nat Genet 51(3):414–430

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  10. Lambert JC et al (2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease. Nat Genet 45(12):1452–1458

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. Neu SC et al (2017) Apolipoprotein E genotype and sex risk factors for Alzheimer disease: a meta-analysis. JAMA Neurol 74(10):1178–1189

    PubMed  PubMed Central  Article  Google Scholar 

  12. Sims R et al (2017) Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease. Nat Genet 49(9):1373–1384

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. Kleinberger G et al (2014) TREM2 mutations implicated in neurodegeneration impair cell surface transport and phagocytosis. Sci Transl Med 6(243):243ra86

    PubMed  Article  CAS  Google Scholar 

  14. Keren-Shaul H et al (2017) A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169(7):1276–1290.e17

    CAS  PubMed  Article  Google Scholar 

  15. Mahley RW (2016) Apolipoprotein E: from cardiovascular disease to neurodegenerative disorders. J Mol Med JMM 94(7):739–746

    CAS  Article  Google Scholar 

  16. Kim J, Basak JM, Holtzman DM (2009) The role of apolipoprotein E in Alzheimer's disease. Neuron 63(3):287–303

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. Grehan S, Tse E, Taylor JM (2001) Two distal downstream enhancers direct expression of the human apolipoprotein E gene to astrocytes in the brain. J Neurosci 21(3):812–822

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Mauch DH et al (2001) CNS synaptogenesis promoted by glia-derived cholesterol. Science 294(5545):1354–1357

    CAS  PubMed  Article  Google Scholar 

  19. Pfrieger FW (2003) Cholesterol homeostasis and function in neurons of the central nervous system. Cell Mol Life Sci 60:1158–1171

    CAS  PubMed  Article  Google Scholar 

  20. Mahley RW, Weisgraber KH, Huang Y (2006) Apolipoprotein E4: a causative factor and therapeutic target in neuropathology, including Alzheimer’s disease. Proc Natl Acad Sci USA 103(5):5644–5651

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  21. Ulrich JD et al (2018) ApoE facilitates the microglial response to amyloid plaque pathology. J Exp Med 215(4):1047–1058

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Sala Frigerio C et al (2019) The major risk factors for Alzheimer’s disease: age, sex, and genes modulate the microglia response to Aβ plaques. Cell Rep 27(4):1293–1306.e6

    CAS  PubMed  Article  Google Scholar 

  23. Zhao Y et al (2018) TREM2 is a receptor for beta-amyloid that mediates microglial function. Neuron 97(5):1023–1031.e7

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. Toledo JB et al (2014) A platform for discovery: the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Alzheimer's dement 10(4):477–484e1

    Article  Google Scholar 

  25. Wolf FA, Angerer P, Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19(1):15

    PubMed  PubMed Central  Article  Google Scholar 

  26. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10008

    Article  Google Scholar 

  27. Jakel S et al (2019) Altered human oligodendrocyte heterogeneity in multiple sclerosis. Nature 566(7745):543–547

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. Mi H et al (2019) Protocol update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). Nat Protoc 14(3):703–721

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Dai H et al (2019) Cell-specific network constructed by single-cell RNA sequencing data. Nucleic Acids Res 47(11):e62

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. Baran Y et al (2019) MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions. Genome Biol 20(1):206

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. Matteson D, James N (2014) A nonparametric approach for multiple change point analysis of multivariate data. J Am Stat Assoc 109(505):334–345

    CAS  Article  Google Scholar 

  32. Liu F et al (2018) Global spectral clustering in dynamic networks. Proc Natl Acad Sci USA 115(5):927–932

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. Trapnell C et al (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32:381

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. Jacomy M et al (2014) ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS ONE 9(6):e98679

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. Buniello A et al (2019) The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47(D1):D1005–D1012

    CAS  PubMed  Article  Google Scholar 

  36. Bankhead P et al (2017) QuPath: open source software for digital pathology image analysis. Sci Rep 7(1):16878

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  37. Jack CR Jr, Bennett DA, Blennow K et al (2018) NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement 14(4):535–562. https://doi.org/10.1016/j.jalz.2018.02.018

    Article  PubMed  PubMed Central  Google Scholar 

  38. Li X, Wang K, Lyu Y et al (2020) Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis. Nat Commun. 11(1):2338. https://doi.org/10.1038/s41467-020-15851-3(Published 2020 May 11)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. Streit WJ et al (2004) Dystrophic microglia in the aging human brain. Glia 45(2):208–212

    PubMed  Article  Google Scholar 

  40. Lopes KO, Sparks DL, Streit WJ (2008) Microglial dystrophy in the aged and Alzheimer's disease brain is associated with ferritin immunoreactivity. Glia 56(10):1048–1060

    PubMed  Article  Google Scholar 

  41. Kristiansen M et al (2001) Identification of the haemoglobin scavenger receptor. Nature 409(6817):198–201

    CAS  PubMed  Article  Google Scholar 

  42. Cao J et al (2019) The single-cell transcriptional landscape of mammalian organogenesis. Nature 566(7745):496–502

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. Qiu X et al (2017) Single-cell mRNA quantification and differential analysis with Census. Nat Methods 14:309

