Abstract
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-gamma (M-LPS+IFN gamma), IFN gamma (M-IFN gamma), 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 IFN gamma 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 IFN gamma-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.
Dokumententyp: | Zeitschriftenartikel |
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Publikationsform: | Publisher's Version |
Keywords: | Meta-analysis; elastic net classification; macrophages; alveolar macrophages (AMs); synovial macrophages (SMs); adipose tissue macrophages (ATMs); macrophage identifier (macIDR) |
Fakultät: | Medizin |
Themengebiete: | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit |
URN: | urn:nbn:de:bvb:19-epub-78418-8 |
ISSN: | 1664-3224 |
Sprache: | Englisch |
Dokumenten ID: | 78418 |
Datum der Veröffentlichung auf Open Access LMU: | 15. Dez. 2021, 14:43 |
Letzte Änderungen: | 13. Apr. 2022, 17:53 |