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Persistence of a regeneration-associated, transitional alveolar epithelial cell state in pulmonary fibrosis

Nature Cell Biology volume 22, pages 934–946 (2020)Cite this article

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Abstract

Stem cells undergo dynamic changes in response to injury to regenerate lost cells. However, the identity of transitional states and the mechanisms that drive their trajectories remain understudied. Using lung organoids, multiple in vivo repair models, single-cell transcriptomics and lineage tracing, we find that alveolar type-2 epithelial cells undergoing differentiation into type-1 cells acquire pre-alveolar type-1 transitional cell state (PATS) en route to terminal maturation. Transitional cells undergo extensive stretching during differentiation, making them vulnerable to DNA damage. Cells in the PATS show an enrichment of TP53, TGFβ, DNA-damage-response signalling and cellular senescence. Gain and loss of function as well as genomic binding assays revealed a direct transcriptional control of PATS by TP53 signalling. Notably, accumulation of PATS-like cells in human fibrotic lungs was observed, suggesting persistence of the transitional state in fibrosis. Our study thus implicates a transient state associated with senescence in normal epithelial tissue repair and its abnormal persistence in disease conditions.

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Fig. 1: Previously unknown alveolar epithelial cell states in ex vivo organoids revealed by scRNA-seq.
Fig. 2: PATS cells emerge transiently and originate from alveolar stem cells after lung injury in vivo.
Fig. 3: Lineage tracing revealed that PATS cells generate AEC1s.
Fig. 4: Gene-expression signatures and signalling pathways enriched in PATS.
Fig. 5: PATS cells undergo stretch-induced DNA damage and senescence in vivo and ex vivo.
Fig. 6: Genetic and pharmacological modulation and genomic binding assays reveal transcriptional control of PATS by TP53 signalling.
Fig. 7: Enrichment of PATS-like states in IPF suggests persistence of this state in pathological milieu.

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Data availability

All NGS sequencing data in this manuscript are available at NCBI GEO under the accession numbers GSE141634 (organoid scRNA-seq), GSE141635 (ChIP-seq) and GSE135893 (scRNA-seq data from normal and IPF human lungs). Previously published sequencing data that were re-analysed here are available under the accession number GSE130148. All other data supporting the findings from this study are available from the corresponding author on reasonable request. Source data are provided with this paper.

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Acknowledgements

We thank B. Hogan for advice and critical reading of the manuscript, L. Macadlo and M. F. d. Soto for technical support, and the members of the Tata laboratory for discussions. We thank K. Lyons for providing Ctgf-GFP mice, D. Kirsch for providing Trp53fl/fl mice, P. v. Galen and Y. Xiang for their help with Mint-ChIP analysis, the Duke Cancer Institute Flow Cytometry Shared Resource for help with cell sorting, the Duke Sequencing and Genomic Technologies Shared Resource for sequencing NGS-libraries, the Duke University Light Microscopy Core Facility for imaging equipment and consultation, the Duke Biorepository and Precision Pathology Center for providing human specimens and the Duke Compute Cluster for help with sequencing data analysis. The monoclonal antibodies (TROMA-I and TROMA-III developed by P. Brulet and R. Kemler) were obtained from the Developmental Studies Hybridoma Bank, created by the NICHD of the NIH and maintained at the Department of Biology, The University of Iowa. We also thank the BioRepository and Precision Pathology Center (BRPC), a shared resource of the Duke University School of Medicine and Duke Cancer Institute. The BRPC receive support from the P30 Cancer Center Support Grant (grant no. P30CA014236). Y.K. is a Japan Society for the Promotion of Science Overseas Research Fellow. A.K. is supported by a medical scientist training program fellowship from NHLBI/NIH (grant no. F30HL143911). Human scRNA-seq was supported by grant no. R01HL145372 (N.E.B. and J.A.K.), Doris Duke Charitable Foundation (J.A.K.), K08HL130595 (J.A.K.) and Boehringer Ingelheim Pharmaceuticals (J.A.K.). This work was supported by a Pathways to Independence award from NHLBI/NIH (grant no. R00HL127181), grant no. R01HL146557 to P.R.T. and funds from Regeneration NeXT and Kaganov-MEDx Pulmonary Initiative at Duke University. This work was partially supported by funds from Whitehead foundation and P.R.T. is a Whitehead Scholar.

