Microglia, the tissue-resident macrophages of the central nervous system (CNS), play critical roles in immune defense, development and homeostasis. However, isolating microglia from humans in large numbers is challenging. Here, we profiled gene expression variation in primary human microglia isolated from 141 patients undergoing neurosurgery. Using single-cell and bulk RNA sequencing, we identify how age, sex and clinical pathology influence microglia gene expression and which genetic variants have microglia-specific functions using expression quantitative trait loci (eQTL) mapping. We follow up one of our findings using a human induced pluripotent stem cell-based macrophage model to fine-map a candidate causal variant for Alzheimer’s disease at the BIN1 locus. Our study provides a population-scale transcriptional map of a critically important cell for human CNS development and disease.
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Patients were consented to share both expression and raw genotype data under managed access, and all data are available under managed access from the EGA, upon approval by the Wellcome Sanger Institute Data Access Committee. More details on how to access these data can be found at https://ega-archive.org/datasets/EGAD00001005736. Raw data (fastq files and CRAM files) of Smart-seq2 and bulk RNA-seq for the primary microglia samples, as well as the raw genotype data (Illumina Omni 2.5) and imputed genotype data by Beagle software, are available from the European Phenome-Genome Archive (EGA) (accession ID: EGAD00001005736). Summary statistics of eQTLs mapped by linear regression and RASQUAL for primary microglia are also available from EGA (accession ID: EGAD00001005736). The 1000 Genomes Phase III integrated variant set can be obtained from the project website (http://www.internationalgenome.org/data). GTEx v.7 summary statistics and brain DroNc-seq data with cell type annotation data can be obtained from the GTEx project website (https://www.gtexportal.org/home/datasets). PBMC 68,000 single-cell data are available from the project GitHub page (https://github.com/10XGenomics/single-cell-3prime-paper/tree/master/pbmc68k_analysis). The BLUEPRINT monocyte RNA-seq data are available from EGA (accession ID: EGAD00001002674). For details on how to access these data, please visit https://ega-archive.org/datasets/EGAD00001002674.
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R.F. was supported by funding from the UK Multiple Sclerosis Society (MS50), the Adelson Medical Research Foundation and a core support grant from the Wellcome Trust and MRC to the Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute (203151/Z/16/Z). A.Y. is supported by a Wellcome Trust Clinicians PhD Fellowship (RRZD/029). All data for this study were generated under Open targets project OTAR039. N.K. and D.J.G. were funded by the Wellcome Trust grant WT206194. We thank the staff in the Cellular Genetics and Phenotyping and Sequencing core facilities at the Wellcome Sanger Institute.
D.J.G. and E.M. were employees of Genomics PLC at the time the manuscript was submitted.
Peer review information Nature Genetics thanks Julie Williams and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, UMAP of bulk RNA-seq for myeloid cells. The ‘Primary microglia’ cluster contains samples collected in this study (pink dots) and previous studies (purple dots) (information on the source of previous study data can be found in Supplementary Table 7). ‘Cultured primary and IPS-derived cells’, includes IPS-derived macrophages and microglia (blue dots), cultured primary microglia and monocyte derived macrophages (orange dots). ‘Monocytes’ (green dots) denotes primary monocytes obtained from the BLUEPRINT project. b, Feature plots of three microglia marker genes (P2RY12, CX3CR1 and TMEM119) using the same UMAP coordinates as Fig. 1d. c, Age versus percentage of infiltrating cells. Red line shows the logistic regression line, the red transparent band shows the 95% confidence interval estimated using a generalised linear mixed model for the binary outcome (Materials and Methods). d, UMAP plot identical to Fig. 1d. e, UMAP plot from the first 12 principal components computed from the same input data for the linear mixed model without any batch correction. f, UMAP of the same 12 PCs where the batch effect was corrected by using Harmony47. g, UMAP of batch corrected data using the canonical correlation analysis method implemented in Seurat V348 with a default setting. We computed the 12 PCs from the integrated data for UMAP plot. h, UMAP of batch corrected data using MNN correct49. Note that points were coloured according to the cell types (same as Fig. 1d): glutamatergic neurons from the PFC (exPFC); pyramidal neurons from the hip CA region (exCA); GABAergic interneurons (GABA); granule neurons from the hip dentate gyrus region (exDG); astrocytes (ASC); oligodendrocytes (ODC); oligodendrocyte precursor cells (OPC); neuronal stem cells (NSC); endothelial cells (END); dendritic cell (DC); B cell (B); hematopoietic progenitor cell (CD34 + ); NK T cell (NK).
a, Marker gene enrichment analysis with Alzheimer’s disease associated microglia20 and glioma associated microglia21. There are three different comparisons for Alzheimer’s disease associated microglia and 14 different populations for glioma associated microglia. Heatmap shows odds ratios and Benjamini-Hochberg (BH) Q-values of the Fisher exact tests between our marker genes and differentially expressed genes in other studies. b, Differentially expressed genes between microglia from different patient pathologies using single cell RNA-seq data. Heatmap shows averaged, normalised expression level (defined as the posterior mean of pathology random effect term, see Materials and Methods) of differentially expressed genes at local true sign rate (ltsr) greater than 0.9 ((Urbut et al. 2019); see Materials and Methods for details). Heatmap is divided into groups based on all possible pairwise groupings of the four cell populations, ordered by most transcriptionally distinct, such that the most different grouping, trauma versus all non-trauma, appears at the top. c, Differential expression of candidate marker genes for immunohistochemistry in fresh frozen patient tissue samples. d, Immunohistochemistry panel of each pathology to validate expression of a differentially expressed gene at the protein level; hydrocephalus (C3), tumour (CCL4), haemorrhage (CD63) and trauma (BIN-1) compared to control. Iba-1 (red) and protein of interest (green). e, RNAScope image of differentially expressed gene panel for cluster C; HAMP (yellow) and RAC2 (purple) with C1QC (green) used to identify microglia. f, RNAScope image of differentially expressed gene panel for cluster D; KLF (yellow) and CCL20 (purple). Scale bar 10 µM.
a, Variance components analysis of log CPM values for the bulk RNA-seq data (N = 102) with biological and technical factors using the linear mixed model (Online methods). b, Heatmap shows the effect size of age for each gene (each row) estimated by the linear mixed model (Online methods). The genes with LTSR > 0.9 in single-cell data are shown. c, PADI2 normalised expression in bulk RNA-seq data against patients’ age. d, P2RY12 expression in bulk RNA-seq data against patients’ age. e, Heatmap shows the average expression of males and females for each gene (each row) estimated by the linear mixed model (Online methods). The genes with LTSR > 0.9 in single-cell data are shown. f, C1QA normalised expression in bulk RNA-seq data for males (M) and females (F). g, HLA-DQB1 normalised expression in bulk RNA-seq data for males (M) and females (F). h, Heatmap shows the average expression for 5 different brain regions estimated by the linear mixed model (Online methods). The genes differentially expressed between a combination of Occipital and Cerebellum and the 3 other regions (LTSR > 0.9) in single-cell data are shown.
a, eQTL effect size comparison for 502 eQTL genes at FDR 5% (linear regression) whose gene body contains at least one feature SNP with sufficient coverage (greater than 5% of average coverage across coding regions). The x-axis shows the eQTL effect size (beta) estimated from linear regression and the y-axis shows the eQTL effect size (pi value) from RASQUAL using only allele-specific count data. The red line shows the least square line crossing (0, 0.5). Note that, x = 0 is the null hypothesis for linear regression and y = 0.5 is the null hypothesis for RASQUAL. b, Examples of colocalised eQTLs in microglia. Colocalisation with Parkinson’s disease at KLHL7-AS1 eQTL (left column), colocalisation with Fed-up feelings at DAG1 eQTL (middle column) and colocalisation with Crohn’s disease at ERAP2 eQTL (right column). The y-axis of each panel shows log10 association Bayes factor for the eQTL or the GWAS trait. The colour of each point indicates LD index (r2 value) to the lead eQTL variant shown by the purple diamond. c, Heatmap of the posterior probability for colocalisation (PP4) between various GWAS traits and cell types/tissues. Each row corresponds to a specific combination of gene and a GWAS trait. Each column corresponds to eQTLs discovered in different cell types and tissues. The first column of the heatmap corresponds to microglia eQTLs, the second column corresponds to eQTLs in IPS cell derived macrophage (IPSDMac) from this study (Materials and Methods), the third column shows eQTLs in primary monocytes from the BLUEPRINT project (Materials and Methods) and the remaining 48 tissues are eQTLs from GTEx V7 (Materials and Methods). The colour of each grid shows the strength of PP4 (white: PP4 = 0.0 and red: PP4 = 1.0). Gray indicates that the gene was very weakly or not expressed, and therefore no eQTL summary statistics were available.
a, Regional association plots at the CD33 locus. b, Coverage plot shows the normalised expression level around the CD33 gene stratified by genotype at the putative splice variant (rs12459419C > T). The zoom-in panel shows a coverage plot of expression level around the second exon (ENST0000262262.4). The coverage shows the first intron expression is negatively correlated with the second exon expression, suggesting the expression of non-coding isoform (ENST00000601785.5) is increased by the alternative allele (T) of the splicing QTL. c, Colocalisation between an association with risk for Alzheimer’s disease on chromosome 2 and an eQTL for the noncoding RNA gene EPHA1-AS1 in microglia, GTEx tissues and myeloid cell types. The x-axis shows the posterior probability of colocalisation (PP4) and y-axis shows the average expression level (log10 TPM) for each tissue or cell type. d, Colocalisation between AD risk and expression of the protein-coding EPHA1 gene. The x-axis shows the posterior probability of colocalisation (PP4) and y-axis shows the average expression level (log10 TPM) for each tissue or cell type. e, Boxplots show the relationship between expression at the PTK2B gene and genotype at the lead eQTL variant (rs28834970C > T) three myeloid cell types. The y-axis shows normalised expression levels (log TPM value). Each dot on the box shows the expression level of a single sample. f, Coverage plot shows chromatin accessibility in iPS cell derived macrophages stratified by three genotype groups of the lead AD GWAS/BIN1 eQTL variant g, Scatter plot of MEF2C (x-axis) and BIN1 (y-axis) expression in GTEx brain tissues and myeloid cell type.
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Young, A.M.H., Kumasaka, N., Calvert, F. et al. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat Genet 53, 861–868 (2021). https://doi.org/10.1038/s41588-021-00875-2
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