ISDoT: in situ decellularization of tissues for high-resolution imaging and proteomic analysis of native extracellular matrix

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Abstract

The extracellular matrix (ECM) is a master regulator of cellular phenotype and behavior. It has a crucial role in both normal tissue homeostasis and disease pathology. Here we present a fast and efficient approach to enhance the study of ECM composition and structure. Termed in situ decellularization of tissues (ISDoT), it allows whole organs to be decellularized, leaving native ECM architecture intact. These three-dimensional decellularized tissues can be studied using high-resolution fluorescence and second harmonic imaging, and can be used for quantitative proteomic interrogation of the ECM. Our method is superior to other methods tested in its ability to preserve the structural integrity of the ECM, facilitate high-resolution imaging and quantitatively detect ECM proteins. In particular, we performed high-resolution sub-micron imaging of matrix topography in normal tissue and over the course of primary tumor development and progression to metastasis in mice, providing the first detailed imaging of the metastatic niche. These data show that cancer-driven ECM remodeling is organ specific, and that it is accompanied by comprehensive changes in ECM composition and topological structure. We also describe differing patterns of basement-membrane organization surrounding different types of blood vessels in healthy and diseased tissues. The ISDoT procedure allows for the study of native ECM structure under normal and pathological conditions in unprecedented detail.

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Figure 1: Surgical modification of vascular flow for decellularization.
Figure 2: Integrity validation and analysis of the structure of ISDoT tissues.
Figure 3: The 3D structure of the primary breast cancer niche.
Figure 4: Mass spectrometry analysis of ISDoT ECM-enriched tissues.
Figure 5: Visualization of metastatic lymph node remodeling.
Figure 6: Visualization of metastatic lung remodeling.

References

  1. 1

    Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell Proteomics 11, M111.014647 (2012).

  2. 2

    Hynes, R.O. The extracellular matrix: not just pretty fibrils. Science 326, 1216–1219 (2009).

  3. 3

    Cox, T.R. & Erler, J.T. Remodeling and homeostasis of the extracellular matrix: implications for fibrotic diseases and cancer. Dis. Model. Mech. 4, 165–178 (2011).

  4. 4

    Bissell, M.J. & Hines, W.C. Why don't we get more cancer? A proposed role of the microenvironment in restraining cancer progression. Nat. Med. 17, 320–329 (2011).

  5. 5

    Cox, T.R. et al. LOX-mediated collagen crosslinking is responsible for fibrosis-enhanced metastasis. Cancer Res. 73, 1721–1732 (2013).

  6. 6

    Byron, A., Humphries, J.D. & Humphries, M.J. Defining the extracellular matrix using proteomics. Int. J. Exp. Pathol. 94, 75–92 (2013).

  7. 7

    Becker, K., Jährling, N., Saghafi, S., Weiler, R. & Dodt, H.U. Chemical clearing and dehydration of GFP expressing mouse brains. PLoS One 7, e33916 (2012).

  8. 8

    Dodt, H.U. et al. Ultramicroscopy: three-dimensional visualization of neuronal networks in the whole mouse brain. Nat. Methods 4, 331–336 (2007).

  9. 9

    Ertürk, A. & Bradke, F. High-resolution imaging of entire organs by 3-dimensional imaging of solvent cleared organs (3DISCO). Exp. Neurol. 242, 57–64 (2013).

  10. 10

    Hama, H. et al. Scale: a chemical approach for fluorescence imaging and reconstruction of transparent mouse brain. Nat. Neurosci. 14, 1481–1488 (2011).

  11. 11

    Ke, M.T., Fujimoto, S. & Imai, T. SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction. Nat. Neurosci. 16, 1154–1161 (2013).

  12. 12

    Kuwajima, T. et al. ClearT: a detergent- and solvent-free clearing method for neuronal and non-neuronal tissue. Development 140, 1364–1368 (2013).

  13. 13

    Moy, A.J., Wiersma, M.P. & Choi, B. Optical histology: a method to visualize microvasculature in thick tissue sections of mouse brain. PLoS One 8, e53753 (2013).

  14. 14

    Susaki, E.A. et al. Whole-brain imaging with single-cell resolution using chemical cocktails and computational analysis. Cell 157, 726–739 (2014).

