Integrated Transcriptomics Establish Macrophage Polarization Signatures and have Potential Applications for Clinical Health and Disease.
Integrated Transcriptomics Establish Macrophage Polarization Signatures and have Potential Applications for Clinical Health and Disease.
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DOI：
10.1038/srep13351
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发表时间：
2015-08-25
影响因子：
4.6
通讯作者：
Klamt F
中科院分区：
文献类型：
--
作者：
Becker M;De Bastiani MA;Parisi MM;Guma FT;Markoski MM;Castro MA;Kaplan MH;Barbé-Tuana FM;Klamt F
越来越多的证据将巨噬细胞 (Mφ) 定义为具有广泛的激活状态和不同标记物表达的塑料细胞，这些标记物依赖于时间和位置。与最初提出的简单的 M1/M2 二分法不同，巨噬细胞表型的广泛多样性已被广泛证明是单核细胞-巨噬细胞分化的特征，突出了通过有限数量的基因定义复杂谱的难度。由于巨噬细胞激活的描述目前存在争议且令人困惑，因此生成一个简单可靠的框架来对复杂临床条件下的主要 Mφ 表型进行分类，对于阐明这些细胞在病理生理学场景中所发挥的不同作用极为相关。在当前的研究中，我们使用生物信息学工具整合转录组数据来生成两个巨噬细胞分子特征。我们在体外实验和临床样本中验证了我们的签名。更重要的是，我们能够将预后和预测值归因于我们的特征的组成部分。我们的研究提供了一个框架来指导在健康和疾病背景下巨噬细胞表型的询问。这里描述的方法可用于提出新的生物标志物，用于不同临床环境的诊断，包括登革热感染、哮喘和败血症解决。
Growing evidence defines macrophages (Mφ) as plastic cells with wide-ranging states of activation and expression of different markers that are time and location dependent. Distinct from the simple M1/M2 dichotomy initially proposed, extensive diversity of macrophage phenotypes have been extensively demonstrated as characteristic features of monocyte-macrophage differentiation, highlighting the difficulty of defining complex profiles by a limited number of genes. Since the description of macrophage activation is currently contentious and confusing, the generation of a simple and reliable framework to categorize major Mφ phenotypes in the context of complex clinical conditions would be extremely relevant to unravel different roles played by these cells in pathophysiological scenarios. In the current study, we integrated transcriptome data using bioinformatics tools to generate two macrophage molecular signatures. We validated our signatures in in vitro experiments and in clinical samples. More importantly, we were able to attribute prognostic and predictive values to components of our signatures. Our study provides a framework to guide the interrogation of macrophage phenotypes in the context of health and disease. The approach described here could be used to propose new biomarkers for diagnosis in diverse clinical settings including dengue infections, asthma and sepsis resolution.