Using large transcriptome studies to characterize the role of microglia in neurological disease

Tanvi Anandampillai

A comprehensive transcriptome assessment revealed that many neurological disease susceptibility loci modulate neurological disease risk by altering gene expression in microglia, key players in brain aging and pathology.

Microglia, the immune cells of the brain, have been implicated in various neurological diseases such as Alzheimer’s Disease (AD) and Parkinson’s Disease (PD)1. These cells are involved in inflammatory responses, neurodevelopment, regulation of brain homeostasis and neurogenesis1. Being immune cells, they are strongly influenced by their environment, leading to a highly heterogenous transcriptome across various brain regions, ages and pathologies2. This heterogeneity complicates the task of characterizing causal variants that modulate disease risk as large sample sizes are required to identify statistically significant variants2. In the most recent issue of Nature Genetics, Lopes et al.2 tackled this very task by creating the Microglia Genomic Atlas (MiGA). As the most public and comprehensive microglial transcriptomic resource to date, it was used to understand the drivers of microglial heterogeneity and identify potential causal variants in these neurological diseases (Figure 1)2. This publicly available resource will help inform future genetic studies for the broader neuroscience community2.

Figure 1: The database MiGA was built using 255 microglial samples isolated from 4 different brain regions of 100 individuals with varying neurological conditions. The RNA was isolated and sequenced. Genome wide genotyping of the DNA was performed. All of this information was stored in MiGA. Figure from2

Lopes and colleagues’ study began with the identification of the biological factors that drive the heterogeneity of the microglial transcriptome. Their analysis concluded that age and brain region were drivers of variance in the microglial transcriptome, with a subset of genes that strongly varied between the different brain regions2. Within this subset, the largest number of differentially expressed genes (DEGs) were between the subventricular zone and the cortical regions, while the smallest number of DEGs were between the two cortical regions (Figure 1)2. This finding emphasizes that differing brain environments leads to differing microglial transcriptomes, and this must be factored in when studying the role of microglia in disease. The authors also observed that the expression of 1693 genes varied, about 1/5th of which were upregulated and the rest downregulated, across the chronological age of the donors2. Similarly, 150 genes had 255 differentially spliced transcripts that varied, with a shift in balance between the long and short isoforms of some genes, across different ages. A majority of the genes that varied with age overlapped with previously associated loci in AD3 and PD4, as determined by genome wide association studies (GWAS). The identification of age related changes in both gene expression and splicing in microglial cells, that overlap with disease-associated loci will help inform future research on these neurodegenerative disorders. In particular, these genes can be looked at as potential drug targets to curb the progression of these age-related disorders. Further, the inclusion of these findings in MiGA, a public resource, speaks to the impact of this work in informing future studies.

The authors2 then chose to examine the genetic drivers of microglial heterogeneity by establishing quantitative trait loci (QTLs). As both gene expression and splicing varied across their samples, Lopes et al.2 established expression QTLs (eQTLs – loci that explain variation in mRNA levels) and splicing QTLS (sQTLs – loci that regulate pre-mRNA splicing) for their microglial samples (Figure 2). The authors found that AD and PD had the highest number of colocalizing GWAS loci in both QTL datasets, relative to other diseases such as schizophrenia and bipolar disorder2. This finding validates the role that microglia are known to play in the progression of these two diseases5,6. The colocalization of microglial QTLs with disease loci can be leveraged by researchers in this field to discern the exact location of a causal variant and help identify potential drug targets.

Lopes et al.2 then described two examples of how their comprehensive eQTL and sQTL database can help hone in on disease risk loci in both AD and PD. Specifically, their database can be of use when a single nucleotide polymorphism (SNP) sits in an intergenic location, and the causal gene is still unknown. For example, the lead SNP of a GWAS study was found to lie between ECHDC3 and USP6NL7. The authors determined that the latter gene harbored an eQTL SNP that increases its expression in microglia2. They then used fine mapping to determine that both the GWAS SNP and the USP6NL eQTL SNP overlapped with a microglial specific enhancer2. However, this microglial enhancer only had long range connections with the promoter of USP6NL, suggesting that between ECHDC3 and USP6NL, the latter is the AD risk gene. This was an interesting and novel finding as, in the past, the analysis of ECHDC3 was prioritized as it was found to be upregulated in post-mortem samples of AD patients2. Similarly, with the use of their eQTL database and fine-mapping, Lopes et al.2 suggested that P2RY12, a gene that sits within the GWAS associated MED12L locus is the exact PD risk gene. This demonstration of zooming in on the disease-associated locus using their eQTLs coupled with the incorporation of the eQTLs and sQTLs into MiGA, speaks to the usefulness of this database. Correctly identifying the disease-risk loci can lead to target identification and can aid therapeutic drug development.

Figure 2: The MiGA database was used to perform the following analyses: Age-related heterogeneity, brain-region related heterogeneity, eQTL and sQTL analysis, colocalization and fine-mapping of eQTLs with disease associated GWAS loci. Figure from2

This paper culminated in the formation of the comprehensive MiGA database, whose translational applications include the discovery of causal variants and the subsequent identification of drug targets for neurological disorders that currently lack promising therapeutic options.


1.        Ransohoff, R. M. & el Khoury, J. Microglia in Health and Disease. Cold Spring Harbor Perspectives in Biology 8, (2016).

2.        Lopes, K. de P. et al. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nature genetics 54, 4–17 (2022).

3.        Raj, T. et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer’s disease susceptibility. Nature genetics 50, 1584–1592 (2018).

4.        Li, Y. I., Wong, G., Humphrey, J. & Raj, T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nature communications 10, (2019).

5.        Kam, T. I., Hinkle, J. T., Dawson, T. M. & Dawson, V. L. Microglia and astrocyte dysfunction in parkinson’s disease. Neurobiology of disease 144, (2020).

6.        Fakhoury, M. Microglia and Astrocytes in Alzheimer’s Disease: Implications for Therapy. Current neuropharmacology 16, (2018).

7.        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. Nature genetics 51, 414–430 (2019).

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