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.

A microenvironment-inspired synthetic three-dimensional model for pancreatic ductal adenocarcinoma organoids

Abstract

Experimental in vitro models that capture pathophysiological characteristics of human tumours are essential for basic and translational cancer biology. Here, we describe a fully synthetic hydrogel extracellular matrix designed to elicit key phenotypic traits of the pancreatic environment in culture. To enable the growth of normal and cancerous pancreatic organoids from genetically engineered murine models and human patients, essential adhesive cues were empirically defined and replicated in the hydrogel scaffold, revealing a functional role of laminin–integrin α36 signalling in establishment and survival of pancreatic organoids. Altered tissue stiffness—a hallmark of pancreatic cancer—was recapitulated in culture by adjusting the hydrogel properties to engage mechano-sensing pathways and alter organoid growth. Pancreatic stromal cells were readily incorporated into the hydrogels and replicated phenotypic traits characteristic of the tumour environment in vivo. This model therefore recapitulates a pathologically remodelled tumour microenvironment for studies of normal and pancreatic cancer cells in vitro.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Defining adhesive requirements of PCCs.
Fig. 2: Optimizing PEG hydrogel composition for pancreatic organoids.
Fig. 3: Formation of hPDOs in defined PEG matrices.
Fig. 4: Recapitulating the stiffness range of PDA in PEG hydrogels.
Fig. 5: 3D PEG-VS CBF-0.5 gels support stromal co-cultures.

Data availability

All original source data are freely available. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE69 partner repository with the following dataset identifiers: NP matrisome atlas (PXD022555 and 10.6019/PXD022555); IAC datasets (PXD022487 and 10.6019/PXD022487); cell-derived matrix datasets (PXD022509 and 10.6019/PXD022509); 3D PEG CBF-0.5 LC–MS (PXD022520 and 10.6019/PXD022520); Tumour Matrisome LC–MS (PXD022767 and 10.6019/PXD022767). Raw CyTOF data, IF images and AFM force curves as well as source data for all figures (Figs. 15 and Supplementary Figs. 1–28) have been deposited to https://zenodo.org/record/4664132.

Code availability

All original R scripts have been deposited to https://zenodo.org/record/4664132 and are freely available.

References

  1. 1.

    Egeblad, M., Nakasone, E. S. & Werb, Z. Tumors as organs: complex tissues that interface with the entire organism. Dev. Cell 18, 884–901 (2010).

    CAS  Article  Google Scholar 

  2. 2.

    Feig, C. et al. The pancreas cancer microenvironment. Clin. Cancer Res. 18, 4266–4276 (2012).

    CAS  Article  Google Scholar 

  3. 3.

    Sahai, E. et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat. Rev. Cancer 6, 174–186 (2020).

    Article  CAS  Google Scholar 

  4. 4.

    DeNardo, D. G. & Ruffell, B. Macrophages as regulators of tumour immunity and immunotherapy. Nat. Rev. Immunol. 7, 369–382 (2019).

    Article  CAS  Google Scholar 

  5. 5.

    Biankin, A. V. et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature 491, 399–405 (2012).

    CAS  Article  Google Scholar 

  6. 6.

    Miller, B. W. et al. Targeting the LOX/hypoxia axis reverses many of the features that make pancreatic cancer deadly: inhibition of LOX abrogates metastasis and enhances drug efficacy. EMBO Mol. Med. 7, 1063–1076 (2015).

    CAS  Article  Google Scholar 

  7. 7.

    Jiang, H. et al. Targeting focal adhesion kinase renders pancreatic cancers responsive to checkpoint immunotherapy. Nat. Med. 22, 851–860 (2016).

  8. 8.

    Shi, Y. et al. Targeting LIF-mediated paracrine interaction for pancreatic cancer therapy and monitoring. Nature 569, 131–135 (2019).

    CAS  Article  Google Scholar 

  9. 9.

    Sherman, M. H. et al. Vitamin D receptor-mediated stromal reprogramming suppresses pancreatitis and enhances pancreatic cancer therapy. Cell 159, 80–93 (2014).

    CAS  Article  Google Scholar 

  10. 10.