Author information

Author notes

  1. These authors contributed equally: Yoshihiko Kobayashi, Aleksandra Tata.

Authors and Affiliations

  1. Department of Cell Biology, Duke University School of Medicine, Durham, NC, USA

    Yoshihiko Kobayashi, Aleksandra Tata, Arvind Konkimalla, Hiroaki Katsura, Rebecca F. Lee, Jianhong Ou & Purushothama Rao Tata

  2. Medical Scientist Training Program, Duke University School of Medicine, Durham, NC, USA

    Arvind Konkimalla

  3. Regeneration Next, Duke University, Durham, NC, USA

    Jianhong Ou & Purushothama Rao Tata

  4. Translational Genomics Research Institute, Phoenix, AZ, USA

    Nicholas E. Banovich

  5. Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    Jonathan A. Kropski

  6. Department of Veterans Affairs Medical Center, Nashville, TN, USA

    Jonathan A. Kropski

  7. Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, USA

    Jonathan A. Kropski

  8. Duke Cancer Institute, Duke University School of Medicine, Durham, NC, USA

    Purushothama Rao Tata

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Contributions

Y.K. designed, conceived and performed the next-generation-sequencing-related experiments and the computational analysis, and co-wrote the manuscript. A.T. designed, conceived and performed the in vivo and ex vivo experiments and immunostaining, and co-wrote the manuscript. A.K. designed and performed the AEC1-ablation and pneumonectomy experiments. H.K. performed the organoid experiments. R.F.L. performed the histological analyses. J.O. built a pipeline for the Mint-ChIP analysis. N.E.B. and J.A.K. provided scRNA-seq data from healthy and IPF human lungs. P.R.T. designed, conceived and supervised the work and co-wrote the manuscript. All authors reviewed and edited the manuscript.

Corresponding author

Correspondence to Purushothama Rao Tata.

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Competing interests

The authors declare the following competing interests. A provisional patent application (US Patent application number: 62/975,294) related to this work has been filed; Y.K., A.T., A.K. and P.R.T. are listed as co-inventors on this application. P.R.T. serves as a consultant, unrelated to this work, for Cellarity Inc.

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Extended data

Extended Data Fig. 1 scRNA-seq identifies distinct alveolar cell populations in organoid cultures and LPS-treated lungs in vivo.

a, Representative gating for FACS sorting of Sftpc-tdTomato+ AEC2s and PDGFRα+ fibroblasts utilized for organoid cultures. Singlet cells were used for further gating based on antibody staining (CD31, CD45 and PDGFRα) or tdTomato expression. Unstained control is shown in left bottom. b, Pearson correlation plot visualizes the number of genes per cell (nGene) and unique molecular identifier (nUMI) in total cells derived from alveolar organoids (left panel, 10,948 cells). UMAP shows major cell populations including epithelial cells (green, 5,163 cells), fibroblasts (red, 5,686 cells), and some minor populations such as endothelial cells (blue, 66 cells) and macrophages (purple, 33 cells) in alveolar organoids (right panel). c, Integrated UMAP showing cells derived from alveolar organoids (orange, 4,787 cells), in vivo homeostatic mouse lung (blue, 1,878 cells) and LPS-treated mouse lung (magenta, 14,323 cells) (left). Expression of indicated genes in the integrated UMAP (right). d and e, UMAP plots show the expression of indicated genes in AEC2, AEC2-proliferating, AEC1, and novel alveolar epithelial cell state in alveolar organoid scRNA-seq dataset (d, 4,573 cells) and LPS treated or control lungs (13,204 cells). In panel e, UMAP shows homeostatic (red) and LPS-treated (blue) lung.

Extended Data Fig. 2 Expression pattern of novel alveolar epithelial cell state enriched genes in organoids and LPS or bleomycin-treated lungs in vivo.

a, Schematic of bleomycin-induced lung injury in Ctgf-GFP mice. b, Immunostaining for Ctgf-GFP (green), KRT8 (red) and SFTPC (grey) in control lung (left) and bleomycin treated lungs on day-12 (right). Representative of 3 mice. Magnified single channel images are shown on the right. White line box indicates magnified region. c, Proximity ligation in situ hybridization analysis for Ctgf (grey) and Sftpc (green) on sections derived from Sftpc-tdTomato mice administered with bleomycin. Red arrows indicate Ctgf+tdTomato+Sftpc- cells. Green arrows indicate Ctgf+tdTomato+Sftpc+ cells. d, Violin plots show the expression of Krt8 and Sfn in epithelial cell populations derived from alveolar organoid (left, n = 4,573 cells) and LPS -injured lung scRNA-seq datasets (right, n = 13,204 cells). Violin plots indicate distribution of the cells. Scale bars: 30 µm.