  15. 15

    Tainaka, K. et al. Whole-body imaging with single-cell resolution by tissue decolorization. Cell 159, 911–924 (2014).

  16. 16

    Tomer, R., Ye, L., Hsueh, B. & Deisseroth, K. Advanced CLARITY for rapid and high-resolution imaging of intact tissues. Nat. Protoc. 9, 1682–1697 (2014).

  17. 17

    Tseng, S.J. et al. Integration of optical clearing and optical sectioning microscopy for three-dimensional imaging of natural biomaterial scaffolds in thin sections. J. Biomed. Opt. 14, 044004 (2009).

  18. 18

    Chung, K. & Deisseroth, K. CLARITY for mapping the nervous system. Nat. Methods 10, 508–513 (2013).

  19. 19

    Janssen, B., Debets, J., Leenders, P. & Smits, J. Chronic measurement of cardiac output in conscious mice. Am. J. Physiol. Regul. Integr. Comp. Physiol. 282, R928–R935 (2002).

  20. 20

    Rezakhaniha, R. et al. Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy. Biomech. Model. Mechanobiol. 11, 461–473 (2012).

  21. 21

    Acton, S.E. et al. Dendritic cells control fibroblastic reticular network tension and lymph node expansion. Nature 514, 498–502 (2014).

  22. 22

    Tozluoğlu, M. et al. Matrix geometry determines optimal cancer cell migration strategy and modulates response to interventions. Nat. Cell Biol. 15, 751–762 (2013).

  23. 23

    Faulk, D.M. et al. The effect of detergents on the basement membrane complex of a biologic scaffold material. Acta Biomater. 10, 183–193 (2014).

  24. 24

    Ott, H.C. et al. Perfusion-decellularized matrix: using nature's platform to engineer a bioartificial heart. Nat. Med. 14, 213–221 (2008).

  25. 25

    Uygun, B.E. et al. Organ reengineering through development of a transplantable recellularized liver graft using decellularized liver matrix. Nat. Med. 16, 814–820 (2010).

  26. 26

    Zhou, P. et al. Decellularized liver matrix as a carrier for the transplantation of human fetal and primary hepatocytes in mice. Liver Transpl. 17, 418–427 (2011).

  27. 27

    Matsumoto, M., Nishinakagawa, H., Kurohmaru, M., Hayashi, Y. & Otsuka, J. Pregnancy and lactation affect the microvasculature of the mammary gland in mice. J. Vet. Med. Sci. 54, 937–943 (1992).

  28. 28

    Djonov, V., Andres, A.C. & Ziemiecki, A. Vascular remodelling during the normal and malignant life cycle of the mammary gland. Microsc. Res. Tech. 52, 182–189 (2001).

  29. 29

    Ingman, W.V., Wyckoff, J., Gouon-Evans, V., Condeelis, J. & Pollard, J.W. Macrophages promote collagen fibrillogenesis around terminal end buds of the developing mammary gland. Dev. Dyn. 235, 3222–3229 (2006).

  30. 30

    Cavallotti, C., Cavallotti, D., Pescosolido, N. & Pacella, E. Age-related changes in rat optic nerve: morphological studies. Anat. Histol. Embryol. 32, 12–16 (2003).

  31. 31

    Koopmans, G., Hasse, B. & Sinis, N. Chapter 19: The role of collagen in peripheral nerve repair. Int. Rev. Neurobiol. 87, 363–379 (2009).

  32. 32

    Cornbrooks, C.J., Carey, D.J., McDonald, J.A., Timpl, R. & Bunge, R.P. In vivo and in vitro observations on laminin production by Schwann cells. Proc. Natl. Acad. Sci. USA 80, 3850–3854 (1983).

  33. 33

    Hill, R.E. & Williams, R.E. A quantitative analysis of perineurial cell basement membrane collagen IV, laminin and fibronectin in diabetic and non-diabetic human sural nerve. J. Anat. 201, 185–192 (2002).

  34. 34

    Llewellyn, M.E., Thompson, K.R., Deisseroth, K. & Delp, S.L. Orderly recruitment of motor units under optical control in vivo. Nat. Med. 16, 1161–1165 (2010).

  35. 35

    Poliak, S. & Peles, E. The local differentiation of myelinated axons at nodes of Ranvier. Nat. Rev. Neurosci. 4, 968–980 (2003).

