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Nat Genet. 2022 Jan;54(1):4-17. doi: 10.1038/s41588-021-00976-y. Epub 2022 Jan 06.

Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies.

Nature genetics

Katia de Paiva Lopes, Gijsje J L Snijders, Jack Humphrey, Amanda Allan, Marjolein A M Sneeboer, Elisa Navarro, Brian M Schilder, Ricardo A Vialle, Madison Parks, Roy Missall, Welmoed van Zuiden, Frederieke A J Gigase, Raphael Kübler, Amber Berdenis van Berlekom, Emily M Hicks, Chotima Bӧttcher, Josef Priller, René S Kahn, Lot D de Witte, Towfique Raj

Affiliations

  1. Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  2. Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  3. Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  4. Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  5. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  6. Mental Illness Research, Education and Clinical Center, James J Peters VA Medical Center, New York, NY, USA.
  7. Department of Translational Neuroscience, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, the Netherlands.
  8. Department of Neuropsychiatry and Laboratory of Molecular Psychiatry, Charité-Universitätsmedizin Berlin, Berlin, Germany.
  9. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected].
  10. Mental Illness Research, Education and Clinical Center, James J Peters VA Medical Center, New York, NY, USA. [email protected].
  11. Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected].
  12. Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected].
  13. Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected].
  14. Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA. [email protected].

PMID: 34992268 DOI: 10.1038/s41588-021-00976-y

Abstract

Microglia have emerged as important players in brain aging and pathology. To understand how genetic risk for neurological and psychiatric disorders is related to microglial function, large transcriptome studies are essential. Here we describe the transcriptome analysis of 255 primary human microglial samples isolated at autopsy from multiple brain regions of 100 individuals. We performed systematic analyses to investigate various aspects of microglial heterogeneities, including brain region and aging. We mapped expression and splicing quantitative trait loci and showed that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia. Fine-mapping of these loci nominated candidate causal variants that are within microglia-specific enhancers, finding associations with microglial expression of USP6NL for Alzheimer's disease and P2RY12 for Parkinson's disease. We have built the most comprehensive catalog to date of genetic effects on the microglial transcriptome and propose candidate functional variants in neurological and psychiatric disorders.

© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.

References

  1. Priller, J. & Prinz, M. Targeting microglia in brain disorders. Science 365, 32–33 (2019). - PubMed
  2. Ransohoff, R. M. & El Khoury, J. Microglia in health and disease. Cold Spring Harb. Perspect. Biol. 8, a020560 (2015). - PubMed
  3. Prinz, M., Jung, S. & Priller, J. Microglia biology: one century of evolving concepts. Cell 179, 292–311 (2019). - PubMed
  4. Tan, Y.-L., Yuan, Y. & Tian, L. Microglial regional heterogeneity and its role in the brain. Mol. Psychiatry 25, 351–367 (2020). - PubMed
  5. van der Poel, M. et al. Transcriptional profiling of human microglia reveals grey–white matter heterogeneity and multiple sclerosis-associated changes. Nat. Commun. 10, 1139 (2019). - PubMed
  6. Grabert, K. et al. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nat. Neurosci. 19, 504–516 (2016). - PubMed
  7. De Biase, L. M. et al. Local cues establish and maintain region-specific phenotypes of basal ganglia microglia. Neuron 95, 341–356.e6 (2017). - PubMed
  8. Soreq, L. et al. Major shifts in glial regional identity are a transcriptional hallmark of human brain aging. Cell Rep. 18, 557–570 (2017). - PubMed
  9. Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019). - PubMed
  10. Olah, M. et al. A transcriptomic atlas of aged human microglia. Nat. Commun. 9, 539 (2018). - PubMed
  11. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019). - PubMed
  12. 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, 599 (2018). - PubMed
  13. 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). - PubMed
  14. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017). - PubMed
  15. Masuda, T. et al. Author correction: spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 568, E4 (2019). - PubMed
  16. Galatro, T. F. et al. Transcriptomic analysis of purified human cortical microglia reveals age-associated changes. Nat. Neurosci. 20, 1162–1171 (2017). - PubMed
  17. McGeer, P. L. et al. Microglia in degenerative neurological disease. Glia 7, 84–92 (1993). - PubMed
  18. Kreutzberg, G. W. Microglia: a sensor for pathological events in the CNS. Trends Neurosci. 19, 312–318 (1996). - PubMed
  19. Trépanier, M. O., Hopperton, K. E., Mizrahi, R., Mechawar, N. & Bazinet, R. P. Postmortem evidence of cerebral inflammation in schizophrenia: a systematic review. Mol. Psychiatry 21, 1009–1026 (2016). - PubMed
  20. Hopperton, K. E., Mohammad, D., Trépanier, M. O., Giuliano, V. & Bazinet, R. P. Markers of microglia in post-mortem brain samples from patients with Alzheimer’s disease: a systematic review. Mol. Psychiatry 23, 177–198 (2018). - PubMed
  21. Parikshak, N. N. et al. Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism. Nature 540, 423–427 (2016). - PubMed
  22. Raj, T. et al. Polarization of the effects of autoimmune and neurodegenerative risk alleles in leukocytes. Science 344, 519–523 (2014). - PubMed
  23. Kunkle, B. W. et al. Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat. Genet. 51, 414–430 (2019). - PubMed
  24. McCauley, M. E. & Baloh, R. H. Inflammation in ALS/FTD pathogenesis. Acta Neuropathol. 137, 715–730 (2019). - PubMed
  25. Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018). - PubMed
  26. Young, A. M. H. et al. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat. Genet. 53, 861–868 (2021). - PubMed
  27. Masuda, T., Sankowski, R., Staszewski, O. & Prinz, M. Microglia heterogeneity in the single-cell era. Cell Rep. 30, 1271–1281 (2020). - PubMed
  28. Li, Y. I. et al. RNA splicing is a primary link between genetic variation and disease. Science 352, 600–604 (2016). - PubMed
  29. Raj, T. et al. CD33: increased inclusion of exon 2 implicates the Ig V-set domain in Alzheimer’s disease susceptibility. Hum. Mol. Genet. 23, 2729–2736 (2014). - PubMed
  30. Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016). - PubMed
  31. Hoffman, G. E. & Roussos, P. Dream: powerful differential expression analysis for repeated measures designs. Bioinformatics 37, 192–201 (2021). - PubMed
  32. Hartigan, J. A. & Wong, M. A. A K-means clustering algorithm. J. R. Stat. Soc. Ser. C Appl. Stat. 28, 100–108 (1979). - PubMed
  33. Srinivasan, K. et al. Alzheimer’s patient microglia exhibit enhanced aging and unique transcriptional activation. Cell Rep. 31, 107843 (2020). - PubMed
  34. Gosselin, D. et al. An environment-dependent transcriptional network specifies human microglia identity. Science 356, eaal3222 (2017). - PubMed
  35. Stahl, E. A. et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat. Genet. 51, 793–803 (2019). - PubMed
  36. Gusev, A. et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nat. Genet. 50, 538–548 (2018). - PubMed
  37. Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nat. Genet. 50, 1584–1592 (2018). - PubMed
  38. Li, Y. I., Wong, G., Humphrey, J. & Raj, T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat. Commun. 10, 994 (2019). - PubMed
  39. Peters, M. J. et al. The transcriptional landscape of age in human peripheral blood. Nat. Commun. 6, 8570 (2015). - PubMed
  40. Urbut, S. M., Wang, G., Carbonetto, P. & Stephens, M. Flexible statistical methods for estimating and testing effects in genomic studies with multiple conditions. Nat. Genet. 51, 187–195 (2019). - PubMed
  41. Fairfax, B. P. et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343, 1246949 (2014). - PubMed
  42. Navarro, E. et al. Dysregulation of mitochondrial and proteolysosomal genes in Parkinson’s disease myeloid cells. Nat. Aging 1, 850–863 (2021). - PubMed
  43. Ng, B. et al. An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome. Nat. Neurosci. 20, 1418–1426 (2017). - PubMed
  44. Storey, J. D. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann. Stat. 31, 2013–2035 (2003). - PubMed
  45. Han, B. & Eskin, E. Random-effects model aimed at discovering associations in meta-analysis of genome-wide association studies. Am. J. Hum. Genet. 88, 586–598 (2011). - PubMed
  46. Marioni, R. E. et al. GWAS on family history of Alzheimer’s disease. Transl. Psychiatry 8, 99 (2018). - PubMed
  47. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014). - PubMed
  48. Lambert, J. C. et al. Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat. Genet. 45, 1452–1458 (2013). - PubMed
  49. Jansen, I. E. et al. Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nat. Genet. 51, 404–413 (2019). - PubMed
  50. Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019). - PubMed
  51. Ripke, S. et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014). - PubMed
  52. Patsopoulos, N. A. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019). - PubMed
  53. Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013). - PubMed
  54. Desikan, R. S. et al. Polygenic overlap between C-reactive protein, plasma lipids, and Alzheimer disease. Circulation 131, 2061–2069 (2015). - PubMed
  55. Zhernakova, D. V. et al. Identification of context-dependent expression quantitative trait loci in whole blood. Nat. Genet. 49, 139–145 (2017). - PubMed
  56. Nott, A. et al. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association. Science 366, 1134–1139 (2019). - PubMed
  57. Grenn, F. P. et al. The Parkinson’s disease genome-wide association study locus browser. Mov. Disord. 35, 2056–2067 (2020). - PubMed
  58. Hollingworth, P. et al. Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat. Genet. 43, 429–435 (2011). - PubMed
  59. Böttcher, C. et al. Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat. Neurosci. 22, 78–90 (2019). - PubMed
  60. Mittelbronn, M., Dietz, K., Schluesener, H. J. & Meyermann, R. Local distribution of microglia in the normal adult human central nervous system differs by up to one order of magnitude. Acta Neuropathol. 101, 249–255 (2001). - PubMed
  61. Olah, M. et al. Identification of a microglia phenotype supportive of remyelination. Glia 60, 306–321 (2012). - PubMed
  62. Stevens, B. et al. The classical complement cascade mediates CNS synapse elimination. Cell 131, 1164–1178 (2007). - PubMed
  63. Badimon, A. et al. Negative feedback control of neuronal activity by microglia. Nature 586, 417–423 (2020). - PubMed
  64. Savage, J. C. et al. Nuclear receptors license phagocytosis by TREM2 - PubMed
  65. Courtney, R. & Landreth, G. E. LXR regulation of brain cholesterol: from development to disease. Trends Endocrinol. Metab. 27, 404–414 (2016). - PubMed
  66. Kao, Y.-C., Ho, P.-C., Tu, Y.-K., Jou, I.-M. & Tsai, K.-J. Lipids and Alzheimer’s disease. Int. J. Mol. Sci. 21, 1505 (2020). - PubMed
  67. Proitsi, P. et al. Alzheimer’s disease susceptibility variants in the MS4A6A gene are associated with altered levels of MS4A6A expression in blood. Neurobiol. Aging 35, 279–290 (2014). - PubMed
  68. Huang, K.-L. et al. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease. Nat. Neurosci. 20, 1052–1061 (2017). - PubMed
  69. Deming, Y. et al. The MS4A gene cluster is a key modulator of soluble TREM2 and Alzheimer’s disease risk. Sci. Transl. Med. 11, eaau2291 (2019). - PubMed
  70. Novikova, G. et al. Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat. Commun. 12, 1610 (2021). - PubMed
  71. Mildner, A., Huang, H., Radke, J., Stenzel, W. & Priller, J. P2Y - PubMed
