Pearson correlation plots of gene expression profiles between different cell populations were generated using Express Matrix software. data generation pipelines1. Here, we have turned attention to gene expression and regulatory networks in tissue resident macrophages. Macrophages are professional phagocytic cells, often long-lived, that reside in all organs to maintain tissue integrity, obvious debris, and respond rapidly to initiate repair upon injury or innate immunity following infection2,3. Accordingly, macrophages are specialized for degrading and detoxifying engulfed cargo and they are potent secretagogues with the capacity to display Rabbit Polyclonal to SPHK2 (phospho-Thr614) an array of phenotypes4. Macrophages can also present antigens, but lack the potency in stimulating T cells observed in dendritic cells, and usually fail to mobilize to lymphoid tissues where na?ve T cells are abundant. Partially overlapping functions between macrophages and dendritic cells, reflected by overlapping molecular profiles, have for decades fueled some debate over the origins and overall distinction between macrophages and dendritic cells (DCs)5. In the last several years, significant progress has been made in identifying precursors specific to DCs6C8. Moreover, transcription factors have been identified, such as resident tissue macrophages16. Though some classical macrophages, such as Kupffer cells of the liver and metallophilic as well marginal zone macrophages of the spleen proved elusive for definitive identification and/or isolation by flow cytometric cell sorting, four resting macrophage populations submitted to Immgen met the criteria of macrophage populations: peritoneal macrophages, red pulp splenic macrophages, lung macrophages, and microglia (brain macrophages). We thus focused our initial analysis on these four key macrophage populations. Principal component analysis (PCA) of all genes expressed by the four sorted populations and several DC populations revealed a relatively greater distance between the different macrophages compared with DCs (Fig. 1a). Pearson correlation values were high for replicates within a given DC or macrophage population as per the quality control standards of Immgen; variability within replicates for a single population varied from 0.908 0.048 for microglia to 0.995 0.001 for peritoneal macrophages. Pearson correlations in gene expression profiles between different populations of DCs yielded coefficients ranging from 0.877 (liver CD11b+ versus spleen CD8+ DCs) to 0.966 (spleen CD4+CD11b+ versus spleen CD8+ DCs) (mean of all DC populations 0.931), whereas the correlation coefficients between different tissue macrophages ranged from 0.784 (peritoneal versus splenic red pulp) to 0.863 (peritoneal versus lung) with a mean of 0.812 (Fig. 1b). Several thousand mRNA transcripts were differentially expressed by at least 2-fold Cyclosporin H when, for example, lung macrophages were compared to red pulp splenic macrophages (Fig. 1c). This degree of diversity was greater than that observed when DCs from different subsets (CD103+ versus CD11b+) were compared from different organs (Fig. 1c). Finally, a dendrogram applied to the various populations showed that DCs clustered more closely than did macrophages (Fig. 1d), and this was true whether we considered all gene transcripts in the array (data not shown) or only the top 15% Cyclosporin H ranked by cross-population max/min ratio or coefficient of variation (Fig. 1d). Overall, these comparisons indicate a pronounced diversity among tissue macrophage populations. Open in a separate window 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. Distinct molecular signatures among tissue macrophages The diversity among these four Cyclosporin H classical macrophage populations extended to gene families previously associated with macrophage function – chemokine receptors, Toll-like receptors, C-type lectins, and efferocytic receptors. For example, at least one distinct chemokine receptor observed in each population was prominently expressed above the others (Supplementary Fig. 1a). Diversity among Toll-like receptors, C-type lectin domain members and efferocytic receptors was also remarkable (Supplementary Fig. 1bCd). Indeed, only a few of the mRNA transcripts profiled in these Cyclosporin H categories, including mRNA encoding the Mer tyrosine kinase receptor (MerTK) involved in phagocytosis of apoptotic cells17, and toll-like receptors Tlr4, Tlr7, Tlr8, and Tlr13, showed relatively uniform expression across all macrophages compared. Hundreds of mRNA transcripts were selectively increased or decreased by at least 2-fold in only one of the macrophage populations (Fig. 2a), and microglia in particular.