Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Metastatic-niche labelling reveals parenchymal cells with stem features

An Author Correction to this article was published on 13 November 2019

This article has been updated

Abstract

Direct investigation of the early cellular changes induced by metastatic cells within the surrounding tissue remains a challenge. Here we present a system in which metastatic cancer cells release a cell-penetrating fluorescent protein, which is taken up by neighbouring cells and enables spatial identification of the local metastatic cellular environment. Using this system, tissue cells with low representation in the metastatic niche can be identified and characterized within the bulk tissue. To highlight its potential, we applied this strategy to study the cellular environment of metastatic breast cancer cells in the lung. We report the presence of cancer-associated parenchymal cells, which exhibit stem-cell-like features, expression of lung progenitor markers, multi-lineage differentiation potential and self-renewal activity. In ex vivo assays, lung epithelial cells acquire a cancer-associated parenchymal-cell-like phenotype when co-cultured with cancer cells and support their growth. These results highlight the potential of this method as a platform for new discoveries.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: The mCherry-niche labelling strategy.
Fig. 2: The mCherry-niche strategy enables characterization of metastatic-niche neutrophils.
Fig. 3: The mCherry-niche strategy identifies an epithelial component of metastatic TME.
Fig. 4: Lung epithelial cells in the metastatic niche display a progenitor phenotype.
Fig. 5: CAPs show multi-lineage differentiation potential.

Similar content being viewed by others

Data availability

The RNA-sequencing datasets have been deposited in the Gene Expression Omnibus with accession number GSE117930; the single-cell RNA-sequencing datasets have been deposited with accession number GSE131508. The proteomic datasets have been deposited in the Proteomics Identifications Database with accession number PXD010597.

Change history

  • 13 November 2019

    An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

References

  1. Hanahan, D. & Coussens, L. M. Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21, 309–322 (2012).

    CAS  PubMed  Google Scholar 

  2. Quail, D. F. & Joyce, J. A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19, 1423–1437 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Barash, S., Wang, W. & Shi, Y. Human secretory signal peptide description by hidden Markov model and generation of a strong artificial signal peptide for secreted protein expression. Biochem. Biophys. Res. Commun. 294, 835–842 (2002).

    CAS  PubMed  Google Scholar 

  4. Flinterman, M. et al. Delivery of therapeutic proteins as secretable TAT fusion products. Mol. Ther. 17, 334–342 (2009).

    CAS  PubMed  Google Scholar 

  5. Shaner, N. C., Steinbach, P. A. & Tsien, R. Y. A guide to choosing fluorescent proteins. Nat. Methods 2, 905–909 (2005).

    CAS  PubMed  Google Scholar 

  6. del Pozo Martin, Y. et al. Mesenchymal cancer cell-stroma crosstalk promotes niche activation, epithelial reversion, and metastatic colonization. Cell Rep. 13, 2456–2469 (2015).

    PubMed  PubMed Central  Google Scholar 

  7. Peinado, H. et al. Pre-metastatic niches: organ-specific homes for metastases. Nat. Rev. Cancer 17, 302–317 (2017).

    CAS  PubMed  Google Scholar 

  8. Wculek, S. K. & Malanchi, I. Neutrophils support lung colonization of metastasis-initiating breast cancer cells. Nature 528, 413–417 (2015).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  9. Coffelt, S. B., Wellenstein, M. D. & de Visser, K. E. Neutrophils in cancer: neutral no more. Nat. Rev. Cancer 16, 431–446 (2016).

    CAS  PubMed  Google Scholar 

  10. Singhal, S. et al. Origin and role of a subset of tumor-associated neutrophils with antigen-presenting cell features in early-stage human lung cancer. Cancer Cell 30, 120–135 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Blomberg, O. S., Spagnuolo, L. & de Visser, K. E. Immune regulation of metastasis: mechanistic insights and therapeutic opportunities. Dis. Model. Mech. 11, dmm036236 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Kessenbrock, K., Plaks, V. & Werb, Z. Matrix metalloproteinases: regulators of the tumor microenvironment. Cell 141, 52–67 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Kowanetz, M. et al. Granulocyte-colony stimulating factor promotes lung metastasis through mobilization of Ly6G+Ly6C+ granulocytes. Proc. Natl Acad. Sci. USA 107, 21248–21255 (2010).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  14. Qian, B.-Z. et al. CCL2 recruits inflammatory monocytes to facilitate breast-tumour metastasis. Nature 475, 222–225 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Acharyya, S. et al. A CXCL1 paracrine network links cancer chemoresistance and metastasis. Cell 150, 165–178 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Oskarsson, T. et al. Breast cancer cells produce tenascin C as a metastatic niche component to colonize the lungs. Nat. Med. 17, 867–874 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Erez, N. Cancer: Opening LOX to metastasis. Nature 522, 41–42 (2015).

