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A cellular census of human lungs identifies novel cell states in health and in asthma

Nature Medicine volume 25, pages 1153–1163 (2019)Cite this article

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Abstract

Human lungs enable efficient gas exchange and form an interface with the environment, which depends on mucosal immunity for protection against infectious agents. Tightly controlled interactions between structural and immune cells are required to maintain lung homeostasis. Here, we use single-cell transcriptomics to chart the cellular landscape of upper and lower airways and lung parenchyma in healthy lungs, and lower airways in asthmatic lungs. We report location-dependent airway epithelial cell states and a novel subset of tissue-resident memory T cells. In the lower airways of patients with asthma, mucous cell hyperplasia is shown to stem from a novel mucous ciliated cell state, as well as goblet cell hyperplasia. We report the presence of pathogenic effector type 2 helper T cells (TH2) in asthmatic lungs and find evidence for type 2 cytokines in maintaining the altered epithelial cell states. Unbiased analysis of cell–cell interactions identifies a shift from airway structural cell communication in healthy lungs to a TH2-dominated interactome in asthmatic lungs.

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Fig. 1: A human lung cell census identifies zonation of novel epithelial cell states across macro-anatomical location.
Fig. 2: A cellular and molecular map of the stromal and immune components of across the upper and lower human respiratory airways.
Fig. 3: Distinct programs of epithelial cell differentiation in asthmatic versus healthy airways.
Fig. 4: Remodeling of the stromal and immune compartments in asthmatic airways.
Fig. 5: Pathogenic effector TH2 cells are enriched in asthmatic airways.
Fig. 6: Asthma is characterized by unique cell-to-cell signaling networks.

Data availability

Data requests for raw and analyzed data and materials will fall under two categories. Datasets from healthy live volunteers and live volunteers with asthma will be promptly reviewed by the University of Groningen. Any data and materials that can be shared will be released via a Material Transfer Agreement. These datasets can be found on European Genome–phenome Archive (https://www.ebi.ac.uk/ega/home) EGAS00001001755. Datasets generated from lung resection samples using Drop-seq can be accessed in GSE130148. Datasets generated from deceased donors fall under Open Access Policies of the Human Cell Atlas (https://www.humancellatlas.org for details). This data can be accessed at European Genome–phenome Archive (https://www.ebi.ac.uk/ega/home) EGAS00001002649. Interactive exploration tool: www.lungcellatlas.org.

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Acknowledgements

We thank J. Eliasova (scientific illustrator) for support with design of figures, the Sanger Single Cell Genomics Core Facility for support with the SmartSeq2 protocol, E. Rawlins for feedback and critical reading of the manuscript, L. Mamanova for the technical support, as well as all the members of the Teichmann lab for scientific input. We are grateful to the Cambridge Biorepository for Translational Medicine (CBTM) for the provision of tissue from deceased organ donors, to all tissue donors, and to K. Sjollema and the UMCG Imaging and Microscopy Center. We gratefully acknowledge the provision of human biomaterial and clinical data from the CPC-M bioArchive and its partners at the Asklepios Biobank Gauting, the Klinikum der Universität München, and the Ludwig-Maximilians-Universität München. This project was funded by and part of the Open Targets collaboration (https://www.opentargets.org), a GlaxoSmithKline collaborative agreement with University Medical Center Groningen, Wellcome (WT206194), the Lung Foundation Netherlands (projects no. 5.1.14.020 and 4.1.18.226), Health-Holland, Top Sector Life Sciences and Health, and Human Cell Atlas Wellcome Stragetic Science Support (211276/Z/18/Z). R.V.-T. was supported by EMBO and HFSP Long Term fellowships. T.G. by the Marie Curie ENLIGHT-TEN training network. L.M.S. acknowledges funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement no. 753039. H.B.S. acknowledges funding by the Helmholtz Association and the German Center for Lung Research (DZL). F.J.T. acknowledges financial support by the German Research Foundation (DFG) within the Collaborative Research Centre 1243. F.J.T acknowledges subproject A17, by the Helmholtz Association (Incubator grant sparse2big, grant number ZT-I-0007) and by the Chan Zuckerberg Initiative D.A.F. (advised fund of Silicon Valley Community Foundation), grant number 182835. A.C. and P.M.S. acknowledge European Research Council (project 677501 – ZF_Blood). This publication is part of the Human Cell Atlas (www.humancellatlas.org/publications).

