A three-dimensional engineered heterogeneous tumor model for assessing cellular environment and response

Abstract

This protocol describes how to build and implement a three-dimensional (3D) cell culture system, TRACER (tissue roll for analysis of cellular environment and response), that enables analysis of cellular behavior and phenotype in hypoxic gradients. TRACER consists of infiltrating cells encapsulated in a hydrogel extracellular matrix (ECM) within a thin strip of porous cellulose scaffolding that is then rolled around an oxygen-impermeable mandrel for assembly of thick and layered 3D tissue constructs that develop cell-defined oxygen gradients. TRACER differs from other stacked-paper cell culture models because it is assembled from a single-piece scaffold, which facilitates rapid disassembly for analysis of different cell populations and metabolites. The protocol describes how to fabricate TRACER components, cell seeding in the scaffold, and scaffold assembly and disassembly. Furthermore, it provides methods to quantify live, dead, or proliferating cells, as well as gradients of oxygen using the nitroimidazole derivative EF5, in a layer-by-layer analysis with confocal microscopy or by flow cytometry of cells isolated from the TRACER scaffold. Additional methods to isolate live cells from TRACER layers for dose–response analysis with a clonogenic assay, as well as steps to extract RNA or fast-changing metabolites from TRACER layers, are also presented. Finally, we provide alternative steps to establish TRACER co-cultures for assessment of tumor cell invasion and metastasis, in this case in the absence of a hypoxic gradient. Although analysis time varies according to the assay chosen, scaffold fabrication and seeding typically take 2 h, and TRACER assembly takes 20 min on the day following scaffold seeding. The TRACER platform is designed for use by researchers and students who have basic tissue culture experience.

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Fig. 1: A description of the TRACER cell culture system.
Fig. 2: Outline of the protocol structure and assays.
Fig. 3: Components of the TRACER culture platform.
Fig. 4: Key cell-seeding steps.
Fig. 5: Key steps in TRACER assembly.
Fig. 6: Key steps in TRACER disassembly.
Fig. 7: Assessment of oxygen gradients in TRACER through EF5-binding-intensity quantification.
Fig. 8: Key steps in metabolite collection.
Fig. 9: Generation of co-culture TRACERs containing FaDu tumor cells and CAFs for investigation of cancer cell metastasis.
Fig. 10: Expected results.

References

  1. 1.

    Bissell, M. J. & Radisky, D. Putting tumours in context. Nat. Rev. Cancer 1, 46–54 (2001).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. 2.

    Xing, Y., Zhao, S., Zhou, B. P. & Mi, J. Metabolic reprogramming of the tumour microenvironment. FEBS J 282, 3892–3898 (2015).

    Article  PubMed  CAS  Google Scholar 

  3. 3.

    Kumar, R., Kuniyasu, H., Bucana, C. D., Wilson, M. R. & Fidler, I. J. Spatial and temporal expression of angiogenic molecules during tumor growth and progression. Oncol. Res. 10, 301–311 (1998).

    PubMed  CAS  Google Scholar 

  4. 4.

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. 5.

    McAllister, S. S. & Weinberg, R. A. The tumour-induced systemic environment as a critical regulator of cancer progression and metastasis. Nat. Cell Biol. 16, 717–727 (2014).

    Article  PubMed  CAS  Google Scholar 

  6. 6.

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. 7.

    Infanger, D. W., Lynch, M. E. & Fischbach, C. Engineered culture models for studies of tumor-microenvironment interactions. Annu. Rev. Biomed. Eng. 15, 29–53 (2013).

    Article  PubMed  CAS  Google Scholar 

  8. 8.

    Gill, B. J. & West, J. L. Modeling the tumor extracellular matrix: tissue engineering tools repurposed towards new frontiers in cancer biology. J. Biomech. 47, 1969–1978 (2014).

    Article  PubMed  Google Scholar 

  9. 9.

    DelNero, P., Song, Y. H. & Fischbach, C. Microengineered tumor models: insights & opportunities from a physical sciences-oncology perspective. Biomed. Microdevices 15, 583–593 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. 10.

    Rodenhizer, D., Cojocari, D., Wouters, B. G. & McGuigan, A. P. Development of TRACER: tissue roll for analysis of cellular environment and response. Biofabrication 8, 045008 (2016).

