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. 2018 May 29;115(22):E5096-E5105.
doi: 10.1073/pnas.1802611115. Epub 2018 May 14.

Distinct macrophage populations direct inflammatory versus physiological changes in adipose tissue

David A Hill  1   2 , Hee-Woong Lim  1   3 , Yong Hoon Kim  1   4 , Wesley Y Ho  1   4 , Yee Hoon Foong  1   4 , Victoria L Nelson  1   4 , Hoang C B Nguyen  1   4 , Kavya Chegireddy  1   4 , Jihoon Kim  1   4 , Andreas Habertheuer  5 , Prashanth Vallabhajosyula  5 , Taku Kambayashi  6 , Kyoung-Jae Won  1   3 , Mitchell A Lazar  7   4
Affiliations

Distinct macrophage populations direct inflammatory versus physiological changes in adipose tissue

David A Hill et al. Proc Natl Acad Sci U S A. .

Abstract

Obesity is characterized by an accumulation of macrophages in adipose, some of which form distinct crown-like structures (CLS) around fat cells. While multiple discrete adipose tissue macrophage (ATM) subsets are thought to exist, their respective effects on adipose tissue, and the transcriptional mechanisms that underlie the functional differences between ATM subsets, are not well understood. We report that obese fat tissue of mice and humans contain multiple distinct populations of ATMs with unique tissue distributions, transcriptomes, chromatin landscapes, and functions. Mouse Ly6c ATMs reside outside of CLS and are adipogenic, while CD9 ATMs reside within CLS, are lipid-laden, and are proinflammatory. Adoptive transfer of Ly6c ATMs into lean mice activates gene programs typical of normal adipocyte physiology. By contrast, adoptive transfer of CD9 ATMs drives gene expression that is characteristic of obesity. Importantly, human adipose tissue contains similar ATM populations, including lipid-laden CD9 ATMs that increase with body mass. These results provide a higher resolution of the cellular and functional heterogeneity within ATMs and provide a framework within which to develop new immune-directed therapies for the treatment of obesity and related sequela.

