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Microglial brain region−dependent diversity and selective regional sensitivities to aging

Nature Neuroscience volume 19, pages 504–516 (2016)Cite this article

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Abstract

Microglia have critical roles in neural development, homeostasis and neuroinflammation and are increasingly implicated in age-related neurological dysfunction. Neurodegeneration often occurs in disease-specific, spatially restricted patterns, the origins of which are unknown. We performed to our knowledge the first genome-wide analysis of microglia from discrete brain regions across the adult lifespan of the mouse, and found that microglia have distinct region-dependent transcriptional identities and age in a regionally variable manner. In the young adult brain, differences in bioenergetic and immunoregulatory pathways were the major sources of heterogeneity and suggested that cerebellar and hippocampal microglia exist in a more immune-vigilant state. Immune function correlated with regional transcriptional patterns. Augmentation of the distinct cerebellar immunophenotype and a contrasting loss in distinction of the hippocampal phenotype among forebrain regions were key features during aging. Microglial diversity may enable regionally localized homeostatic functions but could also underlie region-specific sensitivities to microglial dysregulation and involvement in age-related neurodegeneration.

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Figure 1: Validation of multiregion microglial purification.
Figure 2: The adult mouse microglial transcriptome is regionally heterogeneous.
Figure 3: Three major patterns of gene coexpression underpin regional microglial transcriptional heterogeneity.
Figure 4: Regional transcriptional heterogeneity in microglial immunophenotype and bioenergetics.
Figure 5: Regional microglial heterogeneity in immunophenotype suggests differences in immune vigilance.
Figure 6: Regional microglial heterogeneity is comparable to intertissue macrophage diversity.
Figure 7: Region-specific microglial aging.
Figure 8: Biological pathways underlying region-specific microglial aging.

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Acknowledgements

We thank members of Edinburgh Genomics, The University of Edinburgh, for performing microarray assays, B. Fleming for technical assistance with microglial purification and Y.-T. Lai for help optimizing regional brain dissection. This work was funded by a PhD scholarship from the Darwin Trust of Edinburgh to K Grabert and grants from the Biotechnology and Biological Sciences Research Council (BBSRC; BB/J004332/1) and the Medical Research Council (MRC; MR/L003384/1). The Roslin Institute and Edinburgh Genomics are partly supported through core grants from the Natural Environment Research Council (R8/H10/56), MRC (MR/K001744/1) and BBSRC (BB/J004243/1, BB/J004332/1).

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Authors and Affiliations

  1. The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, UK

    Kathleen Grabert, Tom Michoel, Michail H Karavolos, Sara Clohisey, J Kenneth Baillie, Mark P Stevens, Tom C Freeman, Kim M Summers & Barry W McColl

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  1. Kathleen Grabert
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Contributions

B.W.M. and K.M.S. conceived the study; B.W.M., K.M.S., K.G. and T.C.F. designed experiments; K.G., B.W.M. performed experiments and analyzed data; T.M. advised on and contributed to data visualization; M.H.K. and M.P.S. advised on and contributed to bacterial assay; S.C. and J.K.B. advised on and performed analysis of transcriptional regulators; B.W.M., K.M.S. and K.G. wrote the paper; all authors contributed to data interpretation and editing of the paper.

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Correspondence to Barry W McColl.

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Integrated supplementary information

Supplementary Figure 1 Validation of regional microglial isolation

(a) Workflow showing key steps in multi-region microglial isolation. (b) Brain cell suspensions were prepared from Csf1r-EGFP reporter mice and the expression of CD11b on GFP+ cells (i.e. microglia) assessed by flow cytometry. Complete overlap between CD11b and GFP is evident. Data are representative of three independent cell preparations (c) CD11b and F4/80 expression were assessed by flow cytometry on the selected and non-selected cell fractions after immunomagnetic separation. Single populations of CD11b+ and F4/80+ cells in the selected fraction and the absence of staining in the non-selected fraction indicates the specificity and efficiency of extraction. Data are representative of four independent samples, each from tissue prepared from eight mice (d) Microarray expression profiles of selected genes in mixed brain cell homogenates prepared from distinct brain regions. Genes chosen have previously established regional patterns of expression in neuronal subpopulations. The expression profiles in brain homogenates showed the expected regional pattern as validated by correlation with spatial expression profiles in the Allen Brain Atlas (http://mouse.brain-map.org/). URLs for images are provided in the Methods section. Image credit: Allen Institute for Brain Science. Data show mean ± SEM, n = 2 independent samples (mice). Str, striatum; Hpp, hippocampus; Ctx, cerebral cortex; Cbm, cerebellum.

Source data

Supplementary Figure 2 Network analysis of regional microglial gene expression

Network analysis of regional microglial gene expression. (a) A transcript-to-transcript correlation network graph of transcripts significantly differentially expressed by brain region was generated in BioLayout Express3D. Nodes represent transcripts (probesets) and edges the degree of correlation between them. The network graph was clustered using a Markov clustering algorithm. Key network graph metrics and description of expression pattern for clusters determined with Pearson correlation threshold of 0.80 or 0.90 are shown. (b) Network diagram showing similar overall topography with Pearson correlation threshold of 0.80 or 0.90.

Supplementary Figure 3 Networks centered on interferon pathways are enriched in cluster 3

Pathway analysis in Ingenuity identified an enriched IRF7-centred interferon network within cluster 3 from the network graph of differentially expressed transcripts generated in BioLayout Express3D. Filled circle/oval: genes represented in the cluster 3 dataset; empty circle/oval: genes not contained in cluster 3 but associated with the network; dashed arrow: indirect connection, solid arrow: direct connection; multiple circles: complex.

Supplementary Figure 4 Enrichment of an antigen presentation pathway in cluster 3

KEGG pathway analysis using DAVID identified antigen processing and presentation as an enriched pathway within cluster 3 from the network graph of differentially expressed transcripts generated in BioLayout Express3D. Stars denote pathway genes present in cluster 3.

Supplementary Figure 5 Regional heterogeneity in expression of microglial cell surface genes

(a) Transcripts differentially expressed according to brain region were analysed for enrichment (p < 0.05 with Benjamini correction) of Gene Ontology (GO) Cellular Component and visualised in GOrilla. (b) Heat map showing the expression pattern of microglial “sensome” genes that were differentially expressed according to brain region in our dataset. Row Z-score intensities represent the mean of four independent samples per region with red indicating high probeset expression and blue low expression. (c) Microarray expression levels of genes encoding cell surface proteins transducing microglial “off” signals. Data show mean ± SD, n = 4 independent samples, each pooled from tissue from eight mice. *p < 0.05, **p < 0.01, ***p < 0.001, one-way ANOVA with Bonferroni correction. Str, striatum; Hpp, hippocampus; Ctx, cerebral cortex; Cbm, cerebellum. Specific p values for all statistical comparisons are presented in Supplementary Table 13.

Source data

Supplementary Figure 6 Regional variations in age-related depression of young adult microglial signature

(a) Fold-changes in expression of selected microglial signature genes between 4 and 22 months in each brain region. Data show mean ± SEM, n = 4 independent samples, each pooled from tissue from eight mice. *p < 0.05, **p < 0.01, ***p < 0.001 vs Cbm, one-way ANOVA with Dunnett post-test. (b-d) Expression profiles of (b) Tgfbr family genes, (c) systemic macrophage signature genes with Itgam as reference, and (d) genes upregulated on activated microglia/macrophages during ageing in each brain region. Data show mean ± SEM, n = 4 independent samples, each pooled from tissue from eight mice. *p < 0.05, **p < 0.01, ***p < 0.001, two-way ANOVA with Bonferroni post-test. Str, striatum; Hpp, hippocampus; Ctx, cerebral cortex; Cbm, cerebellum. Specific p values for all statistical comparisons are presented in Supplementary Table 13.

Source data

Supplementary Figure 7 Regional expression pattern of Mela in mixed brain cell homogenates at 4 months of age

Expression was determined from microarray analysis of mixed brain cell homogenates dissected from discrete brain regions. Data show mean ± SEM, n = 2 independent samples (mice). *p < 0.05 vs all other regions, one-way ANOVA with Bonferroni correction. Str, striatum; Hpp, hippocampus; Ctx, cerebral cortex; Cbm, cerebellum. Specific p values for all statistical comparisons are presented in Supplementary Table 13.

Source data

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 1232 kb)

Supplementary Methods Checklist

(PDF 451 kb)

Supplementary Table 1

Probesets differentially regulated by brain region (XLSX 195 kb)

Supplementary Table 2

Over-represented GO biological processes at 4 months (XLSX 14 kb)

Supplementary Table 3

Annotation of Enrichment Map nodes at 4 months (XLSX 13 kb)

Supplementary Table 4

Transcript composition of clusters of gene co-expression at 4 months (XLSX 78 kb)

Supplementary Table 5

Over-represented GO biological processes in immune gene co-expression cluster at 4 months (XLSX 14 kb)

Supplementary Table 6

Ingenuity analysis of upstream regulators in immune gene co-expression cluster at 4 months (XLSX 11 kb)

Supplementary Table 7

Over-represented KEGG pathways in immune gene co-expression cluster at 4 months (XLSX 12 kb)

Supplementary Table 8

Over-represented GO biological processes in bioenergetics gene co-expression cluster 3 at 4 months (XLSX 14 kb)

Supplementary Table 9

Transcriptional regulators of region-dependent co-expression networks at 4 months (XLSX 11 kb)

Supplementary Table 10

Over-represented GO biological processes in regionally differentially regulated sensome genes at 4 months (XLSX 13 kb)

Supplementary Table 11

Over-represented GO biological processes in gene co-expression cluster 2 from ageing datasets (XLSX 11 kb)

Supplementary Table 12

Over-represented GO biological processes in age down-regulated genes from hippocampal microglia (XLSX 10 kb)

Supplementary Table 13

Tables of specific p values for all statistical comparisons (XLSX 54 kb)

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Grabert, K., Michoel, T., Karavolos, M. et al. Microglial brain region−dependent diversity and selective regional sensitivities to aging. Nat Neurosci 19, 504–516 (2016). https://doi.org/10.1038/nn.4222

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