Extended Data Fig. 3 Expression of markers specific to the novel alveolar population in AEC1-specific ablation mouse model and pneumonectomy-induced alveolar regeneration.

a, Experimental design to ablate AEC1 cells using Ager-CreER;R26R-DTR mouse model. b-e, Immunostaining for b) SFN (green) and LGALS3 (grey) or c) SFTPC (green) and LGALS3 (grey) or d) SFN (green) and KRT19 (red) or e) LGALS3 (green) and Ki67 (red) in control (left panel) and AEC1-ablated lungs on day 6 (middle panel) and day 28 (right panel). f, Schematic of AEC2 lineage tracing using Sftpc-CreER;R26R-tdTomato mice follow by pneumonectomy (PNX) and tissue collection on day 9. g, Immunostaining for SFN (green), Sftpc-tdt (red) and LGALS3 (grey) in center (left panel) and edge (right panel) of the lungs after pneumonectomy. h, immunostaining for CLDN4 (green), Sftpc-tdt (red) and LGALS3 (grey) (right panel) in center (left panel) and edge (right panel) of the lungs after pneumonectomy. DAPI stains nuclei (blue). Scale bars indicate 20 µm. Images in b-e, g and h are representative of three mice repeated independently with similar results.

Extended Data Fig. 4 Expression pattern of PATS and AEC1 markers in Krt19-lineage-traced lungs during alveolar regeneration.

a, Immunostaining for KRT8 (green), Krt19-tdt (red) and SFTPC (grey) (left panel) or AQP5 (grey) (right panel) in control (upper panel) and bleomycin-treated lungs (lower panel). Scale bars indicate 30 µm. Representative of 3 mice. b. Quantification of KRT8+Krt19-tdt+ cells in total Krt19-tdt+ cells. p = 0.0318, (one-tailed, Mann-Whitney). n = 3 mice. c, Dotted lines indicates the methodology used for quantification of AGER+ cells. Representative of 3 mice. Scale bar: 30 µm d. Quantification of AGER+Krt19-tdt+ cells in total Krt19-tdt+ cells p = 0.0318 (one-tailed, Mann-Whitney). n = 3 mice. Data are from three independent experiments and are presented as mean ± s.e.m. White boxed inset indicates individual color channels shown on the right. DAPI stains nuclei (blue).

Source data

Extended Data Fig. 5 Signalling pathways enriched in murine PATS.

a, Heatmap shows expression of known target genes of indicated signalling pathways in AEC2, proliferating AEC2 (AEC2pro), PATS and AEC1 in alveolar organoids (n = 4,573 cells) and LPS-treated murine lung (n = 13,204 cells). Scale indicates z-score where red is high, and blue is low. b, Immunostaining for YAP (red), Ctgf-GFP (green) and LGALS3 (grey) in bleomycin-treated mouse lung (left) and AEC1-ablated lung (right). Arrowheads indicate YAP expression in Ctgf-GFP+ cells. DAPI stains nuclei (blue). Scale bars indicate 25 µm. Representative of 3 mice.

Extended Data Fig. 6 Genetic and pharmacological modulation of TP53 signalling in organoid cultures and during alveolar regeneration in vivo.

a, Schematic of alveolar organoid culture treated with Nutlin-3a. b, Immunostaining for Ki67 (green) and AGER (grey) in control or Nutlin-3a treated alveolar organoids. Scale bar: 30 µm. c, Schematic representation of experimental design to delete Trp53 in AEC2s followed by bleomycin-induced lung injury in Sftpc-tdt;Trp53+/+ and Sftpc-tdt;Trp53fl/fl. d, Immunostaining for SFTPC (green), Sftpc-tdt (red) and AGER (grey) in lungs that show normal appearing regions in bleomycin treated TP53 deleted (Sftpc-tdt;Trp53fl/f) mice. e, Immunostaining for active Caspase 3 (green), Sftpc-tdt (red) and AGER (grey) in control (Sftpc-tdt;Trp53+/+) and TP53 deleted (Sftpc-tdt;Trp53fl/fl) mice. Scale bars: 100 µm unless noted otherwise. DAPI stains nuclei (blue). Images from b, d and e are representative from three mice repeated independently with similar results.

Extended Data Fig. 7 Transcriptional control of PATS by TP53 signalling.

a, Schematic of bleomycin-induced lung injury in Ctgf-GFP mice. b, c, IGV tracks show presence or absence of H3K4me3 and H3K36me3 marks in Fn1 (b) and Sftpc gene loci (c) in AEC2 and PATS. d, Distribution of H3K4me3 peaks in PATS marker gene loci in PATS (red line) and homeostatic AEC2s (blue line). e, Enriched TF motifs in H3K4me3 called peaks in PATS specific gene loci (n = 2, enrichment was detected using HOMER’s findMotifsGenome.pl). f, g, Enrichment for H3K4me3, H3K27ac and H3K36me3 in known TP53 target gene loci (Fas and Mdm2) in PATS compared to AEC2s. Arrowhead indicates location of predicted TP53 binding motifs. Green-shade regions are promoter or enhancer. h, Representative gating for FACS sorting of PATS utilized for ChIP−seq. Ctgf-GFP+/Sftpc-tdTomato+ cells are sorted from Bleomycin-treated mouse lung as PATS. i-l, IGV tracks show significant enrichment for TP53 binding in genomic loci corresponding to known targets of TP53 (i), PATS enriched genes (j) but not on AEC1 (k) and AEC2 (l) gene loci.