    ADS  CAS  PubMed  Google Scholar 

  18. Onnis, B., Fer, N., Rapisarda, A., Perez, V. S. & Melillo, G. Autocrine production of IL-11 mediates tumorigenicity in hypoxic cancer cells. J. Clin. Invest. 123, 1615–1629 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Malanchi, I. et al. Interactions between cancer stem cells and their niche govern metastatic colonization. Nature 481, 85–89 (2012).

    ADS  CAS  Google Scholar 

  20. Su, F., Overholtzer, M., Besser, D. & Levine, A. J. WISP-1 attenuates p53-mediated apoptosis in response to DNA damage through activation of the Akt kinase. Genes Dev. 16, 46–57 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Costa, A. et al. Fibroblast heterogeneity and immunosuppressive environment in human breast cancer. Cancer Cell 33, 463–479 (2018).

    CAS  PubMed  Google Scholar 

  22. Karnoub, A. E. et al. Mesenchymal stem cells within tumour stroma promote breast cancer metastasis. Nature 449, 557–563 (2007).

    ADS  CAS  PubMed  Google Scholar 

  23. Hosaka, K. et al. Pericyte-fibroblast transition promotes tumor growth and metastasis. Proc. Natl Acad. Sci. USA 113, E5618–E5627 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Murgai, M. et al. KLF4-dependent perivascular cell plasticity mediates pre-metastatic niche formation and metastasis. Nat. Med. 23, 1176–1190 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kim, C. F. B. et al. Identification of bronchioalveolar stem cells in normal lung and lung cancer. Cell 121, 823–835 (2005).

    CAS  PubMed  Google Scholar 

  27. Lee, J.-H. et al. Lung stem cell differentiation in mice directed by endothelial cells via a BMP4–NFATc1–thrombospondin-1 axis. Cell 156, 440–455 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Zacharias, W. J. et al. Regeneration of the lung alveolus by an evolutionarily conserved epithelial progenitor. Nature 555, 251–255 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chapman, H. A. et al. Integrin α6β4 identifies an adult distal lung epithelial population with regenerative potential in mice. J. Clin. Invest. 121, 2855–2862 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. McQualter, J. L., Yuen, K., Williams, B. & Bertoncello, I. Evidence of an epithelial stem/progenitor cell hierarchy in the adult mouse lung. Proc. Natl Acad. Sci. USA 107, 1414–1419 (2010).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. Nabhan, A. N., Brownfield, D. G., Harbury, P. B., Krasnow, M. A. & Desai, T. J. Single-cell Wnt signaling niches maintain stemness of alveolar type 2 cells. Science 359, 1118–1123 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Liu, Y. et al. Tumor exosomal RNAs promote lung pre-metastatic niche formation by activating alveolar epithelial TLR3 to recruit neutrophils. Cancer Cell 30, 243–256 (2016).

    PubMed  Google Scholar 

  33. Lee, J. W. et al. Hepatocytes direct the formation of a pro-metastatic niche in the liver. Nature 567, 249–252 (2019).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  34. Guy, C. T., Cardiff, R. D. & Muller, W. J. Induction of mammary tumors by expression of polyomavirus middle T oncogene: a transgenic mouse model for metastatic disease. Mol. Cell. Biol. 12, 954–961 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Okabe, M., Ikawa, M., Kominami, K., Nakanishi, T. & Nishimune, Y. ‘Green mice’ as a source of ubiquitous green cells. FEBS Lett. 407, 313–319 (1997).

