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A guide to the organ-on-a-chip

Nature Reviews Methods Primers volume 2, Article number: 33 (2022) Cite this article

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Abstract

Organs-on-chips (OoCs) are systems containing engineered or natural miniature tissues grown inside microfluidic chips. To better mimic human physiology, the chips are designed to control cell microenvironments and maintain tissue-specific functions. Combining advances in tissue engineering and microfabrication, OoCs have gained interest as a next-generation experimental platform to investigate human pathophysiology and the effect of therapeutics in the body. There are as many examples of OoCs as there are applications, making it difficult for new researchers to understand what makes one OoC more suited to an application than another. This Primer is intended to give an introduction to the aspects of OoC that need to be considered when developing an application-specific OoC. The Primer covers guiding principles and considerations to design, fabricate and operate an OoC, as well as subsequent assaying techniques to extract biological information from OoC devices. Alongside this is a discussion of current and future applications of OoC technology, to inform design and operational decisions during the implementation of OoC systems.

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Fig. 1: Experimental set-up for a generic two-organ system with supporting peripheral equipment.
Fig. 2: Material selection and fabrication according to the purpose of the experiment.
Fig. 3: Schematic drawings of the single-OoCs and multi-OoCs highlighted in this section.
Fig. 4: Collecting biological results from OoC systems.
Fig. 5: Biological complexity and form factor of an OoC system.

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References

  1. Bhatia, S. N. & Ingber, D. E. Microfluidic organs-on-chips. Nat. Biotechnol. 32, 760–772 (2014).

    Google Scholar 

  2. Low, L. A., Mummery, C., Berridge, B. R., Austin, C. P. & Tagle, D. A. Organs-on-chips: into the next decade. Nat. Rev. Drug. Discov. 20, 345–361 (2021).

    Google Scholar 

  3. Manz, A., Graber, N. & Widmer, H. M. Miniaturized total chemical analysis systems: a novel concept for chemical sensing. Sens. Actuator B Chem. 1, 244–248 (1990). This pioneering paper introduces the concept of micro total analysis systems integrated into microfluidic chips, which led, in turn, to the introduction of OoCs and integrated read-out systems.

    Google Scholar 

  4. Harrison, D. J., Manz, A., Fan, Z. H., Ludi, H. & Widmer, H. M. Capillary electrophoresis and sample injection systems integrated on a planar glass chip. Anal. Chem. 64, 1926–1932 (1992).

    Google Scholar 

  5. Whitesides, G. M. The origins and the future of microfluidics. Nature 442, 368–373 (2006).

    ADS  Google Scholar 

  6. Sahlgren, C. et al. Tailored approaches in drug development and diagnostics: from molecular design to biological model systems. Adv. Healthc. Mater. 6, 1700258 (2017).

    Google Scholar 

  7. Jackson, E. L. & Lu, H. Three-dimensional models for studying development and disease: moving on from organisms to organs-on-a-chip and organoids. Integr. Biol. 8, 672–683 (2016).

    Google Scholar 

  8. Sin, A., Baxter, G. & Shuler, M. Animal on a Chip: A Microscale Cell Culture Analog Device for Evaluating Toxicological and Pharmacological Profiles Vol. 4560 PWM (SPIE, 2001).

  9. Sin, A. et al. The design and fabrication of three-chamber microscale cell culture analog devices with integrated dissolved oxygen sensors. Biotechnol. Prog. 20, 338–345 (2004). This paper is the first to provide a detailed description of the construction of a multi-OoC system based on a physiologically based PK model.

    Google Scholar 

  10. Huh, D. et al. Reconstituting organ-level lung functions on a chip. Science 328, 1662–1668 (2010). This paper is one of the pioneering works on lung OoC, which demonstrates a functional alveolar–capillary interface that is responsive to pathogen infection.

    ADS  Google Scholar 

  11. Ronaldson-Bouchard, K. & Vunjak-Novakovic, G. Organs-on-a-chip: a fast track for engineered human tissues in drug development. Cell Stem Cell 22, 310–324 (2018).

    Google Scholar 

  12. World Economic Forum. Top 10 emerging technologies of 2016. World Economic Forum https://www.weforum.org/reports/top-10-emerging-technologies-of-2016 (2016).

  13. Jang, K.-J. & Suh, K.-Y. A multi-layer microfluidic device for efficient culture and analysis of renal tubular cells. Lab Chip 10, 36–42 (2010).

    Google Scholar 

  14. Kim, H. J., Huh, D., Hamilton, G. & Ingber, D. E. Human gut-on-a-chip inhabited by microbial flora that experiences intestinal peristalsis-like motions and flow. Lab Chip 12, 2165–2174 (2012). This work is one of the first gut OoC papers showing that physical stretching of the cell culture substrate and fluid perfusion can enhance the differentiation of Caco-2 cells into an intestinal epithelium with physiological architectures and functions.

    Google Scholar 

  15. Bokhari, M., Carnachan, R. J., Cameron, N. R. & Przyborski, S. A. Culture of HepG2 liver cells on three dimensional polystyrene scaffolds enhances cell structure and function during toxicological challenge. J. Anat. 211, 567–576 (2007).

    Google Scholar 

  16. Shahin-Shamsabadi, A. & Selvaganapathy, P. R. A 3D self-assembled in vitro model to simulate direct and indirect interactions between adipocytes and skeletal muscle cells. Adv. Biosyst. 4, 2000034 (2020).

    Google Scholar 

  17. Wang, Y., Wang, L., Guo, Y., Zhu, Y. & Qin, J. Engineering stem cell-derived 3D brain organoids in a perfusable organ-on-a-chip system. RSC Adv. 8, 1677–1685 (2018).

    ADS  Google Scholar 

  18. Lee, S.-R. et al. Modeling neural circuit, blood–brain barrier, and myelination on a microfluidic 96 well plate. Biofabrication 11, 035013 (2019).

    ADS  Google Scholar 

  19. Jang, J. M., Lee, J., Kim, H., Jeon, N. L. & Jung, W. One-photon and two-photon stimulation of neurons in a microfluidic culture system. Lab Chip 16, 1684–1690 (2016).

    Google Scholar 

  20. Park, S. M. et al. Reconstruction of in vivo-like in vitro model: enabling technologies of microfluidic systems for dynamic biochemical/mechanical stimuli. Microelectron. Eng. 203–204, 6–24 (2019).

    Google Scholar 

  21. Herzog, N., Katzenberger, N., Martin, F., Schmidtke, K. U. & Küpper, J.-H. Generation of cytochrome P450 3A4-overexpressing HepG2 cell clones for standardization of hepatocellular testosterone 6β-hydroxylation activity. J. Cell. Biotechnol. 1, 15–26 (2015).

    Google Scholar 

  22. Moysidou, C.-M., Barberio, C. & Owens, R. M. Advances in engineering human tissue models. Front. Bioeng. Biotechnol. 8, 620962 (2021).

    Google Scholar 

  23. Gallagher, L. B. et al. Pre-culture of mesenchymal stem cells within RGD-modified hyaluronic acid hydrogel improves their resilience to ischaemic conditions. Acta Biomater. 107, 78–90 (2020).

    Google Scholar 

  24. Ramme, A. P. et al. Autologous induced pluripotent stem cell-derived four-organ-chip. Future Sci. OA 5, FSO413 (2019).

    Google Scholar 

  25. Sances, S. et al. Human iPSC-derived endothelial cells and microengineered organ-chip enhance neuronal development. Stem Cell Rep. 10, 1222–1236 (2018).

    Google Scholar 

  26. Rowe, R. G. & Daley, G. Q. Induced pluripotent stem cells in disease modelling and drug discovery. Nat. Rev. Genet. 20, 377–388 (2019).

    Google Scholar 

  27. Ronaldson-Bouchard K., et al. Inter-organ chips with matured tissue niches linked by vascular perfusion. Nat. Biomed. Eng. (in the press). This paper reports a multi-tissue chip system in which matured human tissues are linked by recirculating vascular flow, allowing for the maintenance of their phenotypes, communication across endothelial barriers and recapitulation of interdependent organ functions.

  28. Duffy, D. C., McDonald, J. C., Schueller, O. J. A. & Whitesides, G. M. Rapid prototyping of microfluidic systems in poly(dimethylsiloxane). Anal. Chem. 70, 4974–4984 (1998).

    Google Scholar 

  29. Xia, Y. & Whitesides, G. M. Soft lithography. Annu. Rev. Mater. Sci. 28, 153–184 (1998).

    ADS  Google Scholar 

  30. Reyes, D. R., Iossifidis, D., Auroux, P.-A. & Manz, A. Micro total analysis systems. 1. Introduction, theory, and technology. Anal. Chem. 74, 2623–2636 (2002).

    Google Scholar 

  31. Verpoorte, E. M. J. et al. Three-dimensional micro flow manifolds for miniaturized chemical analysis systems. J. Micromech. Microeng. 4, 246–256 (1994).

    ADS  Google Scholar 

  32. Dittrich, P. S. & Manz, A. Lab-on-a-chip: microfluidics in drug discovery. Nat. Rev. Drug. Discov. 5, 210–218 (2006).

    Google Scholar 

  33. Meyvantsson, I. & Beebe, D. J. Cell culture models in microfluidic systems. Annu. Rev. Anal. Chem. 1, 423–449 (2008).

    Google Scholar 

  34. Kim, L., Toh, Y.-C., Voldman, J. & Yu, H. A practical guide to microfluidic perfusion culture of adherent mammalian cells. Lab Chip 7, 681–694 (2007). This paper offers practical help for setting up a microfluidic perfusion cell culture system, such as dealing with bubbles, proper control of temperature and gas equilibration in media.

    Google Scholar 

  35. Sung, J. H. et al. Recent advances in body-on-a-chip systems. Anal. Chem. 91, 330–351 (2019).

    Google Scholar 

  36. Allwardt, V. et al. Translational roadmap for the organs-on-a-chip industry toward broad adoption. Bioengineering 7, 112 (2020).

    Google Scholar 

  37. Abaci, H. E. & Shuler, M. L. Human-on-a-chip design strategies and principles for physiologically based pharmacokinetics/pharmacodynamics modeling. Integr. Biol. 7, 383–391 (2015).

    Google Scholar 

  38. Zhang, Y. S. et al. Multisensor-integrated organs-on-chips platform for automated and continual in situ monitoring of organoid behaviors. Proc. Natl Acad. Sci. USA 114, E2293–E2302 (2017).

    Google Scholar 

  39. Frey, O., Misun, P. M., Fluri, D. A., Hengstler, J. G. & Hierlemann, A. Reconfigurable microfluidic hanging drop network for multi-tissue interaction and analysis. Nat. Commun. 5, 4250 (2014). This paper introduces interconnected hanging drops and unifies spheroid aggregation of different organ types and their cross-communication in a parallel format.