Extended Data Fig. 8 Markers specific to PATS-like cells are highly enriched in human fibrotic lungs compared with healthy lungs.

a, Heatmap shows expression of marker genes of each cell population in human lungs (scale shows z-score, n = 11,725 cells). b, UMAP plots show the expression of indicated genes in alveolar epithelial populations in heathy controls and fibrotic human lungs (11,725 cells). c, Hematoxylin and Eosin staining on IPF lung tissue sections. Representative image depicting fibrotic (red square box) and non-fibrotic (blue square box) regions in IPF lung. Scale bar indicates 200 µm. d-g, Immunostaining for PATS-like markers in non-fibrotic regions of IPF lungs. d, Immunostaining for SFN (green), CLDN4 (red) and AGER (grey). e, Immunostaining for SFN (green), KRT17 (red) and TP63 (grey). f, Immunostaining for SFN (green), TP63 (blue), HTII-280 (grey) and ACTA2 (red) in healthy (left panel) and IPF lungs (right panel). g, Immunostaining for SFN (green), HTII-280 (red) and AGER (grey) in healthy (left panel) and IPF (right panel) lung. h, Immunostaining for ACTA2 (green), SFN (red) and COL1A1 (grey) in IPF lung. White line box in merged images indicate region of single channel images shown on right. DAPI (blue) stains nuclei. Scale bars in d-h indicate 100 µm. Images from c, d, e, f, g and h are representative of experiments using four human samples repeated independently with similar results.

Extended Data Fig. 9 scRNA-seq analysis revealed enrichment of signalling pathways associated with PATS-like cells in human fibrotic lungs.

a, KEGG pathway enrichment analysis shows signalling pathways highly represented in PATS-like cells in human fibrotic lungs. Scale shows log2 (combined score) obtained from Enrichr (see methods section for details). b, Heatmap shows expression of known target genes of indicated signalling pathways in AEC1, AEC2, and PATS-like state (n = 11,725 cells). Scale indicates z-score where red is high, and blue is low. c-e, Violin plots show the relative gene expression levels of indicated pathways/cellular processes: p53 signalling (c), DNA damage checkpoint (d), and cellular senescence (e) in different cell types in control and IPF lungs (n = 11,725 cells). Violin bodies indicate distribution of the cells in Healthy (blue) and IPF (red) lungs. f, Immunostaining for p21 (green), ACTA2 (red), SFN (blue) and KRT17 (grey). g, Immunostaining for γH2AX (green), SFN (blue), ACTA2 (red) and HTII-280 (grey) in healthy human lung. h, β-galactosidase staining on IPF lung section. Black arrows indicate X-gal staining in epithelial cells. White line box in merged images indicate region of single channel images shown on right. DAPI (blue) stains nuclei. Scale bars in f-h indicate 100 µm. Images in f,g are from 3 experiments, image in h is from 4 experiments, repeated independently with similar results.

Extended Data Fig. 10 Schematic describing emergence of a novel transitional cell state in alveolar stem cell-mediated epithelial regeneration and its persistence in disease pathogenesis.

Alveolar stem cells replicate in response to damage and generate a novel transitional cell state which normally matures into functional alveolar type-1 epithelial cells. The previously unappreciated transitional state is directly regulated by TP53 signalling, vulnerable to DNA damage and undergoes a transient senescent state. This state is enriched in human fibrotic lungs.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Table 1. List of genes that are enriched in AEC2, AEC1, AEC2-proliferating cells, PATS or PATS-like cells in murine organoids, LPS-injury and human interstitial lung diseases. Supplementary Table 2. Sequences of ctgf and Sftpc probes used for PLISH. Supplementary Table 3. Expression of proteins in murine homeostatic lung in indicated cell types.

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Source Data Fig. 2

Raw quantification data used to generate the graphs in Fig. 2.

Source Data Fig. 5

Raw quantification data used to generate the graphs in Fig. 5.

Source Data Fig. 6

Raw quantification data used to generate the graphs in Fig. 6.

Source Data Fig. 7

Raw quantification data used to generate the graphs in Fig. 7.

Source Data Extended Data Fig. 4

Raw quantification data used to generate the graphs in Fig. 4.

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Kobayashi, Y., Tata, A., Konkimalla, A. et al. Persistence of a regeneration-associated, transitional alveolar epithelial cell state in pulmonary fibrosis. Nat Cell Biol 22, 934–946 (2020). https://doi.org/10.1038/s41556-020-0542-8

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