  36. 36

    Yang, G. et al. Genetic targeting of chemical indicators in vivo. Nat. Methods 12, 137–139 (2015).

  37. 37

    Levental, K.R. et al. Matrix crosslinking forces tumor progression by enhancing integrin signaling. Cell 139, 891–906 (2009).

  38. 38

    Pedrola, N. et al. Nidogen 1 and Nuclear Protein 1: novel targets of ETV5 transcription factor involved in endometrial cancer invasion. Clin. Exp. Metastasis 32, 467–478 (2015).

  39. 39

    Harunaga, J.S., Doyle, A.D. & Yamada, K.M. Local and global dynamics of the basement membrane during branching morphogenesis require protease activity and actomyosin contractility. Dev. Biol. 394, 197–205 (2014).

  40. 40

    Chai, H. & Brown, R.E. Field effect in cancer-an update. Ann. Clin. Lab. Sci. 39, 331–337 (2009).

  41. 41

    Lochhead, P. et al. Etiologic field effect: reappraisal of the field effect concept in cancer predisposition and progression. Mod. Pathol. 28, 14–29 (2015).

  42. 42

    Aslakson, C.J. & Miller, F.R. Selective events in the metastatic process defined by analysis of the sequential dissemination of subpopulations of a mouse mammary tumor. Cancer Res. 52, 1399–1405 (1992).

  43. 43

    Dexter, D.L. et al. Heterogeneity of tumor cells from a single mouse mammary tumor. Cancer Res. 38, 3174–3181 (1978).

  44. 44

    Pulaski, B.A. & Ostrand-Rosenberg, S. Mouse 4T1 breast tumor model. Curr. Protoc. Immunol. Chapter 20, Unit 20.2 (2001).

  45. 45

    Kawashiri, S., Kumagai, S., Kojima, K., Harada, H. & Yamamoto, E. Development of a new invasion and metastasis model of human oral squamous cell carcinomas. Eur. J. Cancer B Oral Oncol. 31B, 216–221 (1995).

  46. 46

    Fujiwara, T. & Uehara, Y. The cytoarchitecture of the wall and the innervation pattern of the microvessels in the rat mammary gland: a scanning electron microscopic observation. Am. J. Anat. 170, 39–54 (1984).

  47. 47

    Iijima, T. & Zhang, J.Q. Three-dimensional wall structure and the innervation of dental pulp blood vessels. Microsc. Res. Tech. 56, 32–41 (2002).

  48. 48

    Bajénoff, M. & Germain, R.N. B-cell follicle development remodels the conduit system and allows soluble antigen delivery to follicular dendritic cells. Blood 114, 4989–4997 (2009).

  49. 49

    Baluk, P., Morikawa, S., Haskell, A., Mancuso, M. & McDonald, D.M. Abnormalities of basement membrane on blood vessels and endothelial sprouts in tumors. Am. J. Pathol. 163, 1801–1815 (2003).

  50. 50

    Manning, C. et al. Intravital imaging reveals conversion between distinct tumor vascular morphologies and localized vascular response to Sunitinib. Intravital 2, e24790 (2013).

  51. 51

    Laurie, G.W., Leblond, C.P. & Martin, G.R. Localization of type IV collagen, laminin, heparan sulfate proteoglycan, and fibronectin to the basal lamina of basement membranes. J. Cell Biol. 95, 340–344 (1982).

  52. 52

    Ott, H.C. et al. Regeneration and orthotopic transplantation of a bioartificial lung. Nat. Med. 16, 927–933 (2010).

  53. 53

    Hotaling, N.A., Bharti, K., Kriel, H., Simon, C.G. Jr. & Diameter, J. DiameterJ: A validated open source nanofiber diameter measurement tool. Biomaterials 61, 327–338 (2015).

  54. 54

    Cox, T.R. et al. The hypoxic cancer secretome induces pre-metastatic bone lesions through lysyl oxidase. Nature 522, 106–110 (2015).

  55. 55

    Cox, T.R., Schoof, E.M., Gartland, A., Erler, J.T. & Linding, R. Dataset for the proteomic inventory and quantitative analysis of the breast cancer hypoxic secretome associated with osteotropism. Data Brief 5, 621–625 (2015).

  56. 56

    Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007).

