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:

A permanent window for the murine lung enables high-resolution imaging of cancer metastasis

Nature Methods volume 15, pages 73–80 (2018)Cite this article

Subjects

Abstract

Stable, high-resolution intravital imaging of the lung has become possible through the utilization of vacuum-stabilized imaging windows. However, this technique is extremely invasive and limited to only hours in duration. Here we describe a minimally invasive, permanently implantable window for high-resolution intravital imaging of the murine lung that allows the mouse to survive surgery, recover from anesthesia, and breathe independently. Compared to vacuum-stabilized windows, this window produces the same high-quality images without vacuum-induced artifacts; it is also less invasive, which allows imaging of the same lung tissue over a period of weeks. We further adapt the technique of microcartography for reliable relocalization of the same cells longitudinally. Using commonly employed experimental, as well as more clinically relevant, spontaneous metastasis models, we visualize all stages of metastatic seeding, including: tumor cell arrival; extravasation; growth and progression to micrometastases; as well as tumor microenvironment of metastasis function, the hallmark of hematogenous dissemination of tumor cells.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Figure 1: Design of WHRIL and overview of surgical protocol.
Figure 2: Validation of local and systemic effects of the WHRIL.
Figure 3: Microcartography enables relocalization of positions within the window day after day.
Figure 4: The WHRIL allows visualization of tumor cell arrival (a) and extravasation in both experimental (b) and spontaneous (c) metastasis models.
Figure 5: The WHRIL allows visualization of subcellular structures and tumor cell growth over several days in either experimental or spontaneous metastasis models.
Figure 6: Visualization of TMEM in the lung.

Similar content being viewed by others

References

  1. Looney, M.R. et al. Stabilized imaging of immune surveillance in the mouse lung. Nat. Methods 8, 91–96 (2011).

    Article  CAS  Google Scholar 

  2. Entenberg, D. et al. In vivo subcellular resolution optical imaging in the lung reveals early metastatic proliferation and motility. Intravital 4, 1–11 (2015).

    Article  Google Scholar 

  3. Martin, E.A. Concise Medical Dictionary (Oxford University Press, 2010).

  4. Vinegoni, C., Lee, S., Feruglio, P.F. & Weissleder, R. Advanced motion compensation methods for intravital optical microscopy. IEEE J. Sel. Top. Quantum Electron. 20, 115–121 (2014).

    Article  Google Scholar 

  5. Presson, R.G. Jr. et al. Two-photon imaging within the murine thorax without respiratory and cardiac motion artifact. Am. J. Pathol. 179, 75–82 (2011).

    Article  Google Scholar 

  6. Dunphy, M.P., Entenberg, D., Toledo-Crow, R. & Larson, S.M. In vivo microcartography and subcellular imaging of tumor angiogenesis: a novel platform for translational angiogenesis research. Microvasc. Res. 78, 51–56 (2009).

    Article  CAS  Google Scholar 

  7. Harney, A.S. et al. Real-time imaging reveals local, transient vascular permeability, and tumor cell intravasation stimulated by TIE2hi macrophage-derived VEGFA. Cancer Discov. 5, 932–943 (2015).

    Article  CAS  Google Scholar 

  8. Karagiannis, G.S. et al. Neoadjuvant chemotherapy induces breast cancer metastasis through a TMEM-mediated mechanism. Sci. Transl. Med. 9, eaan0026 (2017).

    Article  Google Scholar 

  9. te Velde, E.A. et al. Impaired healing of cutaneous wounds and colonic anastomoses in mice lacking thrombin-activatable fibrinolysis inhibitor. J. Thromb. Haemost. 1, 2087–2096 (2003).

    Article  CAS  Google Scholar 

  10. Ritsma, L. et al. Intravital microscopy through an abdominal imaging window reveals a pre-micrometastasis stage during liver metastasis. Sci. Transl. Med. 4, 158ra145 (2012).

    Article  Google Scholar 

  11. Ohsawa, K., Imai, Y., Kanazawa, H., Sasaki, Y. & Kohsaka, S. Involvement of Iba1 in membrane ruffling and phagocytosis of macrophages/microglia. J. Cell Sci. 113, 3073–3084 (2000).

