Single-cell transcriptome analyses reveal microglia types associated with proliferative retinopathy
Zhiping Liu, … , Ruth B. Caldwell, Yuqing Huo
Zhiping Liu, … , Ruth B. Caldwell, Yuqing Huo
Published October 20, 2022
Citation Information: JCI Insight. 2022;7(23):e160940. https://doi.org/10.1172/jci.insight.160940.
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Research Article Angiogenesis Ophthalmology

Single-cell transcriptome analyses reveal microglia types associated with proliferative retinopathy

Abstract

Pathological angiogenesis is a major cause of irreversible blindness in individuals of all age groups with proliferative retinopathy (PR). Mononuclear phagocytes (MPs) within neovascular areas contribute to aberrant retinal angiogenesis. Due to their cellular heterogeneity, defining the roles of MP subsets in PR onset and progression has been challenging. Here, we aimed to investigate the heterogeneity of microglia associated with neovascularization and to characterize the transcriptional profiles and metabolic pathways of proangiogenic microglia in a mouse model of oxygen-induced PR (OIR). Using transcriptional single-cell sorting, we comprehensively mapped all microglia populations in retinas of room air (RA) and OIR mice. We have unveiled several unique types of PR-associated microglia (PRAM) and identified markers, signaling pathways, and regulons associated with these cells. Among these microglia subpopulations, we found a highly proliferative microglia subset with high self-renewal capacity and a hypermetabolic microglia subset that expresses high levels of activating microglia markers, glycolytic enzymes, and proangiogenic Igf1. IHC staining shows that these PRAM were spatially located within or around neovascular tufts. These unique types of microglia have the potential to promote retinal angiogenesis, which may have important implications for future treatment of PR and other pathological ocular angiogenesis–related diseases.

Authors

Zhiping Liu, Huidong Shi, Jiean Xu, Qiuhua Yang, Qian Ma, Xiaoxiao Mao, Zhimin Xu, Yaqi Zhou, Qingen Da, Yongfeng Cai, David J.R. Fulton, Zheng Dong, Akrit Sodhi, Ruth B. Caldwell, Yuqing Huo

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Figure 4

scRNA-Seq reveals a highly proliferative microglia cluster associated with OIR.

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scRNA-Seq reveals a highly proliferative microglia cluster associated wi...
(A) Volcano plot showing differential gene expression between cluster 5 cells to cluster 0, 1, and 2 cells. Each dot represents a gene. Genes with absolute average log2 fold change > 1 and adjusted P < 0.01 are highlighted in red. Representative upregulated and downregulated genes are labeled in each panel. P values were determined using the Wilcoxon rank sum test with Bonferroni correction using all genes in the data set. (B) Dot plot showing pathway enrichment results from 3 independent pathway databases. The lists of upregulated genes in cluster 5 versus clusters 0, 1, and 2 are used as inputs for the analysis. The cutoff thresholds for gene inclusion are: average log2 fold change > 0.585, adjusted P < 0.05, and expression in at least 15% of cluster 5 cells. (C) Cnetplot showing a list of genes in Hallmarks G2M checkpoint signatures upregulated in cluster 5 as compared with resting microglia (clusters 0, 1, and 2). (D) Violin plots of 6 unique marker genes representing cluster 5. (E) tSNE plot of cell cycle scores calculated based on a group of 97 cell cycle–related genes. (F) tSNE plot showing E2f1, Tfdp1, and Ezh2 extended regulon activity scores among the 11 clusters of CD11b+F4/80+ cells. (G) Heatmap showing average expression of several histone modification enzymes among the 11 clusters of CD11b+F4/80+ cells.