    Article  PubMed  CAS  Google Scholar 

  11. 11.

    Rodenhizer, D. et al. A three-dimensional engineered tumour for spatial snapshot analysis of cell metabolism and phenotype in hypoxic gradients. Nat. Mater. 15, 227–234 (2016).

    Article  PubMed  CAS  Google Scholar 

  12. 12.

    Derda, R. et al. Multizone paper platform for 3D cell cultures. PLoS ONE 6, e18940 (2011).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. 13.

    Hirschhaeuser, F. et al. Multicellular tumor spheroids: an underestimated tool is catching up again. J. Biotechnol. 148, 3–15 (2010).

    Article  PubMed  CAS  Google Scholar 

  14. 14.

    Clevers, H. Modeling development and disease with organoids. Cell 165, 1586–1597 (2016).

    Article  PubMed  CAS  Google Scholar 

  15. 15.

    Sachs, N. & Clevers, H. Organoid cultures for the analysis of cancer phenotypes. Curr. Opin. Genet. Dev. 24, 68–73 (2014).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  16. 16.

    Francies, H. E. & Garnett, M. J. What role could organoids play in the personalization of cancer treatment? Pharmacogenomics 16, 1523–1526 (2015).

    Article  PubMed  CAS  Google Scholar 

  17. 17.

    Dunne, L. W. et al. Human decellularized adipose tissue scaffold as a model for breast cancer cell growth and drug treatments. Biomaterials 35, 4940–4949 (2014).

    Article  PubMed  CAS  Google Scholar 

  18. 18.

    Nyga, A., Cheema, U. & Loizidou, M. 3D tumour models: novel in vitro approaches to cancer studies. J. Cell Commun. Signal. 5, 239–248 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Villasante, A. & Vunjak-Novakovic, G. Tissue-engineered models of human tumors for cancer research. Expert Opin. Drug Discov. 10, 257–268 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. 20.

    Charbe, N., McCarron, P. A. & Tambuwala, M. M. Three-dimensional bio-printing: a new frontier in oncology research. World J. Clin. Oncol. 8, 21–36 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Sung, K. E. et al. Transition to invasion in breast cancer: a microfluidic in vitro model enables examination of spatial and temporal effects. Integr. Biol. 3, 439–450 (2011).

    Article  CAS  Google Scholar 

  22. 22.

    Zervantonakis, I. K. et al. Three-dimensional microfluidic model for tumor cell intravasation and endothelial barrier function. Proc. Natl. Acad. Sci. USA 109, 13515–13520 (2012).

    Article  PubMed  Google Scholar 

  23. 23.

    Chen, M. B. et al. On-chip human microvasculature assay for visualization and quantification of tumor cell extravasation dynamics. Nat. Protoc. 12, 865–880 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. 24.

    Bai, J., Tu, T. Y., Kim, C., Thiery, J. P. & Kamm, R. D. Identification of drugs as single agents or in combination to prevent carcinoma dissemination in a microfluidic 3D environment. Oncotarget 6, 36603–36614 (2015).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Jeon, J. S. et al. Generation of 3D functional microvascular networks with human mesenchymal stem cells in microfluidic systems. Integr. Biol. 6, 555–563 (2014).

    Article  CAS  Google Scholar 

  26. 26.

    Derda, R. et al. Paper-supported 3D cell culture for tissue-based bioassays. Proc. Natl. Acad. Sci. USA 106, 18457–18462 (2009).

    Article  PubMed  Google Scholar 

  27. 27.

    Young, M. et al. A TRACER 3D co-culture tumour model for head and neck cancer. Biomaterials 164, 54–69 (2018).

    Article  PubMed  CAS  Google Scholar 

  28. 28.

    Amann, A. et al. Development of an innovative 3D cell culture system to study tumour–stroma interactions in non-small cell lung cancer cells. PLoS ONE 9, e92511 (2014).

  29. 29.

    Zhang, J. Z. et al. The use of spectroscopic imaging and mapping techniques in the characterisation and study of DLD-1 cell spheroid tumour models. Integr. Biol. 4, 1072–1080 (2012).

    Article  CAS  Google Scholar 

  30. 30.