  72. Tóth, A., Antal, Z., Bereczki, D. & Sperlágh, B. Purinergic signalling in Parkinson’s disease: a multi-target system to combat neurodegeneration. Neurochem. Res. 44, 2413–2422 (2019). - PubMed
  73. van Wageningen, T. A. et al. Regulation of microglial TMEM119 and P2RY12 immunoreactivity in multiple sclerosis white and grey matter lesions is dependent on their inflammatory environment. Acta Neuropathol. Commun. 7, 206 (2019). - PubMed
  74. Haynes, S. E. et al. The P2Y - PubMed
  75. Marsh, S. E. et al. Single cell sequencing reveals glial specific responses to tissue processing & enzymatic dissociation in mice and humans. Preprint at bioRxiv https://doi.org/10.1101/2020.12.03.408542 (2020). - PubMed
  76. Mattei, D. et al. Enzymatic dissociation induces transcriptional and proteotype bias in brain cell populations. Int. J. Mol. Sci. 21, 7944 (2020). - PubMed
  77. Lee, M. N. et al. Common genetic variants modulate pathogen-sensing responses in human dendritic cells. Science 343, 1246980 (2014). - PubMed
  78. Ramdhani, S. et al. Tensor decomposition of stimulated monocyte and macrophage gene expression profiles identifies neurodegenerative disease-specific trans-eQTLs. PLoS Genet. 16, e1008549 (2020). - PubMed
  79. de Lange, G. M., Rademaker, M., Boks, M. P. & Palmen, S. J. M. C. Brain donation in psychiatry: results of a Dutch prospective donor program among psychiatric cohort participants. BMC Psychiatry 17, 347 (2017). - PubMed
  80. Melief, J. et al. Characterizing primary human microglia: a comparative study with myeloid subsets and culture models. Glia 64, 1857–1868 (2016). - PubMed
  81. Sneeboer, M. A. M. et al. Microglia in post-mortem brain tissue of patients with bipolar disorder are not immune activated. Transl. Psychiatry 9, 153 (2019). - PubMed
  82. Shah, H. PgmNr 1856: RAPiD—An Agile and Dependable RNA-Seq Framework (American Society of Human Genetics, 2015). - PubMed
  83. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). - PubMed
  84. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011). - PubMed
  85. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015). - PubMed
  86. Auton, A. A global reference for human genetic variation. Nature 526, 68–74 (2015). - PubMed
  87. Boettger, L. M., Handsaker, R. E., Zody, M. C. & McCarroll, S. A. Structural haplotypes and recent evolution of the human 17q21.31 region. Nat. Genet. 44, 881–885 (2012). - PubMed
  88. Allcock, R. J. N. et al. The MHC haplotype project: a resource for HLA-linked association studies. Tissue Antigens 59, 520–521 (2002). - PubMed
  89. Li, H. Tabix: fast retrieval of sequence features from generic TAB-delimited files. Bioinformatics 27, 718–719 (2011). - PubMed
  90. Schilder, B. M., Humphrey, J. & Raj, T. echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline. Bioinformatics https://doi.org/10.1093/bioinformatics/btab658 (2021). - PubMed
  91. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). - PubMed
  92. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014). - PubMed
  93. 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
  94. Patir, A., Shih, B., McColl, B. W. & Freeman, T. C. A core transcriptional signature of human microglia: derivation and utility in describing region-dependent alterations associated with Alzheimer’s disease. Glia 67, 1240–1253 (2019). - PubMed
  95. Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010). - PubMed
  96. Fort, A. et al. MBV: a method to solve sample mislabeling and detect technical bias in large combined genotype and sequencing assay datasets. Bioinformatics 33, 1895–1897 (2017). - PubMed
  97. Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015). - PubMed
  98. Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016). - PubMed
  99. Loh, P.-R. et al. Reference-based phasing using the Haplotype Reference Consortium panel. Nat. Genet. 48, 1443–1448 (2016). - PubMed
  100. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012). - PubMed
  101. Aguet, F. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020). - PubMed
  102. Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012). - PubMed
  103. Taylor-Weiner, A. et al. Scaling computational genomics to millions of individuals with GPUs. Genome Biol. 20, 228 (2019). - PubMed
  104. Cotto, K. C. et al. RegTools: integrated analysis of genomic and transcriptomic data for the discovery of splicing variants in cancer. Preprint at bioRxiv https://doi.org/10.1101/436634 (2021). - PubMed
  105. Li, Y. I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018). - PubMed
  106. Stephens, M. False discovery rates: a new deal. Biostatistics 18, 275–294 (2017). - PubMed

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