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A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte

Naturevolume 560pages377381 (2018) | Download Citation


The functions of epithelial tissues are dictated by the types, abundance and distribution of the differentiated cells they contain. Attempts to restore tissue function after damage require knowledge of how physiological tasks are distributed among cell types, and how cell states vary between homeostasis, injury–repair and disease. In the conducting airway, a heterogeneous basal cell population gives rise to specialized luminal cells that perform mucociliary clearance1. Here we perform single-cell profiling of human bronchial epithelial cells and mouse tracheal epithelial cells to obtain a comprehensive census of cell types in the conducting airway and their behaviour in homeostasis and regeneration. Our analysis reveals cell states that represent known and novel cell populations, delineates their heterogeneity and identifies distinct differentiation trajectories during homeostasis and tissue repair. Finally, we identified a novel, rare cell type that we call the ‘pulmonary ionocyte’, which co-expresses FOXI1, multiple subunits of the vacuolar-type H+-ATPase (V-ATPase) and CFTR, the gene that is mutated in cystic fibrosis. Using immunofluorescence, modulation of signalling pathways and electrophysiology, we show that Notch signalling is necessary and FOXI1 expression is sufficient to drive the production of the pulmonary ionocyte, and that the pulmonary ionocyte is a major source of CFTR activity in the conducting airway epithelium.

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We thank members of the Jaffe and Klein labs, and of the Tissue Repair hub for helpful discussions; C. Weinreb for help with implementing SPRING and data analysis; C. Bauer and K. Feldman for help obtaining human bronchial tissue; P. Capodieci, K. Wetzel and J. Judge for help with tissue processing; K. Mansfield, B. Suchomel and C. Saravanan for help with histology; K. Coote and R. Valdez Misiolek for help with electrophysiology; N. Kirkpatrick, A. Szilvasi and D. Ahern-Ridlon for help with flow cytometry and imaging; J. Reece-Hoyes and S. An for help with lentiviral design; the Single Cell Core Facility at Harvard Medical School for inDrop reagents; the Bauer Core Facility for sequencing; T. Bouwmeester and J. Porter for support and input through the course of this work; R. DeBerardinis for reviewing the manuscript; and B. Ward for editorial assistance. L.W.P. would like to thank R. N. Plasschaert for thoughtful discussion. A.M.K. received support from a Career Award at the Scientific Interface from the Burroughs Wellcome Fund, an Edward Mallinckrodt Jr. Foundation Grant. R.Z. received support from the Lithuanian Education Exchanges Support Foundation. A.M.K. and R.Z. are supported by NCI grant R33CA212697-01.

Reviewer information

Nature thanks I. Amit and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

    • Lindsey W. Plasschaert
    • , Rayman Choo-Wing
    •  & Aron B. Jaffe

    Present address: Respiratory Diseases, Novartis Institutes for BioMedical Research, Cambridge, MA, USA

    • Virginia Savova

    Present address: Precision Immunology, Immunology & Inflammation Research Therapeutic Area, Sanofi, Cambridge, MA, USA

  1. These authors contributed equally: Lindsey W. Plasschaert, Rapolas Žilionis


  1. Chemical Biology & Therapeutics, Novartis Institutes for BioMedical Research, Cambridge, MA, USA

    • Lindsey W. Plasschaert
    • , Rayman Choo-Wing
    •  & Aron B. Jaffe
  2. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    • Rapolas Žilionis
    • , Virginia Savova
    •  & Allon M. Klein
  3. Institute of Biotechnology, Vilnius University, Vilnius, Lithuania

    • Rapolas Žilionis
  4. Chemical Biology & Therapeutics, Novartis Institutes for BioMedical Research, Basel, Switzerland

    • Judith Knehr
    •  & Guglielmo Roma


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A.M.K. and A.B.J. conceived the project and performed the initial pilot experiments. L.W.P., R.Z., G.R., A.M.K. and A.B.J. designed experiments and L.W.P. and R.Z. performed most experiments. J.K. and G.R. sequenced cDNA libraries. R.Z, V.S. and A.M.K. performed scRNA-seq analysis. L.W.P. performed histology, lentiviral studies, Notch pathway modulation and electrophysiology. R.C.-W. performed animal work including polidocanol dosing and trachea isolation. L.W.P., R.Z., A.M.K. and A.B.J. wrote the manuscript. All authors read and reviewed the manuscript.

Competing interests

L.W.P., R.C.-W., J.K., G.R., and A.B.J. are employees of Novartis Institutes for BioMedical Research. V.S. is an employee of Sanofi Aventis. A.M.K. is a founder and SAB member of 1Cell-Bio.

Corresponding authors

Correspondence to Allon M. Klein or Aron B. Jaffe.

Extended data figures and tables

  1. Extended Data Fig. 1 Atlas of transcription factors, surface molecules and kinases enriched in proximal airway lineages of mouse and human.

    a, b, Transcription factors, kinases and surface molecules in mouse (a) and human (b) identified among the list of cell-type-specific genes that met the following criteria: significantly enriched in lineage (false discovery rate (FDR) <5%, permutation test), expressed at ≥50 transcripts per million (TPM), expressed in marked lineage at least 1.5× higher than second highest cluster and highest in marked lineage for 4/4 (mouse) or 2/3 (human) biological replicates. c, Pairwise correlation of cell populations identified by single-cell RNA-seq. Genes used for correlation analysis were the 20% most variable genes (identified as described8) of all genes expressed at a level of at least 3 UMI counts in at least 3 cells. Ward’s method was used for hierarchical clustering. Source data

  2. Extended Data Fig. 2 Gene modules identified in mouse tracheal lineages.

    Gene modules were identified by selecting variable genes within the given population that were correlated with at least 4 other genes with rank correlation >0.2. a, b, Gene–gene correlation heat map shows 4 gene modules in mouse airway basal cells (a) and 6 gene modules in mouse airway secretory cells (b). SPRING plots show where gene modules are expressed in a given population. Multiple genes are combined in a single signature defined as the mean rank of expression (dense ranking). Source data

  3. Extended Data Fig. 3 Gene modules identified in human bronchial lineages.

    a, Two major modules of anti-correlated genes were identified by selecting variable genes within the basal-to-secretory continuum that were correlated with at least 4 other genes with rank correlation >0.12. Genes within each module were then separately considered within basal and secretory cells, keeping genes with a correlation >0.35 with at least 4 other genes. b, c, Gene–gene correlation heat map shows 3 gene modules in human airway basal cells (b) and 4 gene modules in human airway secretory cells (c). SPRING plots show where gene modules are active in a given population. Multiple genes are combined in a single signature defined as the mean rank of expression (dense ranking). Source data

  4. Extended Data Fig. 4 Validation of novel lineages in mouse and human by immunofluorescence.

    a, Immunofluorescence in mouse tracheal epithelium for Krt4 (green, arrowheads), Krt5 (basal), Krt8 (luminal), Scgb1a1 (club, secretory) and Foxj1 (ciliated) (n = 3 mice). b, Immunofluorescence in differentiated HBEC cultures for FOXN4 (red, arrows), FOXJ1 (arrowheads mark FOXJ1low cells) and acetylated α-tubulin (cilia) (n = 2 donors). c, Immunofluorescence in HBEC cultures for the ionocyte markers FOXI1, ATP6V1B1 and NGFR (n = 3 donors). Arrowhead shows apical enrichment of ATP6V1B1. Arrows highlight lateral protrusions. Scale bar, 20 μm.

  5. Extended Data Fig. 5 Identification of recovery-specific cell states and population dynamics during regeneration.

    a, Cells from uninjured mouse airway do not equally populate all regions of the SPRING plot of all mouse data combined. Each cell from the uninjured condition voted for its 10 nearest neighbours among all mouse cells profiled, and smoothed vote counts were used as a proxy for uninjured cell density on the map (two left-most plots). By visual inspection of the smooth vote distribution a threshold of 25 votes was chosen to binarize regions of the SPRING plot into present versus depleted in uninjured. b, Bar charts representing abundance of rare populations as a fraction of all cells, over time post-injury. Error bars represent the 95% binomial proportion confidence interval (normal approximation). Total number cells = 7,898 from n = 4 mice (uninjured), 898 from n = 1 mouse (1 dpi), 1,964 from n = 1 mouse (2 dpi), 1,082 from n = 1 mouse (3 dpi) and 2,321 from n = 3 mice (7 dpi). c, Bar charts showing the fraction of all cells in each population that express Foxi1 during recovery. Values shown correspond to the fraction of all cells at each time point (cell and mouse numbers as in b above). Error bars defined as in b. Source data

  6. Extended Data Fig. 6 Analysis of basal-to-ciliated differentiation trajectory following injury.

    a, Population balance analysis (PBA, see Methods) was used to order 609 cells highlighted in black along the pseudotime of their basal-to-ciliated progression, followed by application of a moving average over a window of 100 cells. The resulting ordering of averaged cells is referred to as the basal-to-ciliated trajectory. PBA requires manually selecting source and sink cells for calculating the pseudotime. b, Heat map of the 1,237 differentially expressed genes along the basal-to-ciliated trajectory (permutation test, FDR <5%, fold-changemax ≥ 2; see Methods). Genes ordered by expression-weighted mean position, defined for an expression time series xt as \(\tau =\frac{\sum _{t}t{x}_{t}}{\sum _{t}{x}_{t}}\). c, Heat map of transcription factors only. Hierarchical clustering revealed six major clusters of correlated genes. Clusters were ordered by mean expression weighted mean position. d, Plots of up to 5 transcription factors sampled from each cluster. The y axis shows the average expression of a gene within the window of 100 cells ± s.e.m. (or +1/[window size] for mean values of zero), normalized to the maximum value. The total trajectory includes 609 cells. Source data