    CAS  PubMed  Google Scholar 

  36. Rock, J. R. et al. Multiple stromal populations contribute to pulmonary fibrosis without evidence for epithelial to mesenchymal transition. Proc. Natl Acad. Sci. USA 108, E1475–E1483 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Srinivas, S. et al. Cre reporter strains produced by targeted insertion of EYFP and ECFP into the ROSA26 locus. BMC Dev. Biol. 1, 4 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Rawlins, E. L. et al. The role of Scgb1a1+ Clara cells in the long-term maintenance and repair of lung airway, but not alveolar, epithelium. Cell Stem Cell 4, 525–534 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Lee, J.-H. et al. Anatomically and functionally distinct lung mesenchymal populations marked by Lgr5 and Lgr6. Cell 170, 1149–1163 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Théry, C., Amigorena, S., Raposo, G. & Clayton, A. Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr. Protoc. Cell Biol. 30, 3.22.1–3.22.29 (2006).

    Google Scholar 

  41. Deerink, T. J. et al. NCMIR methods for 3D EM: a new protocol for preparation of biological specimens for serial block face scanning electron microscopy. National Center for Microscopy and Imaging Research https://ncmir.ucsd.edu/sbem-protocol (2010).

  42. Heinze, I. et al. Species comparison of liver proteomes reveals links to naked mole-rat longevity and human aging. BMC Biol. 16, 82 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. Bruderer, R. et al. Optimization of experimental parameters in data-independent mass spectrometry significantly increases depth and reproducibility of results. Mol. Cell. Proteomics 16, 2296–2309 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  46. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  47. Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank E. Sahai, P. Scaffidi (The Francis Crick Institute) and V. Sanz-Moreno (Barts Cancer Institute) for scientific discussions, critical reading of the manuscript and sharing cell lines and mouse strains; M. Izquierdo (CSIC, Madrid) for sharing the CD63–GFP plasmid; E. Nye and the pathologists G. Stamp and E. Herbert from the Experimental Histopathology Unit at the Francis Crick Institute for histological processing and analysis support; J. Bee from the Biological Resources Unit at the Francis Crick Institute for technical support with mice and mouse tissues; R. Goldstone and A. Edwards from the Advanced Sequencing Facility at the Francis Crick Institute for technical support; M. Llorian-Sopena from the Bioinformatics and Biostatistics Unit at the Francis Crick Institute for helping with the RNA sequencing analysis; the Flow Cytometry Unit at the Francis Crick Institute, particularly S. Purewal and J. Cerveira, for invaluable technical help; the Cell Services Unit at the Francis Crick Institute; C. Moore (The Francis Crick Institute) for intra-tracheal injections; and I. Pshenichnaya, P. Humphreys, S. McCallum and Cambridge Stem Cell Institute core facilities for technical assistance. We acknowledge support from the FLI Core Facility Proteomics, which is a member of the Leibniz Association and is financially supported by the Federal Government of Germany and the State of Thuringia. This work was supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001112), the UK Medical Research Council (FC001112), and the Wellcome Trust (FC001112) and the European Research Council grant (ERC CoG-H2020-725492); and by the Wellcome Trust—MRC Stem Cell Institute, which receives funding from the Sir Henry Dale Fellowship from Wellcome, the Royal Society (107633/Z/15/Z) and the European Research Council Starting Grant (679411).

Author information

Authors and Affiliations

Authors

Contributions

L.O. designed and performed most of the experiments, analysed and interpreted the data and contributed to the manuscript preparation. E.N. assisted with data collection, performed all the 3D-scaffold co-culture experiments, the in vivo WISP1 experiments and the scRNA sequencing, and interpreted and analysed the data and contributed to the manuscript preparation. I.K. performed the RT–qPCR analysis, some of the tissue immunofluorescence staining and analysed the data. A.M. and J.-H.L. performed some of the tissue immunofluorescence staining and all the lung organoid experiments, and interpreted and analysed the data. V.B. performed some of the tissue immunofluorescence staining. P.C. and S.H. performed bioinformatics analysis. I.H., J.K. and A.O. performed the proteomics and analysed the data. E.G.-G. helped with the collection of Ly6G+ cells for proteomics. G.M. performed the 3D-scaffold co-culture to analyse CD104+ cells. A.W. and L.C. performed the electron microscopy experiments. E.H. and V.S. provided human samples. L.O., E.N., I.K., V.B. and J.-H.L. critically reviewed the manuscript. J.-H.L. supervised the lung organoid experiments. I.M. designed and supervised the study, interpreted the data and wrote the manuscript.

