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Transcriptomic and epigenetic mechanisms underlying myeloid diversity in the lung

Nature Immunology volume 21, pages 221–231 (2020)Cite this article

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Abstract

The lung is inhabited by resident alveolar and interstitial macrophages as well as monocytic cells that survey lung tissues. Each cell type plays distinct functional roles under homeostatic and inflammatory conditions, but mechanisms establishing their molecular identities and functional potential remain poorly understood. In the present study, systematic evaluation of transcriptomes and open chromatin of alveolar macrophages (AMs), interstitial macrophages (IMs) and lung monocytes from two mouse strains enabled inference of common and cell-specific transcriptional regulators. We provide evidence that these factors drive selection of regulatory landscapes that specify distinct phenotypes of AMs and IMs and entrain qualitatively different responses to toll-like receptor 4 signaling in vivo. These studies reveal a striking divergence in a fundamental innate immune response pathway in AMs and establish a framework for further understanding macrophage diversity in the lung.

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Fig. 1: Transcriptomes of lung MPs at baseline.
Fig. 2: Strain-specific transcriptomes of lung MPs at baseline.
Fig. 3: Epigenetic landscapes of lung MPs.
Fig. 4: Effects of natural genetic variation on lung MP open chromatin.
Fig. 5: Unique gene expression signatures for lung MPs during the course of acute lung inflammation induced by LPS administration i.p. and i.n.
Fig. 6: LPS administration i.n. induces a moderate activation of AMs.

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Data availability

The sequencing data reported in this article are deposited in the Gene Expression Omnibus with the following accession number: GSE136916. Additional data supporting the presented findings are available in the manuscript and upon request from the corresponding author.

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Acknowledgements

These studies were supported by National Institutes of Health (NIH) grants (nos. DK091183 and DK063491). E.S. was supported by a NIH grant (no. K08HL140198). C.E.R. was supported by NIH grants (nos. R00HL123485 and R01HL147187). L.S.P. was supported by NIH grants (nos. HL086324, HL126703 and HL143256) and the Gerber Foundation (grant no. 1823-3830). We thank T. Rombaldo for assistance with FACS, J. Collier for technical assistance and L. van Ael for help with preparation of the manuscript. Sequencing of RNA-seq and ATAC-seq libraries was conducted at the IGM Genomics Center, University of California San Diego, La Jolla, CA.

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Authors and Affiliations

  1. Department of Pediatrics, University of California San Diego, Rady Children’s Hospital, La Jolla, CA, USA

    Eniko Sajti & Lawrence S. Prince

  2. Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA

    Verena M. Link, Zhengyu Ouyang, Nathanael J. Spann, Emma Westin, Casey E. Romanoski, Gregory J. Fonseca & Christopher K. Glass

  3. Metaorganism Immunity Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Verena M. Link

  4. Department of Cellular & Molecular Medicine, Bioscience Research Laboratories, University of Arizona, College of Medicine, Tucson, AZ, USA

    Casey E. Romanoski

  5. Department of Medicine, Division of Quantitative Life Sciences, Meakins-Christie Laboratories, McGill University Health Centre, Montreal, Quebec, Canada

    Gregory J. Fonseca

  6. Department of Medicine, University of California San Diego, La Jolla, CA, USA

    Christopher K. Glass

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Contributions

E.S., N.J.S. and C.K.G. designed the project. E.S., N.J.S., E.W. and G.J.F. performed the experiments. V.M.L, Z.O., C.E.R., E.S. and C.K.G. analyzed the data. E.S., L.S.P. and C.K.G. secured the funding. E.S. and C.K.G. wrote the manuscript. L.S.P, V.M.L. and N.J.S. critically revised the manuscript.

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Correspondence to Eniko Sajti.