Keywords: adipose tissue; exosome; inflammation; macrophage; obesity.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
CD9 and Ly6c define unique populations of ATMs in obese adipose. (A) Flow-cytometric analysis of ATMs from eWAT of mice fed LFD or HFD for 12 wk. Gated on CD45+, CD3−, CD4−, CD8−, CD19−, Ly6g−, and SiglecF−. Numbers indicate percentage of parent gate with mean ± SEM displayed (n = 4–5 per group, statistical comparison by Student’s t test). (B) Number of CD11b+, Ly6c+ (Ly6c+) ATMs in eWAT of LFD or HFD mice. (C) Flow-cytometric analysis of CD11b+, Ly6c− ATMs from eWAT of LFD or HFD mice. (D) Number of CD11b+, Ly6c−, CD64+, F4/80+ (Ly6c−) ATMs in eWAT of LFD or HFD mice. (E) Heatmap of differentially expressed genes in CD11b+, Ly6c−, CD64+, F4/80+ ATMs of HFD mice (n = 37 cells). (F) Examples of differentially expressed genes in HFD2 compared with HFD1 ATMs, calculated as log2 fold change (FC)(HFD2/HFD1). (G) Flow-cytometric analysis of CD11b+, Ly6c−, CD64+, F4/80+ ATMs from eWAT of LFD or HFD mice. (H) Number of CD11b+, Ly6c−, CD64+, F4/80+, CD9− (CD9−) or CD11b+, Ly6c−, CD64+, F4/80+, CD9+ (CD9+) ATMs in eWAT of LFD or HFD mice. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; ns, not significant. Representative of three or more independent experiments.
Fig. 2.
Fig. 2.
CD9 ATMs localize to CLSs and are filled with lipids. (A and B) Immunohistochemistry of eWAT from LFD (A) or HFD (B) mice. Sections were stained for CD11b, CD9, and Ly6c. (C) Comparison of intracellular lipid levels in HFD eWAT CD9 or Ly6c ATMs by mean fluorescence intensity (normalized to mode, n = 5 per group, statistical comparison by Student’s t test, ***P < 0.001). (D) Representative imaging cytometry of HFD eWAT CD9 or Ly6c ATMs. (Scale bars: A and B, 100 μm; D, 10 μm.) (E) Nanoparticle detector analysis of CD9-derived exosomes secreted into culture media (SEM of technical replicates in gray). Representative of two or more independent experiments.
Fig. 3.
Fig. 3.
CD9 ATMs express proinflammatory transcriptomes, while Ly6c ATMs are homeostatic. (A) Heatmap of differentially expressed genes between CD9 and Ly6c ATMs (fold change > 2, false discovery rate < 0.01, n = 4–5 per group). Genes ordered by hierarchical clustering. (B) Venn diagram of genes characteristic of CD9, Ly6c, M1, or M2 transcriptomes. M1- and M2-specific genes were determined by comparing LPS or IL-4 treated bone marrow-derived macrophages (BMDM) to control-treated cells, as detailed in SI Appendix, Fig. S3. (C) Ontology of genes more highly expressed in CD9 or Ly6c ATMs. Most significant, nonredundant biologic process, molecular function, or cellular component terms with gene number and adjusted P value are shown. (D) Representative CD9-specific inflammatory gene tracks. (E) Representative CD9-specific lysosomal gene tracks. Representative of one or more independent experiments.
Fig. 4.
Fig. 4.
CD9 ATMs have an inflammatory chromatin landscape driven by activating transcription factors. (A) Differential analysis of ATAC-seq peaks from CD9 or Ly6c ATMs of HFD-fed B6 mice [n = 3 per arm; fold change > 1.5, false discovery rate < 0.05, and >0.5 reads assigned per million mapped reads (RPM)]. (B) Diagram describing association between cell type-specific peaks and closest cell type-specific gene [within 50 kb of the transcription start site (TSS)]. (C) Percent of ATAC-seq peaks associated with CD9- or Ly6c-specific genes (P values determined by Fisher’s exact test). (D) Gene ontology for cell type-specific genes, associated with corresponding cell-type specific peaks. Most significant, nonredundant biologic process, molecular function, or cellular component terms with gene number and adjusted P value are shown. (E) De novo motif search within ATAC-seq peaks associated with a corresponding cell type-specific gene [consensus motif, transcription factor (TF), P value, and percentage of targets shown]. (F) Percent enrichment of AP-1, NF-κB, and CTCF motifs within CD9- or Ly6c-specific peaks (P values determined y Fisher’s exact test). (G) Occupancy of JunB (AP-1 subunit) and CTCF at CD9- or Ly6c-specific regions of open chromatin (P values determined by Wilcoxon test). (H and I) Representative RNA-seq, ATAC-seq, and ChIP-seq browser tracks displaying CD9- (H) or Ly6c-specific (I) gene loci. Legacy ChIP-seq tracks for JunB (AP-1) or p65 (NF-κB) in lipopolysaccharide-treated BMDM or CTCF in untreated BMDM are displayed. Representative of two or more independent experiments.
Fig. 5.
Fig. 5.
CD9 ATMs confer an inflammatory response to lean adipose tissue. (A) Experimental design of adoptive transfer. (B) PCA plot of transfer groups (n = 4 per group). (C) Hierarchical clustering heatmap and ontology of genes differentially regulated in eWAT after CD9 or Ly6c ATM transfer, compared with PBS injection [fold change (FC) > 2 and false discovery rate < 0.05]. Average of four replicates displayed. Most significant, nonredundant biologic process terms with gene number and adjusted P value are shown. (D) Heatmap of genes expressed in eWAT after 12 wk of HFD [displayed as log2FC(HFD/LFD)]. (E) Gene set enrichment analysis of CD9-induced genes within the HFD eWAT transcriptome. Representative of two or more independent experiments.
Fig. 6.
Fig. 6.
CD9 ATMs are present in CLSs of human adipose and correlate with BMI. (A) Flow-cytometric analysis of VAT ATMs (gating strategy in SI Appendix, Fig. S6). (B) Surface marker expression and intracellular lipid levels of VAT ATMs (normalized to percentage mode of MFI; Neg., Lymphocyte gate; statistical comparison between CD9− and CD9+ ATMs by ratio paired Student’s t test, *P < 0.05, **P < 0.01; n = 8–12). (C) Immunohistochemistry of VAT from obese individuals. Sections stained for DAPI, CD11b, and CD9. (D) Representative imaging flow cytometry of VAT CD9− or CD9+ ATMs. (Scale bars: C, 50 μm; D, 10 μm.) (E) Correlation between CD9+ ATM number and BMI (analysis by linear regression, n = 12).
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