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Transcriptional analysis of cystic fibrosis airways at single-cell resolution reveals altered epithelial cell states and composition

Nature Medicine volume 27, pages 806–814 (2021)Cite this article

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Abstract

Cystic fibrosis (CF) is a lethal autosomal recessive disorder that afflicts more than 70,000 people. People with CF experience multi-organ dysfunction resulting from aberrant electrolyte transport across polarized epithelia due to mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. CF-related lung disease is by far the most important determinant of morbidity and mortality. Here we report results from a multi-institute consortium in which single-cell transcriptomics were applied to define disease-related changes by comparing the proximal airway of CF donors (n = 19) undergoing transplantation for end-stage lung disease with that of previously healthy lung donors (n = 19). Disease-dependent differences observed include an overabundance of epithelial cells transitioning to specialized ciliated and secretory cell subsets coupled with an unexpected decrease in cycling basal cells. Our study yields a molecular atlas of the proximal airway epithelium that will provide insights for the development of new targeted therapies for CF airway disease.

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Fig. 1: Single-cell transcriptome atlas of the epithelium lining proximal airways of control donors and donors with end-stage CF lung disease.
Fig. 2: Expansion of secretory function, including mucus secretion and anti-microbial activity, in CF secretory cells.
Fig. 3: Cilia-related gene expression is vastly expanded outside of the main cilia subgroups in CF.
Fig. 4: Depletion of metabolic stability, basal epithelial function and cellular division is widespread in CF lung basal cells.

Data availability

Sequence data that support the findings of this study have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus ‘GenBank’ with accession code GSE150674.

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Acknowledgements

We would like to thank S. Reynolds for helpful input in reviewing this manuscript. This work was supported by the Cystic Fibrosis Foundation (GOMPER17XX0 (B.N.G.), STRIPP17XX0 (B.R.S.), CARRAR19G0 (G.C.) and BOUCHE15R0 (S.H.R.)), the Tobacco-Related Disease Research Program (HIPRA 29IP-0597) (B.N.G.), National Institutes of Health (NIH) grant R01CA208303 (B.N.G.), the National Heart, Lung, and Blood Institute (PO1 HL108793) (B.R.S.), NIH grant DK065988 (S.H.R.), a grant from the W. M. Keck Foundation (B.N.G.) and a grant from Celgene/BMS (B.R.S.). J.L. was supported by the UCLA Tumor Cell Biology Training Program (USHHS Ruth L. Kirschstein Institutional National Research Service Award T32 CA009056). S.S. was supported by the UCLA Broad Stem Cell Research Center–Rose Hills Foundation Training Award and, currently, the UCLA Dissertation Year Fellowship. K.P. and B.N.G. were supported by the UCLA Broad Stem Cell Research Center, the David Geffen School of Medicine and the Jonsson Comprehensive Cancer Center, and K.P. was supported by the NIH (P01 GM099134). The research of K.P. was also supported, in part, by a Faculty Scholar grant from the Howard Hughes Medical Institute. J.E. was funded by the NIH (DP1DA044371), and J.E. and B.N.G. were supported by the Jonsson Comprehensive Cancer Center and the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research Ablon Scholars Award. C.J.A. and D.W.S. were supported by a UCLA Medical Scientist Training Program grant (NIH NIGMS GM008042), the NIH/NCI NRSA Predoctoral F31 Diversity Fellowship F31CA239655 (C.J.A.), the Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research Training Grant (C.J.A.), the T32 National Research Service Award in Tumor Cell Biology CA009056 (C.J.A.), the Parker B. Francis Foundation Fellowship (C.Y.) and UCLA CTSI KL2- NCATS KL2TR001882 (C.Y.).

Author information

Author notes

  1. These authors contributed equally: Gianni Carraro, Justin Langerman.

  2. These authors jointly supervised this work: Kathrin Plath, John E. Mahoney, Barry R. Stripp, Brigitte N. Gomperts.

Authors and Affiliations

  1. Lung and Regenerative Medicine Institutes, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Gianni Carraro, Guangzhu Zhang, Bindu Konda, Changfu Yao, Edo Israely & Barry R. Stripp

  2. Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Justin Langerman, Shan Sabri, Jason Ernst & Kathrin Plath

  3. Hastings Center for Pulmonary Research and Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Zareeb Lorenzana, Ben A. Calvert & Amy L. Ryan

  4. Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

    Zareeb Lorenzana & Amy L. Ryan

  5. UCLA Children’s Discovery and Innovation Institute, Mattel Children’s Hospital UCLA, Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Arunima Purkayastha, Cody J. Aros, Preethi Vijayaraj, David W. Shia, Andrew J. Lund, Tammy M. Rickabaugh & Brigitte N. Gomperts

  6. Department of Molecular Biology Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA

    Cody J. Aros, David W. Shia & Andrew J. Lund

  7. UCLA Medical Scientist Training Program, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Cody J. Aros & David W. Shia

  8. CFFT Lab, Cystic Fibrosis Foundation, Lexington, MA, USA

    Aleks Szymaniak, Emily Wilson, Michael Mulligan, Priyanka Bhatt, Junjie Lu, Martin Mense & John E. Mahoney

  9. Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, USA

    Jason Ernst, Kathrin Plath & Brigitte N. Gomperts

  10. Eli and Edythe Broad Stem Cell Research Center, University of California, Los Angeles, Los Angeles, CA, USA

    Jason Ernst, Kathrin Plath & Brigitte N. Gomperts

  11. Marsico Lung Institute/Cystic Fibrosis Center, University of North Carolina, Chapel Hill, NC, USA

    Scott H. Randell

  12. Division of Pulmonary Sciences and Critical Care Medicine, Department of Medicine and Department of Cell and Developmental Biology, University of Colorado Denver School of Medicine, Aurora, CO, USA

    Eszter K. Vladar

  13. Division of Pulmonary and Critical Care Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Brigitte N. Gomperts

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  1. Gianni Carraro
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Contributions

A.L.R. was previously known as Amy L. Firth. G.C., J.L. and J.M. designed and performed experiments, analyzed the data and prepared the manuscript. S.S., Z.L., A.P., G.Z., B.K., C.J.A., B.A.C., P.V., C.Y., D.W.S., E.I., T.M.R., E.W., A.S., M.M., A.L. and J.L. assisted in tissue handling, sampling, processing and sorting for scRNA-seq cell culture. J.E. supervised J.L. and S.S. S.H.R., E.K.V., A.L.R. and M.M. provided expertise and/or tissue analysis. K.P., J.M., B.R.S. and B.N.G. supervised the study and prepared the manuscript. All authors reviewed and edited the final manuscript.

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Correspondence to Kathrin Plath, John E. Mahoney, Barry R. Stripp or Brigitte N. Gomperts.

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Additional information

Peer review information Nature Medicine thanks Tuomas Tammela, Jeffrey Whitsett, and the other, anonymous reviewers for their contribution to the peer review of this work. Editor recognition statement: Jerome Staal 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 Cell subsets identified across institutions.

a, Visualization of the distribution of cells from the three institutions in the integrated embedding, showed by institution and (b) by samples of origin, visualized by UMAP. c–f, Network distributions with differences between institutions, visualized by UMAP. g, Major cell types identified using previously described markers, visualized by UMAP. h, Ionocyte and NE cell subsets analyzed independently of other cell types, visualized by UMAP. i, CO and CF sample contribution to cell populations and subsets, visualized by a stacked column chart. The ‘s’ indicates submucosal gland samples derived from matching ‘*’ CO and CF lungs. j, Signatures of major cell types in 10706 ALI cells, created using previously published ALI gene lists, shown by violin plots. Overlaid are boxplots showing the quartiles, whiskers showing 1.5 times interquartile range, and dots showing outliers. k, Distribution of major cell type proportions in freshly isolated and ALI datasets, for 38 and 5 independent biological samples respectively. Error bars show the standard error of the mean. l, CFTR expression level per subtype, scaled over all cells.

Extended Data Fig. 2. Secretory cell networks.

a, Heatmap showing the percent of normalized expression of the seven secretory networks across the secretory subset groups, divided by CO and CF. Each cell shows the average expression of all cells in that category, normalized by row. b, Heatmap showing the percent of normalized expression within the secretory subset groups for the top five genes selected from each secretory network based on their pan-institutional identity as either the most Up or Down in CF within the given network. Up/Down and Network classification is shown by annotation to left of heatmap and in key at right. Note for Net S7, only three genes qualified as pan-institutional. c, Bar plots showing the average expression of all genes in the remaining individual secretory networks per secretory subset group, in CO or CF cells.

