The lung microenvironment shapes a dysfunctional response of alveolar macrophages in aging
Alexandra C. McQuattie-Pimentel, … , Alexander V. Misharin, G.R. Scott Budinger
Alexandra C. McQuattie-Pimentel, … , Alexander V. Misharin, G.R. Scott Budinger
Published February 15, 2021
Citation Information: J Clin Invest. 2021;131(4):e140299. https://doi.org/10.1172/JCI140299.
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Research Article Aging Immunology

The lung microenvironment shapes a dysfunctional response of alveolar macrophages in aging

Abstract

Alveolar macrophages orchestrate the response to viral infections. Age-related changes in these cells may underlie the differential severity of pneumonia in older patients. We performed an integrated analysis of single-cell RNA-Seq data that revealed homogenous age-related changes in the alveolar macrophage transcriptome in humans and mice. Using genetic lineage tracing with sequential injury, heterochronic adoptive transfer, and parabiosis, we found that the lung microenvironment drove an age-related resistance of alveolar macrophages to proliferation that persisted during influenza A viral infection. Ligand-receptor pair analysis localized these changes to the extracellular matrix, where hyaluronan was increased in aged animals and altered the proliferative response of bone marrow–derived macrophages to granulocyte macrophage colony-stimulating factor (GM-CSF). Our findings suggest that strategies targeting the aging lung microenvironment will be necessary to restore alveolar macrophage function in aging.

Authors

Alexandra C. McQuattie-Pimentel, Ziyou Ren, Nikita Joshi, Satoshi Watanabe, Thomas Stoeger, Monica Chi, Ziyan Lu, Lango Sichizya, Raul Piseaux Aillon, Ching-I Chen, Saul Soberanes, Zhangying Chen, Paul A. Reyfman, James M. Walter, Kishore R. Anekalla, Jennifer M. Davis, Kathryn A. Helmin, Constance E. Runyan, Hiam Abdala-Valencia, Kiwon Nam, Angelo Y. Meliton, Deborah R. Winter, Richard I. Morimoto, Gökhan M. Mutlu, Ankit Bharat, Harris Perlman, Cara J. Gottardi, Karen M. Ridge, Navdeep S. Chandel, Jacob I. Sznajder, William E. Balch, Benjamin D. Singer, Alexander V. Misharin, G.R. Scott Budinger

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Figure 4

Heterochronic parabiosis does not reverse age-related transcriptomic changes in TRAMs or AT2 cells.

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Heterochronic parabiosis does not reverse age-related transcriptomic cha...
(A) Parabionts were generated from young adult (4–6 months, green) and old (18–24 months, gray) pairs, and TRAMs and AT2 cells were harvested after 60 days. (B) Percentage of circulating CD45+ cells from the young or old parabiont pair determined by flow cytometry using CD45.1/CD45.2. P = NS by ANOVA. (C) PCA plot (PC1 and PC2) of TRAM transcriptomes of young and old mice linked to an isochronic or heterochronic parabiont pair. Each symbol represents an individual animal. (D) Heatmap shows k-means clustering of differentially expressed genes in TRAMs (FDR < 0.01 in ANOVA-like test) between old and young mice with isochronic or heterochronic parabiont pairs (see also Supplemental Table 5). (E–G) Volcano plots show differentially expressed genes in TRAMs from young-young versus old/old versus young/old parabiotic pairs (FDR < 0.05) (see also Supplemental Figure 4 and Supplemental Tables 6–8). (H) AT2 cells were harvested from the same parabiont pairs as in A after 60 days. (I) PCA plot of transcriptomes of AT2 cells from young and old mice linked to an isochronic or heterochronic parabiont pair. Each symbol represents an individual animal. (J) Heatmap shows k-means clustering of differentially expressed genes in AT2 cells (FDR < 0.01 in ANOVA-like test) between old and young mice with isochronic or heterochronic parabiont pairs (see also Supplemental Table 9). (K–M) Volcano plot showing differentially expressed genes in AT2 cells from young/young versus old/old versus young/old parabiotic pairs (FDR < 0.05) (see also Supplemental Figure 4 and Supplemental Tables 10–12).