Mighty MiGA: Microglial Genomic Atlas Zeros in on Causal AD Risk Variants

Microglia play a pivotal role in neurodegeneration—so much so that changes in their transcriptomes could mean the difference between sliding toward dementia or staying sharp into old age. To learn how genetic variation alters gene expression in microglia, and how those changes influence disease risk, researchers led by Lot de Witte and Towfique Raj of Icahn School of Medicine at Mount Sinai, New York, have assembled the Microglial Genomic Atlas. MiGA is the largest catalogue to date of genetic effects on the microglial transcriptome. Published January 6 in Nature Genetics, MiGA surveys the gene expression landscape of live microglia plucked, at autopsy, from different brain regions of 100 brain donors.

  • The microglial genomic atlas catalogs the transcriptomes of microglia from 255 samples plucked from 100 donors.
  • It correlates genetic variants with changes in expression or splicing of thousands of genes.
  • MiGA pegs USP6NL and P2RY12 as risk genes for AD and PD, respectively.

Beth Stevens and colleagues at Children’s Hospital, Boston, called the work fantastic. “The MiGA paper clearly demonstrates that a high-quality dataset (especially in combination with other new datasets) can enable much stronger interpretation of GWAS loci in microglia, which can more precisely identify the risk gene of interest and the microglial-specific contribution to disease risk,” they wrote (full comment below).

The atlas identifies how microglia change with age and disease, shoring up support for candidate risk genes. In particular, this study pinpoints USP6NL and P2RY12 as gene culprits in Alzheimer’s and Parkinson’s, respectively. In all, the findings build a case that neurodegenerative disease marches to the beat of the microglia drum.

Alzforum covered most of the findings last summer, when they were presented at AAIC and in a bioRxiv preprint (Aug 2021 conference news; de Paiva Lopes et al., 2020). The published work adds further analyses linking genetic variation to changes in splicing in AD risk genes. 

Understanding how risk variants influence the expression of genes in microglia is challenging. For starters, it requires microglial samples from sufficient numbers of people to study the effect of genetic variation, and those samples are in limited supply. Initially, scientists relied on transcriptomic data from other myeloid cell types, or made do with live microglia extracted from very few people (Aug 2019 news; Jun 2017 news; Nov 2019 news). Nowadays, they are ramping up sample numbers. One study profiled transcriptomes of live microglia from 112 tissue samples acquired from 141 donors undergoing neurosurgery (Young et al., 2021). 

Mighty MiGA. Autopsy tissue from four brain regions was used to profile bulk transcriptomes of microglia. With QTL and other analyses, MiGA lays out how genetic variation influences gene expression in microglia, and how that relates to disease risk. [Courtesy of Katia de Paiva Lopes et al., Nature Genetics, 2022.]

MiGA upped the ante even further. Co-first authors Katia de Paiva Lopes, Gijsje Snijders, and Jack Humphrey and colleagues surveyed the transcriptomes of live microglia from 255 tissue samples taken at autopsy from 100 donors, who ranged from having no signs of neurological disease to having AD, PD, and other brain disorders. Live microglia hailed from four regions: the medial frontal gyrus and superior temporal gyrus in the cortex, and the thalamus and subventricular zone. As covered previously, the researchers detected nearly 1,700 genes that changed expression with age, including those involved in lipid metabolism and immune responses. A smaller subset were differentially expressed across brain regions. The study detected no genes with significant expression differences between the sexes.

“Their data strongly supports that lipid homeostasis is among the genetic pathways that were most impacted in microglia by aging,” noted Matheus Victor and Li-Huei Tsai of Massachusetts Institute of Technology.

The scientists analyzed quantitative trait loci (QTL), including those that altered expression (eQTLs) or splicing (sQTLs) of nearby genes, which the authors called eGenes and sGenes, respectively. By integrating their analysis across all four brain regions to boost statistical power, the scientists unearthed a total of 3,611 eGenes and 4,614 sGenes.

Next, the scientists aligned this trove of QTLs with GWAS hits for various diseases, and identified where the two overlapped. They found 15 MiGA QTLs that influenced expression and/or splicing of genes associated with 10 AD risk loci.

By lining up the genetic locations of the QTLs with GWAS polymorphisms, as well as integrating other genomic methods into fold, the investigators were able to strengthen support for causal connections between genetic variants and nearby genes. For example, they pinned USP6NL, rather than ECHDC3, as the likely gene driving AD risk at the ECHDC3 risk locus. USP6NL encodes a GTPase-activating protein involved in the control of endocytosis; it is highly expressed in microglia compared to monocytes. Similarly, they pegged the P2RY12 gene, as opposed to the MED12L gene, as the likely culprit driving PD risk from the MED12L locus. P2YR12 is a P2Y metabotropic G protein-coupled purinergic receptor, which is highly expressed in microglia compared to other brain and myeloid cell types. P2RY12 expression plummets when microglia become activated, and the receptor plays a role in their migration.