Extended Data Fig. 3 Ciliated cell networks.

a, Heatmap showing the percent of normalized expression of all ten ciliated networks across the ciliated subset groups, divided by CO and CF. Each cell shows the average expression of all cells in that category, normalized by row. b, Heatmap showing the percent of normalized expression within the ciliated subset groups for the top five genes selected from each ciliated network based on their pan-institutional identity as either the most Up or Down in CF within the given network. Up/Down and Network classification is shown by annotation to left of heatmap and in key at right. c, Bar plots showing the average expression of all genes in the remaining individual ciliated networks per ciliated subset group, in CO or CF cells.

Extended Data Fig. 4 Changes in CO and CF cilia biogenesis.

a–j, For distinct categories of genes related to cilia biogenesis, the expansion of cilia gene expression is shown by violin plots and UMAP, indicating the changes in CO and CF for each cell subset. Overlaid are boxplots showing the quartiles, whiskers showing 1.5 times interquartile range, and dots showing outliers. Each Pair of CO and CF show the associated P value (Wilcox test).

Extended Data Fig. 5 Surface markers of basal cell subsets.

a, Scaled expression of the top differentially expressed CD marker genes that inform specific basal cell subsets, visualized by heatmap. b, FACS plots showing segregation of total basal cells (CD326+, CD271+, CD45-, CD31−) into basal subsets based on their preferential expression of CD66 and CD266, in freshly isolated CO (upper panel) and primary hBE culture (lower panel).

Extended Data Fig. 6 Basal cell networks.

a, Heatmap showing the percent of normalized expression of the ten basal networks across the basal subset groups, divided by CO and CF. Each cell shows the average expression of all cells in that category, normalized by row. b, Heatmap showing the percent of normalized expression within the basal subset groups for the top five genes selected from each basal network based on their pan-institutional identity as either the most Up or Down in CF within the given network. Up/Down and Network classification is shown by annotation to left of heatmap and in key at right c, Bar plots showing the average expression of all genes in the remaining individual basal networks per basal subset group, in CO or CF cells.

Extended Data Fig. 7. Proliferative basal cells in CO and CF.

a, Scoring of the proliferative state (generated using a gene signature from Basal2 subset, Supplementary Table 2), of primary hBE from CO and CF, visualized by UMAP. b, Same scoring showed as violin plots with pairwise t-test comparison of CO and CF, *: p < 2.22e-16 (Wilcox test). Overlaid are boxplots showing the quartiles, whiskers showing 1.5 times interquartile range, and dots showing outliers. 3 clones were sampled for each condition.

Extended Data Fig. 8 Counting proliferative basal cell in CO and CF.

a, Representative IF images of airways showing KRT5 (green) and PCNA (cyan), all nuclei are counterstained with DAPI (blue) in the merged image. Scale bar shows 75 μm. b, Representative examples of watershed segmentation for isolated KRT5 and PCNA staining. c, Representative images indicating counting of KRT5 (green) and PCNA (cyan) expressing cells in the segmented images. Scale bar shows 75 μm. Red and yellow boxes highlight areas that provide 4x zoomed images. d, Segmentation data assumes a normal distribution. Each data point represents a possible cell and its corresponding area. Red line represents the mean area of the data and black line represents two standard deviations above the mean area. Representative tiles scan regions taken at 20x magnification for non-CF (e) and CF (f) subjects stained for KRT5 (green), PCNA (cyan) and nuclei are counterstained with DAPI (blue). Dimensions of the airways are indicated by the white lines. In all cases, images are representative of 14 CF and 17 CO fields of view.

Extended Data Fig. 9 FACs isolation of airway epithelial cells.

Representative FACS plots for isolation of epithelial cells to use in scRNAseq with 10X Genomics. Cell debris were excluded on the basis of FSC-A versus SSC-A, then doublets were removed using Trigger Pulse Width versus FSC-A (Influx). Dead cells were identified and excluded on the base of staining with DAPI. Negative gating for CD45, CD31, and CD235a, combined with positive gating for EPCAM (CD326) were used to identify epithelial cells.

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Carraro, G., Langerman, J., Sabri, S. et al. Transcriptional analysis of cystic fibrosis airways at single-cell resolution reveals altered epithelial cell states and composition. Nat Med 27, 806–814 (2021). https://doi.org/10.1038/s41591-021-01332-7

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