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Transcriptomic analysis of purified human cortical microglia reveals age-associated changes

Nature Neuroscience volume 20, pages 1162–1171 (2017)Cite this article

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Abstract

Microglia are essential for CNS homeostasis and innate neuroimmune function, and play important roles in neurodegeneration and brain aging. Here we present gene expression profiles of purified microglia isolated at autopsy from the parietal cortex of 39 human subjects with intact cognition. Overall, genes expressed by human microglia were similar to those in mouse, including established microglial genes CX3CR1, P2RY12 and ITGAM (CD11B). However, a number of immune genes, not identified as part of the mouse microglial signature, were abundantly expressed in human microglia, including TLR, Fcγ and SIGLEC receptors, as well as TAL1 and IFI16, regulators of proliferation and cell cycle. Age-associated changes in human microglia were enriched for genes involved in cell adhesion, axonal guidance, cell surface receptor expression and actin (dis)assembly. Limited overlap was observed in microglial genes regulated during aging between mice and humans, indicating that human and mouse microglia age differently.

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Figure 1: Comparison of human microglial and parietal cortex expression profiles.
Figure 2: Comparison of the human microglial expression profile to human cortical and mouse microglial signatures.
Figure 3: Predicted transcriptional regulators of the human microglial core genes.
Figure 4: Protein validation of microglial gene expression.
Figure 5: Aging affects CNS-associated function and motility of human microglia.
Figure 6: Human microglia and the effect of aging.

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References

  1. Salter, M.W. & Beggs, S. Sublime microglia: expanding roles for the guardians of the CNS. Cell 158, 15–24 (2014).

    CAS  PubMed  Google Scholar 

  2. Kierdorf, K. et al. Microglia emerge from erythromyeloid precursors via Pu.1- and Irf8-dependent pathways. Nat. Neurosci. 16, 273–280 (2013).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  4. 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 

  5. Chiu, I.M. et al. A neurodegeneration-specific gene-expression signature of acutely isolated microglia from an amyotrophic lateral sclerosis mouse model. Cell Rep. 4, 385–401 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Zhang, Y. et al. An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex. J. Neurosci. 34, 11929–11947 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Gosselin, D. et al. Environment drives selection and function of enhancers controlling tissue-specific macrophage identities. Cell 159, 1327–1340 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Lavin, Y. et al. Tissue-resident macrophage enhancer landscapes are shaped by the local microenvironment. Cell 159, 1312–1326 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Buttgereit, A. et al. Sall1 is a transcriptional regulator defining microglia identity and function. Nat. Immunol. 17, 1397–1406 (2016).

    CAS  PubMed  Google Scholar 

  10. Hanisch, U.K. & Kettenmann, H. Microglia: active sensor and versatile effector cells in the normal and pathologic brain. Nat. Neurosci. 10, 1387–1394 (2007).

    CAS  PubMed  Google Scholar 

  11. Perry, V.H. & Holmes, C. Microglial priming in neurodegenerative disease. Nat. Rev. Neurol. 10, 217–224 (2014).

    CAS  PubMed  Google Scholar 

  12. Orre, M. et al. Acute isolation and transcriptome characterization of cortical astrocytes and microglia from young and aged mice. Neurobiol. Aging 35, 1–14 (2014).

    CAS  PubMed  Google Scholar 

  13. Holtman, I.R. et al. Induction of a common microglia gene expression signature by aging and neurodegenerative conditions: a co-expression meta-analysis. Acta Neuropathol. Commun. 3, 31 (2015).

    PubMed  PubMed Central  Google Scholar 

  14. Smith, A.M. & Dragunow, M. The human side of microglia. Trends Neurosci. 37, 125–135 (2014).

    CAS  PubMed  Google Scholar 

  15. Zhang, Y. et al. Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89, 37–53 (2016).

    CAS  PubMed  Google Scholar 

  16. Hawrylycz, M. et al. Canonical genetic signatures of the adult human brain. Nat. Neurosci. 18, 1832–1844 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Bozek, K. et al. Organization and evolution of brain lipidome revealed by large-scale analysis of human, chimpanzee, macaque, and mouse tissues. Neuron 85, 695–702 (2015).

    CAS  PubMed  Google Scholar 

  18. Bayés, A. et al. Comparative study of human and mouse postsynaptic proteomes finds high compositional conservation and abundance differences for key synaptic proteins. PLoS One 7, e46683 (2012).

    PubMed  PubMed Central  Google Scholar 

  19. Burns, T.C., Li, M.D., Mehta, S., Awad, A.J. & Morgan, A.A. Mouse models rarely mimic the transcriptome of human neurodegenerative diseases: A systematic bioinformatics-based critique of preclinical models. Eur. J. Pharmacol. 759, 101–117 (2015).