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. Wolf FA et al (2019) PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol 20(1):59

    PubMed  PubMed Central  Article  Google Scholar 

  45. Deming Y et al (2019) The MS4A gene cluster is a key modulator of soluble TREM2 and Alzheimer’s disease risk. Sci Transl Med 11(505):eaau2291

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. Krasemann S et al (2017) The TREM2-APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47(3):566–581.e9

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. Prokop S, Miller KR, Labra SR et al (2019) Impact of TREM2 risk variants on brain region-specific immune activation and plaque microenvironment in Alzheimer's disease patient brain samples. Acta Neuropathol 138(4):613–630

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  48. Gjoneska E et al (2015) Conserved epigenomic signals in mice and humans reveal immune basis of Alzheimer’s disease. Nature 518(7539):365–369

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. Heneka MT, Golenblock DT, Latz E (2010) Innate immunity in Alzheimer’s disease. Nat Immunol 16(3):229–236

    Article  CAS  Google Scholar 

  50. del Rio-Hortega P (1919) El Tercer Elemento de los Centros Nerviosos. IV. Poder Fagocitario y Movilidad de la Microglía. Bol Soc Esp Biol VIII 1919:154–171

    Google Scholar 

  51. Sierra A, Paolicelli RC, Kettenmann H (2019) Cien Anos de microglia: milestones in a century of microglial research. Trends Neurosci 42(11):778–792

    CAS  PubMed  Article  Google Scholar 

  52. Stratoulias V et al (2019) Microglial subtypes: diversity within the microglial community. EMBO J 38:1–18

    Article  CAS  Google Scholar 

  53. Kodama L, Guzman E, Etchegaray JI et al (2020) Microglial microRNAs mediate sex-specific responses to tau pathology. Nat Neurosci 23(2):167–171. https://doi.org/10.1038/s41593-019-0560-7

    CAS  Article  PubMed  Google Scholar 

  54. Mathys H et al (2019) Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570(7761):332–337

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. Zhao N, Ren Y, Yamazaki Y et al (2020) Alzheimer's risk factors age, APOE genotype, and sex drive distinct molecular pathways. Neuron 106(5):727–742.e6. https://doi.org/10.1016/j.neuron.2020.02.034

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  56. Zhou Y et al (2020) Human and mouse single-nucleus transcriptomics reveal TREM2-dependent and TREM2-independent cellular responses in Alzheimer’s disease. Nat Med 26:131–142

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. Gordon S (2001) Homeostasis: a scavenger receptor for haemoglobin. Curr Biol 11(10):R399–R401. https://doi.org/10.1016/s0960-9822(01)00218-4

    CAS  Article  PubMed  Google Scholar 

  58. Sarrias MR, Grønlund J, Padilla O, Madsen J, Holmskov U, Lozano F (2004) The Scavenger Receptor Cysteine-Rich (SRCR) domain: an ancient and highly conserved protein module of the innate immune system. Crit Rev Immunol 24(1):1–37. https://doi.org/10.1615/critrevimmunol.v24.i1.10

    CAS  Article  PubMed  Google Scholar 

  59. Kristiansen M, Graversen JH, Jacobsen C et al (2001) Identification of the haemoglobin scavenger receptor. Nature 409(6817):198–201. https://doi.org/10.1038/35051594

    CAS  Article  PubMed  Google Scholar 

  60. Bover LC, Cardó-Vila M, Kuniyasu A et al (2007) A previously unrecognized protein-protein interaction between TWEAK and CD163: potential biological implications. J Immunol 178(12):8183–8194. https://doi.org/10.4049/jimmunol.178.12.8183

    CAS  Article  PubMed  Google Scholar 

  61. Kneidl J, Löffler B, Erat MC et al (2012) Soluble CD163 promotes recognition, phagocytosis and killing of Staphylococcus aureus via binding of specific fibronectin peptides. Cell Microbiol 14(6):914–936. https://doi.org/10.1111/j.1462-5822.2012.01766.x

    CAS  Article  PubMed  Google Scholar 

  62. Galea J, Cruickshank G, Teeling JL et al (2012) The intrathecal CD163-haptoglobin-hemoglobin scavenging system in subarachnoid hemorrhage. J Neurochem 121(5):785–792. https://doi.org/10.1111/j.1471-4159.2012.07716.x

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  63. Pey P, Pearce RK, Kalaitzakis ME, Griffin WS, Gentleman SM (2014) Phenotypic profile of alternative activation marker CD163 is different in Alzheimer's and Parkinson's disease. Acta Neuropathol Commun. 2:21. https://doi.org/10.1186/2051-5960-2-21(Published 2014 Feb 14)

    Article  PubMed  PubMed Central  Google Scholar 

  64. Roberts ES, Masliah E, Fox HS (2004) CD163 identifies a unique population of ramified microglia in HIV encephalitis (HIVE). J Neuropathol Exp Neurol 63(12):1255–1264. https://doi.org/10.1093/jnen/63.12.1255