  57. 57

    Zeeberg, B.R. et al. High-Throughput GoMiner, an 'industrial-strength' integrative gene ontology tool for interpretation of multiple-microarray experiments, with application to studies of Common Variable Immune Deficiency (CVID). BMC Bioinformatics 6, 168 (2005).

  58. 58

    de Hoon, M.J., Imoto, S., Nolan, J. & Miyano, S. Open source clustering software. Bioinformatics 20, 1453–1454 (2004).

  59. 59

    Saldanha, A.J. Java Treeview—extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004).

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Acknowledgements

We thank I. Novak and N. Meyn Christensen (Centre for Advanced Bioimaging (CAB), University of Copenhagen) for imaging assistance. We thank J.R. Brewer (University of Southern Denmark) for providing access to his custom-built two-photon microscope. We thank J. Koch for help with decellularization procedures. We thank E. Sahai (Francis Crick Institute) for providing the MATLAB code. We thank J. Couchman, K.B. Jensen (both from Biotech Research & Innovation Centre, University of Copenhagen) and E. Sahai for their critical reading of the manuscript. We thank R. Linding (BRIC, University of Copenhagen) for providing access to mass spectrometry facilities. This work was supported by the Danish Cancer Society (R56-A3342, R124-A7862) (A.E.M.-G. and E.R.H.); the Novo Nordisk Foundation (Hallas Møller Stipend; to C.D.M. and J.T.E.); the European Research Council (ERC-2015-CoG-682881-MATRICAN; to A.E.M.-G. and E.R.H.); the Ragnar Söderberg Foundation Sweden (N19/15; C.D.M.); Cancerfonden Sweden (CAN 2016/283); the Innovation Foundation Denmark (1311-00010B; to T.R.C.); the National Health and Medical Research Council (NHMRC) Australia (APP1129766; T.R.C.); and the Danish Council for Independent Research YDUN grant (1084181001; F.V.A.).

Author information

A.E.M.-G. conceived the project, and the project was developed together with C.D.M., T.R.C. and J.T.E. Together, A.E.M.-G., C.D.M., T.R.C. and J.T.E. designed all experiments. A.E.M.-G. designed the surgical decellularization experiments. C.D.M. performed all imaging, assisted by A.E.M.-G., and T.R.C. performed and analyzed all mass spectrometry data. E.R.H. performed the clustering analysis. T.R.C. performed fiber orientation, diameter, gap and periodicity analyses, and was assisted by C.D.M. E.R.H., F.A.V. and A.E.M.-G. performed ECM staining. A.E.M.-G., C.D.M., T.R.C. and J.T.E. wrote and edited the manuscript.

Correspondence to Janine T Erler.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Figures and Tables

Supplementary Figures 1–10 and Supplementary Tables 1–3 (PDF 26132 kb)

Fully narrated surgical procedure for lung decellularization and decellularization set-up. (MP4 105511 kb)

Z-stack of a healthy lung freshly resected (AVI 240 kb)

Z-stack of a healthy decellularized lung (AVI 6776 kb)

Z-stack of a healthy lymph node freshly resected. (AVI 4121 kb)

Z-stack of a healthy decellularized lymph node. (AVI 2678 kb)

Z-stack of a healthy decellularized peripheral nerve bundle stained for collagen-4. (AVI 1144 kb)

Z-stack of a decellularized 4T1 breast tumour immunostained for Nidogen-1. (AVI 3989 kb)

3D representation of a decellularized 4T1 satellite lymph nodes immunostained for Collagen type IV. (AVI 11435 kb)

Z-stacks of decellularized healthy (left panel) and decellularized B16F10 tongue melanoma satellite (right panel) lymph nodes immunostained for Collagen type IV. (AVI 6705 kb)

Z-stacks of decellularized healthy (left panel) and decellularized B16F10 tongue melanoma satellite (right panel) lymph nodes immunostained for Nidogen 1 The Z-stacks descend from cortex into the medulla. (AVI 11607 kb)

Z-stack of a decellularized B16F10 tongue melanoma satellite lymph node. (AVI 2737 kb)

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Mayorca-Guiliani, A., Madsen, C., Cox, T. et al. ISDoT: in situ decellularization of tissues for high-resolution imaging and proteomic analysis of native extracellular matrix. Nat Med 23, 890–898 (2017) doi:10.1038/nm.4352

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