Expression profiling by array Third-party reanalysis
Summary
Macrophage activation is associated with profound transcriptional reprogramming. Although much progress has been made in the understanding of macrophage activation, polarization and function, the transcriptional programs regulating these processes remain poorly characterized. We stimulated human macrophages with diverse activation signals, acquiring a dataset of 299 macrophage transcriptomes. Analysis of this dataset revealed a spectrum of macrophage activation states extending the current M1 versus M2-polarization model. Network analyses identified central transcriptional regulators associated with all macrophage activation complemented by regulators related to stimulus-specific programs. Applying these transcriptional programs to human alveolar macrophages from smokers and patients with chronic obstructive pulmonary disease (COPD) revealed an unexpected loss of inflammatory signatures in COPD patients. Finally, by integrating murine data from the ImmGen project we propose a refined, activation-independent core signature for human and murine macrophages. This resource serves as a framework for future research into regulation of macrophage activation in health and disease.
Overall design
Illumina: To better understand the transcriptional program of human macrophages a set of different stimuli were used to activate and differentiate human macrophages in vitro. These macrophages were then assessed by transcriptomics and analyzed by different approaches using gene co-regulation analysis, SOM-clustering, hierarchical clustering, reverse network engineering and statistical models such as ANOVA.
Affymetrix: To understand the relationship of in vivo macrophages with in vitro stimulations, two alveaolar macrophage datasets GSE13896 (COPD patients, smokers and non-smokers) and GSE2125 (asthmatic patients, smokers and non-smokers) were downloaded and processed together. In total, the combined dataset consists of 12 COPD samples, 15 asthmatic, 49 smoker samples and 39 non-smokers as control. Clustering of the samples (such as correlation network, principal component analysis and hierarchical clustering) and Gene Set Enrichment Analysis was performed on differentially expressed genes from 28 in vitro conditions.
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