Table_4_Meta-Analysis of in vitro-Differentiated Macrophages Identifies Transcriptomic Signatures That Classify Disease Macrophages in vivo.xlsx

Macrophages are heterogeneous leukocytes regulated in a tissue- and disease-specific context. While in vitro macrophage models have been used to study diseases empirically, a systematic analysis of the transcriptome thereof is lacking. Here, we acquired gene expression data from eight commonly-used in vitro macrophage models to perform a meta-analysis. Specifically, we obtained gene expression data from unstimulated macrophages (M0) and macrophages stimulated with lipopolysaccharides (LPS) for 2–4 h (M-LPSearly), LPS for 24 h (M-LPSlate), LPS and interferon-γ (M-LPS+IFNγ), IFNγ (M-IFNγ), interleukin-4 (M-IL4), interleukin-10 (M-IL10), and dexamethasone (M-dex). Our meta-analysis identified consistently differentially expressed genes that have been implicated in inflammatory and metabolic processes. In addition, we built macIDR, a robust classifier capable of distinguishing macrophage activation states with high accuracy (>0.95). We classified in vivo macrophages with macIDR to define their tissue- and disease-specific characteristics. We demonstrate that alveolar macrophages display high resemblance to IL10 activation, but show a drop in IFNγ signature in chronic obstructive pulmonary disease patients. Adipose tissue-derived macrophages were classified as unstimulated macrophages, but acquired LPS-activation features in diabetic-obese patients. Rheumatoid arthritis synovial macrophages exhibit characteristics of IL10- or IFNγ-stimulation. Altogether, we defined consensus transcriptional profiles for the eight in vitro macrophage activation states, built a classification model, and demonstrated the utility of the latter for in vivo macrophages.

巨噬细胞（Macrophages）是一类异质性白细胞，其功能调控具有组织与疾病特异性。尽管既往常通过体外巨噬细胞模型开展疾病实证研究，但针对这些模型的转录组（transcriptome）系统分析仍存在空白。本研究获取了8种常用体外巨噬细胞模型的基因表达谱数据，以此开展荟萃分析（meta-analysis）：具体包括未受刺激的巨噬细胞（M0），经脂多糖（lipopolysaccharides, LPS）刺激2~4小时的巨噬细胞（M-LPSearly）、经LPS刺激24小时的巨噬细胞（M-LPSlate）、经LPS与干扰素γ（interferon-γ, IFNγ）共同刺激的巨噬细胞（M-LPS+IFNγ）、经IFNγ刺激的巨噬细胞（M-IFNγ）、经白细胞介素4（interleukin-4, IL4）刺激的巨噬细胞（M-IL4）、经白细胞介素10（interleukin-10, IL10）刺激的巨噬细胞（M-IL10），以及经地塞米松（dexamethasone）刺激的巨噬细胞（M-dex）。本次荟萃分析鉴定出了一批与炎症及代谢过程密切相关的稳定差异表达基因（differentially expressed genes）。此外，我们构建了macIDR——一款性能稳健的分类器（classifier），可实现高精度（>0.95）的巨噬细胞激活状态识别。我们利用macIDR对体内巨噬细胞进行分类，以明确其组织与疾病特异性特征。研究表明，肺泡巨噬细胞（alveolar macrophages）与IL10激活表型高度相似，但在慢性阻塞性肺疾病（chronic obstructive pulmonary disease）患者体内，其IFNγ特征信号出现下调。脂肪组织来源巨噬细胞（adipose tissue-derived macrophages）原本被归类为未激活状态，但在糖尿病肥胖患者体内则获得了LPS激活特征。类风湿关节炎滑膜巨噬细胞（rheumatoid arthritis synovial macrophages）则表现出IL10或IFNγ刺激的特征表型。综上，本研究明确了8种体外巨噬细胞激活状态的共识转录谱，构建了分类模型，并证实了该模型在体内巨噬细胞分析中的应用价值。