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

    CAS  Article  Google Scholar 

  11. 11.

    Tuveson, D. & Clevers, H. Cancer modeling meets human organoid technology. Science 364, 952–955 (2019).

    CAS  Article  Google Scholar 

  12. 12.

    Drost, J. & Clevers, H. Organoids in cancer research. Nat. Rev. Cancer 18, 407–418 (2018).

    CAS  Article  Google Scholar 

  13. 13.

    Hughes, C. S., Postovit, L. M. & Lajoie, G. A. Matrigel: a complex protein mixture required for optimal growth of cell culture. Proteomics 10, 1886–1890 (2010).

    CAS  Article  Google Scholar 

  14. 14.

    Brassard, J. A. & Lutolf, M. P. Engineering stem cell self-organization to build better organoids. Stem Cell 24, 860–876 (2019).

    CAS  Google Scholar 

  15. 15.

    Socovich, A. M. & Naba, A. The cancer matrisome: from comprehensive characterization to biomarker discovery. Semin. Cell Dev. Biol. 89, 157–166 (2019).

    CAS  Article  Google Scholar 

  16. 16.

    Cook, C. D. et al. Local remodeling of synthetic extracellular matrix microenvironments by co-cultured endometrial epithelial and stromal cells enables long-term dynamic physiological function. Integr. Biol. 9, 271–289 (2017).

    CAS  Article  Google Scholar 

  17. 17.

    Gjorevski, N. et al. Designer matrices for intestinal stem cell and organoid culture. Nature 539, 560–564 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Kratochvil, M. J. et al. Engineered materials for organoid systems. Nat. Rev. Mater. 4, 606–622 (2019).

    CAS  Article  Google Scholar 

  19. 19.

    Valdez, J. et al. On-demand dissolution of modular, synthetic extracellular matrix reveals local epithelial-stromal communication networks. Biomaterials 130, 90–103 (2017).

    CAS  Article  Google Scholar 

  20. 20.

    Naba, A., Clauser, K. R. & Hynes, R. O. Enrichment of extracellular matrix proteins from tissues and digestion into peptides for mass spectrometry analysis. J. Vis. Exp. 101, e53057 (2015).

    Google Scholar 

  21. 21.

    Hingorani, S. R. et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell 7, 469–483 (2005).

    CAS  Article  Google Scholar 

  22. 22.

    Hruban, R. H. et al. Pathology of genetically engineered mouse models of pancreatic exocrine cancer: consensus report and recommendations. Cancer Res. 66, 95–196 (2006).

    CAS  Article  Google Scholar 

  23. 23.

    Schönhuber, N. et al. A next-generation dual-recombinase system for time- and host-specific targeting of pancreatic cancer. Nat. Med. 20, 1340–1347 (2014).

    Article  CAS  Google Scholar 

  24. 24.

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

    Article  CAS  Google Scholar 

  25. 25.

    Tian, C. et al. Proteomic analyses of ECM during pancreatic ductal adenocarcinoma progression reveal different contributions by tumor and stromal cells. Proc. Natl Acad. Sci. USA 116, 19609–19618 (2019).

    CAS  Article  Google Scholar 

  26. 26.

    Horton, E. R. et al. Definition of a consensus integrin adhesome and its dynamics during adhesion complex assembly and disassembly. Nat. Cell Biol. 17, 1577–1587 (2015).

    CAS  Article  Google Scholar 

  27. 27.

    Humphries, J. D. Integrin ligands at a glance. J. Cell. Sci. 119, 3901–3903 (2006).

    CAS  Article  Google Scholar 

  28. 28.

    Hamidi, H. & Ivaska, J. Every step of the way: integrins in cancer progression and metastasis. Nat. Rev. Cancer 18, 533–548 (2018).

    CAS  Article  Google Scholar 

  29. 29.

    Jones, M. C. et al. Isolation of integrin-based adhesion complexes. Curr. Protoc. Cell Biol. 66, 9.8.1–9.8.15 (2015).

    Article  Google Scholar 

  30. 30.

    Robertson, J. et al. Defining the phospho-adhesome through the phosphoproteomic analysis of integrin signalling. Nat. Commun. 6, 6265 (2015).

