IFNγ+LPS up-regulated DEGs identified by RRA and fold-change analysis.

(A) On the left, the heatmap of the thirteen datasets showing the top 20 most robust genes upregulated is depicted. Value in the boxes represents fold-change and shade of blue represents RRA score. On the right, the corresponding average RRA score and fold-change. (B) On the left, the heatmap of the thirteen datasets showing the top 20 most upregulated genes which also present a significant RRA score. Value in the boxes represents fold-change and shade of blue represents RRA score. On the right, the corresponding average RRA score and fold-change.</p

IL-4 up-regulated DEGs identified by RRA and fold-change analysis.

(A) On the left, the heatmap of the seventeen datasets showing the top 20 most robust genes upregulated is depicted. Value in the boxes represents fold-change and shade of blue represents RRA score. On the right, the corresponding average RRA score and fold-change. (B) On the left, the heatmap of the seventeen datasets showing the top 20 most upregulated genes which also present a significant RRA score. Value in the boxes represents fold-change and shade of blue represents RRA score. On the right, the corresponding average RRA score and fold-change.</p

Comparison between the M1-“historical” murine genes with our analyses.

Numbers in parenthesis highlight the rank from our analysis.</p

Supplementary figures.

Macrophages, key players in the innate immune system, showcase remarkable adaptability. Derived from monocytes, these phagocytic cells excel in engulfing and digesting pathogens and foreign substances as well as contributing to antigen presentation, initiating and regulating adaptive immunity. Macrophages are highly plastic, and the microenvironment can shaper their phenotype leading to numerous distinct polarized subsets, exemplified by the two ends of the spectrum: M1 (classical activation, inflammatory) and M2 (alternative activation, anti-inflammatory). RNA sequencing (RNA-Seq) has revolutionized molecular biology, offering a comprehensive view of transcriptomes. Unlike microarrays, RNA-Seq detects known and novel transcripts, alternative splicing, and rare transcripts, providing a deeper understanding of genome complexity. Despite the decreasing costs of RNA-Seq, data consolidation remains limited, hindering noise reduction and the identification of authentic signatures. Macrophages polarization is routinely ascertained by qPCR to evaluate those genes known to be characteristic of M1 or M2 skewing. Yet, the choice of these genes is literature- and experience-based, lacking therefore a systematic approach. This manuscript builds on the significant increase in deposited RNA-Seq datasets to determine an unbiased and robust murine M1 and M2 polarization profile. We now provide a consolidated list of global M1 differentially expressed genes (i.e. robustly modulated by IFN-γ, LPS, and LPS+ IFN-γ) as well as consolidated lists of genes modulated by each stimulus (IFN-γ, LPS, LPS+ IFN-γ, and IL-4).</div

UPSTREAM regulators analysis.

Macrophages, key players in the innate immune system, showcase remarkable adaptability. Derived from monocytes, these phagocytic cells excel in engulfing and digesting pathogens and foreign substances as well as contributing to antigen presentation, initiating and regulating adaptive immunity. Macrophages are highly plastic, and the microenvironment can shaper their phenotype leading to numerous distinct polarized subsets, exemplified by the two ends of the spectrum: M1 (classical activation, inflammatory) and M2 (alternative activation, anti-inflammatory). RNA sequencing (RNA-Seq) has revolutionized molecular biology, offering a comprehensive view of transcriptomes. Unlike microarrays, RNA-Seq detects known and novel transcripts, alternative splicing, and rare transcripts, providing a deeper understanding of genome complexity. Despite the decreasing costs of RNA-Seq, data consolidation remains limited, hindering noise reduction and the identification of authentic signatures. Macrophages polarization is routinely ascertained by qPCR to evaluate those genes known to be characteristic of M1 or M2 skewing. Yet, the choice of these genes is literature- and experience-based, lacking therefore a systematic approach. This manuscript builds on the significant increase in deposited RNA-Seq datasets to determine an unbiased and robust murine M1 and M2 polarization profile. We now provide a consolidated list of global M1 differentially expressed genes (i.e. robustly modulated by IFN-γ, LPS, and LPS+ IFN-γ) as well as consolidated lists of genes modulated by each stimulus (IFN-γ, LPS, LPS+ IFN-γ, and IL-4).</div

Cartoon depicting M1 and M2 characteristics, including the classical genes usually used to determine polarization.

Cartoon depicting M1 and M2 characteristics, including the classical genes usually used to determine polarization.</p

RRA score analysis of the up- and downregulated genes.

RRA score analysis of the up- and downregulated genes.</p

Ingenuity pathway analysis (IPA) of differentially expressed genes in M1- and M2-macrophages.

Upstream regulator analysis of differentially expressed genes in (A) IFNγ- (B) LPS- (C) LPS+IFNγ- (D) IL-4-treated macrophages.</p

Genes with a RRA p<10⁻⁷ in the LPS, IFNγ, LPS+IFNγ datasets not present in the IL-4 dataset.

Genes with a RRA p−7 in the LPS, IFNγ, LPS+IFNγ datasets not present in the IL-4 dataset.</p

Interpolation of the RRA analyses.

Four-set Venn diagram analysis of the statistically significant (p29].</p