    Article  PubMed  Google Scholar 

  65. Yu H, Liu X, Zhong Y (2017) The effect of osteopontin on microglia. Biomed Res Int 2017:1879437. https://doi.org/10.1155/2017/1879437

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  66. Shin YJ, Kim HL, Choi JS, Choi JY, Cha JH, Lee MY (2011) Osteopontin: correlation with phagocytosis by brain macrophages in a rat model of stroke. Glia 59(3):413–423. https://doi.org/10.1002/glia.21110

    Article  PubMed  Google Scholar 

  67. Mori K, Yokoyama A, Yang L et al (2004) L-serine-mediated release of apolipoprotein E and lipids from microglial cells. Exp Neurol 185(2):220–231. https://doi.org/10.1016/j.expneurol.2003.10.010

    CAS  Article  PubMed  Google Scholar 

  68. Olah M, Patrick E, Villani AC et al (2018) A transcriptomic atlas of aged human microglia. Nat Commun. 9(1):539. https://doi.org/10.1038/s41467-018-02926-5(Published 2018 Feb 7)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  69. Polazzi E, Mengoni I, Peña-Altamira E et al (2015) Neuronal regulation of neuroprotective microglial apolipoprotein E secretion in rat in vitro models of brain pathophysiology. J Neuropathol Exp Neurol 74(8):818–834. https://doi.org/10.1097/NEN.0000000000000222

    CAS  Article  PubMed  Google Scholar 

  70. Qin S, Colin C, Hinners I, Gervais A, Cheret C, Mallat M (2006) System Xc- and apolipoprotein E expressed by microglia have opposite effects on the neurotoxicity of amyloid-beta peptide 1–40. J Neurosci 26(12):3345–3356. https://doi.org/10.1523/JNEUROSCI.5186-05.2006

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  71. Rangaraju S, Dammer EB, Raza SA et al (2018) Quantitative proteomics of acutely-isolated mouse microglia identifies novel immune Alzheimer's disease-related proteins. Mol Neurodegener. 13(1):34. https://doi.org/10.1186/s13024-018-0266-4(Published 2018 Jun 28)

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  72. Saura J, Petegnief V, Wu X, Liang Y, Paul SM (2003) Microglial apolipoprotein E and astroglial apolipoprotein J expression in vitro: opposite effects of lipopolysaccharide. J Neurochem 85(6):1455–1467. https://doi.org/10.1046/j.1471-4159.2003.01788.x

    CAS  Article  PubMed  Google Scholar 

  73. Uchihara T, Duyckaerts C, He Y et al (1995) ApoE immunoreactivity and microglial cells in Alzheimer's disease brain. Neurosci Lett 195(1):5–8. https://doi.org/10.1016/0304-3940(95)11763-m

    CAS  Article  PubMed  Google Scholar 

  74. Lue LF, Schmitz CT, Serrano G, Sue LI, Beach TG, Walker DG (2015) TREM2 protein expression changes correlate with Alzheimer's disease neurodegenerative pathologies in post-mortem temporal cortices. Brain Pathol 25(4):469–480. https://doi.org/10.1111/bpa.12190

    CAS  Article  PubMed  Google Scholar 

  75. Condello C, Yuan P, Schain A, Grutzendler J (2015) Microglia constitute a barrier that prevents neurotoxic protofibrillar Aβ42 hotspots around plaques. Nat Commun. 6:6176. https://doi.org/10.1038/ncomms7176(Published 2015 Jan 29)

    CAS  Article  PubMed  Google Scholar 

  76. Mehta D et al (2017) Why do trials for Alzheimer’s disease drugs keep failing? A discontinued drug perspective for 2010–2015. Expert Opin Investig Drugs 26(6):735–739

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  77. Hardy J, Selkoe D (2002) The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 297:353–356

    CAS  Article  PubMed  Google Scholar 

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Funding

National Institutes of Health Grants R01NS095793 (EBL), R56AG063344 (EBL), P30AG010124 (EBL), T32AG000255 (ATN), R01GM108600 (ML), R01GM125301 (ML), R37MH057881 (KR), University of Pennsylvania Institute on Aging Pilot Grant (ML).

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EBL, ATN, and ML designed and conceived of the experiments. ATN and JA performed the experiments. KW, GH, ZM, XW, DC, KR, and ML performed bioinformatics analysis. ES and VMV performed genotype analysis. ATN and EBL wrote the manuscript and all authors edited and approved of the final manuscript.

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Correspondence to Mingyao Li or Edward B. Lee.

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Nguyen, A.T., Wang, K., Hu, G. et al. APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer’s disease. Acta Neuropathol 140, 477–493 (2020). https://doi.org/10.1007/s00401-020-02200-3

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  • DOI: https://doi.org/10.1007/s00401-020-02200-3

Keywords

  • APOE
  • TREM2
  • Microglia
  • Alzheimer’s disease
  • Transcriptomics
  • snRNA-seq