Corresponding authors

Correspondence to Joo-Hyeon Lee or Ilaria Malanchi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Peer review information Nature thanks Marie-Liesse Asselin-Labat, Thomas Tüting and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Fig. 1 The mCherry-niche system in vitro.

a, sLP–mCherry design. b, Fluorescence images of labelling-4T1 cells after thawing. Scale bar, 10 μm. c, Representative FACS plot of labelling-4T1 cells. d, In vitro cultures of the indicated cell types with LCM: culture scheme and representative fluorescence images of HC11 (mouse mammary epithelial cells) and hNLF (human normal lung fibroblasts) with LCM (scale bar, 10 μm). e, FACS plots of 4T1, HC11, RAW264.7 (mouse macrophages), hNLF and mouse breast CAFs cultured with LCM. f, FACS analysis of 293T cells cultured with LCM, at different time points after LCM removal (black dots); white dots show the theoretical decrease considering the cell proliferation rate only (the amount of 293T cells labelled with mCherry after 24 h incubation with LCM was set to 100%). g, Representative fluorescence image of 4T1-CD63–GFP cells cultured with LCM. Scale bars: main panels, 5 μm; enlarged region, 1 μm. h, Representative correlative light and electron microscopy of labelling-4T1 cells showing re-uptake of sLP–mCherry (n = 5 different cells analysed). Top left, bright-field image overlaid with mCherry immunofluorescence (700 nm optical section). Bottom left, electron microscopy of the same cell (70-nm section thickness). Centre, best approximation of immunofluorescence–bright-field–electron microscopy overlay (scale bar, 5 μm). Right, electron microscopy of the outlined regions (centre, labelled a–c) (black arrows point at vesicular structures containing mCherry; scale bar, 1 μm). i, j, Analysis of in vitro labelling potential of soluble fraction and extracellular vesicles isolated from LCM by FACS. i, Schematic representation of LCM fractionation. j, HC11 cells cultured with either LCM, soluble fraction after depletion of extracellular vesicles (soluble) or purified extracellular vesicles. k, ImageStream analysis of mCherry+ extracellular vesicles in LCM (16% of total extracellular vesicles are mCherry+). Data are representative of three (b), ten (c) or two (dg, j, k) independent experiments.

Source data

Extended Data Fig. 2 The mCherry-niche system in vivo.

a, b, Distance of labelled cells within metastases. a, Representative fluorescence images (lines measure the maximum distance of labelled cells (mCherry+) from labelling-4T1 cells (mCherry+GFP+); scale bar, 50 μm). b, Quantification of labelling distance in micro-metastases (n = 11) and macro-metastases (n = 4). c, Correlation between the percentage of mCherry-labelled niche cells and the percentage of cancer cells in metastatic lungs analysed by FACS. Left, analysis of lungs with a small number of cancer cells (n = 14 mice). Right, analysis with all cancer cell frequencies (n = 31 mice). Statistical analysis by Pearson correlation. df, CD45+ cell frequency on live cells in distal lung, mCherry+ niche and not-injected naive lungs by FACS. d, BALB/c mice injected with labelling-4T1 cells (n = 5 mice per group). e, BALB/c mice injected with labelling-HC11 cells (n = 4 mice). f, RAG1-knockout mice injected with labelling-4T1 cells (n = 10 mice). Statistical analysis by paired two-tailed t-test. Data are represented as mean ± s.e.m.

Source data

Extended Data Fig. 3 mCherry+-niche neutrophils increase ROS production.

a, b, CD11b+ (a) and Ly6G+ (b) cell frequencies among live cells in distal lung and mCherry+ niche by FACS (n = 9 mice per group). c, Enriched processes by MetaCore analysis and GSEA based on proteomic data by comparing mCherry+-niche (n = 3) and distal lung (n = 3) neutrophils; dominant mCherry+-niche proteins were obtained by using WebGestalt (http://www.webgestalt.org/option.php). d, PCA of proteins found in unlabelled or mCherry+-niche neutrophils (n = 3, each with 10 mice, small circles; large circles represent the average of the triplicates). e, f, Representative FACS plot (e) and scatter plot (f) of intrinsic ROS in Ly6G+ cells (n = 6 mice). g, GFP signal quantification of 3D co-culture with GFP+ MMTV–PyMT cancer cells and MACS-sorted Ly6G+ cells from either naive or metastatic lungs with or without the ROS inhibitor TEMPO (n = 3, each with 3 technical replicates). Data are normalized to cancer cell growth (statistical analysis on biological replicates). h, Representative cancer cell growth on the scaffold (from 14 independent experiments): integrated density of the GFP signal was measured on the scaffold using ImageJ and the corresponding fluorescent image of GFP+ cancer cell growth (scale bar, 400 μm). Statistical analysis by paired two-tailed t-test (a, b, f), hypergeometric test with Benjamini–Hochberg correction (c, Metacore), weighted Kolmogorov–Smirnov-like statistic with Benjamini–Hochberg correction (c, GSEA) and two-way ANOVA (g). Data are presented as mean ± s.d. (f) and mean ± s.e.m. (g).

