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. 2012 Nov;13(11):1118-28.
doi: 10.1038/ni.2419. Epub 2012 Sep 30.

Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages

Emmanuel L Gautier  1 , Tal Shay, Jennifer Miller, Melanie Greter, Claudia Jakubzick, Stoyan Ivanov, Julie Helft, Andrew Chow, Kutlu G Elpek, Simon Gordonov, Amin R Mazloom, Avi Ma'ayan, Wei-Jen Chua, Ted H Hansen, Shannon J Turley, Miriam Merad, Gwendalyn J Randolph; Immunological Genome Consortium
Collaborators, Affiliations

Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages

Emmanuel L Gautier et al. Nat Immunol. 2012 Nov.

Abstract

We assessed gene expression in tissue macrophages from various mouse organs. The diversity in gene expression among different populations of macrophages was considerable. Only a few hundred mRNA transcripts were selectively expressed by macrophages rather than dendritic cells, and many of these were not present in all macrophages. Nonetheless, well-characterized surface markers, including MerTK and FcγR1 (CD64), along with a cluster of previously unidentified transcripts, were distinctly and universally associated with mature tissue macrophages. TCEF3, C/EBP-α, Bach1 and CREG-1 were among the transcriptional regulators predicted to regulate these core macrophage-associated genes. The mRNA encoding other transcription factors, such as Gata6, was associated with single macrophage populations. We further identified how these transcripts and the proteins they encode facilitated distinguishing macrophages from dendritic cells.

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Conflict of interest statement

Conflict of interest: The authors have declared no conflict of interest related to this work

Figures

Figure 1
Figure 1. Analysis of macrophage diversity
(a) Relative distance between different types of macrophages and DCs was assessed using principal component analysis. (b) Correlation matrix of macrophages and dendritic cells based on all genes probes. (c) Examples of the relatively greater diversity between macrophage populations than DCs were plotted. The number of probes increased by a minimum of 2-fold for each population is indicated. (d) Hierarchical clustering of macrophages and dendritic cells based on the top 15% most variable genes.
Figure 2
Figure 2. Unique gene expression profiles of macrophages from different organs
(a) Scatter plots depict in distinct colors the mRNA transcripts that are ≥ 2-fold increased (left) or decreased (right) in one macrophage population compared to the remaining three populations. (b) Heat map and gene lists reveal mRNA transcripts uniquely expressed by single macrophage populations by ≥ 5 fold. (c) Transcription factor mRNA transcripts increased in only one of the four macrophage populations by ≥ 2 fold. (d) Specific cell surface markers for each macrophage populations, identified from the gene expression profiling data were validated by flow cytometry. Macrophages reacting with the antibodies tested matched the pattern of gene expression observed in (b). Shaded blue line shows isotype control and red line specific antibody.
Figure 3
Figure 3. Identification of gene modules enriched for macrophage-related gene signatures and their predicted regulators
(a) The overlap size of ImmGen modules of co-expressed genes with all macrophage-associated genes signatures (Table 1 and 2) is depicted graphically as a heat map. Only modules significantly enriched for at least one signature are shown. Stars mark significant overlap size by hypergeometric test (Methods). (b) Simplified hematopoietic tree showing mean expression of genes in module 161 (red – high expression; blue – low expression). Listed are genes that constitute module 161 (top) and the predicted positive regulators of the module (bottom). (c) A bar graph listing the positive regulators (activators) predicted by the Ontogenet algorithm to regulate two or more modules listed in a. The frequency that each factor was associated with the 14 modules is depicted. (d). Physical and regulatory interactions between the 18 most frequently represented regulators across the 14 macrophage-associated modules were interrogated using Ingenuity analysis tools. The scheme uses arrows to depict links where there are established physical interactions, or known pathways of co-activation or inhibition.
Figure 4
Figure 4. Expression of macrophage core genes by other populations of mononuclear phagocytes
(a) Heat map depicts the 39 gene transcripts increased in spleen, brain, peritoneal, and lung macrophages compared to classical and migratory DCs. Genes from module 161 that were included among these 39 genes are segregated and labeled “161a.” Other members of module 161 that did not meet the criteria for inclusion on Table 1 are labeled “161b.” Populations of tissue-derived mononuclear phagocytes that were not included in the generation of this list of genes are shown in the middle of the heat map. Various subsets of blood monocytes and plasmacytoid DCs are depicted further to the right on the heat map. (b) The frequency that the 39 genes in a were expressed in these populations at a signal intensity at least 2-fold higher than the highest expressing DC in the original comparison is depicted. (c) A dendrogram depicting the relationship between a wide variety of mononuclear phagocytes based on their expression of the list of 39 common macrophage-enriched genes.
Figure 5
Figure 5. Examination of macrophage core transcripts at the protein level in multiple tissues
(a) Histograms of brain, peritoneum, spleen, and lung stained for CD14, CD64, and MerTK to examine expression in macrophages and DCs from these organs. Shaded blue line shows isotype control and red line specific antibody. (b) MerTK+CD64+ cells were more than 95% Siglec F+ macrophages, but some MHC II+Siglec F− cells were also found in this population. c) Lung DC gating strategy is shown, with DCs being CD45+ cells lacking Siglec F, expressing CD11c and MHC II. d) Gating on lung DCs DCs revealed a significant reactivity for CD14, CD64, and MerTK in the CD11b+ CD24− putative DCs, but lack of MerTK and CD64 in CD24+ CD11b+ DCs. (e) Liver CD45+ cells were plotted to show F4/80 and CD11c staining. Eosinophils were gated (Siglec F+ high SSC+) to reveal that they overlay with another population in gate 1. Replotting gates without (w/o) eosinophils revealed 4 low SSC subsets of cells that differentially express F4/80 and CD11c. These 4 gates were examined with regard to expression of MerTK, CD64, and MHC II. Finally, reverse gating on MerTK+ CD64+ cells was carried these gated cells were plotted based on F4/80 and CD11c. f) A similar approach was as in “e” was carried out here in adipose tissue. Each analysis in this figure was based on studies from at least two replicative experiments with 3 mice per group.
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