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-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.
Highlights
The spatiotemporal regulation of tissue homeostasis relies on the complex network of diverse and heterogeneous immune cell populations
We identified genes that were consistently differentially expressed across studies by means of a random effects meta-analysis (Supplementary Table 1)
Interleukin-1 beta (IL1B), C-C Motif Chemokine Ligand 17 (CCL17) and Cluster of Differentiation 163 (CD163) were consistently upregulated in M-LPSlate, M-IL4, and M-dex compared with M0, respectively [5]
Summary
The spatiotemporal regulation of tissue homeostasis relies on the complex network of diverse and heterogeneous immune cell populations. As a highly plastic and multifunctional immune cell, macrophages play a decisive role in the balance between pro-inflammatory defense and anti-inflammatory tissue repair [1]. To mimic in vivo macrophages encountering various triggers, MDMs are stimulated in vitro with lipopolysaccharides (LPS) and/or interferon-γ (IFNγ) to generate pro-inflammatory macrophages (M1), or activated with interleukin-4 (IL4), interleukin-10 (IL10) or glucocorticoids to generate anti-inflammatory macrophages (M2) [2]. While in vitro model systems based on differential activation of MDMs emerge as a practical heuristic, they are not identical to in vivo tissue resident macrophages or infiltrating MDMs, which are often shaped by a complex and dynamic milieu within the microenvironment [1]. Within the fast-growing field of systems immunology a crucial need exists for identifying and defining in vivo macrophage populations, as well as identifying an in vitro model capable of mimicking the tissue physiology and the systemic perturbation associated with diseases [3]
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