Source data

Extended Data Fig. 4 RNA sequencing of non-immune mCherry+-niche cells.

a, b, GSEA of upregulated genes in mCherry+-niche cells. a, Percentage of correlating processes related to the indicated activity. b, Specific signalling pathways (indicated by the in a) at early or late time point. c, MetaCore analysis of genes differentially expressed in RNA-seq data, comparing early (n = 3) or late (n = 3) mCherry+ samples versus the respective mCherry samples (see Fig. 3a, b). Statistical analysis by hypergeometric test with Benjamini–Hochberg correction.

Extended Data Fig. 5 WISP1 supports metastatic growth.

a, b, Representative immunofluorescence images of lung metastatic tissues (n = 2 mice) stained for GFP (green) to detect labelling-4T1 cells, WISP1 (red) and DAPI (blue), showing distal lung and metastatic areas (a; scale bar, 50 μm), and a representative image showing the enrichment of WISP1+ cells within lung metastasis including niche cells (white arrows) (b; scale bar, 50 μm). ce, WISP1-blocking antibody treatment in vivo. c, Experimental design (IT, intratracheal injection; IP, intraperitoneal injection). d, Metastatic outcome measured as the percentage of lung area covered by metastases (quantification was performed on two lung levels 100 μm apart). e, Representative H&E staining (n = 5 mice per group; black arrows show metastatic foci). Scale bar, 500 μm. Two experiments with lower overall metastatic frequency are quantified in Fig. 3e. Statistical analysis by two-way ANOVA (d). Data are presented as mean ± s.e.m.

Source data

Extended Data Fig. 6 Lung pneumocytes react to cancer cells in human breast pulmonary metastases.

ac, Histology of sections of human breast tumour lung metastases. a, Representative image of distal lung (scale bar, 100 μm). b, Image from the tumour–lung interface showing expression of the pneumocyte marker thyroid transcription factor 1 (TTF1) (scale bar, 50 μm). c, Representative histology of the metastatic border (scale bar, 100 μm). df, Alveolar cell proliferation in human breast tumour lung metastases analysed by immunofluorescence. Representative images from distal lung (d) and metastatic border (e) showing TTF1 (red), Ki67 (green) and DAPI (blue). Scale bars: all 100 μm, except e (far right), 50 μm. f, Quantification of alveolar proliferation. Box edges show 25th and 75th percentiles, the horizontal line shows the median and whiskers show the range of values. Statistical analysis by paired two-tailed t-test. Tissue sections from n = 4 independent patients were analysed.

Source data

Extended Data Fig. 7 Epithelial cells support cancer cell growth ex vivo.

a, GFP+ MMTV–PyMT cancer cell proliferation in 2D co-culture with MACS-sorted EPCAM+ and Ly6G+ cells stained with EdU and analysed by FACS (n = 3 independent experiments). Data are normalized to cancer cell proliferation. bd, Three dimensional co-culture of GFP+ MMTV-PyMT cancer cells with MACS-sorted EPCAM+ and Ly6G+ cells. b, Co-culture scheme. c, Representative images from four independent experiments (day 4; scale bar, 400 μm). d, Quantification of GFP signal. Data are normalized to cancer cell growth (n = 4 independent experiments (dots), each with 3–4 technical replicates). Statistical analysis of biological replicates by one-sample two-tailed t-test (a) and two-way ANOVA (d). Data are represented as mean ± s.e.m.

Source data

Extended Data Fig. 8 scRNA-seq analysis reveals different sub-pools of stromal cells in the niche.

a, t-SNE plots of CD45 cells isolated from distal lung (n = 1,996) or mCherry+ niche (n = 1,473) after scRNA-seq analysis. Stromal cells are coloured on the basis of expression levels of the indicated genes. b, t-SNE niche plots from data in a; each plot shows (in red) the cells expressing the indicated stromal marker. c, MetaCore pathway enrichment analysis using the list of genes detected in at least 50% of the indicated marker-defined cells (n = 66 THY1+ cells, n = 175 PDGFRB1+ cells, n = 322 PDGFRA+ cells, n = 330 ACTA2+ cells, n = 25 LGR6+ cells). Statistical analysis by hypergeometric test with Benjamini–Hochberg correction.