    Ohnishi, K. et al. Plastic induction of CD133AC133-positive cells in the microenvironment of glioblastoma spheroids. Int. J. Oncol. 45, 581–586 (2014).

    Article  PubMed  CAS  Google Scholar 

  31. 31.

    van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    Article  PubMed  CAS  Google Scholar 

  32. 32.

    Gao, D. et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 159, 176–187 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. 33.

    Boj, S. F. et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).

    Article  PubMed  CAS  Google Scholar 

  34. 34.

    Pradhan, S., Hassani, I., Seeto, W. J. & Lipke, E. A. PEG-fibrinogen hydrogels for three-dimensional breast cancer cell culture. J. Biomed. Mater. Res. A 105, 236–252 (2017).

    Article  PubMed  CAS  Google Scholar 

  35. 35.

    Kievit, F. M. et al. Chitosan-alginate 3D scaffolds as a mimic of the glioma tumor microenvironment. Biomaterials 31, 5903–5910 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. 36.

    Lü, W. D. et al. Development of an acellular tumor extracellular matrix as a three-dimensional scaffold for tumor engineering. PLoS ONE 9, e103672 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. 37.

    Gill, B. J. et al. A synthetic matrix with independently tunable biochemistry and mechanical properties to study epithelial morphogenesis and EMT in a lung adenocarcinoma model. Cancer Res. 72, 6013–6023 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. 38.

    Jeon, J. S. et al. Human 3D vascularized organotypic microfluidic assays to study breast cancer cell extravasation. Proc. Natl. Acad. Sci. USA 112, 214–219 (2015).

    Article  PubMed  CAS  Google Scholar 

  39. 39.

    Ayuso, J. M. et al. Development and characterization of a microfluidic model of the tumour microenvironment. Sci. Rep. 6, 36086 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  40. 40.

    Albrengues, J. et al. LIF mediates proinvasive activation of stromal fibroblasts in cancer. Cell Rep. 7, 1664–1678 (2014).

    Article  PubMed  CAS  Google Scholar 

  41. 41.

    Gaggioli, C. et al. Fibroblast-led collective invasion of carcinoma cells with differing roles for RhoGTPases in leading and following cells. Nat. Cell Biol. 9, 1392–1400 (2007).

    Article  PubMed  CAS  Google Scholar 

  42. 42.

    Boyce, M. W., LaBonia, G. J., Hummon, A. B. & Lockett, M. R. Assessing chemotherapeutic effectiveness using a paper-based tumor model. Analyst 142, 2819–2827 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. 43.

    Zanoni, M. et al. 3D tumor spheroid models for in vitro therapeutic screening: a systematic approach to enhance the biological relevance of data obtained. Sci. Rep. 6, 19103 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. 44.

    Sirenko, O. et al. High-content assays for characterizing the viability and morphology of 3D cancer spheroid cultures. Assay Drug Dev. Technol. 13, 402–414 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. 45.

    Beaumont, K. A., Anfosso, A., Ahmed, F., Weninger, W. & Haass, N. K. Imaging- and flow cytometry-based analysis of cell position and the cell cycle in 3D melanoma spheroids. J. Vis. Exp. (106), e53486 (2015).

  46. 46.

    Hubert, C. G. et al. A three-dimensional organoid culture system derived from human glioblastomas recapitulates the hypoxic gradients and cancer stem cell heterogeneity of tumors found in vivo. Cancer Res. 76, 2465–2477 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. 47.

    Giesbrecht, J. L., Wilson, W. R. & Hill, R. P. Radiobiological studies of cells in multicellular spheroids using a sequential trypsinization technique. Radiat. Res. 86, 368–386 (1981).

    Article  PubMed  CAS  Google Scholar 

  48. 48.

    Taubenberger, A. V. et al. 3D extracellular matrix interactions modulate tumour cell growth, invasion and angiogenesis in engineered tumour microenvironments. Acta Biomater. 36, 73–85 (2016).

    Article  PubMed  CAS  Google Scholar 

  49. 49.

    Kim, S. A., Lee, E. K. & Kuh, H. J. Co-culture of 3D tumor spheroids with fibroblasts as a model for epithelial-mesenchymal transition in vitro. Exp. Cell Res. 335, 187–196 (2015).

    Article  PubMed  CAS  Google Scholar 

  50. 50.

    Ehsan, S. M., Welch-Reardon, K. M., Waterman, M. L., Hughes, C. C. & George, S. C. A three-dimensional in vitro model of tumor cell intravasation. Integr. Biol. 6, 603–610 (2014).

    Article  CAS  Google Scholar 

  51. 51.

    Liu, T., Lin, B. & Qin, J. Carcinoma-associated fibroblasts promoted tumor spheroid invasion on a microfluidic 3D co-culture device. Lab Chip 10, 1671–1677 (2010).

    Article  PubMed  CAS  Google Scholar 

  52. 52.

    Young, M. et al. A TRACER 3D co-culture tumour model for head and neck cancer. Biomaterials 164, 54–69 (2018).

  53. 53.

    Mosadegh, B. et al. A paper-based invasion assay: assessing chemotaxis of cancer cells in gradients of oxygen. Biomaterials 52, 262–271 (2015).

    Article  PubMed  CAS  Google Scholar 

  54. 54.

    Camci-Unal, G., Newsome, D., Eustace, B. K. & Whitesides, G. M. Fibroblasts enhance migration of human lung cancer cells in a paper-based coculture system. Adv. Healthcare Mater. 5, 641–647 (2016).

    Article  CAS  Google Scholar 

  55. 55.

    Achilli, T. M., McCalla, S., Meyer, J., Tripathi, A. & Morgan, J. R. Multilayer spheroids to quantify drug uptake and diffusion in 3D. Mol. Pharm. 11, 2071–2081 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. 56.

    Tung, Y. C. et al. High-throughput 3D spheroid culture and drug testing using a 384 hanging drop array. Analyst 136, 473–478 (2011).

    Article  PubMed  CAS  Google Scholar 

  57. 57.

    Galateanu, B. et al. Impact of multicellular tumor spheroids as an in vivo-like tumor model on anticancer drug response. Int. J. Oncol. 48, 2295–2302 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. 58.

    Diniz, F. B. et al. Evaluation of carcass traits and meat characteristics of Guzerat-crossbred bulls. Meat Sci. 112, 58–62 (2016).

    Article  PubMed  Google Scholar 

  59. 59.

    Walsh, A. J., Cook, R. S., Sanders, M. E., Arteaga, C. L. & Skala, M. C. Drug response in organoids generated from frozen primary tumor tissues. Sci. Rep. 6, 18889 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. 60.

    Walsh, A. J., Castellanos, J. A., Nagathihalli, N. S., Merchant, N. B. & Skala, M. C. Optical imaging of drug-induced metabolism changes in murine and human pancreatic cancer organoids reveals heterogeneous drug response. Pancreas 45, 863–869 (2016).

    Article  PubMed  CAS  Google Scholar 

  61. 61.

    Walsh, A. J. et al. Quantitative optical imaging of primary tumor organoid metabolism predicts drug response in breast cancer. Cancer Res. 74, 5184–5194 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. 62.

    Raghavan, S. et al. Personalized medicine-based approach to model patterns of chemoresistance and tumor recurrence using ovarian cancer stem cell spheroids. Clin. Cancer Res. 23, 6934–6945 (2017).

  63. 63.

    Ruppen, J. et al. Towards personalized medicine: chemosensitivity assays of patient lung cancer cell spheroids in a perfused microfluidic platform. Lab Chip 15, 3076–3085 (2015).

    Article  PubMed  CAS  Google Scholar 

  64. 64.

    Grist, S. M., Schmok, J. C., Liu, M. C., Chrostowski, L. & Cheung, K. C. Designing a microfluidic device with integrated ratiometric oxygen sensors for the long-term control and monitoring of chronic and cyclic hypoxia. Sensors 15, 20030–20052 (2015).

    Article  PubMed  CAS  Google Scholar 

  65. 65.

    Raza, A. et al. Oxygen mapping of melanoma spheroids using small molecule platinum probe and phosphorescence lifetime imaging microscopy. Sci. Rep. 7, 10743 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Grimes, D. R., Kelly, C., Bloch, K. & Partridge, M. A method for estimating the oxygen consumption rate in multicellular tumour spheroids. J. R. Soc. Interface 11, 20131124 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Ashton, T. M. et al. The anti-malarial atovaquone increases radiosensitivity by alleviating tumour hypoxia. Nat. Commun. 7, 12308 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. 68.

    Jain, M. et al. Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336, 1040–1044 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. 69.

    Armitage, E. G. et al. Metabolic profiling reveals potential metabolic markers associated with Hypoxia Inducible Factor-mediated signalling in hypoxic cancer cells. Sci. Rep. 5, 15649 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. 70.

    Gunda, V., Yu, F. & Singh, P. K. Validation of metabolic alterations in microscale cell culture lysates using hydrophilic interaction liquid chromatography (HILIC)-tandem mass spectrometry-based metabolomics. PLoS ONE 11, e0154416 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. 71.

    Rodenhizer, D. et al. A three-dimensional engineered tumour for spatial snapshot analysis of cell metabolism and phenotype in hypoxic gradients. Nat. Mater. 15, 227–234 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  72. 72.

    Matsumoto, B. Cell Biological Applications of Confocal Microscopy (Elsevier Science, San Diego, 2003).

    Google Scholar 

  73. 73.

    Shapiro, H. M. Practical Flow Cytometry 4th Edition (John Wiley & Sons, New Jersey, 2003).

  74. 74.

    Franken, N. A., Rodermond, H. M., Stap, J., Haveman, J. & van Bree, C. Clonogenic assay of cells in vitro. Nat. Protoc. 1, 2315–2319 (2006).

    Article  PubMed  CAS  Google Scholar 

  75. 75.

    Xiao, J. F., Zhou, B. & Ressom, H. W. Metabolite identification and quantitation in LC-MS/MS-based metabolomics. Trends Anal. Chem. 32, 1–14 (2012).

    Article  CAS  Google Scholar 

  76. 76.

    Hoogsteen, I. J. et al. Hypoxia in larynx carcinomas assessed by pimonidazole binding and the value of CA-IX and vascularity as surrogate markers of hypoxia. Eur. J. Cancer 45, 2906–2914 (2009).

    Article  PubMed  CAS  Google Scholar 

  77. 77.

    Wilson, W. R. & Hay, M. P. Targeting hypoxia in cancer therapy. Nat. Rev. Cancer 11, 393–410 (2011).

    Article  PubMed  CAS  Google Scholar 

  78. 78.

    Koch, C. J. Measurement of absolute oxygen levels in cells and tissues using oxygen sensors and 2-nitroimidazole EF5. Methods Enzymol. 352, 3–31 (2002).

    Article  PubMed  CAS  Google Scholar 

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Acknowledgements

We thank S.-U. Ngo-Trong for filming and editing the video protocols. This work was funded by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant Accelerator supplement (RGPIN-314056 to A.P.M.).

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Authors

Contributions

D.R., T.D., D.C., B.X., and A.P.M. designed the research; D.R., T.D., D.C., and B.X. performed the research; D.R., T.D., B.X., and D.C. analyzed the data; and D.R., T.D., and A.P.M. wrote the manuscript.

Corresponding author

Correspondence to Alison P. McGuigan.

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The authors declare no competing interests.

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Key references using this protocol

1. Rodenhizer, D. et al. Nat. Mater. 15, 227–234 (2016) https://doi.org/10.1038/nmat4482.

2. Rodenhizer, D. et al. Biofabrication 8, 045008 (2016) https://doi.org/10.1088/1758-5090/8/4/045008.

3. Young, M. et al. Biomaterials 164, 54–69 (2018) https://doi.org/10.1016/j.biomaterials.2018.01.038.

Supplementary information

Combined Supplementary Information

Supplementary Figures 1–16

Supplementary Video 1

Video showing biocomposite

Supplementary Video 2

Video showing TRACER assembly by rolling (Steps 20–30)

Supplementary Video 3

Video showing TRACER disassembly for general analysis (Steps 31–40)

Supplementary Video 4

Video Showing TRACER disassembly for metabolomics analysis (Step 41K)

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Rodenhizer, D., Dean, T., Xu, B. et al. A three-dimensional engineered heterogeneous tumor model for assessing cellular environment and response. Nat Protoc 13, 1917–1957 (2018). https://doi.org/10.1038/s41596-018-0022-9

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