Author information

Author notes

  1. These authors contributed equally: F. A. Vieira Braga, G. Kar, M. Berg, O. A. Carpaij, K. Polanski.

  2. These authors jointly supervised this work: M. C. Nawijn, S. A. Teichmann.

Authors and Affiliations

  1. Wellcome Sanger Institute, Cambridge, UK

    Felipe A. Vieira Braga, Gozde Kar, Krzysztof Polanski, Tomás Gomes, Eirini S. Fasouli, Mirjana Efremova, Roser Vento-Tormo, Carlos Talavera-López, Paulina M. Strzelecka, Helen V. Firth, Ana Cvejic, Kerstin B. Meyer & Sarah A. Teichmann

  2. Open Targets, Cambridge, UK

    Felipe A. Vieira Braga, Gozde Kar, Eirini S. Fasouli & Sarah A. Teichmann

  3. Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands

    Marijn Berg, Sharon Brouwer, Laura Hesse, Jian Jiang, Marnix R. Jonker, Marjan Luinge, Corry-Anke Brandsma, Wim Timens & Martijn C. Nawijn

  4. Groningen Research Institute for Asthma and COPD, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands

    Marijn Berg, Orestes A. Carpaij, Sharon Brouwer, Laura Hesse, Jian Jiang, Marnix R. Jonker, Marjan Luinge, Corry-Anke Brandsma, Wim Timens, Gerard H. Koppelman, Maarten van den Berge & Martijn C. Nawijn

  5. Department of Pulmonology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands

    Orestes A. Carpaij & Maarten van den Berge

  6. Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany

    Lukas M. Simon, Subarna Palit & Fabian J. Theis

  7. Allergic Inflammation Discovery Performance Unit, Respiratory Therapy Area, GlaxoSmithKline, Stevenage, UK

    Karen Affleck & Antoon J. van Oosterhout

  8. TranslaTUM, Technische Universität München, Munich, Germany

    Subarna Palit

  9. Institute of Virology, Technische Universität München, Munich, Germany

    Subarna Palit

  10. German Center for Infection Research, Partner Site Munich, Munich, Germany

    Subarna Palit

  11. Department of Haematology, University of Cambridge, Cambridge, UK

    Paulina M. Strzelecka & Ana Cvejic

  12. Cambridge Stem Cell Institute, Cambridge, UK

    Paulina M. Strzelecka & Ana Cvejic

  13. Department of Surgery, NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK

    Krishnaa T. Mahbubani & Kourosh Saeb-Parsy

  14. Helmholtz Zentrum München, Institute of Lung Biology and Disease, Member of the German Center for Lung Research (DZL), Munich, Germany

    Ilias Angelidis, Maximilian Strunz & Herbert B. Schiller

  15. Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children’s Hospital, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands

    Gerard H. Koppelman

  16. Department of Mathematics, Technische Universität München, Garching, Germany

    Fabian J. Theis

  17. Theory of Condensed Matter Group, Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge, UK

    Sarah A. Teichmann

Authors
  1. Felipe A. Vieira Braga
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  2. Gozde Kar
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  3. Marijn Berg
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  4. Orestes A. Carpaij
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  13. Roser Vento-Tormo
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  18. Paulina M. Strzelecka
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  34. Martijn C. Nawijn
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  35. Sarah A. Teichmann
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Contributions

S.A.T., M.C.N., M.v.d.B., K.A., A.J.v.O. and H.B.S. designed the project. F.A.V.B., M.C.N. and S.A.T. wrote the paper. F.A.V.B., O.A.C., S.B., L.H., J.J., E.S.F., P.M.S., K.T.M., I.A., M.R.J. and M.S. generated the data. F.A.V.B., G.K., M.B., L.M.S., T.G., J.J., M.E., S.P., K.P., H.V.F. and F.J.T. analyzed the data. F.A.V.B., G.K., O.A.C., L.M.S., T.G., J.J., R.V.-T., K.A., S.P., A.C., K.S.-P., W.T., G.H.K., A.J.v.O., C.T.-L., H.B.S., M.v.d.B, F.J.T., M.B. and K.B.M. interpreted the data. All authors read the manuscript, offered feedback, and approved it before submission.

Corresponding authors

Correspondence to Martijn C. Nawijn or Sarah A. Teichmann.

Ethics declarations

Competing interests

K.A. and A.J.v.O. are employees of GlaxoSmithKline. M.B., O.A.C., S.B., M.v.d.B. and M.C.N. received project funding from GlaxoSmithKline.

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Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Unbiased clustering of upper airway, lower airway, and parenchymal lung tissue cells and strategy to divide the dataset used in Figs. 1 and 2.

a, t-SNE plot depicting unbiased cluster assignment of combined dataset of parenchyma and upper and lower airways, highlighting EPCAM-high cell clusters (epithelial cells) in blue and other cell types in green. b, t-SNE as in a, highlighting expression of cell lineage markers. c, t-SNE plot of only epithelial cells, as defined in a. d, t-SNE as in c, showing expression of individual genes used for cell-type assignment of different epithelial subsets. e, Table depicting cell-type assignment of clusters identified in c. Alveolar type 1 cells were not identified in an individual cluster in this analysis and they were selected by the high expression of the type 1 marker AGER. Panels a–e, n = 12 individuals. Complete sample distribution in Fig. 1b.

Extended Data Fig. 2 Resection lung material analysis via Drop-seq.

a, t-SNE showing single cells obtained from lung resection samples analyzed using Drop-seq. b, Heat map depicting top differentially expressed genes by log fold change among the clusters present in a. c, matchSCore analysis comparing the clusters present in the Drop-seq analysis of lung resection material to the clusters identified in Figs. 1 and 2. Panels a and b, n = 4 individuals. Panel c, n = 16 individuals.

Extended Data Fig. 3 Ionocyte cell identity and molecular profile.

a, Immunohistochemistry staining for FOXI1, using hematoxylin as counterstain. b, Fluorescent staining for ionocyte markers: mouse anti-FOXI1 and rabbit anti-CFTR. c–f, Fluorescent staining for FOXI1 (ionocyte-specific) and other epithelial markers: neuroendocrine cell marker (rabbit anti-synaptophysin) (c), ciliated cell marker (mouse anti-α-tubulin) (d), basal cell marker (mouse anti-α-KRT5) (e), and secretory cell marker (mouse anti-MUC5AC) (f). DAPI (blue) stains nuclei. Representative picture out of ten individuals analyzed.

Extended Data Fig. 4 Marker gene analysis of specific goblet, ciliated, and neuroendocrine cells in human lungs.

a, Heat map depicting the expression of marker genes identified by differential expression analysis comparing goblet 1 versus goblet 2 cell types. b, Heat map depicting the expression of marker genes identified by differential expression analysis. Ciliated 1 genes and shared nasal signature genes generated by comparing ciliated 1 versus ciliated 2. Ciliated 2 genes generated by comparing ciliated 2 versus the combination of ciliated 1, goblet 1, and goblet 2, to subtract the nasal signature. c, t-SNE plot depicting the expression of the neuroendocrine marker CHGA. d, Heat map depicting the expression of neuroendocrine markers in neuroendocrine cells as well as in the other clusters identified in our analysis. Neuroendocrine cells identified based on CHGA expression. Neuroendocrine markers obtained from ref. 24. Panels a–d, n = 12 individuals. Complete sample distribution in Fig. 1b.

Extended Data Fig. 5 Cell-type assignment strategy for the assignment of non-epithelial cells discussed in Fig. 2.

a, t-SNE colored by unbiased clustering of the non-epithelial dataset described in Fig. 2. b, t-SNE depicting single cells colored by their respective sample origin. c, t-SNE as in a, showing the expression of lineage markers used for cell-type assignment. d, Table with the strategy used for cluster cell assignment of the cell types present in Fig. 2. e, Cluster distribution in the two donors from which we collected paired nasal brushes, airway brushes, and airway biopsies. f, Violin plots depicting the expression of immunoglobulin genes in the B cell cluster divided by tissue of origin. Representative genes of IgG, IgA, IgE, and IgM producing B cells depicted. Panels a–f, n = 12 individuals. Complete sample distribution in Fig. 2a.