    ADS  Google Scholar 

  40. Santbergen, M. J. C., van der Zande, M., Bouwmeester, H. & Nielen, M. W. F. Online and in situ analysis of organs-on-a-chip. TrAC Trends Anal. Chem. 115, 138–146 (2019).

    Google Scholar 

  41. Misun, P. M., Rothe, J., Schmid, Y. R. F., Hierlemann, A. & Frey, O. Multi-analyte biosensor interface for real-time monitoring of 3D microtissue spheroids in hanging-drop networks. Microsyst. Nanoeng. 2, 16022 (2016).

    Google Scholar 

  42. Hargrove-Grimes, P., Low, L. A. & Tagle, D. A. Microphysiological systems: stakeholder challenges to adoption in drug development. Cell Tissues Organs 211, 69–81 (2022).

    Google Scholar 

  43. FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource (US Food and Drug Administration & National Institutes of Health, 2016).

  44. US Food and Drug Administration. Advancing alternative methods at FDA. US Food and Drug Administration https://www.fda.gov/science-research/about-science-research-fda/advancing-alternative-methods-fda#:~:text=FDA%20Definitions&text=MPS%20platforms%20may%20comprise%20mono,inclusion%20of%20organoid%20cell%20formations (2022).

  45. Zhang, B. & Radisic, M. Organ-on-a-chip devices advance to market. Lab Chip 17, 2395–2420 (2017). This review discusses the commercialization trajectory of OoC technologies into the drug discovery market.

    Google Scholar 

  46. Verpoorte, E. et al. in 2015 Transducers — 2015 18th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS) 224–227 (IEEE, 2015).

  47. Shin, J. et al. Monolithic digital patterning of polydimethylsiloxane with successive laser pyrolysis. Nat. Mater. 20, 100–107 (2021). This study describes a novel laser-based fabrication technique for PDMS devices; a high-resolution technique suitable for rapid prototyping and production of a small series of devices (10–100 devices), while also being a good alternative to replication moulding.

    ADS  Google Scholar 

  48. Toepke, M. W. & Beebe, D. J. PDMS absorption of small molecules and consequences in microfluidic applications. Lab Chip 6, 1484–1486 (2006).

    Google Scholar 

  49. Wang, J. D., Douville, N. J., Takayama, S. & ElSayed, M. Quantitative analysis of molecular absorption into PDMS microfluidic channels. Ann. Biomed. Eng. 40, 1862–1873 (2012).

    Google Scholar 

  50. Ko, J. et al. Tumor spheroid-on-a-chip: a standardized microfluidic culture platform for investigating tumor angiogenesis. Lab Chip 19, 2822–2833 (2019).

    Google Scholar 

  51. Lee, Y. et al. Microfluidics within a well: an injection-molded plastic array 3D culture platform. Lab Chip 18, 2433–2440 (2018). This paper presents the basis for high-throughput microfluidic organ-on-a-chip design that is amenable for 3D co-cultures. Furthermore, the device designs described here are compatible with 3D printing and injection moulding such that they offer an alternative to traditional PDMS-based chips.

    Google Scholar 

  52. Homan, K. A. et al. Flow-enhanced vascularization and maturation of kidney organoids in vitro. Nat. Methods 16, 255–262 (2019).

    Google Scholar 

  53. Berthier, E., Young, E. W. K. & Beebe, D. Engineers are from PDMS-land, biologists are from Polystyrenia. Lab Chip 12, 1224–1237 (2012). This paper highlights how differences in the inherent material properties of PS and PDMS can lead to disparity between conventional and microfluidic cell cultures.

    Google Scholar 

  54. Jensen, C. & Teng, Y. Is it time to start transitioning from 2D to 3D cell culture? Front. Mol. Biosci. 7, 33 (2020).

    Google Scholar 

  55. Kim, H. J., Li, H., Collins, J. J. & Ingber, D. E. Contributions of microbiome and mechanical deformation to intestinal bacterial overgrowth and inflammation in a human gut-on-a-chip. Proc. Natl Acad. Sci. USA 113, E7–E15 (2016).

    ADS  Google Scholar 

  56. Maoz, B. M. et al. A linked organ-on-chip model of the human neurovascular unit reveals the metabolic coupling of endothelial and neuronal cells. Nat. Biotechnol. 36, 865–874 (2018).

    Google Scholar 

  57. Brown, J. A. et al. Recreating blood–brain barrier physiology and structure on chip: a novel neurovascular microfluidic bioreactor. Biomicrofluidics 9, 054124 (2015).

    Google Scholar 

  58. Ahn, S. I. et al. Microengineered human blood–brain barrier platform for understanding nanoparticle transport mechanisms. Nat. Commun. 11, 175 (2020).

    ADS  Google Scholar 

  59. Esch, M. B. et al. On chip porous polymer membranes for integration of gastrointestinal tract epithelium with microfluidic ‘body-on-a-chip’ devices. Biomed. Microdevices 14, 895–906 (2012).

    Google Scholar 

  60. Wang, Y. et al. Capture and 3D culture of colonic crypts and colonoids in a microarray platform. Lab Chip 13, 4625–4634 (2013).

    Google Scholar 

  61. Uemura, M. et al. Matrigel supports survival and neuronal differentiation of grafted embryonic stem cell-derived neural precursor cells. J. Neurosci. Res. 88, 542–551 (2010).

    Google Scholar 

  62. Afshar Bakooshli, M. et al. A 3D culture model of innervated human skeletal muscle enables studies of the adult neuromuscular junction. eLife 8, e44530 (2019).

    Google Scholar 

  63. Horowitz, L. F., Rodriguez, A. D., Ray, T. & Folch, A. Microfluidics for interrogating live intact tissues. Microsyst. Nanoeng. 6, 69 (2020). This article is a reference for readers interested in the integration of live intact tissue slices into a microfluidic system.

    ADS  Google Scholar 

  64. Shi, Y., Inoue, H., Wu, J. C. & Yamanaka, S. Induced pluripotent stem cell technology: a decade of progress. Nat. Rev. Drug Discov. 16, 115–130 (2017).

    Google Scholar 

  65. van den Berg, A., Mummery, C. L., Passier, R. & van der Meer, A. D. Personalised organs-on-chips: functional testing for precision medicine. Lab Chip 19, 198–205 (2019).

    Google Scholar 

  66. Diederichs, S. & Tuan, R. S. Functional comparison of human-induced pluripotent stem cell-derived mesenchymal cells and bone marrow-derived mesenchymal stromal cells from the same donor. Stem Cell Dev. 23, 1594–1610 (2014).

    Google Scholar 

  67. Verma, A., Verma, M. & Singh, A. in Animal Biotechnology 2nd edn (eds Verma, A. S. & Singh, A.) 269–293 (Academic, 2020).

  68. Yeste, J., Illa, X., Alvarez, M. & Villa, R. Engineering and monitoring cellular barrier models. J. Biol. Eng. 12, 18 (2018).

    Google Scholar 

  69. Yang, F., Cohen, R. N. & Brey, E. M. Optimization of co-culture conditions for a human vascularized adipose tissue model. Bioengineering 7, 114 (2020).

    Google Scholar 

  70. Chang, S.-Y. et al. Human liver–kidney model elucidates the mechanisms of aristolochic acid nephrotoxicity. JCI Insight 2, e95978 (2017).

    Google Scholar 

  71. Zhang, C., Zhao, Z., Abdul Rahim, N. A., van Noort, D. & Yu, H. Towards a human-on-chip: culturing multiple cell types on a chip with compartmentalized microenvironments. Lab Chip 9, 3185–3192 (2009).

    Google Scholar 

  72. Jang, K. J. et al. Reproducing human and cross-species drug toxicities using a liver-chip. Sci. Transl. Med. 11, eaax5516 (2019).

    Google Scholar 

  73. Wufuer, M. et al. Skin-on-a-chip model simulating inflammation, edema and drug-based treatment. Sci. Rep. 6, 37471 (2016).

    ADS  Google Scholar 

  74. Even, M. S., Sandusky, C. B. & Barnard, N. D. Serum-free hybridoma culture: ethical, scientific and safety considerations. Trends Biotechnol. 24, 105–108 (2006).

    Google Scholar 

  75. Kongsuphol, P. et al. In vitro micro-physiological model of the inflamed human adipose tissue for immune-metabolic analysis in type II diabetes. Sci. Rep. 9, 4887 (2019).

    ADS  Google Scholar 

  76. Massa, S. et al. Bioprinted 3D vascularized tissue model for drug toxicity analysis. Biomicrofluidics 11, 044109 (2017).

    Google Scholar 

  77. Ong, L. J. Y. et al. Self-aligning Tetris-Like (TILE) modular microfluidic platform for mimicking multi-organ interactions. Lab Chip 19, 2178–2191 (2019). This paper demonstrates a modular design of assembling pre-established tissue chips into a recirculating multi-OoC system.

    Google Scholar 

  78. Satoh, T. et al. A pneumatic pressure-driven multi-throughput microfluidic circulation culture system. Lab Chip 16, 2339–2348 (2016).

    Google Scholar 

  79. Lee, K. K. et al. Human stomach-on-a-chip with luminal flow and peristaltic-like motility. Lab Chip 18, 3079–3085 (2018).

    Google Scholar 

  80. Ong, L. J. Y. et al. A pump-free microfluidic 3D perfusion platform for the efficient differentiation of human hepatocyte-like cells. Biotechnol. Bioeng. 114, 2360–2370 (2017).

    Google Scholar 

  81. Yu, F. et al. A pump-free tricellular blood–brain barrier on-a-chip model to understand barrier property and evaluate drug response. Biotechnol. Bioeng. 117, 1127–1136 (2020).

    Google Scholar 

  82. Lohasz, C., Rousset, N., Renggli, K., Hierlemann, A. & Frey, O. Scalable microfluidic platform for flexible configuration of and experiments with microtissue multiorgan models. SLAS Technol. 24, 79–95 (2019). This paper describes a scalable and automation-compatible multi-spheroid culture system in a microtitre plate format based on gravity-driven flow that is actuated by tilting.

    Google Scholar 

  83. Wang, Y. I. & Shuler, M. L. UniChip enables long-term recirculating unidirectional perfusion with gravity-driven flow for microphysiological systems. Lab Chip 18, 2563–2574 (2018).

    Google Scholar 

  84. Oleaga, C. et al. Multi-organ toxicity demonstration in a functional human in vitro system composed of four organs. Sci. Rep. 6, 20030 (2016).