    CAS  PubMed  Google Scholar 

  12. Kumar, V., Abbas, A.K. & Aster, J.C. Robbins Basic Pathology E-Book (Elsevier Health Sciences, 2017).

  13. Baker, R. & Nissim, J. Expressions for combining standard errors of two groups and for sequential standard error. Nature 198, 1020 (1963).

    Article  Google Scholar 

  14. Hanna, G. et al. Automated measurement of blood flow velocity and direction and hemoglobin oxygen saturation in the rat lung using intravital microscopy. Am. J. Physiol. Lung Cell. Mol. Physiol. 304, L86–L91 (2013).

    Article  CAS  Google Scholar 

  15. Fiole, D. & Tournier, J.N. Intravital microscopy of the lung: minimizing invasiveness. J. Biophotonics 9, 868–878 (2016).

    Article  Google Scholar 

  16. Entenberg, D. et al. Setup and use of a two-laser multiphoton microscope for multichannel intravital fluorescence imaging. Nat. Protoc. 6, 1500–1520 (2011).

    Article  CAS  Google Scholar 

  17. Entenberg, D. et al. In Current Protocols in Cell Biology (Eds. Bonifacino, J.S. et al.) Chapter 19 (John Wiley & Sons, Inc., 2013).

  18. De Alva, W.E. & Rainer, W.G. A method of high speed in vivo pulmonary microcinematography under physiologic conditions. Angiology 14, 160–164 (1963).

    Article  CAS  Google Scholar 

  19. Fingar, V.H., Taber, S.W. & Wieman, T.J. A new model for the study of pulmonary microcirculation: determination of pulmonary edema in rats. J. Surg. Res. 57, 385–393 (1994).

    Article  CAS  Google Scholar 

  20. Kimura, H. et al. Real-time imaging of single cancer-cell dynamics of lung metastasis. J. Cell. Biochem. 109, 58–64 (2010).

    CAS  PubMed  Google Scholar 

  21. Ritsma, L., Vrisekoop, N. & van Rheenen, J. In vivo imaging and histochemistry are combined in the cryosection labelling and intravital microscopy technique. Nat. Commun. 4, 2366 (2013).

    Article  Google Scholar 

  22. Travis, W.D. Classification of lung cancer. Semin. Roentgenol. 46, 178–186 (2011).

    Article  Google Scholar 

  23. Scholten, E.T. & Kreel, L. Distribution of lung metastases in the axial plane. A combined radiological-pathological study. Radiol. Clin. (Basel) 46, 248–265 (1977).

    CAS  Google Scholar 

  24. Braman, S.S. & Whitcomb, M.E. Endobronchial metastasis. Arch. Intern. Med. 135, 543–547 (1975).

    Article  CAS  Google Scholar 

  25. Herold, C.J., Bankier, A.A. & Fleischmann, D. Lung metastases. Eur. Radiol. 6, 596–606 (1996).

    Article  CAS  Google Scholar 

  26. Bernal, P.J. et al. Nitric-oxide-mediated zinc release contributes to hypoxic regulation of pulmonary vascular tone. Circ. Res. 102, 1575–1583 (2008).

    Article  CAS  Google Scholar 

  27. Miyao, N. et al. Various adhesion molecules impair microvascular leukocyte kinetics in ventilator-induced lung injury. Am. J. Physiol. Lung Cell. Mol. Physiol. 290, L1059–L1068 (2006).

    Article  CAS  Google Scholar 

  28. Rodriguez-Tirado, C. et al. Long-term high-resolution intravital microscopy in the lung with a vacuum stabilized imaging window. J. Vis. Exp. http://dx.doi.org/10.3791/54603 (2016).

  29. Fidler, I.J. The relationship of embolic homogeneity, number, size and viability to the incidence of experimental metastasis. Eur. J. Cancer 9, 223–227 (1973).

    Article  CAS  Google Scholar 

  30. Liotta, L.A., Saidel, M.G. & Kleinerman, J. The significance of hematogenous tumor cell clumps in the metastatic process. Cancer Res. 36, 889–894 (1976).

    CAS  PubMed  Google Scholar 

  31. Aceto, N. et al. Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell 158, 1110–1122 (2014).

    Article  CAS  Google Scholar 

  32. Entenberg, D. et al. A protocol for the implantation of a permanent window for high-resolution imaging of the murine lung. Protocol Exchange http://dx.doi.org/10.1038/protex.2017.134 (2017).

