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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

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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 8

RNA velocity analysis reveals transcriptional dynamics of microglia during OIR.

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RNA velocity analysis reveals transcriptional dynamics of microglia duri...
(A) The UMAP plot shows 9 distinct clusters among the RA and OIR microglia populations after subsetting and reintegration. Clusters are color coded corresponding to numbers as in Figure 1B. (B) The UMAP plot of RNA velocities shows the dynamics and trajectory of mRNA transcription states among RA and OIR microglia. The direction of cell state transition and RNA velocities are projected onto UMAP embedding as streamlines. (C) The directed partition-based graph abstraction (PAGA) plot shows the direction of velocity-inferred cell state transitions between clusters. Solid black arrows indicate predicted cell transition directions with high confidence. Dotted lines indicate potential connections between clusters that are suggested by transcriptome similarity but do not have sufficient support by RNA velocity to indicate confident transition. (D) Heatmap of 50 latent time-dependent lineage driver genes identified by CellRank shows a trend of gradual gene expression changes along the latent time trajectory. Each column shows gene expression levels at each point in latent time, while each row represents a corresponding gene.

Copyright © 2023 American Society for Clinical Investigation
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