Extended Data Fig. 6 Unbiased clustering of lower airway biopsy samples of healthy volunteers and volunteers with asthma and the strategy used to divide the dataset depicted in Fig. 3.

a, t-SNE plot depicting unbiased cluster assignment of combined dataset of healthy control and asthma lower airway biopsies, highlighting EPCAM-high cell clusters (epithelial cells) in blue and other cell types in green. b, t-SNE as in a, highlighting expression of cell lineage markers. c, t-SNE plot of only epithelial cells, as defined in a. d, t-SNE as in c, showing expression of individual genes used for cell-type assignment of different epithelial subsets. e, Table depicting cell-type assignment of clusters identified in c. f, Fluorescence in situ hybridization image of lung airway biopsy of one asthma patient. Green marks FOXJ1 (ciliated marker), red marks MUC5AC (secretory marker), and blue marks DAPI (nuclei). White arrow points to areas in which both MUC5AC and FOXJ1 are co-expressed in the exact same region (yellow color) or are contained in the vicinities of one nucleus, suggesting co-expression of the transcripts in the same cell. n = 6 healthy volunteers and 6 volunteers with asthma. Full sample distribution in Fig. 3b. Panels a–e, n = 12 individuals. Panel f depicts the analysis performed in n = 1 asthma donor.

Extended Data Fig. 7 IL-4/IL-13- and Notch-driven gene transcription signatures in goblet cell metaplasia in asthma.

a, Violin plot depicting IL-4/IL-13 genes signature expression in each cluster of biopsy epithelial cells (signature obtained from ref. 24). b, Violin plot depicting the expression of the IL-4/IL-13 signature (as in a) in healthy control and asthma cells of selected clusters. c, Violin plot depicting Notch signature genes (HES4, HES5, HEY1, HEY2, HEYL, and NRARP) expression in each cluster of biopsy epithelial cells. d, Violin plot depicting the expression of Notch signature genes (as in c) in healthy control and asthma cells of selected clusters. e–g, Expression of secretory specific (HES4 and SPDEF) and ciliated specific (FOXJ1) transcription factors in club (e), goblet (f), ciliated (g), and mucous ciliated (h) cells. Panels a–h, n = 12 individuals. Complete sample distribution in Fig. 3b.

Extended Data Fig. 8 Clustering and cell-type assignment of non-epithelial cells in the airways of healthy control patients and patients with asthma and their expression of prostaglandin enzymes.

a, t-SNE depicting unbiased clustering of the non-epithelial dataset described in Fig. 4. b, t-SNE as in a, showing the expression of lineage markers used for cell-type assignment. c, Table with the strategy used for cluster cell assignment of the cell types present in Fig. 4. d, Cartoon illustrating the eicosanoids pathway, its receptors and in which cells each gene is expressed in the lungs, with special notation for asthma alterations. Panels a–c, n = 12 individuals. Complete sample distribution in Fig. 4a. Panel d was generated based on n = 15 individuals. Complete sample distribution in Figs. 3b, 4a, and 5b.

Extended Data Fig. 9 Comparative analysis of bulk transcriptomes versus single-cell RNA-seq data on matched airway biopsies.

a, UMAP displaying bulk transcriptomes of biopsies before digestion (blue), of the single-cell suspension obtained from digestion and used to load the 10x microfluidics device (orange) and pseudo-bulk samples generated by collapsing all the data obtained from single-cell transcriptomes (green). b, Heat map depicting expression of several epithelial and non-epithelial cell markers, as shown in Extended Data Figs. 6 and 8. Gene markers of rare cells (neuroendocrine, eosinophils, and tuft) not identified in our single-cell clusters depicted in specific colors. Panels a–c, n = 8 individuals.

Extended Data Fig. 10 Canonical T cell markers expression in TH CD4 clusters.

a, Heat map depicting genes differentially expressed in the nine clusters of CD4 T cells using an initial unbiased clustering method, followed by manual selection of TH1, TH2, and TH17 clusters based on canonical cytokines. a–f, Violin plots depicting canonical markers of tissue-resident memory cells (b) and TH1 (c), TH2 (d), TH17 (e), and Treg (f) CD4 T cells. Depicted markers based on literature search of well-established T cell subsets. Panels a–f, n = 15 individuals. Complete sample distribution in Fig. 5b.

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Source Data Fig. 1

Cell numbers per cluster used to design Fig. 1d,e

Source Data Fig. 2

Cell numbers per cluster used to design Fig. 2c,d

Source Data Fig. 3

Cell numbers per cluster used to design Fig. 3c

Source Data Fig. 4

Cell numbers per cluster used to design Fig. 4c

Source Data Fig. 5

Cell numbers per cluster used to design Fig. 5d,e,h

Source Data Fig. 6

P values calculated from cellPhone DB used to design Fig. 6a–d

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Vieira Braga, F.A., Kar, G., Berg, M. et al. A cellular census of human lungs identifies novel cell states in health and in asthma. Nat Med 25, 1153–1163 (2019). https://doi.org/10.1038/s41591-019-0468-5

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