    CAS  PubMed  Google Scholar 

  20. Durrenberger, P.F. et al. Effects of antemortem and postmortem variables on human brain mRNA quality: a BrainNet Europe study. J. Neuropathol. Exp. Neurol. 69, 70–81 (2010).

    PubMed  Google Scholar 

  21. Kondo, T., Kawai, T. & Akira, S. Dissecting negative regulation of Toll-like receptor signaling. Trends Immunol. 33, 449–458 (2012).

    CAS  PubMed  Google Scholar 

  22. Mitchell, R.A. et al. Macrophage migration inhibitory factor (MIF) sustains macrophage proinflammatory function by inhibiting p53: regulatory role in the innate immune response. Proc. Natl. Acad. Sci. USA 99, 345–350 (2002).

    CAS  PubMed  Google Scholar 

  23. Grabert, K. et al. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nat. Neurosci. 19, 504–516 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Matcovitch-Natan, O. et al. Microglia development follows a stepwise program to regulate brain homeostasis. Science 353, aad8670 (2016).

    PubMed  Google Scholar 

  25. Leitner, J., Herndler-Brandstetter, D., Zlabinger, G.J., Grubeck-Loebenstein, B. & Steinberger, P. CD58/CD2 is the primary costimulatory pathway in human CD28−CD8+ T cells. J. Immunol. 195, 477–487 (2015).

    CAS  PubMed  Google Scholar 

  26. Chen, H. et al. ERAP1-ERAP2 dimers trim MHC I-bound precursor peptides; implications for understanding peptide editing. Sci. Rep. 6, 28902 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Krensky, A.M. & Clayberger, C. Granulysin: a novel host defense molecule. Am. J. Transplant. 5, 1789–1792 (2005).

    CAS  PubMed  Google Scholar 

  28. Realegeno, S. et al. S100A12 is part of the antimicrobial network against Mycobacterium leprae in human macrophages. PLoS Pathog. 12, e1005705 (2016).

    PubMed  PubMed Central  Google Scholar 

  29. Mizutani, M. et al. The fractalkine receptor but not CCR2 is present on microglia from embryonic development throughout adulthood. J. Immunol. 188, 29–36 (2012).

    CAS  PubMed  Google Scholar 

  30. Saeed, S. et al. Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity. Science 345, 1251086 (2014).

    PubMed  PubMed Central  Google Scholar 

  31. Wehrspaun, C.C., Haerty, W. & Ponting, C.P. Microglia recapitulate a hematopoietic master regulator network in the aging human frontal cortex. Neurobiol. Aging 36, 2443.e9–2443.e20 (2015).

    CAS  Google Scholar 

  32. Andrews, L.P., Marciscano, A.E., Drake, C.G. & Vignali, D.A. LAG3 (CD223) as a cancer immunotherapy target. Immunol. Rev. 276, 80–96 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Erny, D. et al. Host microbiota constantly control maturation and function of microglia in the CNS. Nat. Neurosci. 18, 965–977 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Stenglein, M.D., Burns, M.B., Li, M., Lengyel, J. & Harris, R.S. APOBEC3 proteins mediate the clearance of foreign DNA from human cells. Nat. Struct. Mol. Biol. 17, 222–229 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Ryan, E.J. et al. Dendritic cell-associated lectin-1: a novel dendritic cell-associated, C-type lectin-like molecule enhances T cell secretion of IL-4. J. Immunol. 169, 5638–5648 (2002).

    CAS  PubMed  Google Scholar 

  36. Shimokawa, T. & Ra, C. C/EBPα functionally and physically interacts with GABP to activate the human myeloid IgA Fc receptor (FcαR, CD89) gene promoter. Blood 106, 2534–2542 (2005).

    CAS  PubMed  Google Scholar 

  37. Macauley, M.S., Crocker, P.R. & Paulson, J.C. Siglec-mediated regulation of immune cell function in disease. Nat. Rev. Immunol. 14, 653–666 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Griciuc, A. et al. Alzheimer's disease risk gene CD33 inhibits microglial uptake of amyloid beta. Neuron 78, 631–643 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Meissner, T.B. et al. NLR family member NLRC5 is a transcriptional regulator of MHC class I genes. Proc. Natl. Acad. Sci. USA 107, 13794–13799 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Muffat, J. et al. Efficient derivation of microglia-like cells from human pluripotent stem cells. Nat. Med. 22, 1358–1367 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. van Eekelen, J.A. et al. Expression pattern of the stem cell leukaemia gene in the CNS of the embryonic and adult mouse. Neuroscience 122, 421–436 (2003).

    CAS  PubMed  Google Scholar 

  42. Wilson, N.K. et al. Combinatorial transcriptional control in blood stem/progenitor cells: genome-wide analysis of ten major transcriptional regulators. Cell Stem Cell 7, 532–544 (2010).

