Meta-Analysis of -Differentiated Macrophages Identifies Transcriptomic Signatures That Classify Disease Macrophages .

Macrophages are heterogeneous leukocytes regulated in a tissue- and disease-specific context. While 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 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-LPS), LPS for 24 h (M-LPS), 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 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 macrophage activation states, built a classification model, and demonstrated the utility of the latter for macrophages.