  33. Ovchinnikov, D.A. et al. Expression of Gal4-dependent transgenes in cells of the mononuclear phagocyte system labeled with enhanced cyan fluorescent protein using Csf1r-Gal4VP16/UAS-ECFP double-transgenic mice. J. Leukoc. Biol. 83, 430–433 (2008).

    Article  CAS  Google Scholar 

  34. DuPage, M., Dooley, A.L. & Jacks, T. Conditional mouse lung cancer models using adenoviral or lentiviral delivery of Cre recombinase. Nat. Protoc. 4, 1064–1072 (2009).

    Article  CAS  Google Scholar 

  35. Choi, J.A. & Lim, I.K. TIS21/BTG2 inhibits invadopodia formation by downregulating reactive oxygen species level in MDA-MB-231 cells. J. Cancer Res. Clin. Oncol. 139, 1657–1665 (2013).

    Article  CAS  Google Scholar 

  36. Kearns, M.T. et al. Fas ligand-expressing lymphocytes enhance alveolar macrophage apoptosis in the resolution of acute pulmonary inflammation. Am. J. Physiol. Lung Cell. Mol. Physiol. 307, L62–L70 (2014).

    Article  CAS  Google Scholar 

  37. Wagner, W.W. Jr. & Filley, G.F. Microscopic observation of the lung in vivo. Vasc. Dis. 2, 229–241 (1965).

    PubMed  Google Scholar 

  38. Schneider, C.A., Rasband, W.S. & Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).

    Article  CAS  Google Scholar 

  39. Harney, A.S., Wang, Y., Condeelis, J.S. & Entenberg, D. Extended time-lapse intravital imaging of real-time multicellular dynamics in the tumor microenvironment. J. Vis. Exp. 112, e54042 (2016).

    Google Scholar 

  40. Thévenaz, P., Ruttimann, U.E. & Unser, M. A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7, 27–41 (1998).

    Article  Google Scholar 

Download references

Acknowledgements

This technology was developed in the Gruss-Lipper Biophotonics Center and the Integrated Imaging Program at Albert Einstein College of Medicine. We acknowledge the support of these Centers in this work: Einstein's Integrated Imaging Program, Montefiore's Ruth L. Kirschstein T32 Training Grant of Surgeons for the Study of the Tumor Microenvironment (CA200561), NIH grants CA100324, CA216248, P30CA013330, and SIG #1S10OD019961-01. We thank M. Rottenkolber, R. Ibagon and A. Leggiadro of the Einstein Machine Shop for their skilled craftsmanship and design insight; and we thank C. Rodriguez-Tirado, B. Canella and U. Steidl's lab for help with evaluating blood counts.

Author information

Author notes

  1. David Entenberg and Sonia Voiculescu: These authors contributed equally to this work.

Authors and Affiliations

  1. Anatomy and Structural Biology, Einstein College of Medicine, Montefiore Medical Center, Bronx, New York, USA

    David Entenberg, Sonia Voiculescu, Peng Guo, Lucia Borriello, Yarong Wang, George S Karagiannis, Joan Jones, Maja Oktay & John Condeelis

  2. Gruss-Lipper Biophotonics Center, Einstein College of Medicine, Montefiore Medical Center, Bronx, New York, USA

    David Entenberg, Peng Guo, Lucia Borriello, Yarong Wang, George S Karagiannis, Joan Jones, Maja Oktay & John Condeelis

  3. Integrated Imaging Program, Einstein College of Medicine, Montefiore Medical Center, Bronx, New York, USA

    David Entenberg, Yarong Wang, George S Karagiannis, Joan Jones, Maja Oktay & John Condeelis