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Peer review information L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Single-cell RNA-seq clusters, FACS sorting strategy and quality control of FACS and RNA-seq results.

a. Single-cell RNA-seq gene expression in clusters used to determine cluster identities. Heat map is representing expression values for the most significant genes in each cluster. Cells from the lungs of 3 male and 1 female DBA/2J mice were pooled. b. Zoomed-in view of velocity analysis for single-cell RNA-seq from Fig. 1a. c. Flow cytometry analysis and sorting strategy to obtain subsets of lung mononuclear phagocytes (MPs). d. Validation of sorting strategy with gene expression in sorted lung MPs in C57BL/6J (B6) mice (top panel) and DBA/2J (DBA) mice (bottom panel). Bars represent transcripts per million (TPM) for one mouse. Two replicates are shown. e. Spearman correlation heat map of all RNA-seq replicates, N=2 for each cell type in both mouse strains. f. Comparison of gene expression for AM vs. IM and iMo vs. pMo in B6 mice. Scatter plots are showing genes with TPM > 16. Blue dots for AM, orange dots for IM and bordeaux dots for iMo show genes with fold change (FC) 2 or higher. g. Comparison of gene expression in pMo isolated from lung vs pMo isolated from circulating blood. Scatter plots show genes with TPM > 16. Pale blue dots show genes with FC 2 or higher, dark blue dots show genes with FC 4 or higher. h. Ingenuity pathway analysis (IPA) functional pathways for genes differentially regulated in pMo isolated from lung vs pMo isolated from circulating blood.

Extended Data Fig. 2 ATAC-seq quality control and HOMER de novo motif analysis.

a. Spearman correlation heat map of all ATAC-seq replicates, N=2 for each cell type in both mouse strains. b. Comparison of open chromatin regions for iMo vs pMo in B6 mice. Scatter plot shows log2 (tag counts+1) of ATAC-seq peaks, colored dots show ATAC-seq peaks with fold change (FC) 4 or higher, bordeaux for iMo and purple for pMo. c. HOMER de novo motif enrichment analysis for transcription factor (TF) binding sites in distal regions of open chromatin (> 3 kb from a TSS) likely representing enhancers using GC matched background for B6 mice. Boxes display –log10 p-values for enrichment of the motif, rank order in parenthesis and percentage of motif occurrence in peaks vs background, N=2 for each cell type in both mouse strains. d. HOMER de novo motif enrichment analysis for TF binding sites in distal regions of open chromatin (> 3 kb from a TSS) likely representing enhancers using GC matched background for DBA mice. Boxes display –log10 p-values for enrichment of the motif, rank order in parenthesis and percentage of motif occurrence in peaks vs background, N=2 for each cell type in both mouse strains.

Extended Data Fig. 3 Flow cytometry and ingenuity pathway analysis (IPA) of lung mononuclear phagocytes (MPs) after LPS administration.

a. Flow cytometry analysis of lung MP subsets and neutrophils after i.p. LPS administration. Bars represent the average percentage of given cell subset out of CD45+ leukocytes ± s.d. Three independent experiments were pooled, 0h N=11, for all other time points N=8. Non-parametric Wilcoxon signed-rank test with Bonferroni correction was used, * p < 0.05, ** p < 0.01, *** p < 0.001. b. Venn diagram of 4,499 differentially expressed genes in AM, IM and iMo after i.p. LPS administration. c. Flow cytometry analysis of lung MP subsets and neutrophils after i.n. LPS administration. Bars represent the average percentage of given cell subset out of CD45+ leukocytes ± s.d. Two independent experiments were pooled, 0h N= 11, for all other time points N=4. Non-parametric Wilcoxon signed-rank test with Bonferroni correction was used, * p < 0.05, ** p < 0.01, *** p < 0.001. d. Venn diagram of 806 differentially expressed genes in AM, IM and iMo after i.n. LPS administration.

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Sajti, E., Link, V.M., Ouyang, Z. et al. Transcriptomic and epigenetic mechanisms underlying myeloid diversity in the lung. Nat Immunol 21, 221–231 (2020). https://doi.org/10.1038/s41590-019-0582-z

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