Abstract

Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here, we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologs of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request. RNA-seq data have been deposited in the GEO repository under accession numbers GSE111664, GSE111690, and GSE114005 and at ArrayExpress under accession number E-MTAB-7142.

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Acknowledgements

This work was supported by a UCSF Marcus Award to M.B. and A.R.A., a National Institutes of Health grant (HL131560) to M.B., a Gruss Lipper Postdoctoral Fellowship to D.A., a UCSF Nina Ireland Program award to R.P.N. and P.J.W., a National Institutes of Health grant (HL139897) to P.J.W., and a National Institutes of Health award (National Institute of Allergy and Infectious Diseases Bioinformatics Support Contract HHSN272201200028C) to A.J.B. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank D. Erle, A. Barczak, W. Eckalbar, and M. Adkisson of the UCSF Functional Genomics Core Facility, the UCSF Center for Advanced Technology, the Gladstone Institutes’ Histology & Light Microscopy Core, and D. Sheppard for his insightful comments on the manuscript.

Author information

Author notes

  1. These authors contributed equally: D. Aran, A.P. Looney, L. Liu.

Affiliations

  1. Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA

    • Dvir Aran
    •  & Atul J. Butte
  2. Division of Pulmonary, Critical Care, Allergy, and Sleep Medicine, Department of Medicine, University of California, San Francisco, San Francisco, CA, USA

    • Agnieszka P. Looney
    • , Esther Wu
    • , Valerie Fong
    • , Suzanna Chak
    • , Ram P. Naikawadi
    • , Paul J. Wolters
    •  & Mallar Bhattacharya
  3. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA

    • Leqian Liu
    •  & Adam R. Abate
  4. Gladstone Institute of Cardiovascular Disease, San Francisco, CA, USA

    • Austin Hsu
  5. California Institute for Quantitative Biosciences, University of California, San Francisco, San Francisco, CA, USA

    • Adam R. Abate
  6. Chan Zuckerberg Biohub, San Francisco, CA, USA

    • Adam R. Abate

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Contributions

D.A. developed SingleR and performed computational analysis of single-cell data under the guidance of A.J.B. A.P.L. performed in vivo and in vitro experiments under the guidance of M.B. and with the assistance of E.W. and S.C. L.L. performed microfluidic capture of single-cell transcriptomes, library preparation, and sequencing under the guidance of A.R.A. V.F., A.H., and E.W. prepared breeding and experimental stocks of genetically modified mice and performed lung injury models under the guidance of M.B. P.J.W. contributed acquisition, storage, and processing of human samples and, with R.P.N., acquired lung microarray data from mice with telomere dysfunction. D.A. prepared the figures. M.B. conceived of the work, supervised experimental planning and execution, and wrote the manuscript with input from D.A., A.P.L., and L.L.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Mallar Bhattacharya.

Integrated supplementary information

  1. Supplementary Figure 1 Comparison of annotations of PBMC data with reference-based methods for cell type annotation.

    This figure accompanies Fig. 1c. t-SNE plots of the PBMC-4K scRNA-seq data21. The plots are colored by the top annotation using the methods presented by Kang et al.22 (top left), Li et al.23 (top right) and SingleR (bottom left). Kang et al. correlated 173 differentially expressed genes learned by comparison of scRNA-seq clusters of PBMCs. Li et al. (RCA) used a bulk microarray reference dataset for correlation. SingleR introduced a fine-tuning step to refine correlation with bulk datasets. While all methods agreed on the general annotations of monocytes, T cells, B cells and NK cells, annotations differed for cellular subtypes: RCA annotated the left-most cells in cluster 4 as NK cells, while the method of Kang et al. and SingleR annotated them as CD8+ T cells, which was supported by the expression of individual genes CD3E (general T cell marker) and CD8A. It is of note that NKG7 and GNLY are commonly used as NK cell markers but are also expressed in activated CD8+ T cells. The method of Kang et al. and RCA annotated the cells in cluster 3 as CD4+ T cells, while SingleR annotated 47.7% of this cluster as naive CD8+ T cells and 30.4% as central memory CD8+ T cells (TCM). The CD8A marker, along with CCR7 and SELL (markers for naïve cells), supported this annotation. The expression of IL7R (marker for memory T cells) in some of those cells suggested that this cluster also contained memory cells, as annotated by SingleR. FOXP3 (a marker of regulatory T cells) was found only in a small proportion of Treg cells (in the sorted Treg cells, only 5.6% of cells expressed FOXP3); however, SingleR suggested that many of the cells in cluster 1 were in fact Treg cells. In this comparison, only SingleR was able to distinguish highly similar cell states, and these differential annotations were supported by gene markers viewed individually.