    CAS  Article  Google Scholar 

  31. 31.

    Takizawa, M. et al. Mechanistic basis for the recognition of laminin-511 by α6β1 integrin. Sci. Adv. 3, e1701497 (2017).

    Article  CAS  Google Scholar 

  32. 32.

    Samarelli, A. V. et al. Neuroligin 1 induces blood vessel maturation by cooperating with the α6 integrin. J. Biol. Chem. 289, 19466–19476 (2014).

    CAS  Article  Google Scholar 

  33. 33.

    Aumailley, M., Timpl, R. & Sonnenberg, A. Antibody to integrin α6 subunit specifically inhibits cell-binding to laminin fragment 8. Exp. Cell Res. 188, 55–60 (1990).

    CAS  Article  Google Scholar 

  34. 34.

    Lee, S. P. et al. Sickle cell adhesion to laminin: potential role for the α5 chain. Blood 92, 2951–2958 (1998).

    CAS  Article  Google Scholar 

  35. 35.

    Hernandez-Gordillo, V. et al. Fully synthetic matrices for in vitro culture of primary human intestinal enteroids and endometrial organoids. Biomaterials 254, 120125 (2020).

    CAS  Article  Google Scholar 

  36. 36.

    Johnson, G. & Moore, S. W. Identification of a structural site on acetylcholinesterase that promotes neurite outgrowth and binds laminin-1 and collagen IV. Biochem. Biophys. Res. Commun. 319, 448–455 (2004).

    CAS  Article  Google Scholar 

  37. 37.

    Brown, A. et al. Engineering PEG-based hydrogels to foster efficient endothelial network formation in free-swelling and confined microenvironments. Biomaterials 243, 119921 (2020).

    CAS  Article  Google Scholar 

  38. 38.

    Knight, C. G. et al. The collagen-binding A-domains of integrins α1β1 and α2β1 recognize the same specific amino acid sequence, GFOGER, in native (triple-helical) collagens. J. Biol. Chem. 275, 35–40 (2000).

    CAS  Article  Google Scholar 

  39. 39.

    Kuhlman, W., Taniguchi, I., Griffith, L. G. & Mayes, A. M. Interplay between PEO tether length and ligand spacing governs cell spreading on RGD-modified PMMA-g-PEO comb copolymers. Biomacromolecules 8, 3206–3213 (2007).

    CAS  Article  Google Scholar 

  40. 40.

    Eble, J. A., Bruckner, P. & Mayer, U. Vipera lebetina venom contains two disintegrins inhibiting laminin-binding β1 integrins. J. Biol. Chem. 278, 26488–26496 (2003).

    CAS  Article  Google Scholar 

  41. 41.

    Cavaco, A. C. M. et al. The interaction between laminin-332 and α3β1 integrin determines differentiation and maintenance of CAFs, and supports invasion of pancreatic duct adenocarcinoma cells. Cancers 11, 14–20 (2019).

    CAS  Article  Google Scholar 

  42. 42.

    Gasmi, A. et al. Amino acid structure and characterization of a heterodimeric disintegrin from Vipera lebetina venom. Biochim. Biophys. Acta 1547, 51–56 (2001).

    CAS  Article  Google Scholar 

  43. 43.

    Arruda Macêdo, J. K., Fox, J. W. & de Souza Castro, M. Disintegrins from snake venoms and their applications in cancer research and therapy. Curr. Protein Pept. Sci. 16, 532–548 (2015).

    Article  CAS  Google Scholar 

  44. 44.

    Tiriac, H. et al. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer. Cancer Discov. 8, 1112–1129 (2018).

    CAS  Article  Google Scholar 

  45. 45.

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

    CAS  Article  Google Scholar 

  46. 46.

    Laklai, H. et al. Genotype tunes pancreatic ductal adenocarcinoma tissue tension to induce matricellular fibrosis and tumor progression. Nat. Med. 22, 497–505 (2016).

    CAS  Article  Google Scholar 

  47. 47.

    Rice, A. J. et al. Matrix stiffness induces epithelial–mesenchymal transition and promotes chemoresistance in pancreatic cancer cells. Oncogenesis 6, e352 (2019).

    Article  CAS  Google Scholar 

  48. 48.

    Rubiano, A. et al. Viscoelastic properties of human pancreatic tumors and in vitro constructs to mimic mechanical properties. Acta Biomaterialia 67, 331–340 (2018).

    Article  Google Scholar 

  49. 49.

    Panciera, T. et al. Reprogramming normal cells into tumour precursors requires ECM stiffness and oncogene-mediated changes of cell mechanical properties. Nat. Mater. 19, 797–806 (2020).

    CAS  Article  Google Scholar 

  50. 50.

    Öhlund, D. et al. Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer. J. Exp. Med. 214, 579–596 (2017).

    Article  CAS  Google Scholar 

  51. 51.

    Elyada, E. et al. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. Cancer Discov. 9, 1102–1123 (2019).

    CAS  Article  Google Scholar 

  52. 52.

    Wu, J. et al. Generation of a pancreatic cancer model using a Pdx1-Flp recombinase knock-in allele. PLoS ONE 12, e0184984 (2017).

    Article  CAS  Google Scholar 

  53. 53.

    Ouyang, H. et al. Immortal human pancreatic duct epithelial cell lines with near normal genotype and phenotype. Am. J. Pathol. 157, 1623–1631 (2010).

    Article  Google Scholar 

  54. 54.

    Furukawa, T. et al. Long-term culture and immortalization of epithelial cells from normal adult human pancreatic ducts transfected by the E6E7 gene of human papilloma virus 16. Am. J. Pathol. 148, 1763–1770 (1996).

    CAS  Google Scholar 

  55. 55.

    Huch, M. et al. Unlimited in vitro expansion of adult bi-potent pancreas progenitors through the Lgr5/R-spondin axis. EMBO J. 32, 2708–2721 (2013).

    CAS  Article  Google Scholar 

  56. 56.

    Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).

  57. 57.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  Article  Google Scholar 

  58. 58.

    Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCt method. Methods 25, 402–408 (2001).

    CAS  Article  Google Scholar 

  59. 59.

    Qin, X. et al. Cell-type-specific signaling networks in heterocellular organoids. Nat. Methods 17, 335–342 (2020).

    CAS  Article  Google Scholar 

  60. 60.

    Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).

    CAS  Article  Google Scholar 

  61. 61.

    Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  Google Scholar 

  62. 62.

    Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal 6, pl1 (2013).

    Article  CAS  Google Scholar 

  63. 63.

    Naba, A. et al. The extracellular matrix: tools and insights for the ‘omics’ era. Matrix Biol. 49, 10–24 (2016).

    CAS  Article  Google Scholar 

  64. 64.

    Bult, C. J. et al. Mouse genome database (MGD) 2019. Nucleic Acids Res. 47, D801–D806 (2019).

    CAS  Article  Google Scholar 

  65. 65.

    Zhang, H., Meltzer, P. & Davis, S. RCircos: an R package for Circos 2D track plots. BMC Bioinformatics 14, 244–245 (2013).

    Article  Google Scholar 

  66. 66.

    Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. Circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    CAS  Article  Google Scholar 

  67. 67.

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  Article  Google Scholar 

  68. 68.

    Szklarczyk, D. et al. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 45, D362–D368 (2017).

    CAS  Article  Google Scholar 

  69. 69.

    Pérez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2018).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by Cancer Research UK Program grant no. C13329/A21671 (M.J.H., C.J.), Cancer Research UK Institute Awards A19258 (C.J.) and A17196 (J.P.M.), Experimental Medicine Programme Award A25236 (C.J. and J.P.M.), Rosetrees Trust grant no. M286 (C.J.), European Research Council Consolidator Award ERC-2017-COG 772577 (C.J.), National Science Foundation grant no. CBET-0939511 (L.G.G.), National Institutes of Health grants no. R01EB021908 and T32GM008334 (L.G.G.) and Defense Advanced Research Projects Agency grant no. W911NF-12-2-0039 (L.G.G.). J.A.E. is financially supported by the Deutsche Forschungsgemeinschaft (DFG grant no. SFB1009 project A09). We thank D. Liu, A. Thrasher, T. Roberts, B. Torok-Storb, I. Verma, D. Trono and T. Somervaille for kindly sharing plasmids, M.-S. Tsao (UHN) for HPDE H6c7 cells, M. Ball and E. Mckenzie at Manchester Institute of Biotechnology for sortase expression and purification, C. J. Tape at University College London for technical advice, K. Beattie for assistance at FingerPrints Proteomics Facility (University of Dundee), the Cancer Research UK Glasgow Centre (A25142), the Biological Service Unit facilities at CRUK BI and members of Systems Oncology Group at CRUK MI for constructive input.

Author information

Affiliations

Authors

Contributions

C.R.B., J.K., A. Brown, B.Y.L., J.D.H., D.L.S., L.G.G., M.J.H. and C.J. designed the research; C.R.B., J.K., A. Brown, A. Banyard, J.D.H., J.X., C.L., D.K., A.M., N.H., D.L.S., J.B., C.C. and B.Y.L. conducted experiments; C.R.B., J.K., A. Brown, A. Banyard and C.J. analysed data; A. Banyard, C.C., V.H.-G., L.S., J.A.E., B.S., X.Z., D.L.S., D.K., J.A., G.A. and C.H. provided technical support; J.P.M. maintained the genetically engineered murine models and provided murine samples; J.P.M., L.S., L.G.G., J.A.E. and B.S. provided reagents and cell lines; M.A.G., J.G., L.F. and D.A.O. helped with clinical sample collection; L.F. provided pathological support; C.R.B. and C.J. wrote the paper and C.J. and L.G.G. oversaw the project. J.X. contributed to this work while an employee at CRUK MI.

Corresponding authors

Correspondence to Linda G. Griffith or Claus Jørgensen.

Ethics declarations

Competing interests

L.G.G. has patent application pending related to the hydrogel system. The rest of the authors have no competing interests.

Additional information

Peer review information Nature Materials thanks the anonymous reviewers for their contribution to the peer review of this work.

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–28, Tables 1 and 2, Methods, legends for Supplementary Videos 1–15, uncropped blots and FACS gating strategy.

Reporting Summary

Supplementary Video 1

Time-lapse image series of KPC-1 PCCs adhering to laminin 511.

Supplementary Video 2

Time-lapse image series of KPC-1 PCCs adhering to a non-coated surface.

Supplementary Video 3

Time-lapse image series of KPC-1 PCCs adhering to laminin 511.

Supplementary Video 4

Time-lapse image series of KPC-1 PCCs adhering to laminin 521.

Supplementary Video 5

Time-lapse image series of KPC-1 PCCs adhering to a combination of laminin 511 and laminin 521.

Supplementary Video 6

Time-lapse image series of KPC-1 PCCs adhering to a combination of laminin 511, laminin 521 and FN.

Supplementary Video 7

Time-lapse image series of KPC-1 PCCs adhering to FN.

Supplementary Video 8

Time-lapse image series of KPC-1 PCCs adhering to collagen-1.

Supplementary Video 9

Time-lapse image series of KPC-1 PCCs adhering to a non-coated glass surface.

Supplementary Video 10

3D reconstruction of a representative mPDO from Supplementary Fig. 12d.

Supplementary Video 11

3D reconstruction of a representative mPDO from Supplementary Fig. 12e.

Supplementary Video 12

Maximum intensity projection (MIPs) videos of co-cultures from Supplementary Fig. 25.

Supplementary Video 13

Maximum intensity projection (MIPs) videos of co-cultures from Supplementary Fig. 25.

Supplementary Video 14

Maximum intensity projection (MIPs) videos of co-cultures from Supplementary Fig. 25.

Supplementary Video 15

Maximum intensity projection (MIPs) videos of co-cultures from Supplementary Fig. 25.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Below, C.R., Kelly, J., Brown, A. et al. A microenvironment-inspired synthetic three-dimensional model for pancreatic ductal adenocarcinoma organoids. Nat. Mater. (2021). https://doi.org/10.1038/s41563-021-01085-1

Download citation

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing