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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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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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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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