    ADS  Google Scholar 

  85. Chen, L., Yang, Y., Ueno, H. & Esch, M. B. Body-in-a-cube: a microphysiological system for multi-tissue co-culture with near-physiological amounts of blood surrogate. Microphys. Syst. 4, 1 (2020).

    Google Scholar 

  86. Yang, Y. et al. Pumpless microfluidic devices for generating healthy and diseased endothelia. Lab Chip 19, 3212–3219 (2019).

    Google Scholar 

  87. Maschmeyer, I. et al. A four-organ-chip for interconnected long-term co-culture of human intestine, liver, skin and kidney equivalents. Lab Chip 15, 2688–2699 (2015).

    Google Scholar 

  88. Vernetti, L. et al. Functional coupling of human microphysiology systems: intestine, liver, kidney proximal tubule, blood–brain barrier and skeletal muscle. Sci. Rep. 7, 42296 (2017). This paper combines multiple single-OoCs by manual transfer of culture medium from one organ to the next to simulate multi-organ functions; termed ‘functional’ coupling rather than direct coupling of organs in a flow-through system.

    ADS  Google Scholar 

  89. Novak, R. et al. Robotic fluidic coupling and interrogation of multiple vascularized organ chips. Nat. Biomed. Eng. 4, 407–420 (2020).

    Google Scholar 

  90. van Midwoud, P. M. et al. On-line HPLC analysis system for metabolism and inhibition studies in precision-cut liver slices. Anal. Chem. 83, 84–91 (2011).

    Google Scholar 

  91. Young, E. W. & Beebe, D. J. Fundamentals of microfluidic cell culture in controlled microenvironments. Chem. Soc. Rev. 39, 1036–1048 (2010). This review covers fundamental concepts on how microfluidics alter physical and biochemical microenvironmental factors, which are important for successful microscale cell culture.

    Google Scholar 

  92. Blagovic, K., Kim, L. Y. & Voldman, J. Microfluidic perfusion for regulating diffusible signaling in stem cells. PLoS ONE 6, e22892 (2011).

    ADS  Google Scholar 

  93. Przybyla, L. M. & Voldman, J. Attenuation of extrinsic signaling reveals the importance of matrix remodeling on maintenance of embryonic stem cell self-renewal. Proc. Natl Acad. Sci. USA 109, 835–840 (2012).

    ADS  Google Scholar 

  94. Bhattacharjee, N. & Folch, A. Large-scale microfluidic gradient arrays reveal axon guidance behaviors in hippocampal neurons. Microsyst. Nanoeng. 3, 17003 (2017).

    Google Scholar 

  95. McCarty, W. J., Usta, O. B. & Yarmush, M. L. A microfabricated platform for generating physiologically-relevant hepatocyte zonation. Sci. Rep. 6, 26868 (2016).

    ADS  Google Scholar 

  96. Tilles, A. W., Baskaran, H., Roy, P., Yarmush, M. L. & Toner, M. Effects of oxygenation and flow on the viability and function of rat hepatocytes cocultured in a microchannel flat-plate bioreactor. Biotechnol. Bioeng. 73, 379–389 (2001).

    Google Scholar 

  97. Lindner, M., Laporte, A., Block, S., Elomaa, L. & Weinhart, M. Physiological shear stress enhances differentiation, mucus-formation and structural 3D organization of intestinal epithelial. Cell Vitro. Cell 10, 2062 (2021).

    Google Scholar 

  98. Marrero, D. et al. Gut-on-a-chip: mimicking and monitoring the human intestine. Biosens. Bioelectron. 181, 113156 (2021).

    Google Scholar 

  99. Trieu, D., Waddell, T. K. & McGuigan, A. P. A microfluidic device to apply shear stresses to polarizing ciliated airway epithelium using air flow. Biomicrofluidics 8, 064104 (2014).

    Google Scholar 

  100. Arora, S., Srinivasan, A., Leung, M. C. & Toh, Y.-C. Bio-mimicking shear stress environments for enhancing mesenchymal stem cell differentiation. Curr. Stem Cell Res. Ther. 15, 414–427 (2020).

    Google Scholar 

  101. Ergir, E., Bachmann, B., Redl, H., Forte, G. & Ertl, P. Small force, big impact: next generation organ-on-a-chip systems incorporating biomechanical cues. Front. Physiol. 9, 1417 (2018).

    Google Scholar 

  102. Takano, A., Tanaka, M. & Futai, N. On-chip multi-gas incubation for microfluidic cell cultures under hypoxia. Biomicrofluidics 8, 061101 (2014).

    Google Scholar 

  103. Toh, Y.-C. et al. A novel 3D mammalian cell perfusion-culture system in microfluidic channels. Lab Chip 7, 302–309 (2007).

    Google Scholar 

  104. Imura, Y., Asano, Y., Sato, K. & Yoshimura, E. A microfluidic system to evaluate intestinal absorption. Anal. Sci. 25, 1403–1407 (2009).

    Google Scholar 

  105. Bein, A. et al. Enteric coronavirus infection and treatment modeled with an immunocompetent human intestine-on-a-chip. Front. Pharmacol. 12, 718484 (2021).

    Google Scholar 

  106. Viravaidya, K., Sin, A. & Shuler, M. L. Development of a microscale cell culture analog to probe naphthalene toxicity. Biotechnol. Prog. 20, 316–323 (2004).

    Google Scholar 

  107. Edington, C. D. et al. Interconnected microphysiological systems for quantitative biology and pharmacology studies. Sci. Rep. 8, 4530 (2018).

    ADS  Google Scholar 

  108. McAleer, C. W. et al. Multi-organ system for the evaluation of efficacy and off-target toxicity of anticancer therapeutics. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aav1386 (2019). Together with Viravaidya et al. (2004) and Edington et al. (2018), this paper and the above-mentioned papers have been selected as case studies to highlight evolution in the design of multi-OoC systems for ADME-Tox studies.

    Article  Google Scholar 

  109. Grist, S. M., Chrostowski, L. & Cheung, K. C. Optical oxygen sensors for applications in microfluidic cell culture. Sensors 10, 9286–9316 (2010).

    ADS  Google Scholar 

  110. Rivera, K. R., Yokus, M. A., Erb, P. D., Pozdin, V. A. & Daniele, M. Measuring and regulating oxygen levels in microphysiological systems: design, material, and sensor considerations. Analyst 144, 3190–3215 (2019).

    ADS  Google Scholar 

  111. Oomen, P. E., Skolimowski, M. D. & Verpoorte, E. Implementing oxygen control in chip-based cell and tissue culture systems. Lab Chip 16, 3394–3414 (2016).

    Google Scholar 

  112. Brennan, M. D., Rexius-Hall, M. L., Elgass, L. J. & Eddington, D. T. Oxygen control with microfluidics. Lab Chip 14, 4305–4318 (2014).

    Google Scholar 

  113. Scheidecker, B. et al. Induction of in vitro metabolic zonation in primary hepatocytes requires both near-physiological oxygen concentration and flux. Front. Bioeng. Biotechnol. 8, 524 (2020).

    Google Scholar 

  114. Farzaneh, Z. et al. Dissolved oxygen concentration regulates human hepatic organoid formation from pluripotent stem cells in a fully controlled bioreactor. Biotechnol. Bioeng. 117, 3739–3756 (2020).

    Google Scholar 

  115. Kietzmann, T. Metabolic zonation of the liver: the oxygen gradient revisited. Redox Biol. 11, 622–630 (2017).

    Google Scholar 

  116. Sung, J. H., Choi, J. R., Kim, D. & Shuler, M. L. Fluorescence optical detection in situ for real-time monitoring of cytochrome P450 enzymatic activity of liver cells in multiple microfluidic devices. Biotechnol. Bioeng. 104, 516–525 (2009).

    Google Scholar 

  117. Choi, J. R., Sung, J. H., Shuler, M. L. & Kim, D. Investigation of portable in situ fluorescence optical detection for microfluidic 3D cell culture assays. Opt. Lett. 35, 1374–1376 (2010).

    ADS  Google Scholar 

  118. Weltin, A. et al. Cell culture monitoring for drug screening and cancer research: a transparent, microfluidic, multi-sensor microsystem. Lab Chip 14, 138–146 (2014).

    Google Scholar 

  119. Toh, Y. C. & Voldman, J. Fluid shear stress primes mouse embryonic stem cells for differentiation in a self-renewing environment via heparan sulfate proteoglycans transduction. FASEB J. 25, 1208–1217 (2011).

    Google Scholar 

  120. Vit, F. F. et al. A modular, reversible sealing, and reusable microfluidic device for drug screening. Analytica Chim. Acta 1185, 339068 (2021).

    Google Scholar 

  121. Morsink, M. A. J., Willemen, N. G. A., Leijten, J., Bansal, R. & Shin, S. R. Immune organs and immune cells on a chip: an overview of biomedical applications. Micromachines 11, 849 (2020).

    Google Scholar 

  122. Chen, Y. Y. et al. Clarifying intact 3D tissues on a microfluidic chip for high-throughput structural analysis. Proc. Natl Acad. Sci. USA 113, 14915–14920 (2016).

    ADS  Google Scholar 

  123. Wardwell-Swanson, J. et al. A framework for optimizing high-content imaging of 3D models for drug discovery. SLAS Discov. 25, 709–722 (2020).

    Google Scholar 

  124. Chen, Z. et al. Automated evaluation of tumor spheroid behavior in 3D culture using deep learning-based recognition. Biomaterials 272, 120770 (2021).

    Google Scholar 

  125. Wolff, A., Antfolk, M., Brodin, B. & Tenje, M. In vitro blood-brain barrier models — an overview of established models and new microfluidic approaches. J. Pharm. Sci. 104, 2727–2746 (2015).

    Google Scholar 

  126. Brown, C. D. A. et al. Characterisation of human tubular cell monolayers as a model of proximal tubular xenobiotic handling. Toxicol. Appl. Pharmacol. 233, 428–438 (2008).

    Google Scholar 

  127. Henry, O. Y. F. et al. Organs-on-chips with integrated electrodes for trans-epithelial electrical resistance (TEER) measurements of human epithelial barrier function. Lab Chip 17, 2264–2271 (2017).

    Google Scholar 

  128. van der Helm, M. W. et al. Direct quantification of transendothelial electrical resistance in organs-on-chips. Biosens. Bioelectron. 85, 924–929 (2016).

    Google Scholar 

  129. Demircan Yalcin, Y. & Luttge, R. Electrical monitoring approaches in 3-dimensional cell culture systems: toward label-free, high spatiotemporal resolution, and high-content data collection in vitro. Organs-on-a-Chip 3, 100006 (2021).

    Google Scholar 

  130. Elbakary, B. & Badhan, R. K. S. A dynamic perfusion based blood–brain barrier model for cytotoxicity testing and drug permeation. Sci. Rep. 10, 3788 (2020).

    ADS  Google Scholar 

  131. Bürgel, S. C., Diener, L., Frey, O., Kim, J. Y. & Hierlemann, A. Automated, multiplexed electrical impedance spectroscopy platform for continuous monitoring of microtissue spheroids. Anal. Chem. 88, 10876–10883 (2016).

    Google Scholar 

  132. Srinivasan, B. et al. TEER measurement techniques for in vitro barrier model systems. J. Lab. Autom. 20, 107–126 (2015). This paper reviews different methods of making TEER measurements in barrier tissues, and their strengths and weaknesses, and addresses issues where the technique is applied incorrectly.

    Google Scholar 

  133. Soucy, J. R., Bindas, A. J., Koppes, A. N. & Koppes, R. A. Instrumented microphysiological systems for real-time measurement and manipulation of cellular electrochemical processes. iScience 21, 521–548 (2019).

    ADS  Google Scholar 

  134. Ferrari, E., Palma, C., Vesentini, S., Occhetta, P. & Rasponi, M. Integrating biosensors in organs-on-chip devices: a perspective on current strategies to monitor microphysiological systems. Biosensors 10, 110 (2020). This review covers the integration of sensors into OoC devices for real-time measurements of oxygen, soluble metabolites (for example, glucose or lactate cytokines), TEER and electrical activity.

    Google Scholar 

  135. Oleaga, C. et al. A human in vitro platform for the evaluation of pharmacology strategies in cardiac ischemia. APL. Bioeng. 3, 036103 (2019).

    Google Scholar 

  136. Kussauer, S., David, R. & Lemcke, H. hiPSCs derived cardiac cells for drug and toxicity screening and disease modeling: what micro-electrode-array analyses can tell us. Cells 8, 1331 (2019).

    Google Scholar 

  137. van de Wijdeven, R. et al. A novel lab-on-chip platform enabling axotomy and neuromodulation in a multi-nodal network. Biosens. Bioelectron. 140, 111329 (2019).

    Google Scholar 

  138. Oiwa, K. et al. A device for co-culturing autonomic neurons and cardiomyocytes using micro-fabrication techniques. Integr. Biol. 8, 341–348 (2016).

    Google Scholar 

  139. Yuan, X. et al. Versatile live-cell activity analysis platform for characterization of neuronal dynamics at single-cell and network level. Nat. Commun. 11, 4854 (2020).

    ADS  Google Scholar 

  140. Sung, J. H., Wang, Y. & Shuler, M. L. Strategies for using mathematical modeling approaches to design and interpret multi-organ microphysiological systems (MPS). APL Bioeng. 3, 021501 (2019). This paper provides a useful guide to design principles and scaling rules for multi-OoC systems to be used for PK and PD studies.

    Google Scholar 

  141. Moutaux, E., Charlot, B., Genoux, A., Saudou, F. & Cazorla, M. An integrated microfluidic/microelectrode array for the study of activity-dependent intracellular dynamics in neuronal networks. Lab Chip 18, 3425–3435 (2018).

    Google Scholar 

  142. Stancescu, M. et al. A phenotypic in vitro model for the main determinants of human whole heart function. Biomaterials 60, 20–30 (2015).

    Google Scholar 

  143. Coln, E. A. et al. Piezoelectric BioMEMS cantilever for measurement of muscle contraction and for actuation of mechanosensitive cells. MRS Commun. 9, 1186–1192 (2019).

    Google Scholar 

  144. Maoz, B. M. et al. Organs-on-chips with combined multi-electrode array and transepithelial electrical resistance measurement capabilities. Lab Chip 17, 2294–2302 (2017).

    Google Scholar 

  145. Balijepalli, A. & Sivaramakrishan, V. Organs-on-chips: research and commercial perspectives. Drug. Discov. Today 22, 397–403 (2017).

    Google Scholar 

  146. Livingston, C. A., Fabre, K. M. & Tagle, D. A. Facilitating the commercialization and use of organ platforms generated by the microphysiological systems (Tissue Chip) program through public–private partnerships. Comput. Struct. Biotechnol. J. 14, 207–210 (2016).

    Google Scholar 

  147. Reyes, D. R. et al. Accelerating innovation and commercialization through standardization of microfluidic-based medical devices. Lab Chip 21, 9–21 (2021).

    Google Scholar 

  148. Fabre, K. et al. Introduction to a manuscript series on the characterization and use of microphysiological systems (MPS) in pharmaceutical safety and ADME applications. Lab Chip 20, 1049–1057 (2020).

    Google Scholar 

  149. Sung, J. H., Kam, C. & Shuler, M. L. A microfluidic device for a pharmacokinetic–pharmacodynamic (PK–PD) model on a chip. Lab Chip 10, 446–455 (2010).

    Google Scholar 

  150. Tatosian, D. A. & Shuler, M. L. A novel system for evaluation of drug mixtures for potential efficacy in treating multidrug resistant cancers. Biotechnol. Bioeng. 103, 187–198 (2009).

    Google Scholar 

  151. Stokes, C. L., Cirit, M. & Lauffenburger, D. A. Physiome-on-a-chip: the challenge of “scaling” in design, operation, and translation of microphysiological systems. CPT Pharmacomet. Syst. Pharmacol. 4, 559–562 (2015).

    Google Scholar 

  152. Wikswo, J. P. et al. Scaling and systems biology for integrating multiple organs-on-a-chip. Lab Chip 13, 3496–3511 (2013).

    Google Scholar 

  153. Yu, J. et al. Quantitative systems pharmacology approaches applied to microphysiological systems (MPS): data interpretation and multi-MPS integration. CPT Pharmacomet. Syst. Pharmacol. 4, 585–594 (2015).

    Google Scholar 

  154. Bai, J. et al. A novel 3D vascular assay for evaluating angiogenesis across porous membranes. Biomaterials 268, 120592 (2021).

    Google Scholar 

  155. Carter, S. D., Barbe, L., Tenje, M. & Mestres, G. Exploring microfluidics as a tool to evaluate the biological properties of a titanium alloy under dynamic conditions. Biomater. Sci. 8, 6309–6321 (2020).

    Google Scholar 

  156. Chueh, B. H. et al. Leakage-free bonding of porous membranes into layered microfluidic array systems. Anal. Chem. 79, 3504–3508 (2007).

    Google Scholar 

  157. Winkler, T. E., Feil, M., Stronkman, E., Matthiesen, I. & Herland, A. Low-cost microphysiological systems: feasibility study of a tape-based barrier-on-chip for small intestine modeling. Lab Chip 20, 1212–1226 (2020).

    Google Scholar 

  158. Schneider, S., Gruner, D., Richter, A. & Loskill, P. Membrane integration into PDMS-free microfluidic platforms for organ-on-chip and analytical chemistry applications. Lab Chip 21, 1866–1885 (2021).

    Google Scholar 

  159. Kuo, C. H., Xian, J., Brenton, J. D., Franze, K. & Sivaniah, E. Complex stiffness gradient substrates for studying mechanotactic cell migration. Adv. Mater. 24, 6059–6064 (2012).

    Google Scholar 

  160. Zink, C., Hall, H., Brunette, D. M. & Spencer, N. D. Orthogonal nanometer–micrometer roughness gradients probe morphological influences on cell behavior. Biomaterials 33, 8055–8061 (2012).

    Google Scholar 

  161. Kim, T. H. et al. Creating stiffness gradient polyvinyl alcohol hydrogel using a simple gradual freezing–thawing method to investigate stem cell differentiation behaviors. Biomaterials 40, 51–60 (2015).

    Google Scholar 

  162. Oh, S. H., An, D. B., Kim, T. H. & Lee, J. H. Wide-range stiffness gradient PVA/HA hydrogel to investigate stem cell differentiation behavior. Acta Biomater. 35, 23–31 (2016).

    Google Scholar 

  163. Mei, Y. et al. Combinatorial development of biomaterials for clonal growth of human pluripotent stem cells. Nat. Mater. 9, 768–778 (2010).

    ADS  Google Scholar 

  164. Jana, S., Levengood, S. K. & Zhang, M. Anisotropic materials for skeletal-muscle-tissue engineering. Adv. Mater. 28, 10588–10612 (2016).

    Google Scholar 

  165. Kim, D.-H. et al. Mechanosensitivity of fibroblast cell shape and movement to anisotropic substratum topography gradients. Biomaterials 30, 5433–5444 (2009).

    Google Scholar 

  166. Park, J. et al. Directed migration of cancer cells guided by the graded texture of the underlying matrix. Nat. Mater. 15, 792–801 (2016).

    ADS  Google Scholar 

  167. Zhou, Q. et al. Screening platform for cell contact guidance based on inorganic biomaterial micro/nanotopographical gradients. ACS Appl. Mater. Interfaces 9, 31433–31445 (2017).

    Google Scholar 

  168. Perlman, R. L. Mouse models of human disease: an evolutionary perspective. Evol. Med. Public Health 2016, 170–176 (2016).

    Google Scholar 

  169. Mestas, J. & Hughes, C. C. W. Of mice and not men: differences between mouse and human immunology. J. Immunol. 172, 2731–2738 (2004).

    Google Scholar 

  170. Dellaquila, A., Thomée, E. K., McMillan, A. H. & Lesher-Pérez, S. C. in Organ-on-a-Chip (eds Hoeng, J., Bovard, D. & Peitsch, M. C.) 133–180 (Academic, 2020).

  171. Bliley, J. M. et al. Dynamic loading of human engineered heart tissue enhances contractile function and drives a desmosome-linked disease phenotype. Sci. Transl. Med. 13, eabd1817 (2021).

    Google Scholar 

  172. Trapecar, M. et al. Gut–liver physiomimetics reveal paradoxical modulation of IBD-related inflammation by short-chain fatty acids. Cell Syst. 10, 223–239.e9 (2020).

    Google Scholar 

  173. Ahn, J., Sei, Y. J., Jeon, N. L. & Kim, Y. Tumor microenvironment on a chip: the progress and future perspective. Bioengineering 4, 64 (2017).

    Google Scholar 

  174. Park, D., Lim, J., Park, J. Y. & Lee, S. H. Concise review: stem cell microenvironment on a chip: current technologies for tissue engineering and stem cell biology. Stem Cell Transl. Med. 4, 1352–1368 (2015).

    Google Scholar 

  175. Selimović, Š., Kaji, H., Bae, H. & Khademhosseini, A. in Microfluidic Cell Culture Systems 2nd edn (eds Borenstein, J. T., Tandon, V., Tao, S. L & Charest, J. L) 31–63 (Elsevier, 2019).

  176. Shang, M., Soon, R. H., Lim, C. T., Khoo, B. L. & Han, J. Microfluidic modelling of the tumor microenvironment for anti-cancer drug development. Lab Chip 19, 369–386 (2019).

    Google Scholar 

  177. Przybyla, L. & Voldman, J. Probing embryonic stem cell autocrine and paracrine signaling using microfluidics. Annu. Rev. Anal. Chem. 5, 293–315 (2012). This review paper explains the physics governing the use of microfluidic cell culture to control the presentation of soluble factors to cells in order to investigate paracrine and autocrine signalling.

    Google Scholar 

  178. Toh, A. G. G., Wang, Z. P., Yang, C. & Nguyen, N.-T. Engineering microfluidic concentration gradient generators for biological applications. Microfluidics Nanofluidics 16, 1–18 (2014).

    Google Scholar 

  179. Wang, X., Liu, Z. & Pang, Y. Concentration gradient generation methods based on microfluidic systems. RSC Adv. 7, 29966–29984 (2017).

    ADS  Google Scholar 

  180. Gómez-Sjöberg, R., Leyrat, A. A., Pirone, D. M., Chen, C. S. & Quake, S. R. Versatile, fully automated, microfluidic cell culture system. Anal. Chem. 79, 8557–8563 (2007).

    Google Scholar 

  181. Titmarsh, D. M. et al. Induction of human iPSC-derived cardiomyocyte proliferation revealed by combinatorial screening in high density microbioreactor arrays. Sci. Rep. 6, 24637 (2016).

    ADS  Google Scholar 

  182. Cosson, S. & Lutolf, M. P. Hydrogel microfluidics for the patterning of pluripotent stem cells. Sci. Rep. 4, 4462 (2014).

    ADS  Google Scholar 

  183. Manfrin, A. et al. Engineered signaling centers for the spatially controlled patterning of human pluripotent stem cells. Nat. Methods 16, 640–648 (2019).

    Google Scholar 

  184. Rhee, S. W. et al. Patterned cell culture inside microfluidic devices. Lab Chip 5, 102–107 (2005).

    Google Scholar 

  185. Chiu, D. T. et al. Patterned deposition of cells and proteins onto surfaces by using three-dimensional microfluidic systems. Proc. Natl Acad. Sci. USA 97, 2408–2413 (2000).

    ADS  Google Scholar 

  186. Jeon, K. J. et al. Combined effects of flow-induced shear stress and micropatterned surface morphology on neuronal differentiation of human mesenchymal stem cells. J. Biosci. Bioeng. 117, 242–247 (2014).

    Google Scholar 

  187. Tu, C. et al. A microfluidic chip for cell patterning utilizing paired microwells and protein patterns. Micromachines 8, 1 (2017).

    Google Scholar 

  188. Skelley, A. M., Kirak, O., Suh, H., Jaenisch, R. & Voldman, J. Microfluidic control of cell pairing and fusion. Nat. Methods 6, 147–152 (2009).

    Google Scholar 

  189. Flaim, C. J., Chien, S. & Bhatia, S. N. An extracellular matrix microarray for probing cellular differentiation. Nat. Methods 2, 119–125 (2005).

    Google Scholar 

  190. McCain, M. L., Yuan, H., Pasqualini, F. S., Campbell, P. H. & Parker, K. K. Matrix elasticity regulates the optimal cardiac myocyte shape for contractility. Am. J. Physiol. Heart Circul. Physiol. 306, H1525–H1539 (2014).

    Google Scholar 

  191. Vázquez-Victorio, G. et al. Building a microfluidic cell culture platform with stiffness control using Loctite 3525 glue. Lab Chip 19, 3512–3525 (2019).

    Google Scholar 

  192. Pasman, T., Grijpma, D., Stamatialis, D. & Poot, A. Flat and microstructured polymeric membranes in organs-on-chips. J. R. Soc. Interface 15, 20180351 (2018).

    Google Scholar 

  193. Arora, S., Lin, S., Cheung, C., Yim, E. K. F. & Toh, Y.-C. Topography elicits distinct phenotypes and functions in human primary and stem cell derived endothelial cells. Biomaterials 234, 119747 (2020).

    Google Scholar 

  194. Chau, L., Doran, M. & Cooper-White, J. A novel multishear microdevice for studying cell mechanics. Lab Chip 9, 1897–1902 (2009).

    Google Scholar 

  195. Arora, S., Lam, A. J. Y., Cheung, C., Yim, E. K. F. & Toh, Y.-C. Determination of critical shear stress for maturation of human pluripotent stem cell-derived endothelial cells towards an arterial subtype. Biotechnol. Bioeng. 116, 1164–1175 (2019).

    Google Scholar 

  196. Visone, R. et al. A microscale biomimetic platform for generation and electro-mechanical stimulation of 3D cardiac microtissues. APL. Bioeng. 2, 046102 (2018).

    Google Scholar 

  197. Duc, P. et al. Human neuromuscular junction on micro-structured microfluidic devices implemented with a custom micro electrode array (MEA). Lab Chip 21, 4223–4236 (2021).

    Google Scholar 

  198. Beckwitt, C. H. et al. Liver ‘organ on a chip’. Exp. Cell Res. 363, 15–25 (2018).

    Google Scholar 

  199. Deng, J. et al. Engineered liver-on-a-chip platform to mimic liver functions and its biomedical applications: a review. Micromachines 10, 676 (2019).

    Google Scholar 

  200. Starokozhko, V. & Groothuis, G. M. Judging the value of ‘liver-on-a-chip’ devices for prediction of toxicity. Expert Opin. Drug Metab. Toxicol. 13, 125–128 (2017).

    Google Scholar 

  201. Khetani, S. R. & Bhatia, S. N. Microscale culture of human liver cells for drug development. Nat. Biotechnol. 26, 120–126 (2008).

    Google Scholar 

  202. Sivaraman, A. et al. A microscale in vitro physiological model of the liver: predictive screens for drug metabolism and enzyme induction. Curr. Drug Metab. 6, 569–591 (2005).

    Google Scholar 

  203. Boon, R. et al. Amino acid levels determine metabolism and CYP450 function of hepatocytes and hepatoma cell lines. Nat. Commun. 11, 1393 (2020).

    ADS  Google Scholar 

  204. Chao, P., Maguire, T., Novik, E., Cheng, K. C. & Yarmush, M. L. Evaluation of a microfluidic based cell culture platform with primary human hepatocytes for the prediction of hepatic clearance in human. Biochem. Pharmacol. 78, 625–632 (2009).

    Google Scholar 

  205. Goral, V. N. et al. Perfusion-based microfluidic device for three-dimensional dynamic primary human hepatocyte cell culture in the absence of biological or synthetic matrices or coagulants. Lab Chip 10, 3380–3386 (2010).

    Google Scholar 

  206. Hegde, M. et al. Dynamic interplay of flow and collagen stabilizes primary hepatocytes culture in a microfluidic platform. Lab Chip 14, 2033–2039 (2014).

    Google Scholar 

  207. Lee, P. J., Hung, P. J. & Lee, L. P. An artificial liver sinusoid with a microfluidic endothelial-like barrier for primary hepatocyte culture. Biotechnol. Bioeng. 97, 1340–1346 (2007).

    Google Scholar 

  208. Toh, Y. C. et al. A microfluidic 3D hepatocyte chip for drug toxicity testing. Lab Chip 9, 2026–2035 (2009).

    Google Scholar 

  209. Clark, A. M. et al. A liver microphysiological system of tumor cell dormancy and inflammatory responsiveness is affected by scaffold properties. Lab Chip 17, 156–168 (2016).

    Google Scholar 

  210. Deng, J. et al. A liver-on-a-chip for hepatoprotective activity assessment. Biomicrofluidics 14, 064107 (2020).

    Google Scholar 

  211. Vernetti, L. A. et al. A human liver microphysiology platform for investigating physiology, drug safety, and disease models. Exp. Biol. Med. 241, 101–114 (2016).

    Google Scholar 

  212. Ströbel, S. et al. A 3D primary human cell-based in vitro model of non-alcoholic steatohepatitis for efficacy testing of clinical drug candidates. Sci. Rep. 11, 22765 (2021).

    ADS  Google Scholar 

  213. Müller, F. A. & Sturla, S. J. Human in vitro models of nonalcoholic fatty liver disease. Curr. Opin. Toxicol. 16, 9–16 (2019).

    Google Scholar 

  214. Ronaldson-Bouchard, K. et al. Advanced maturation of human cardiac tissue grown from pluripotent stem cells. Nature 556, 239–243 (2018).

    ADS  Google Scholar 

  215. Zhao, Y. et al. A platform for generation of chamber-specific cardiac tissues and disease modeling. Cell 176, 913–927.e918 (2019).

    Google Scholar 

  216. Kaisar, M. A. et al. New experimental models of the blood–brain barrier for CNS drug discovery. Expert Opin. Drug Discov. 12, 89–103 (2017).

    Google Scholar 

  217. Herland, A. et al. Distinct contributions of astrocytes and pericytes to neuroinflammation identified in a 3D human blood–brain barrier on a chip. PLoS ONE 11, e0150360 (2016).

    Google Scholar 

  218. Wang, J. D., Khafagy el, S., Khanafer, K., Takayama, S. & ElSayed, M. E. Organization of endothelial cells, pericytes, and astrocytes into a 3D microfluidic in vitro model of the blood–brain barrier. Mol. Pharm. 13, 895–906 (2016).

    Google Scholar 

  219. Wevers, N. R. et al. A perfused human blood–brain barrier on-a-chip for high-throughput assessment of barrier function and antibody transport. Fluids Barriers CNS 15, 23 (2018).

    Google Scholar 

  220. Park, T.-E. et al. Hypoxia-enhanced blood–brain barrier chip recapitulates human barrier function and shuttling of drugs and antibodies. Nat. Commun. 10, 2621 (2019).

    ADS  Google Scholar 

  221. Jin, L. Y. et al. Blood–spinal cord barrier in spinal cord injury: a review. J. Neurotrauma 38, 1203–1224 (2021).

    Google Scholar 

  222. Zheng, W. et al. Differentiation of glial cells from hiPSCs: potential applications in neurological diseases and cell replacement therapy. Front. Cell. Neurosci. 12, 239 (2018).

    Google Scholar 

  223. Chiaradia, I. & Lancaster, M. A. Brain organoids for the study of human neurobiology at the interface of in vitro and in vivo. Nat. Neurosci. 23, 1496–1508 (2020).

    Google Scholar 

  224. Eugène, E. et al. An organotypic brain slice preparation from adult patients with temporal lobe epilepsy. J. Neurosci. Methods 235, 234–244 (2014).