  4. Department of Surgery, Einstein College of Medicine, Montefiore Medical Center, Bronx, New York, USA

    Sonia Voiculescu & Francis Baccay

  5. Analytical Imaging Facility, Einstein College of Medicine, Montefiore Medical Center, Bronx, New York, USA

    Peng Guo

  6. Department of Pathology, Einstein College of Medicine, Montefiore Medical Center, Bronx, New York, USA

    Joan Jones & Maja Oktay

Authors
  1. David Entenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sonia Voiculescu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peng Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lucia Borriello
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yarong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. George S Karagiannis
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Joan Jones
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Francis Baccay
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Maja Oktay
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. John Condeelis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.E. and J.C. conceived of the technique; J.C. conceived of the biological goals for performance; D.E. designed the window and stage plate; D.E., S.V. and F.B. developed the surgical protocol; S.V., D.E., G.S.K., M.O. and J.C. designed the validation experiments; D.E. and S.V. performed the imaging; S.V. and L.B. performed the validation experiments; Y.W. bred the mice; P.G. performed 3D reconstructions; D.E., L.B. and P.G. performed the blood-flow measurements; S.V., D.E., G.S.K., M.O. and J.J. evaluated the validation results; D.E. and G.S.K. performed statistical analyses; M.O. and J.J. evaluated HE, IBA1 IHC and TMEM IHC in fixed lung sections; and D.E., S.V. and J.C. wrote the paper.

Corresponding authors

Correspondence to David Entenberg or John Condeelis.

Ethics declarations

Competing interests

D.E., S.V., and J.C. have submitted a provisional patent application on the window (US provisional patent application no. 62/548,455).

Integrated supplementary information

Supplementary Figure 1 Quantification of macrophages in lung tissue under the window.

A) IBA1 staining in the lung under the window shows no statistical difference (p>0.29) in macrophage number over the two weeks of window implantation. Control is from a mouse without a window. B) Example low magnification (left) and high magnification (right) images of IBA1 staining in lungs from mice without windows (top, Control) and mice bearing windows post-operative day 4 (bottom, POD 4). Number of dots indicate the number of mice for each day. Error bars = s.e.m. Significance determined by one-way ANOVA comparing each POD to control with Dunnett correction for multiple comparisons.

Supplementary Figure 2 Imaging capabilities of WHRIL.

Stills from a time-lapse video showing circulating monocytes (yellow arrow heads) and resident macrophages (yellow arrows). B) The high spatial stability allows the use of blood averaging. Unlabeled circulating erythrocytes and leukocytes cause temporary occlusions of the intravascular fluorescent dextran leading to an inability to distinguish the vascular boundaries. Blood averaging smoothes these occlusions and clearly defines the vascular boundary.

Supplementary Figure 3 Image of tumor cells trapped in the lung vasculature where the endothelia are transgenically labeled with the fluorescent protein tdTomato.

x = luminal side of endothelia, * = abluminal side of endothelia. Transgenic mouse = B6.FVB-Tg(Cdh5-cre)7Mlia/J x B6.Cg-Gt(ROSA)26Sortm14(CAG-tdTomato)Hze. Red = td-Tomato Ve-Cad labeled endothelial cells, Green = GFP labeled E0771 tumor cells.

Supplementary Figure 4 Quantification of blood flow rate.

Quantification of blood flow rates can be accomplished by imaging the passage of 2.5 μm fluorescent microspheres injected retro-orbitally through the lung vasculature. Some spheres become trapped in the vasculature (cyan arrows), presumably by macrophages, while others flow through with the circulation. Use of a microscope with an EMCCD detector enables continuous acquisition of photons during the exposure time. This results in visualization of the entire microsphere track as a blurred streak through the image (yellow arrows). Measurement of the length of the tracks, divided by the acquisition time of the frame, allows quantification of the cell speeds which is presented as a histogram. 144 tracks in n=2 mice were analyzed. Error = s.e.m.

Supplementary Figure 5 Visualization of tumor cell extravasation in the lung.

A) Stills of data, without the application of blood averaging, corresponding to Figure 4B showing extravasation of tumor cells. B) 3-dimensional reconstructions of imaging data shown in Figure 4B. C) Stills of data, without the application of blood averaging, corresponding to Figure 4C showing extravasation of a tumor cell. Green = GFP tumor cells, Cyan = CFP macrophages, Red = Ve-Cad labeled endothelia and 155kD TMR dextran.

Supplementary Figure 6 Relocalization using microcartography enables identification of tumor cell fate between imaging sessions.

A&B) Left: Image of a tumor cell trapped in the vasculature on POD 1. Right: Image of the same field of view relocated using microcartography 24 hours later. A) The tumor cell (white dotted outline) can be seen to have left the field of view either by recirculation or apoptosis. B) The tumor cell can be seen to have crossed the endothelium into the extravascular space (yellow dotted outline). Red = td-Tomato Ve-Cad labeled endothelial cells and TMR labeled dextran, Green = GFP labeled E0771 tumor cells, Blue = SHG.

Supplementary Figure 7 The SHG signal from the collagen I network can be used to verify that microcartography accurately returns to the same region of interest day after day in both the spontaneous and the experimental metastasis models.

Similar patterns in the network (colored arrows in each day) can be identified in day despite slight morphological changes like straightening of the fibers. A) Experimental metastasis model. Extraction of just SHG channel taken from the same field of view shown in Figure 5B. Z-projection of 8 slices taken 3 μm apart. B) Spontaneous metastasis model. Extraction of just the SHG channel taken from the same field of view shown in Figure 5C. Z-projection of 2 slices taken 3 μm apart.

Supplementary Figure 8 Growth curve of primary mammary tumors correlate strongly with the number of disseminated tumor cells observed on the left lung (measured as number of disseminated tumor cells per mm2 of lung).

n=8 mice, line=linear regression with slope ± s.e.m., R2 = goodness of fit).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 (PDF 2114 kb)

Life Sciences Reporting Summary

Life Sciences Reporting Summary (PDF 160 kb)

Supplementary Protocol

A Protocol for the Implantation of a Permanent Window for High-Resolution Imaging of the Murine Lung (PDF 658 kb)

Supplementary Data 1

Microcartography software setup file. (ZIP 159363 kb)

Supplementary Data 2

Raw data for supplementary figures. (XLSX 10 kb)

The WHRIL is well tolerated.

WHRIL bearing mice are able to perform tasks of daily living (grooming, feeding, nesting, stretching, etc.) without impediment. (MOV 6163 kb)

Time-lapse video of healthy lung vasculature shown in Supplemental Figure 1.

Left: raw, unprocessed video showing single cell resolution imaging. Right: Same video with red (blood) channel averaged over time to improve definition of the vascular boundaries. Time between frames = 1 sec, FOV = 170 μm. Cyan = CFP labeled myeloid cells, Red = vasculature labeled by 155kD TMR-dextran. (AVI 48388 kb)

Time lapse video of raw data shown in Figure 4A showing arrival of a tumor cell to join other cells trapped in the lung vasculature.

Time between frames = 1.35 min. Time stamp = hh:mm:ss. FOV = 117x67 μm. Green = tumor cell, red = vasculature, cyan = macrophages. (AVI 159005 kb)

Time lapse video of blood averaged data shown in Figure 4B showing extravasation of an experimentally metastasized tumor cell from inside the lung vasculature into an alveolus.

Left: Maximum intensity projection of 3 slices (9 μm) within the lung vasculature. Right: 3D reconstruction of the entire data set (8 slices, 24 μm) FOV = 60 μm. Time between frames = 64 sec. Time stamp = hh:mm:ss. (AVI 314502 kb)

Visualization of tumor cell extravasation in an experimental metastasis model.

Raw unprocessed data corresponding to Figure 4B showing a single optical section of the lung vasculature. Time between frames = 64 sec. Green = GFP tumor cells, Cyan = CFP macrophages, Red = Ve-Cad labeled endothelia and 155kD TMR dextran. Time stamp = hh:mm:ss. (AVI 157254 kb)

Video of data presented in Figure 5A showing one cell of a group of spontaneously metastasized tumor cells appearing to undergo division in the lung.

Time between frames = 1.42 min. FOV = 75 μm. Green = GFP labeled tumor cells, Red = 155kD TMR dextran, Blue = SHG from collagen I fibers. Time stamp =mm:ss (AVI 41476 kb)

41592_2018_BFnmeth4511_MOESM12_ESM.avi

Two frames from Supplemental Figure 3B showing co-registration of vasculature and an extravasated tumor cell. (AVI 17284 kb)

Video of functional TMEM in the lung showing transient vasculature leakage (permeability).

Yellow arrow indicates location of transient vascular leakage seen as red dextran appearing in the extravascular space. (AVI 39748 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Entenberg, D., Voiculescu, S., Guo, P. et al. A permanent window for the murine lung enables high-resolution imaging of cancer metastasis. Nat Methods 15, 73–80 (2018). https://doi.org/10.1038/nmeth.4511

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.4511

This article is cited by

Search

Advanced 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