Comprehensive gene expression meta-analysis identifies signature genes that distinguish microglia from peripheral monocytes/macrophages in health and glioma

Verena Haage†,
Marcus Semtner†,
Ramon Oliveira Vidal†,
Daniel Perez Hernandez,
Winnie W. Pong,
Zhihong Chen,
Dolores Hambardzumyan,
Vincent Magrini,
Amy Ly,
Jason Walker,
Elaine Mardis,
Philipp Mertins,
Sascha Sauer†,
Helmut Kettenmann† and
David H. Gutmann†Email author

†Contributed equally

Acta Neuropathologica Communications20197:20

https://doi.org/10.1186/s40478-019-0665-y

Received: 17 December 2018
Accepted: 22 January 2019
Published: 14 February 2019

Abstract

Monocytes/macrophages have begun to emerge as key cellular modulators of brain homeostasis and central nervous system (CNS) disease. In the healthy brain, resident microglia are the predominant macrophage cell population; however, under conditions of blood-brain barrier leakage, peripheral monocytes/macrophages can infiltrate the brain and participate in CNS disease pathogenesis. Distinguishing these two populations is often challenging, owing to a paucity of universally accepted and reliable markers. To identify discriminatory marker sets for microglia and peripheral monocytes/macrophages, we employed a large meta-analytic approach using five published murine transcriptional datasets. Following hierarchical clustering, we filtered the top differentially expressed genes (DEGs) through a brain cell type-specific sequencing database, which led to the identification of eight microglia and eight peripheral monocyte/macrophage markers. We then validated their differential expression, leveraging a published single cell RNA sequencing dataset and quantitative RT-PCR using freshly isolated microglia and peripheral monocytes/macrophages from two different mouse strains. We further verified the translation of these DEGs at the protein level. As top microglia DEGs, we identified P2ry12, Tmem119, Slc2a5 and Fcrls, whereas Emilin2, Gda, Hp and Sell emerged as the best DEGs for identifying peripheral monocytes/macrophages. Lastly, we evaluated their utility in discriminating monocyte/macrophage populations in the setting of brain pathology (glioma), and found that these DEG sets distinguished glioma-associated microglia from macrophages in both RCAS and GL261 mouse models of glioblastoma. Taken together, this unbiased bioinformatic approach facilitated the discovery of a robust set of microglia and peripheral monocyte/macrophage expression markers to discriminate these monocyte populations in both health and disease.