    Google Scholar 

  225. Schwarz, N. et al. Long-term adult human brain slice cultures as a model system to study human CNS circuitry and disease. eLife 8, e48417 (2019).

    Google Scholar 

  226. Cakir, B. et al. Engineering of human brain organoids with a functional vascular-like system. Nat. Methods 16, 1169–1175 (2019).

    Google Scholar 

  227. Liu, J., Pan, L., Cheng, X. & Berdichevsky, Y. Perfused drop microfluidic device for brain slice culture-based drug discovery. Biomed. Microdevices 18, 46 (2016).

    Google Scholar 

  228. Wang, Y., Wang, L., Zhu, Y. & Qin, J. Human brain organoid-on-a-chip to model prenatal nicotine exposure. Lab Chip 18, 851–860 (2018).

    Google Scholar 

  229. Sharma, A. D. et al. Engineering a 3D functional human peripheral nerve in vitro using the nerve-on-a-chip platform. Sci. Rep. 9, 8921 (2019).

    ADS  Google Scholar 

  230. Hyung, S. et al. A 3D disease and regeneration model of peripheral nervous system-on-a-chip. Sci. Adv. 7, eabd9749 (2021).

    ADS  Google Scholar 

  231. Johnson, B. N. et al. 3D printed nervous system on a chip. Lab Chip 16, 1393–1400 (2016).

    Google Scholar 

  232. Benam, K. H. et al. Small airway-on-a-chip enables analysis of human lung inflammation and drug responses in vitro. Nat. Methods 13, 151–157 (2016).

    Google Scholar 

  233. Humayun, M., Chow, C. W. & Young, E. W. K. Microfluidic lung airway-on-a-chip with arrayable suspended gels for studying epithelial and smooth muscle cell interactions. Lab Chip 18, 1298–1309 (2018).

    Google Scholar 

  234. Shrestha, J. et al. A rapidly prototyped lung-on-a-chip model using 3D-printed molds. Organs-on-a-Chip 1, 100001 (2019).

    Google Scholar 

  235. Si, L. et al. A human-airway-on-a-chip for the rapid identification of candidate antiviral therapeutics and prophylactics. Nat. Biomed. Eng. 5, 815–829 (2021).

    Google Scholar 

  236. Zamprogno, P. et al. Second-generation lung-on-a-chip with an array of stretchable alveoli made with a biological membrane. Commun. Biol. 4, 168 (2021).

    Google Scholar 

  237. Ataç, B. et al. Skin and hair on-a-chip: in vitro skin models versus ex vivo tissue maintenance with dynamic perfusion. Lab Chip 13, 3555–3561 (2013).

    Google Scholar 

  238. Lee, S. et al. Construction of 3D multicellular microfluidic chip for an in vitro skin model. Biomed. Microdevices 19, 22 (2017).

    Google Scholar 

  239. Sriram, G. et al. Full-thickness human skin-on-chip with enhanced epidermal morphogenesis and barrier function. Mater. Today 21, 326–340 (2018).

    Google Scholar 

  240. de Haan, P. et al. A versatile, compartmentalised gut-on-a-chip system for pharmacological and toxicological analyses. Sci. Rep. 11, 4920 (2021).

    ADS  Google Scholar 

  241. Kasendra, M. et al. Duodenum intestine-chip for preclinical drug assessment in a human relevant model. eLife 9, e50135 (2020).

    Google Scholar 

  242. Abdalkader, R. & Kamei, K. I. Multi-corneal barrier-on-a-chip to recapitulate eye blinking shear stress forces. Lab Chip 20, 1410–1417 (2020).

    Google Scholar 

  243. Bai, J., Fu, H., Bazinet, L., Birsner, A. E. & D’Amato, R. J. A method for developing novel 3D cornea-on-a-chip using primary murine corneal epithelial and endothelial cells. Front. Pharmacol. 11, 453 (2020).

    Google Scholar 

  244. Bennet, D., Estlack, Z., Reid, T. & Kim, J. A microengineered human corneal epithelium-on-a-chip for eye drops mass transport evaluation. Lab Chip 18, 1539–1551 (2018).

    Google Scholar 

  245. Cao, X. et al. Invited review: human air–liquid-interface organotypic airway tissue models derived from primary tracheobronchial epithelial cells — overview and perspectives. In Vitro Cell Dev. Biol. Anim. 57, 104–132 (2021).

    Google Scholar 

  246. Chiba, H., Osanai, M., Murata, M., Kojima, T. & Sawada, N. Transmembrane proteins of tight junctions. Biochim. Biophys. Acta 1778, 588–600 (2008).

    Google Scholar 

  247. Park, J. Y. et al. Development of a functional airway-on-a-chip by 3D cell printing. Biofabrication 11, 015002 (2018).

    ADS  Google Scholar 

  248. Stucki, J. D. et al. Medium throughput breathing human primary cell alveolus-on-chip model. Sci. Rep. 8, 14359 (2018).

    ADS  Google Scholar 

  249. Gijzen, L. et al. An intestine-on-a-chip model of plug-and-play modularity to study inflammatory processes. SLAS Technol. 25, 585–597 (2020).

    Google Scholar 

  250. van der Helm, M. W. et al. Non-invasive sensing of transepithelial barrier function and tissue differentiation in organs-on-chips using impedance spectroscopy. Lab Chip 19, 452–463 (2019).

    Google Scholar 

  251. Zoio, P., Lopes-Ventura, S. & Oliva, A. Barrier-on-a-chip with a modular architecture and integrated sensors for real-time measurement of biological barrier function. Micromachines 12, 816 (2021).

    Google Scholar 

  252. Ramadan, Q. & Ting, F. C. W. In vitro micro-physiological immune-competent model of the human skin. Lab Chip 16, 1899–1908 (2016).

    Google Scholar 

  253. Yu, Z., Hao, R., Zhang, Y. & Yang, H. in 2021 IEEE 34th Int. Conf. Micro Electro Mechanical Systems (MEMS) 982–985 (IEEE, 2021).

  254. Roberts, N. & Horsley, V. Developing stratified epithelia: lessons from the epidermis and thymus. WIREs Dev. Biol. 3, 389–402 (2014).

    Google Scholar 

  255. Ma, C., Peng, Y., Li, H. & Chen, W. Organ-on-a-chip: a new paradigm for drug development. Trends Pharmacol. Sci. 42, 119–133 (2021).

    Google Scholar 

  256. Viravaidya, K. & Shuler, M. L. Incorporation of 3T3-L1 cells to mimic bioaccumulation in a microscale cell culture analog device for toxicity studies. Biotechnol. Prog. 20, 590–597 (2004).

    Google Scholar 

  257. Renggli, K. & Frey, O. in Organ-on-a-chip (eds Hoeng, J., Bovard, D. & Peitsch, M. C.) 393–427 (Academic, 2020).

  258. Picollet-D’hahan, N., Zuchowska, A., Lemeunier, I. & Le Gac, S. Multiorgan-on-a-chip: a systemic approach to model and decipher inter-organ communication. Trends Biotechnol. 39, 788–810 (2021). This paper provides a comprehensive review of the various applications of multi-OoCs and the different design approaches of coupling individual organ systems.

    Google Scholar 

  259. Lee, J. & Kim, S. Kidney-on-a-chip: a new technology for predicting drug efficacy, interactions, and drug-induced nephrotoxicity. Curr. Drug Metab. 19, 577–583 (2018).

    Google Scholar 

  260. Lee, J., Kim, K. & Kim, S. in Methods in Cell Biology Vol. 146 (eds Doh, J., Fletcher, D. & Piel, J.) 85–104 (Academic, 2018).

  261. Miller, P. G. & Shuler, M. L. Design and demonstration of a pumpless 14 compartment microphysiological system. Biotechnol. Bioeng. 113, 2213–2227 (2016).

    Google Scholar 

  262. Piergiovanni, M., Leite, S. B., Corvi, R. & Whelan, M. Standardisation needs for organ on chip devices. Lab Chip 21, 2857–2868 (2021).

    Google Scholar 

  263. Kilic, T., Navaee, F., Stradolini, F., Renaud, P. & Carrara, S. Organs-on-chip monitoring: sensors and other strategies. Microphysiol. Syst. 2, 1–32 (2018).

    Google Scholar 

  264. EU Science Hub. European Union Reference Laboratory for alternatives to animal testing. EURL ECVAM. Europrean Commission https://ec.europa.eu/jrc/en/eurl/ecvam (2021).

  265. Piergiovanni, M. et al. Organ on Chip: Building a Roadmap Towards Standardisation (European Commission, 2021).

  266. Piergiovanni, M. et al. Putting Science into Standards workshop on standards for organ-on-chip. Stem Cell Rep. 16, 2076–2077 (2021).

    Google Scholar 

  267. Peirsman, A. et al. MISpheroID: a knowledgebase and transparency tool for minimum information in spheroid identity. Nat. Methods 18, 1294–1303 (2021).

    Google Scholar 

  268. Greaney, A. M. et al. Platform effects on regeneration by pulmonary basal cells as evaluated by single-cell RNA sequencing. Cell Rep. 30, 4250–4265.e6 (2020).

    Google Scholar 

  269. Vunjak-Novakovic, G., Ronaldson-Bouchard, K. & Radisic, M. Organs-on-a-chip models for biological research. Cell 184, 4597–4611 (2021). This article is complementary to the present Primer and provides a perspective of how the tissue engineering paradigm, which involves the integration of cells, scaffolds and bioreactors, has been applied to drive the development of OoCs intended for biological research.

    Google Scholar 

  270. Johnson, B. P. et al. A microphysiological approach to evaluate effectors of intercellular hedgehog signaling in development. Front. Cell Dev. Biol. 9, 621442 (2021).

    Google Scholar 

  271. Komeya, M. et al. Pumpless microfluidic system driven by hydrostatic pressure induces and maintains mouse spermatogenesis in vitro. Sci. Rep. 7, 15459 (2017).

    ADS  Google Scholar 

  272. Glieberman, A. L. et al. Synchronized stimulation and continuous insulin sensing in a microfluidic human islet on a chip designed for scalable manufacturing. Lab Chip 19, 2993–3010 (2019).

    Google Scholar 

  273. Modena, M. M., Chawla, K., Misun, P. M. & Hierlemann, A. Smart cell culture systems: integration of sensors and actuators into microphysiological systems. ACS Chem. Biol. 13, 1767–1784 (2018).

    Google Scholar 

  274. Liao, Z. et al. Microfluidic chip coupled with optical biosensors for simultaneous detection of multiple analytes: a review. Biosens. Bioelectron. 126, 697–706 (2019).

    Google Scholar 

  275. Hiramoto, K., Ino, K., Nashimoto, Y., Ito, K. & Shiku, H. Electric and electrochemical microfluidic devices for cell analysis. Front. Chem. 7, 396 (2019).

    ADS  Google Scholar 

  276. van Meer, B. J. et al. Small molecule absorption by PDMS in the context of drug response bioassays. Biochem. Biophys. Res. Commun. 482, 323–328 (2017).

    Google Scholar 

  277. Kim, S. et al. Anchor-IMPACT: a standardized microfluidic platform for high-throughput antiangiogenic drug screening. Biotechnol. Bioeng. 118, 2524–2535 (2021).

    Google Scholar 

  278. Werner, M. et al. Surface curvature differentially regulates stem cell migration and differentiation via altered attachment morphology and nuclear deformation. Adv. Sci. 4, 1600347 (2017).

    Google Scholar 

  279. Wang, J. D. Development of a 3D in Vitro Model of the Blood–Brain Barrier in Layered Microfluidic Devices. PhD thesis, Univ. of Michigan (2015).

  280. Shin, W., Hinojosa, C. D., Ingber, D. E. & Kim, H. J. Human intestinal morphogenesis controlled by transepithelial morphogen gradient and flow-dependent physical cues in a microengineered gut-on-a-chip. iScience 15, 391–406 (2019).

    ADS  Google Scholar 

  281. Albrecht, W. et al. Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations. Arch. Toxicol. 93, 1609–1637 (2019).

    Google Scholar 

  282. Park, J. et al. A 3D human triculture system modeling neurodegeneration and neuroinflammation in Alzheimer’s disease. Nat. Neurosci. 21, 941–951 (2018).

    Google Scholar 

  283. Ekert, J. E. et al. Recommended guidelines for developing, qualifying, and implementing complex in vitro models (CIVMs) for drug discovery. SLAS Discov. 25, 1174–1190 (2020).

    Google Scholar 

  284. Kopec, A. K. et al. Microphysiological systems in early stage drug development: perspectives on current applications and future impact. J. Toxicol. Sci. 46, 99–114 (2021).

    Google Scholar 

  285. Chou, D. B. et al. On-chip recapitulation of clinical bone marrow toxicities and patient-specific pathophysiology. Nat. Biomed. Eng. 4, 394–406 (2020).

    Google Scholar 

  286. LaValley, D. J., Miller, P. G. & Shuler, M. L. Pumpless, unidirectional microphysiological system for testing metabolism-dependent chemotherapeutic toxicity. Biotechnol. Prog. 37, e3105 (2021).

    Google Scholar 

  287. McCracken, K. W. et al. Modelling human development and disease in pluripotent stem-cell-derived gastric organoids. Nature 516, 400–404 (2014).

    ADS  Google Scholar 

  288. Park, D., Lee, J., Chung, J. J., Jung, Y. & Kim, S. H. Integrating organs-on-chips: multiplexing, scaling, vascularization, and innervation. Trends Biotechnol. 38, 99–112 (2020).

    Google Scholar 

  289. Vila, O. F. et al. Bioengineered optogenetic model of human neuromuscular junction. Biomaterials 276, 121033 (2021).

    Google Scholar 

  290. Habert, R. et al. Concerns about the widespread use of rodent models for human risk assessments of endocrine disruptors. Reproduction 147, R119–R129 (2014).

    Google Scholar 

  291. Sun, H., Jia, Y., Dong, H., Dong, D. & Zheng, J. Combining additive manufacturing with microfluidics: an emerging method for developing novel organs-on-chips. Curr. Opin. Chem. Eng. 28, 1–9 (2020). This review provides an update on the integration of additive manufacturing technologies such as 3D printing to fabricate OoC devices.

    Google Scholar 

  292. Trietsch, S. J., Israëls, G. D., Joore, J., Hankemeier, T. & Vulto, P. Microfluidic titer plate for stratified 3D cell culture. Lab Chip 13, 3548–3554 (2013). This paper presents a stratified 3D cell culture platform with side-by-side gel and liquid lanes in a microtitre plate format. Cells in neighbouring lanes can interact, and analysis can be done in high throughput.

    Google Scholar 

  293. Esch, M. B., Ueno, H., Applegate, D. R. & Shuler, M. L. Modular, pumpless body-on-a-chip platform for the co-culture of GI tract epithelium and 3D primary liver tissue. Lab Chip 16, 2719–2729 (2016).

    Google Scholar 

  294. Loskill, P., Marcus, S. G., Mathur, A., Reese, W. M. & Healy, K. E. μOrgano: a lego®-like plug & play system for modular multi-organ-chips. PLoS ONE 10, e0139587 (2015).

    Google Scholar 

  295. Vollertsen, A. R. et al. Facilitating implementation of organs-on-chips by open platform technology. Biomicrofluidics 15, 051301 (2021).

    Google Scholar 

  296. Castillo-Armengol, J., Fajas, L. & Lopez-Mejia, I. C. Inter-organ communication: a gatekeeper for metabolic health. EMBO Rep. 20, e47903 (2019).

    Google Scholar 

  297. Gancheva, S., Jelenik, T., Álvarez-Hernández, E. & Roden, M. Interorgan metabolic crosstalk in human insulin resistance. Physiol. Rev. 98, 1371–1415 (2018).

    Google Scholar 

  298. Ogurtsova, K. et al. IDF Diabetes Atlas: global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res. Clin. Pract. 128, 40–50 (2017).

    Google Scholar 

  299. Yang, J. et al. Integrated gut–liver-on-a-chip platform as an in vitro human model of non-alcoholic fatty liver disease. Preprint at bioRxiv https://doi.org/10.1101/2020.06.10.141606 (2020).

    Article  Google Scholar 

  300. Lee, S. Y. & Sung, J. H. Gut–liver on a chip toward an in vitro model of hepatic steatosis. Biotechnol. Bioeng. 115, 2817–2827 (2018).

    Google Scholar 

  301. Jeon, J.-W., Lee, S. H., Kim, D. & Sung, J. H. In vitro hepatic steatosis model based on gut–liver-on-a-chip. Biotechnol. Prog. 37, e3121 (2021).

    Google Scholar 

  302. Essaouiba, A. et al. Development of a pancreas–liver organ-on-chip coculture model for organ-to-organ interaction studies. Biochem. Eng. J. 164, 107783 (2020).

    Google Scholar 

  303. Bauer, S. et al. Functional coupling of human pancreatic islets and liver spheroids on-a-chip: towards a novel human ex vivo type 2 diabetes model. Sci. Rep. 7, 14620 (2017).

    ADS  Google Scholar 

  304. Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 13, 260–270 (2012).

    Google Scholar 

  305. Tan, H.-Y. & Toh, Y.-C. What can microfluidics do for human microbiome research? Biomicrofluidics 14, 051303 (2020). This review highlights OoC systems that specifically model microbial–mammalian host tissue interactions.

    Google Scholar 

  306. Polini, A. et al. Towards the development of human immune-system-on-a-chip platforms. Drug Discov. Today 24, 517–525 (2019). This review provides an overview of key characteristics of the human immune system and how different aspects of human immunity can be modelled by OoC systems.

    Google Scholar 

  307. Miller, C. P., Shin, W., Ahn, E. H., Kim, H. J. & Kim, D.-H. Engineering microphysiological immune system responses on chips. Trends Biotechnol. 38, 857–872 (2020).

    Google Scholar 

  308. Park, D. et al. High-throughput microfluidic 3D cytotoxicity assay for cancer immunotherapy (CACI-IMPACT Platform). Front. Immunol. 10, 1133 (2019).

    Google Scholar 

  309. Lee, S. et al. Modeling 3D human tumor lymphatic vessel network using high-throughput platform. Adv. Biol. 5, 2000195 (2021).

    Google Scholar 

  310. Hind, L. E., Ingram, P. N., Beebe, D. J. & Huttenlocher, A. Interaction with an endothelial lumen increases neutrophil lifetime and motility in response to P. aeruginosa. Blood 132, 1818–1828 (2018).

    Google Scholar 

  311. Hind, L. E. et al. Immune cell paracrine signaling drives the neutrophil response to A. fumigatus in an infection-on-a-chip model. Cell. Mol. Bioeng. 14, 133–145 (2021).

    Google Scholar 

  312. Weerappuli, P. D. et al. Extracellular trap-mimicking DNA–histone mesostructures synergistically activate dendritic cells. Adv. Healthc. Mater. 8, 1900926 (2019).

    Google Scholar 

  313. Tao, T. et al. Microengineered multi-organoid system from hipscs to recapitulate human liver–islet axis in normal and type 2 diabetes. Adv. Sci. 9, e2103495 (2022).

    Google Scholar 

  314. Yin, F. et al. HiPSC-derived multi-organoids-on-chip system for safety assessment of antidepressant drugs. Lab Chip 21, 571–581 (2021).

    Google Scholar 

  315. Osaki, T., Uzel, S. G. M. & Kamm, R. D. On-chip 3D neuromuscular model for drug screening and precision medicine in neuromuscular disease. Nat. Protoc. 15, 421–449 (2020).

    Google Scholar 

  316. Douville, N. J. et al. Fabrication of two-layered channel system with embedded electrodes to measure resistance across epithelial and endothelial barriers. Anal. Chem. 82, 2505–2511 (2010).

    Google Scholar 

  317. Poussin, C. et al. 3D human microvessel-on-a-chip model for studying monocyte-to-endothelium adhesion under flow — application in systems toxicology. Altex 37, 47–63 (2020).

    Google Scholar 

  318. Mori, N., Morimoto, Y. & Takeuchi, S. Skin integrated with perfusable vascular channels on a chip. Biomaterials 116, 48–56 (2017).

    Google Scholar 

  319. Ong, L. J. Y. et al. A 3D printed microfluidic perfusion device for multicellular spheroid cultures. Biofabrication 9, 045005 (2017).

    ADS  Google Scholar 

  320. Asif, A., Kim, K. H., Jabbar, F., Kim, S. & Choi, K. Real-time sensors for live monitoring of disease and drug analysis in microfluidic model of proximal tubule. Microfluid. Nanofluidics 24, 1–10 (2020).

    Google Scholar 

  321. Khoshfetrat Pakazad, S., Savov, A., van de Stolpe, A. & Dekker, R. A novel stretchable micro-electrode array (SMEA) design for directional stretching of cells. J. Micromech. Microeng. 24, 034003 (2014).

    ADS  Google Scholar 

  322. Liu, Y. et al. Adipose-on-a-chip: a dynamic microphysiological in vitro model of the human adipose for immune-metabolic analysis in type II diabetes. Lab Chip 19, 241–253 (2019).

    Google Scholar 

  323. Huh, D. et al. Microfabrication of human organs-on-chips. Nat. Protoc. 8, 2135–2157 (2013).

    Google Scholar 

  324. Zhang, W., Zhang, H., Williams, S. E. & Zhou, A. Microfabricated three-electrode on-chip PDMS device with a vibration motor for stripping voltammetric detection of heavy metal ions. Talanta 132, 321–326 (2015).

    Google Scholar 

  325. Lee, B. et al. 3D micromesh-based hybrid bioprinting: multidimensional liquid patterning for 3D microtissue engineering. NPG Asia Mater. 14, 6 (2022).

    ADS  Google Scholar 

  326. Kang, H.-W. et al. A 3D bioprinting system to produce human-scale tissue constructs with structural integrity. Nat. Biotechnol. 34, 312–319 (2016).

    Google Scholar 

  327. Lee, S.-Y., Kim, D.-S., Kim, E.-S. & Lee, D.-W. Nano-textured polyimide cantilever for enhancing the contractile behavior of cardiomyocytes and its application to cardiac toxicity screening. Sens. Actuators B Chem. 301, 126995 (2019).

    Google Scholar 

  328. Riahi, R. et al. Automated microfluidic platform of bead-based electrochemical immunosensor integrated with bioreactor for continual monitoring of cell secreted biomarkers. Sci. Rep. 6, 24598 (2016).

    ADS  Google Scholar 

  329. Schmittlein, C., Meier, R. J., Sauer, L., Liebsch, G. & Wegener, J. Monitoring oxygenation in microfluidic cell culture using 2D sensor foils as growth substrate. PreSens https://www.presens.de/knowledge/publications/application-note/monitoring-oxygenation-in-microfluidic-cell-culture-using-2d-sensor-foils-as-growth-substrate-full-text-1681 (2019).

  330. 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).

    ADS  Google Scholar 

  331. LeCluyse, E. L., Witek, R. P., Andersen, M. E. & Powers, M. J. Organotypic liver culture models: meeting current challenges in toxicity testing. Crit. Rev. Toxicol. 42, 501–548 (2012).

    Google Scholar 

  332. Zhang, S. et al. A robust high-throughput sandwich cell-based drug screening platform. Biomaterials 32, 1229–1241 (2011).

    Google Scholar 

  333. Bircsak, K. M. et al. A 3D microfluidic liver model for high throughput compound toxicity screening in the OrganoPlate®. Toxicology 450, 152667 (2021).

    Google Scholar 

  334. Domansky, K. et al. Perfused multiwell plate for 3D liver tissue engineering. Lab Chip 10, 51–58 (2010).

    Google Scholar 

  335. Boos, J. A., Misun, P. M., Michlmayr, A., Hierlemann, A. & Frey, O. Microfluidic multitissue platform for advanced embryotoxicity testing in vitro. Adv. Sci. 6, 1900294 (2019).

    Google Scholar 

  336. Freag, M. S. et al. Human nonalcoholic steatohepatitis on a chip. Hepatol. Commun. 5, 217–233 (2021).

    Google Scholar 

  337. Herland, A. et al. Quantitative prediction of human pharmacokinetic responses to drugs via fluidically coupled vascularized organ chips. Nat. Biomed. Eng. 4, 421–436 (2020).

    Google Scholar 

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Acknowledgements

This work was supported by National Institute of Health (NIH) (U10A214300) awarded to M.L.S.; NIH (UG3EB025765, CA249799, P41 EB027062 and R01HL136414), National Science Foundation (NSF1647837), National Aeronautics and Space Administration (NASA NNX16A069A) and BARDA (75A50121C00017) awarded to G.V.-N.; National Research Foundation of Korea (No. 2021R1A3B1077481) awarded to N.L.J.; Australian Research Council (FT180100157, DP200101658) awarded to Y.-C.T.; and National University of Singapore Graduate School (NUSGS) Integrative Sciences and Engineering Programme (ISEP) scholarship awarded to C.M.L.

Author information

Author notes

  1. Zhu Chen

    Present address: Bio-Rad Laboratories, Hercules, CA, USA

  2. These authors contributed equally: Chak Ming Leung, Pim de Haan.

Authors and Affiliations

  1. Integrative Sciences and Engineering Programme, National University of Singapore, Singapore, Singapore

    Chak Ming Leung

  2. Pharmaceutical Analysis Group, Groningen Research Institute of Pharmacy, University of Groningen, Groningen, Netherlands

    Pim de Haan & Elisabeth Verpoorte

  3. Laboratory for Stem Cells and Tissue Engineering, Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Kacey Ronaldson-Bouchard & Gordana Vunjak-Novakovic

  4. School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Ge-Ah Kim

  5. Department of Mechanical Engineering, Seoul National University, Seoul, Republic of Korea

    Jihoon Ko & Noo Li Jeon

  6. MERLN Institute for Technology Inspired Regenerative Medicine, Maastricht University, Maastricht, Netherlands

    Hoon Suk Rho & Pamela Habibovic

  7. Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA

    Zhu Chen & Michael L. Shuler

  8. The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory School of Medicine, Atlanta, GA, USA

    Shuichi Takayama

  9. Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA

    Shuichi Takayama

  10. InSphero AG, Schlieren, Switzerland

    Olivier Frey

  11. School of Mechanical, Medical and Process Engineering, Queensland University of Technology, Brisbane, Queensland, Australia

    Yi-Chin Toh

  12. Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, Queensland, Australia

    Yi-Chin Toh

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Contributions

Introduction (E.V., Y.-C.T., O.F. and G.V.-N.); Experimentation (E.V., C.M.L., Y.-C.T., P.d.H., K.R.-B., G.V.-N., J.K. and N.L.J.); Results (Y.-C.T., O.F. and M.L.S.); Applications (C.M.L., Y.-C.T., K.R.-B., G.V.-N., M.L.S., Z.C., H.S.R. and P.H.); Reproducibility and data deposition (O.F., S.T. and P.d.H.); Limitations and optimizations (G.-A.K. and S.T.); Outlook (C.M.L., Y.-C.T., G.V.-N., E.V., G.-A.K. and S.T.); Overview of the Primer (E.V., Y.-C.T., C.M.L. and P.d.H.).

Corresponding authors

Correspondence to Pamela Habibovic, Noo Li Jeon, Shuichi Takayama, Michael L. Shuler, Gordana Vunjak-Novakovic, Olivier Frey, Elisabeth Verpoorte or Yi-Chin Toh.

Ethics declarations

Competing interests

O.F. is member of the management team of InSphero commercializing 3D organ systems. K.R.-B. and G.V.-N. are co-founders of Tara Biosystems, a Columbia University start-up company commercializing organs-on-a-chip with human heart muscle. M.L.S. is CEO and President of Hesperos, Inc. All other authors declare no competing interests.

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Nature Reviews Methods Primers thanks Mandy Esch, Zhongze Gu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Glossary

Microsystems technology

A set of technologies for fabrication of planar devices having microscale and nanoscale features. Organ-on-a-chip (OoC)-containing microfluidic channels and integrated electrical and non-electrical components are often fabricated using microsystems technology.

Lithographic pattern transfer

A microfabrication technique in which micrometre-sized features are transferred from a mould into a silicone polymer, usually poly(dimethylsiloxane) (PDMS).

Single-organ systems

Organ-on-a-chip systems with only one organ or tissue modelled.

Multi-organ systems

Organ-on-a-chip systems with multiple tissues that are representative of organ systems are being modelled. A fluidic circuit is embedded in the device to connect the different organ compartments and allow for inter-organ communication via soluble paracrine signalling factors.

Solid organ chips

Organ-on-a-chip systems that are representative of parenchymal or mesenchymal organ tissues, such as the liver, tumour, pancreas, bone and cartilage. Cells are often cultured as a 3D tissue mass or embedded in an extracellular matrix (ECM) analogue where they may directly interact with one another and with the culture substrate and medium in a defined manner.

Barrier tissue chips

Organ-on-a-chip systems that are representative of endothelial and epithelial tissues, such as the vascular endothelium, gut, and corneal and skin epithelium, that function as a living barrier to regulate active and/or passive transport of molecules. Cells are often attached to a porous surface separating two different compartments.

Micro-milling

A microfabrication technique in which structures are carved into a solid block of material by a computer-controlled milling tool.

PDMS-based soft lithography

A collection of techniques for replicating structures in the elastic silicone rubber, poly(dimethylsiloxane) (PDMS), or using PDMS stamps to print molecules in patterns onto device surfaces.

Media perfusion

The delivery of cell culture medium to the living elements inside a microchannel using convective fluid flow driven by external forces generated by pumps or gravity, in order to supply nutrients and remove its metabolites.

Shear stress

Friction on a cell surface caused by the moving of fluids over that surface.

Reynolds number

(Re). A dimensionless parameter that describes the ratio of a fluid’s inertial forces to viscous forces. Commonly used as a measure to determine whether fluid flow is laminar or turbulent.

Péclet number

(Pe). A dimensionless parameter that describes the ratio of convective to diffusive mass transport. Commonly used as a measure to determine the primary mode of mass transport.

Damköhler number

(Da). A dimensionless parameter that describes the ratio of diffusion to reactive timescales. Commonly used as a measure to determine the overall efficacy of a reaction process.

Effective culture time

The period of time whereby a cell culture is able to receive sufficient biochemical factors essential for survival and maintenance of its phenotype.

Transepithelial electrical resistance

(TEER). The electrical resistance across a cell layer or tissue layer. Generally used as a measure to determine the overall integrity and permeability of the cell/tissue layer; a higher TEER indicates better integrity or cell confluency.

Excitation threshold

The minimal electrical potential required for the tissue to contract in response to electrical signal.

Maximum capture rate

The highest beat rate that is the maximum rate of synchronous contraction under an electric field voltage corresponding to twice the excitation threshold.

Donor-to-donor variability

Variation in phenotypes and functions of primary cells that are due to inherent genetic and physiological differences (such as age, sex, ethnicity) between the donors.

Batch-to-batch variability

Variation that arises from in vitro cell culture processes, such as cell expansion and differentiation. As cell culture is a batch process (as opposed to a continuous process), variation can arise due to different operators, equipment and reagent sources used across the different batches.

Line-to-line variability

Variation that exists between different induced pluripotent stem cell (iPSC) lines due to the inter-patient heterogeneity from which the iPSC lines are derived.

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Leung, C.M., de Haan, P., Ronaldson-Bouchard, K. et al. A guide to the organ-on-a-chip. Nat Rev Methods Primers 2, 33 (2022). https://doi.org/10.1038/s43586-022-00118-6

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