    CAS  PubMed  Google Scholar 

  43. Huang, G. et al. PU.1 is a major downstream target of AML1 (RUNX1) in adult mouse hematopoiesis. Nat. Genet. 40, 51–60 (2008).

    CAS  PubMed  Google Scholar 

  44. Gürtler, C. & Bowie, A.G. Innate immune detection of microbial nucleic acids. Trends Microbiol. 21, 413–420 (2013).

    PubMed  PubMed Central  Google Scholar 

  45. Luan, Y., Lengyel, P. & Liu, C.-J. p204, a p200 family protein, as a multifunctional regulator of cell proliferation and differentiation. Cytokine Growth Factor Rev. 19, 357–369 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Askew, K. et al. Coupled proliferation and apoptosis maintain the rapid turnover of microglia in the adult brain. Cell Rep. 18, 391–405 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Vincent, C., Siddiqui, T.A. & Schlichter, L.C. Podosomes in migrating microglia: components and matrix degradation. J. Neuroinflammation 9, 190 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Krabbe, G. et al. Functional impairment of microglia coincides with Beta-amyloid deposition in mice with Alzheimer-like pathology. PLoS One 8, e60921 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Chen, Y., Won, S.J., Xu, Y. & Swanson, R.A. Targeting microglial activation in stroke therapy: pharmacological tools and gender effects. Curr. Med. Chem. 21, 2146–2155 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Lenz, K.M. & McCarthy, M.M. A starring role for microglia in brain sex differences. Neuroscientist 21, 306–321 (2015).

    CAS  PubMed  Google Scholar 

  51. Galatro, T.F., Vainchtein, I.D., Brouwer, N., Boddeke, E.W.G.M. & Eggen, B.J.L. Isolation of microglia and immune infiltrates from mouse and primate central nervous system. Methods Mol. Biol. 1559, 333–342 (2017).

    CAS  PubMed  Google Scholar 

  52. Babraham Bioinformatics. FastQC: a quality control tool for high throughput sequence data http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed 5 February 2017).

  53. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  54. Liao, Y., Smyth, G.K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    CAS  PubMed  Google Scholar 

  55. DeLuca, D.S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530–1532 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. George, N.I. & Chang, C.-W. DAFS: a data-adaptive flag method for RNA-sequencing data to differentiate genes with low and high expression. BMC Bioinformatics 15, 92 (2014).

    PubMed  PubMed Central  Google Scholar 

  57. The R Project for Statistical Computing. R. https://www.r-project.org/ (accessed 5 February 2017).

  58. Durinck, S., Spellman, P.T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Ritchie, M.E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  60. Leek, J.T., Johnson, W.E., Parker, H.S., Jaffe, A.E. & Storey, J.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Gautier, L., Cope, L., Bolstad, B.M. & Irizarry, R.A. Affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

    CAS  PubMed  Google Scholar 

  62. Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).

    CAS  PubMed  Google Scholar 

  63. Nicolle, R., Radvanyi, F. & Elati, M. CoRegNet: reconstruction and integrated analysis of co-regulatory networks. Bioinformatics 31, 3066–3068 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank I. Huitinga, the director of the Netherlands Brain Bank, for the Dutch brain samples. We thank E.G. Kallás for the use of the FACS facility at School of Medicine. We thank G. Mesander, H. Moes, R.J. van der Lei and P. Ramos Costa for technical assistance with FACS, M. Meijer for confocal microscopy support, and H. van Weering for artwork. We thank I. Moretti, C. Silva, M. Molina, V. Galdeno and C.E. Brantis for assistance in laboratory work and library preparation. We thank São Paulo Research Foundation (FAPESP), grants 2013/07704-3, 2013/06315-3, 2013/02162-8 and 2014-50137-5, CAPES-NUFFIC (062/15), Conselho Nacional de Pesquisa (CNPq 305730/2015-0) and Fundação Faculdade de Medicina (FFM) for financial support. We thank the Dutch MS Research Foundation and the Gemmy & Mibeth Tichelaar Foundation.

Author information

Author notes

  1. Thais F Galatro and Inge R Holtman: These authors contributed equally to this work.

  2. Suely K N Marie and Bart J L Eggen: These authors jointly directed this work.

Authors and Affiliations

  1. Department of Neuroscience, Section Medical Physiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Thais F Galatro, Inge R Holtman, Ilia D Vainchtein, Nieske Brouwer, Jon D Laman, Erik W G M Boddeke & Bart J L Eggen

  2. Department of Neurology, Laboratory of Molecular and Cellular Biology, School of Medicine, University of São Paulo, São Paulo, Brazil

    Thais F Galatro, Paula R Sola, Sueli M Oba-Shinjo & Suely K N Marie

  3. Department of Internal Medicine, Division of Metabolism, Endocrinology, and Diabetes, University of Michigan, Ann Arbor, Michigan, USA

    Antonio M Lerario

  4. Brazilian Aging Brain Study Group, School of Medicine, University of São Paulo, São Paulo, Brazil

    Mariana M Veras, Renata E P Leite & Carlos A Pasqualucci

  5. Center for Studies of Cellular and Molecular Therapy (NAP-NETCEM-NUCEL), University of São Paulo, São Paulo, Brazil

    Tulio F Pereira, Mari C Sogayar & Suely K N Marie

  6. Department of Biochemistry, Chemistry Institute, University of São Paulo, São Paulo, Brazil

    Tulio F Pereira

  7. Neuroinflammation Disease Biology Unit, Lundbeck Research USA, Paramus, New Jersey, USA

    Thomas Möller & Paul D Wes

  8. Department of Pathology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Wilfred den Dunnen

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Contributions

E.W.G.M.B., S.K.N.M. and B.J.L.E. conceived the study. I.R.H. and A.M.L. established the methods. T.F.G., I.R.H., A.M.L., I.D.V., N.B., P.R.S., M.M.V., T.F.P., R.E.P.L., P.D.W., W.d.D. and S.M.O.-S. performed experiments and/or analyses. T.M., M.C.S., J.D.L., C.A.P., E.W.G.M.B., S.K.N.M. and B.J.L.E. provided supervision. T.F.G., I.R.H., S.K.N.M. and B.J.L.E. wrote the manuscript. All authors contributed to the editing of the paper.

Corresponding author

Correspondence to Bart J L Eggen.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 FACS gating and isolation of human microglia.

The complete FACS sorting strategy used to obtain CD11B+/CD45+ microglia from sample 11963 is depicted. First, cells (53.9%) are gated based on SSC/FSC. In the cells gate, single cells (90.8%) are selected. Viable, single cells (13.8%) were selected using the LIVE/DEAD® Fixable Red Dead Cell stain. When plotted for CD11B and CD45, microglia are sorted as a CD11B+/CD45+ population, 72% of the cell population.

The CD11B/CD45 plots, and the microglia gates used, for samples 11965, 10914, 11837 and 10889 are depicted in the lower row.

Supplementary Figure 2 Comparison of the human microglial expression profile to macrophage and monocyte expression data.

A) Principal component analysis of microglia, monocyte and macrophage gene expression data. All three data sets segregate and microglia are most different. Each dot depicts an individual sample.

B) Microglia-monocyte-macrophage expression data were compared pair-wise. The number of differentially expressed genes (up and down) between these data sets is depicted.

C) GO analysis - categories significantly with genes differentially expressed between microglia, monocytes and macrophages are depicted. GO categories enriched in microglia are depicted in red, in macrophages in green and monocytes in blue.

D) A heatmap depicting genes differentially expressed between microglia, monocytes and macrophages.

Supplementary Figure 3 Protein validation of microglial gene expression (full blot)

Sorted microglia and parietal cortex protein samples were obtained from 1 donor and analyzed on a western blot with the antibodies indicated (1 experiment). IBA1 was only detected in pure microglia, whereas proteins expressed by other CNS cells were only detected in unsorted parietal cortex protein samples. Molecular weight standard sizes are indicated. ACTB: β-actin, VIM: vimentin, TUBB3: β-III-tubulin, CNP: CNPase, GFAP: glial fibrillary acidic protein.

Supplementary Figure 4 Venn diagram of genes differentially expressed in human and mouse cortical microglia during aging.

Venn diagram depicting the respective overlap between genes in human and mouse37 cortical microglia that are either up- or down- in expression levels in association to aging. The number of genes and percent overlap are depicted. The overlap in differentially expressed genes in human and mouse microglia during aging is very low.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4 (PDF 792 kb)

Supplementary Methods Checklist (PDF 432 kb)

Supplementary Table 1

Donor information (XLSX 15 kb)

Supplementary Table 2

Gene expression in human microglia, parietal cortex tissue and temporal lobe epilepsy surgery biopsies (XLSX 19290 kb)

Supplementary Table 3

GOs associated with the human microglia core genes (XLSX 72 kb)

Supplementary Table 4

Gene expression in human microglia vs. human CD45+ cells and mouse microglia (XLSX 368 kb)

Supplementary Table 5

Gene expression in microglia, monocytes and macrophages (XLSX 3553 kb)

Supplementary Table 6

CoRegNet target genes (XLSX 24 kb)

Supplementary Table 7

Gene expression in human microglia versus aging (XLSX 61 kb)

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Galatro, T., Holtman, I., Lerario, A. et al. Transcriptomic analysis of purified human cortical microglia reveals age-associated changes. Nat Neurosci 20, 1162–1171 (2017). https://doi.org/10.1038/nn.4597

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