In addition, the MiGA revealed an sQTL associated with less retention of exon 2 in the CD33 gene. The sQTL was co-inherited with the lead CD33 AD risk polymorphism, which previously had been tied to the same splicing aberration in monocytes (Apr 2011 news). Finally, a crop of sQTLs turned up in MS4A, a multigene locus tied to AD. In particular, one sQTL associated with increased usage of an intron in the MS4A6A gene that harbored a premature polyadenylation site, thus rendering a stunted transcript. The findings implicate mechanisms underlying genetic risk for AD at the MS4A locus.—Jessica Shugart

Comments

  1. Microglia are central players in multiple brain diseases. While mouse datasets have been instrumental in deepening our understanding of the role of microglia in disease onset and progression, human transcriptomic datasets are needed to fully understand how microglia change across sex, age, brain regions, and in disease. In this fantastic study, de Paiva Lopes, Snijders, Humphrey et al. provide an important resource by generating a deep transcriptomic dataset of human microglia that includes both sexes, a wide age span, and four different brain regions dubbed the Microglial Genomic Atlas (MiGA).

    While human microglia transcriptomic datasets have become more prevalent in recent years, the number of patients profiled is often low. Furthermore, as we and others have shown, microglia are extremely sensitive to changes in their environment and experimental handling, which can lead to difficulties in comparing across datasets (Bohlen et al., 2017; Gosselin et al., 2017; Marsh et al., 2020). As this study demonstrates, there is a great need for large datasets, such as MiGA, to help overcome the inherent variability in human gene-expression profiles simply between individuals, but also as result of highly variable factors such as cause of death.

    The MiGA project uses bulk RNA sequencing to deeply characterize microglial transcriptomes, including splicing and isoform usage that have been challenging to generate in isolated microglia. Many previous studies simply used monocytes or other myeloid cells as proxies for microglia given this challenge. However, the MiGA paper clearly demonstrates that a high-quality dataset (especially in combination with other new datasets, i.e., Nott et al., 2019) can enable much stronger interpretation of GWAS loci in microglia, which can more precisely identify the risk gene of interest and the microglial-specific contribution to disease risk (Huang et al., 2017; Nott et al., 2019). Overall, this level of information will identify new potential pathways regulating microglia states and microglial contribution to disease risk in many different neurological diseases.

    MiGA highlights the importance of some of the key AD risk genes, but the study does have limitations. The loss of granularity due to the bulk sequencing method is probably underestimating the impact of many biological factors, including the impact of disease. It will be important in future studies to expand upon the work of MiGA using single-cell techniques. As recent studies have established, microglial responses to disease, injury, and other factors induce further changes in gene expression patterns, which may also be subject to technical pre-/postmortem variables (Keren-Shaul et al., 2017; Hammond et al., 2019; Marsh et al., 2020; Kamath et al., 2021). Therefore, it will be critical for similarly large-scale experiments, such as MiGA, to use these techniques to enable deeper characterization of eQTLs and disease risk across different subtypes of microglia across different contexts.

    Also, the use of individuals of European descent only for eQTL studies adds bias, reducing applicability of the findings to genetically diverse populations. Further sequencing and analysis of a more diverse population will be needed to make more generalizable interpretations of the impact of various AD risk genes and microglial expression patterns. These large-scale experiments will enable deeper characterization of eQTLs and disease risk across different states of microglia, a critical endeavor in our quest to understand the impact of microglia in disease contexts and opening new therapeutic avenues for many neurological diseases.

    References:

    Huang KL, Marcora E, Pimenova AA, Di Narzo AF, Kapoor M, Jin SC, Harari O, Bertelsen S, Fairfax BP, Czajkowski J, Chouraki V, Grenier-Boley B, Bellenguez C, Deming Y, McKenzie A, Raj T, Renton AE, Budde J, Smith A, Fitzpatrick A, Bis JC, DeStefano A, Adams HH, Ikram MA, van der Lee S, Del-Aguila JL, Fernandez MV, Ibañez L, International Genomics of Alzheimer's Project, Alzheimer's Disease Neuroimaging Initiative, Sims R, Escott-Price V, Mayeux R, Haines JL, Farrer LA, Pericak-Vance MA, Lambert JC, van Duijn C, Launer L, Seshadri S, Williams J, Amouyel P, Schellenberg GD, Zhang B, Borecki I, Kauwe JS, Cruchaga C, Hao K, Goate AM. A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease. Nat Neurosci. 2017 Aug;20(8):1052-1061. Epub 2017 Jun 19 PubMed.

    Bohlen CJ, Bennett FC, Tucker AF, Collins HY, Mulinyawe SB, Barres BA. Diverse Requirements for Microglial Survival, Specification, and Function Revealed by Defined-Medium Cultures. Neuron. 2017 May 17;94(4):759-773.e8. PubMed.

    Gosselin D, Skola D, Coufal NG, Holtman IR, Schlachetzki JC, Sajti E, Jaeger BN, O'Connor C, Fitzpatrick C, Pasillas MP, Pena M, Adair A, Gonda DD, Levy ML, Ransohoff RM, Gage FH, Glass CK. An environment-dependent transcriptional network specifies human microglia identity. Science. 2017 Jun 23;356(6344) Epub 2017 May 25 PubMed.

    Marsh SE, Kamath T, Walker AJ, Dissing-Olesen L, Hammond TR, Young AM, Abdulraouf A. Single Cell Sequencing Reveals Glial Specific Responses to Tissue Processing & Enzymatic Dissociation in Mice and Humans. BioRxiv, December 3, 2020

    Nott A, Holtman IR, Coufal NG, Schlachetzki JC, Yu M, Hu R, Han CZ, Pena M, Xiao J, Wu Y, Keulen Z, Pasillas MP, O'Connor C, Nickl CK, Schafer ST, Shen Z, Rissman RA, Brewer JB, Gosselin D, Gonda DD, Levy ML, Rosenfeld MG, McVicker G, Gage FH, Ren B, Glass CK. Brain cell type-specific enhancer-promoter interactome maps and disease-risk association. Science. 2019 Nov 29;366(6469):1134-1139. Epub 2019 Nov 14 PubMed.

    Kamath T, Abdulraouf A, Burris SJ, Gazestani V, Nadaf N, Vanderburg C, Macosko EZ. A molecular census of midbrain dopaminergic neurons in Parkinson’s disease. bioRxiv, June 16, 2021

    Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK, David E, Baruch K, Lara-Astaiso D, Toth B, Itzkovitz S, Colonna M, Schwartz M, Amit I. A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease. Cell. 2017 Jun 15;169(7):1276-1290.e17. Epub 2017 Jun 8 PubMed.

    Hammond TR, Dufort C, Dissing-Olesen L, Giera S, Young A, Wysoker A, Walker AJ, Gergits F, Segel M, Nemesh J, Marsh SE, Saunders A, Macosko E, Ginhoux F, Chen J, Franklin RJ, Piao X, McCarroll SA, Stevens B. Single-Cell RNA Sequencing of Microglia throughout the Mouse Lifespan and in the Injured Brain Reveals Complex Cell-State Changes. Immunity. 2019 Jan 15;50(1):253-271.e6. Epub 2018 Nov 21 PubMed.

    View all comments by Beth Stevens View all comments by Samuel E. Marsh View all comments by Martine Therrien

  2. The authors report that microglial transcriptional heterogeneity, a measure of how well one has captured various microglial states, is quite poor across multiple brain regions, and between males and females, with the only biological factor strongly modulating microglial gene expression being age. Their data strongly supports that lipid homeostasis is among the genetic pathways that were most impacted in microglia by aging.

    Understanding how age-related deficits in lipid metabolism shape the microglial inflammatory response will be critical to fully elucidate the pathobiology of Alzheimer’s disease. Genetic studies like this one, in combination with empirical validation in models that can capture the complexity of human genetics, such as cells derived from human induced pluripotent stem cells, will give us a handle on identifying novel therapies to halt disease progression. Moreover, by integrating the heterogeneity that can be achieved through single-cell approaches with large genomics and transcriptomics studies, such as this one, we can build a better roadmap of the pathogenesis, which is urgently needed to develop effective interventions.

    View all comments by Matheus Victor View all comments by Li-Huei Tsai

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References

News Citations

  1. Polygenic Scores Paint Microglia as Culprits in Alzheimer's
  2. AD Genetic Risk Tied to Changes in Microglial Gene Expression
  3. What Makes a Microglia? Tales from the Transcriptome
  4. Cell-Specific Enhancer Atlas Centers AD Risk in Microglia. Again.
  5. Large Genetic Analysis Pays Off With New AD Risk Genes

Paper Citations

  1. de Paiva Lopes K, Snijders GJ, Humphrey J, Allan A, Sneeboer M, Navarro A, Schilder BM, Vialle RA, Parks M, Missall R, van Zuiden W, Gigase F, Kübler F, van Berlekom AB, Böttcher C, Priller J, Kahn RS, de Witte L, Raj T. Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies. BioRxiv, October 28, 2020 bioRxiv.
  2. Young AM, Kumasaka N, Calvert F, Hammond TR, Knights A, Panousis N, Park JS, Schwartzentruber J, Liu J, Kundu K, Segel M, Murphy NA, McMurran CE, Bulstrode H, Correia J, Budohoski KP, Joannides A, Guilfoyle MR, Trivedi R, Kirollos R, Morris R, Garnett MR, Timofeev I, Jalloh I, Holland K, Mannion R, Mair R, Watts C, Price SJ, Kirkpatrick PJ, Santarius T, Mountjoy E, Ghoussaini M, Soranzo N, Bayraktar OA, Stevens B, Hutchinson PJ, Franklin RJ, Gaffney DJ. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat Genet. 2021 Jun;53(6):861-868. Epub 2021 Jun 3 PubMed.

Further Reading

No Available Further Reading

Primary Papers

  1. Lopes KP, Snijders GJ, Humphrey J, Allan A, Sneeboer MA, Navarro E, Schilder BM, Vialle RA, Parks M, Missall R, van Zuiden W, Gigase FA, Kübler R, van Berlekom AB, Hicks EM, Bӧttcher C, Priller J, Kahn RS, de Witte LD, Raj T. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat Genet. 2022 Jan;54(1):4-17. Epub 2022 Jan 6 PubMed.

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