  7. Extended Data Fig. 7 Specification and characterization of FOXI1 lineage in human bronchial epithelium.

    a, HBECs were transduced at seeding with GFP or GFP:FOXI1 lentivirus, differentiated and sorted for GFP (shown is representative gating strategy, n = 12). b, Fold change in transduced cells (GFP+) compared to non-transduced cells (GFP) was determined by RT–qPCR normalized to GAPDH. Pooled data from 2 donors transduced with GFP (n = 7 samples) or GFP:FOXI1 (n = 8 samples) are represented as mean ± s.e.m. P value: FOXI1, 0.001; CFTR, 0.04; ATP6V1B1, 0.006; FOXJ1, 0.01; SCGB1A1 , 0.02; two-tailed t-test. c, Fluorescent in situ hybridization (RNAscope) for FOXI1 and CFTR in HBEC culture transduced with GFP or GFP:FOXI1. Note that while there is an increase in FOXI1/CFTR co-labelled cells, not all FOXI1 cells express CFTR. (arrowheads versus arrows) (n = 2 independent experiments in 2 donors). d, e, Chromogenic in situ hybridization (RNAscope) in primary human bronchial tissue surface epithelium and gland ducts for CFTR and FOXI1 (d) or FOXJ1 (e). Chromogenic signals were split and pseudocoloured to reveal individual channels; inset regions are shown at higher magnification on the right. Note that CFTR is highly enriched in FOXI1+ but not FOXJ1+ cells (n = 1 donor, 5 regions of bronchial tree analysed). Scale bars, 20 μm. Source data

  8. Extended Data Fig. 8 Single-cell RNA-seq analysis of HBECs transduced with GFP and GFP:FOXI1.

    a, SPRING plot combining cells transduced with GFP (n = 9,436) or GFP:FOXI1 (n = 10,330), with each of the two conditions highlighted in black (total cells n = 19,766). b, The SLC16A7+ population was identified to be absent in the viral transduction experiment after mapping single-cell transcriptome onto the reference state map. Each cell from the viral transduction experiment voted for its nearest neighbour in the reference experiment. The bar chart on the right shows the average number of votes per cluster. c, Cell states unique to the viral transduction experiment were identified as detailed in Extended Data Fig. 5a. d, Cells representing states also found in the reference experiment (conserved cells) inherited the label of their single nearest neighbour in the reference map. Cells specific to the viral transduction experiment were divided into four clusters by spectral clustering, with their top five enriched genes shown in the top part of the heat map (right). Enrichment of gene g in population i is defined as the fold change in expression of g in i versus the second highest expresser. A pseudo value of 10 TPM was added before calculating the fold change, and only genes expressed at >50 TPM in at least one cluster were considered. The bottom of the heat map shows the top 20 enriched genes identified treating all four transduction-specific states as one population. e, Bar chart showing fold changes in population size following GFP:FOXI1 versus GFP transduction (extension of Fig. 3f). f, Expression of transgene in identified cell populations. Source data

  9. Extended Data Fig. 9 Notch pathway component enrichment in airway lineages.

    a, b, SPRING plots show enrichment of Notch pathway components in mouse (a) and human (b) airway lineages. Normalized counts are shown for the Notch ligands JAG1, JAG2 and DLL1, and the Notch receptors NOTCH1, NOTCH2 and NOTCH3. The Notch target gene signature combines HES1, HES5 and NRARP into a single gene signature, defined as the mean expression rank (dense ranking). All gene expression and signature values are smoothed (see Methods for smoothing). Source data

  10. Extended Data Fig. 10 Inhibition of Notch signalling decreases ionocyte markers in HBECs.

    a, Expression of Notch target genes and airway lineage markers in cultures treated with 3.3 µM DAPT compared to cultures treated with DMSO. Notch target genes (NRARP P = 0.03, HES5) and secretory cell markers (MUC5B P = 0.001, MUC5AC) are decreased whereas ciliated cell markers (FOXJ1, DNAI2 P = 0.01) and basal cell markers (ITGA6 P = 0.006 and TP63) are increased upon DAPT treatment. Note that ionocyte markers (FOXI1 P = 0.02, CFTR) are also decreased upon DAPT treatment. Two-tailed t-test; n = 8 experiments in 2 donors. b, FOXI1 cell counts in HBEC cultures treated with antibodies that neutralize individual Notch receptors (n = 5 experiments in 2 donors). All data are mean ± s.e.m. Source data

Supplementary information

  1. Supplementary Tables 1-5

    This zipped file contains Supplementary Tables 1-5: Supplementary Table 1 Genes enriched in mouse tracheal epithelial cell populations identified by scRNA-seq; Supplementary Table 2 Genes enriched in human bronchial epithelial cell populations identified by scRNA-seq.; Supplementary Table 3 Average gene expression in tracheal epithelial cell populations identified by scRNA-seq in this study; Supplementary Table 4 Differentially expressed genes during basal-to-ciliated differentiation and Supplementary Table 5 Sequences of primers used for inDrops scRNA-seq.

  2. Reporting Summary

  3. Supplementary Data 1

    This file contains a yaml file specifying parameters used with the indrop.py pipeline to process raw mouse data.

  4. Supplementary Data 2

    This file contains a yaml file specifying parameters used with the indrop.py pipeline to process raw human data from the reference (untransduced) experiment.

  5. Supplementary Data 3

    This file contains a yaml file specifying parameters used with the indrop.py pipeline to process raw human data from the viral transduction experiment.

Source data

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