  2. Supplementary Figure 2 Quality control of the single-cell experiment and analysis.

    a, t-SNE projection showing non-zero, unique molecular identifiers (UMIs), mitochondria fraction, and cell cycle score38 (n = 8,366 cells from n = 3 biologically independent mice for bleomycin, n = 6 biologically independent mice for control). No observed clusters associated with mitochondrial gene expression; n = 30 cells can be suggested as outliers based on mitochondrial percentage. Clusters of cycling cells represent ~ 10% of macrophages and T cells and were not used in the macrophage analysis. The number of genes per cell and mitochondrial fraction is shown as a function of UMI per cell. b, The t-SNE plot colored by batches. c, The t-SNE plot is colored by the maximal SingleR score. d, The t-SNE plot is colored by –log10 P value for the SingleR annotation using the chi-squared outliers test (gray used for –log10 P value >5). All cells in this analysis received a P value <0.05. The alveolar macrophages and fibroblasts (as defined by SingleR) received higher confidence than other cells.

  3. Supplementary Figure 3 SingleR detailed annotations of mouse lungs.

    This plot accompanies Fig. 2b. Here we present all SingleR annotations (not just the main types). To reduce the number of possible cell types in the data, annotations with fewer than 15 cells were marked as ‘Other’ and are colored in black.

  4. Supplementary Figure 4 Heat map of SingleR scores using lung reference datasets.

    This heat map accompanies Fig. 2c. We used RNA-seq datasets as reference—lung macrophages from Gibbings et al.29 (downloaded from GEO accession number GSE94135) and lung dendritic cells from Altboum et al.28 (downloaded from GEO accession number GSE49932). The two datasets were combined for a lung-specific reference. The heat maps show the SingleR scores after one round, without fine-tuning, of the top-scored cell types. Scores were normalized to [0,1]. a, The majority of cells in the macrophage cluster (see Fig. 2b) were annotated to alveolar macrophages, whereas the remaining cells were most correlated with interstitial macrophage type 3, which includes CD11c+ macrophages and not dendritic cells. Interestingly, the macrophage cluster was split into two subclusters, as we show in Fig. 2c. b, Cells from the dendritic cell cluster (see Fig. 2b) are annotated as dendritic cells, showing that the annotations are not an artifact of the reference datasets.

  5. Supplementary Figure 5 C2 expresses both alveolar and interstitial macrophage genes.

    In all scatterplots, individual genes are plotted and the vertical axis is log fold change between alveolar macrophages (AM) and interstitial macrophages (IM) in independent bulk reference datasets (top, GSE94135; bottom, GSE108844). Positive levels represent genes upregulated in AM. The horizontal axis in the left plots is the log fold change between cluster C1 and cluster C2 in the lung scRNA-seq dataset. Positive levels represent genes upregulated in C1. The horizontal axis in the right plots is the log fold change between C2 (the postulated transitional cluster) and C3 (the interstitial cluster) in the single-cell data. Positive levels represent genes upregulated in C2. Genes are colored red if significant in bulk (log(FC) > 1, adjusted P value < 0.05), green if significant in single cells (adjusted P value < 0.05), blue if significant in both, and purple if non-significant. Percentage is the percent of significant genes in single cells (SC) that are in the upper-right or lower-left quadrants. R is Pearson’s coefficient of significant genes in SC. P value in correlations < 0.00001. Fold ratios were computed with the lung scRNA-seq dataset (n = 3 biologically independent mice for bleomycin, n = 6 biologically independent mice for control).

  6. Supplementary Figure 6 Flow cytometric gating.

    a, Cells were dissociated from lungs of WT mice 14 d after bleomycin injury and stained with SiglecF, CD11c, and MHC II antibodies. SiglecF+CD11c+ cells were sorted into MHCIIhi and MHClo populations, with the threshold defined by MHC II staining in an uninjured mouse. Representative data are shown. b, Lung cells were dissociated from CX3CR1CreERT2 / Rosa26-loxp-STOP-loxp-TdTomato mice induced with tamoxifen 1 d prior to and during bleomycin injury and stained with SiglecF and MHC II antibodies. Representative data are shown.

  7. Supplementary Figure 7 Depletion of the macrophage subpopulation during bleomycin fibrosis.

    a, Fluorescence microscopy demonstrating ablation of macrophages detected by immunofluorescence for SiglecF and macrophage marker MerTK26 in areas of scar by second-harmonic (SH) imaging 21 d after bleomycin injury in wild-type mice (left) and tamoxifen-induced Cx3cr1CreERT2/Rosa26-loxp-STOP-loxp-Diptheria Toxin A mice (right), with quantification in five fields of view per mouse (n = 3 biologically independent mice per group). The Wilcoxon rank-sum test two-sided P value is presented. Scale bar, 50 μm. The box plot center line is the median, box limits are the upper and lower quartiles, and whiskers denote the largest and smallest values no more than 1.5 times the interquartile range from the limits. b, H&E staining 21 d after injury of wild-type mice (left) and tamoxifen-induced Cx3cr1CreERT2 / Rosa26-loxp-STOP-loxP-Diptheria Toxin A mice (right). Scale bar, 0.5 mm. Images are representative of three independent replicates with similar results.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–7 and Supplementary Notes 1 and 2

  2. Reporting Summary

  3. Supplementary Table 1

    Drop-seq batches

  4. Supplementary Table 2

    Differentially expressed genes between cluster C1 and cluster C

  5. Supplementary Table 3

    Mouse and human orthologs of cluster C1 genes

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https://doi.org/10.1038/s41590-018-0276-y