体内
体外
巨噬细胞
M2巨噬细胞
巨噬细胞极化
细胞生物学
造血
脂肪组织巨噬细胞
精氨酸酶
生物
脂多糖
免疫学
分子生物学
化学
干细胞
脂肪组织
生物化学
氨基酸
精氨酸
白色脂肪组织
生物技术
作者
Marco Orecchioni,Yanal Ghosheh,Akula Bala Pramod,Klaus Ley
标识
DOI:10.3389/fimmu.2019.01084
摘要
Macrophages are found in tissues, body cavities, and mucosal surfaces. Most tissue macrophages are seeded in the early embryo before definitive hematopoiesis is established. Others are derived from blood monocytes. The macrophage lineage diversification and plasticity are key aspects of their functionality. Macrophages can also be generated from monocytes in vitro and undergo classical (LPS+IFN-γ) or alternative (IL-4) activation. In vivo, macrophages with different polarization and different activation markers coexist in tissues. Certain mouse strains preferentially promote T-helper-1 (Th1) responses and other Th2 responses. Their macrophages preferentially induce iNOS or arginase and have been called M1 and M2, respectively. In many publications, M1 and classically activated and M2 and alternatively activated are used interchangeably. We tested whether this is justified by comparing the gene lists positively [M1(=LPS+)] or negatively [M2(=LPS-)] correlated with the ratio of IL-12 and arginase 1 in transcriptomes of LPS-treated peritoneal macrophages with in vitro classically (LPS, IFN-γ) versus alternatively activated (IL-4) bone marrow-derived macrophages, both from published datasets. Although there is some overlap between in vivo M1(=LPS+) and in vitro classically activated (LPS+IFNγ) and in vivo M2(=LPS-) and in vitro alternatively activated macrophages, many more genes are regulated in opposite or unrelated ways. Thus, M1(=LPS+) macrophages are not equivalent to classically activated, and M2(=LPS-) macrophages are not equivalent to alternatively activated macrophages. This fundamental discrepancy explains why most surface markers identified on in vitro generated macrophages do not translate to the in vivo situation. Valid in vivo M1/M2 surface markers remain to be discovered.
科研通智能强力驱动
Strongly Powered by AbleSci AI