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利用脂质信号通路靶向特异性促血管生成中性粒细胞亚群进行再生免疫治疗。

Harnessing lipid signaling pathways to target specialized pro-angiogenic neutrophil subsets for regenerative immunotherapy.

机构信息

Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.

Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.

出版信息

Sci Adv. 2020 Oct 30;6(44). doi: 10.1126/sciadv.aba7702. Print 2020 Oct.

DOI:10.1126/sciadv.aba7702
PMID:33127670
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7608810/
Abstract

To gain insights into neutrophil heterogeneity dynamics in the context of sterile inflammation and wound healing, we performed a pseudotime analysis of single-cell flow cytometry data using the spanning-tree progression analysis of density-normalized events algorithm. This enables us to view neutrophil transitional subsets along a pseudotime trajectory and identify distinct VEGFR1, VEGFR2, and CXCR4 high-expressing pro-angiogenic neutrophils. While the proresolving lipid mediator aspirin-triggered resolvin D1 (AT-RvD1) has a known ability to limit neutrophil infiltration, our analysis uncovers a mode of action in which AT-RvD1 leads to inflammation resolution through the selective reprogramming toward a therapeutic neutrophil subset. This accumulation leads to enhanced vascular remodeling in the skinfold window chamber and a proregenerative shift in macrophage and dendritic cell phenotype, resulting in improved wound closure after skin transplantation. As the targeting of functional immune subsets becomes the key to regenerative immunotherapies, single-cell pseudotime analysis tools will be vital in this field.

摘要

为了深入了解在无菌炎症和伤口愈合背景下中性粒细胞异质性的动态变化,我们使用密度归一化事件算法的树突状进展分析对单细胞流式细胞术数据进行了伪时间分析。这使我们能够沿着伪时间轨迹观察中性粒细胞过渡亚群,并鉴定出独特的 VEGFR1、VEGFR2 和 CXCR4 高表达的促血管生成中性粒细胞。虽然已知前体解决脂质介质阿司匹林触发的 resolvin D1(AT-RvD1)能够限制中性粒细胞浸润,但我们的分析揭示了一种作用模式,其中 AT-RvD1 通过选择性重编程为治疗性中性粒细胞亚群来导致炎症消退。这种积累导致皮肤窗室中的血管重塑增强,以及巨噬细胞和树突状细胞表型的再生性转变,从而在皮肤移植后改善伤口闭合。由于靶向功能性免疫亚群成为再生免疫疗法的关键,单细胞伪时间分析工具在该领域将至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d5/7608810/d6be30c80285/aba7702-F6.jpg
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