Extended Data Fig. 9 mCherry+-niche epithelial cells are enriched for stem cell markers.

a, Representative FACS plots showing Lin (CD45CD31Ter119) cells in distal lung and mCherry+ niche from labelling-4T1-injected mice (quantification in Fig. 4i). b, c, Scatter plots showing FACS quantification of EPCAM+SCA1+ cell frequency on Lin (CD45CD31Ter119) cells in distal lung and mCherry+ niche with injection of labelling-RENCA (b; n = 5 mice) and labelling-CT26 (c; n = 4 mice). df, Scatter plot of CD49f+CD104+ cell frequency among Lin (CD45CD31Ter119) cells in distal lung and mCherry+ niche detected by FACS (d; n = 5 mice), representative FACS plots (e) and representative immunofluorescence image of FACS-sorted mCherry+-niche CD49f+CD104+ cells stained for E-cadherin (green) and with DAPI (blue) (f; scale bar, 20 μm). gi, Three-dimensional co-culture of GFP+ MMTV–PyMT cancer cells with MACS-sorted EPCAM+ cells. g, Quantification of integrin β4 (CD104) expression on EPCAM+ cells. h, Number of CD104+ cells proximal to cancer cells (n = 4 from three independent sorts). i, Representative immunofluorescence image from the co-culture stained for CD104 (red), GFP+ cancer cells (green) and with DAPI (blue). Scale bar, 20 μm. Statistical analysis of biological replicates by paired two-tailed t-test (bd, g). Data are presented as mean ± s.e.m.

Source data

Extended Data Fig. 10 Cancer cells change lung epithelial cell-lineage commitment ex vivo.

a, Representative immunofluorescence images of lung metastatic sections (n = 3 mice) co-stained for an airway marker (SCGB1A1 (top; white) or SOX2 (bottom; white)) and mCherry (red), and with DAPI (blue). Scale bar, 100 μm. b, c, Lung organoids from EPCAM+ FACS-sorted cells in co-culture with either lung stromal CD31+ cells or MLg fibroblasts, alone or in the presence of non-labelling 4T1-GFP cells from metastatic lungs in the lower chamber; quantification (b) and representative bright-field images (c; scale bar, 150 μm) of organoids. d, e, Lung organoids with Scgb1a1-CreERT2 lineage cells with or without 4T1-GFP: quantification (d) and representative bright-field images (e; scale bar, 150 μm). f, Representative staining of lineage cells in metastatic lungs from Scgb1a1-CreERT2 mice injected with MMTV–PyMT cancer cells. Scale bars: top left, 200 μm; other panels, 50 μm; top middle inset, 25 μm. Data are generated with sorted EPCAM+ (b) or club-lineage cells (d) and represented as cumulative percentage presented as mean ± s.d. of three co-cultures per sorting. Statistical analysis by two-tailed t-test on original non-cumulative values (b, d). Images are representative of three organoid cultures (c, e).

Source data

Supplementary information

Supplementary Information

FACS gating strategy examples, a list of antibodies used for FACS and immune-staining, a list of primers used for the qRT–PCRs (Fig. 4h) and information about the human pulmonary breast cancer metastases from four patients. In addition, supplementary data shows the sLP-mCherry nucleotide sequence (design shown in Ext.Data Fig.1a) that was cloned in a pRRL lentiviral backbone.

Reporting Summary

Supplementary Data

The entire list of differentially detected proteins (AVG log2 ratio) in mCherry-niche versus unlabelled Ly6G+ neutrophils. Differences were considered when changes were >0.58 and <−0.58.

Supplementary Data

Upregulated genes (>2 fold) in non-immune-mCherry-niche compared to unlabelled lung tissue at both 5 and 10 days post cancer cells seeding.

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ombrato, L., Nolan, E., Kurelac, I. et al. Metastatic-niche labelling reveals parenchymal cells with stem features. Nature 572, 603–608 (2019). https://doi.org/10.1038/s41586-019-1487-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-019-1487-6

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer