Skip to main content

A Perfusion Model to Evaluate Response to Photodynamic Therapy in 3D Tumors

  • Protocol
  • First Online:
Photodynamic Therapy

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2451))

Abstract

Numerous cancer models have been developed to investigate the effects of mechanical stress on the biology of cells. Here we describe a protocol to fabricate a perfusion model to culture 3-dimensional (3D) ovarian cancer nodules under constant flow. The modular design of this model allows for a wide range of treatment regimens and combinations, including PDT and chemotherapy. Finally, methods for a number of readouts are detailed, allowing researchers to investigate a variety of biological and cytotoxic parameters related to mechanical stress and therapeutic modalities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rizvi I, Bulin A-L, Briars E et al (2016) Mind the gap: 3D models in photodynamic therapy. In: Herwig Kostron TH (ed) Photodynamic medicine: from bench to clinic. The Royal Society of Chemistry, London, pp 197–221

    Chapter  Google Scholar 

  2. Bissell MJ, Radisky D (2001) Putting tumours in context. Nat Rev Cancer 1(1):46–54

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Bissell MJ, Hall HG, Parry G (1982) How does the extracellular matrix direct gene expression? J Theor Biol 99(1):31–68

    Article  CAS  PubMed  Google Scholar 

  4. Stock K, Estrada MF, Vidic S et al (2016) Capturing tumor complexity in vitro: comparative analysis of 2D and 3D tumor models for drug discovery. Sci Rep 6:28951

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Griffith LG, Swartz MA (2006) Capturing complex 3D tissue physiology in vitro. Nat Rev Mol Cell Biol 7(3):211–224

    Article  CAS  PubMed  Google Scholar 

  6. Polacheck WJ, German AE, Mammoto A et al (2014) Mechanotransduction of fluid stresses governs 3D cell migration. Proc Natl Acad Sci U S A 111(7):2447–2452

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Mpekris F, Angeli S, Pirentis AP et al (2015) Stress-mediated progression of solid tumors: effect of mechanical stress on tissue oxygenation, cancer cell proliferation, and drug delivery. Biomech Model Mechanobiol 14(6):1391–1402

    Article  PubMed  PubMed Central  Google Scholar 

  8. Rizvi I, Gurkan UA, Tasoglu S et al (2013) Flow induces epithelial-mesenchymal transition, cellular heterogeneity and biomarker modulation in 3D ovarian cancer nodules. Proc Natl Acad Sci U S A 110(22):E1974–E1983

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Calibasi Kocal G, Guven S, Foygel K et al (2016) Dynamic microenvironment induces phenotypic plasticity of esophageal cancer cells under flow. Sci Rep 6:38221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. van Duinen V, Trietsch SJ, Joore J et al (2015) Microfluidic 3D cell culture: from tools to tissue models. Curr Opin Biotechnol 35:118–126

    Article  PubMed  Google Scholar 

  11. Zervantonakis IK, Hughes-Alford SK, Charest JL et al (2012) Three-dimensional microfluidic model for tumor cell intravasation and endothelial barrier function. Proc Natl Acad Sci U S A 109(34):13515–13520

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Lee WG, Kim Y-G, Chung BG et al (2010) Nano/microfluidics for diagnosis of infectious diseases in developing countries. Adv Drug Deliv Rev 62(4):449–457

    Article  CAS  PubMed  Google Scholar 

  13. Afshar S, Nath S, Demirci U et al (2018) Identification of hydrodynamic forces around 3D surrogates using particle image velocimetry in a microfluidic channel. In: SPIE BiOS. SPIE, p 8

    Google Scholar 

  14. Pisano M, Triacca V, Barbee KA et al (2015) An in vitro model of the tumor-lymphatic microenvironment with simultaneous transendothelial and luminal flows reveals mechanisms of flow enhanced invasion. Integr Biol 7(5):525–533

    Article  CAS  Google Scholar 

  15. Swartz MA, Lund AW (2012) Lymphatic and interstitial flow in the tumour microenvironment: linking mechanobiology with immunity. Nat Rev Cancer 12(3):210–219

    Article  CAS  PubMed  Google Scholar 

  16. Adriani G, Pavesi A, Tan AT et al (2016) Microfluidic models for adoptive cell-mediated cancer immunotherapies. Drug Discov Today 21(9):1472–1478

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Haeberle S, Zengerle R (2007) Microfluidic platforms for lab-on-a-chip applications. Lab Chip 7(9):1094–1110

    Article  CAS  PubMed  Google Scholar 

  18. Srinivasan V, Pamula VK, Fair RB (2004) An integrated digital microfluidic lab-on-a-chip for clinical diagnostics on human physiological fluids. Lab Chip 4(4):310–315

    Article  CAS  PubMed  Google Scholar 

  19. Obaid G, Broekgaarden M, Bulin A-L et al (2016) Photonanomedicine: a convergence of photodynamic therapy and nanotechnology. Nanoscale 8(25):12471–12503

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Celli JP, Spring BQ, Rizvi I et al (2010) Imaging and photodynamic therapy: mechanisms, monitoring, and optimization. Chem Rev 110(5):2795–2838

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chudy M, Tokarska K, Jastrzębska E et al (2017) Lab-on-a-chip systems for photodynamic therapy investigations. Biosensors Bioelectron 101:37–51

    Article  Google Scholar 

  22. Bulin A-L, Broekgaarden M, Hasan T (2017) Comprehensive high-throughput image analysis for therapeutic efficacy of architecturally complex heterotypic organoids. Sci Rep 7(1):16645

    Article  PubMed  PubMed Central  Google Scholar 

  23. Celli JP, Rizvi I, Blanden AR et al (2014) An imaging-based platform for high-content, quantitative evaluation of therapeutic response in 3D tumour models. Sci Rep 4:3751

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Institute of Health grants R00CA175292 (to IR) and R01CA158415 (to TH).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Imran Rizvi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Nath, S., Pigula, M., Hasan, T., Rizvi, I. (2022). A Perfusion Model to Evaluate Response to Photodynamic Therapy in 3D Tumors. In: Broekgaarden, M., Zhang, H., Korbelik, M., Hamblin, M.R., Heger, M. (eds) Photodynamic Therapy. Methods in Molecular Biology, vol 2451. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2099-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2099-1_4

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2098-4

  • Online ISBN: 978-1-0716-2099-1

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics