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Microfluidic high-throughput 3D cell culture

Nature Reviews Bioengineering (2024)Cite this article

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Abstract

High-throughput 3D microfluidic cell culture systems can be designed to model aspects of human tissues and organs and may thus serve as non-clinical evaluation tools. They benefit from large-scale production, high throughput, compatibility with automated equipment, standardized analysis and the generation of physiologically relevant results. In this Review, we discuss how microfluidic devices can be designed with different biological complexity, cell sources and cell configurations, as well as physiological parameters to mimic human tissues. We examine standardization, scalability and automation strategies, and outline high-throughput data generation and analysis approaches to interpret readouts of microfluidic 3D cell culture models. Finally, we explore the potential of these tools as non-clinical testing systems for drug development and outline key future challenges in device design and application.

Key points

  • High-throughput microfluidic 3D cell culture systems may provide valuable non-clinical testing tools.

  • To apply microfluidic technology in cell culture, physiological relevance and high throughput need to be balanced.

  • Microfluidics-based 3D cell culture models can be designed and optimized for specific applications, depending on the required level of biological complexity and readout.

  • Automation and artificial intelligence may aid in the standardized analysis of 3D microfluidic cell culture devices.

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Fig. 1: Microfluidic high-throughput 3D cell culture.
Fig. 2: Design considerations for microfluidic 3D cell culture platforms.

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References

  1. Dove, A. Screening for content — the evolution of high throughput. Nat. Biotechnol. 21, 859–864 (2003).

    Article  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).

    Article  Google Scholar 

  3. Esch, E. W., Bahinski, A. & Huh, D. Organs-on-chips at the frontiers of drug discovery. Nat. Rev. Drug. Discov. 14, 248–260 (2015).

    Article  Google Scholar 

  4. Probst, C., Schneider, S. & Loskill, P. High-throughput organ-on-a-chip systems: current status and remaining challenges. Curr. Opin. Biomed. Eng. 6, 33–41 (2018).

    Article  Google Scholar 

  5. Schuster, B. et al. Automated microfluidic platform for dynamic and combinatorial drug screening of tumor organoids. Nat. Commun. 11, 5271 (2020).

    Article  Google Scholar 

  6. van Duinen, V. et al. Perfused 3D angiogenic sprouting in a high-throughput in vitro platform. Angiogenesis 22, 157–165 (2019).

    Article  Google Scholar 

  7. Zhao, Y., Sampson, M. G. & Wen, X. Quantify and control reproducibility in high-throughput experiments. Nat. Methods 17, 1207–1213 (2020).

    Article  Google Scholar 

  8. Zhang, B., Korolj, A., Lai, B. F. L. & Radisic, M. Advances in organ-on-a-chip engineering. Nat. Rev. Mater. 3, 257–278 (2018).

    Article  Google Scholar 

  9. Herbig, M. et al. Best practices for reporting throughput in biomedical research. Nat. Methods 19, 633–634 (2022).

    Article  Google Scholar 

  10. Stresser, D. M. et al. Towards in vitro models for reducing or replacing the use of animals in drug testing. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-023-01154-7 (2023).

  11. Ewart, L. & Roth, A. Opportunities and challenges with microphysiological systems: a pharma end-user perspective. Nat. Rev. Drug. Discov. 20, 327–328 (2021).

    Article  Google Scholar 

  12. Huang, Y. et al. Improving immune–vascular crosstalk for cancer immunotherapy. Nat. Rev. Immunol. 18, 195–203 (2018).

    Article  Google Scholar 

  13. Gao, S., Yang, X., Xu, J., Qiu, N. & Zhai, G. Nanotechnology for boosting cancer immunotherapy and remodeling tumor microenvironment: the horizons in cancer treatment. ACS nano 15, 12567–12603 (2021).

    Article  Google Scholar 

  14. Zhou, Z. et al. Harnessing 3D in vitro systems to model immune responses to solid tumours: a step towards improving and creating personalized immunotherapies. Nat. Rev. Immunol. 24, 18–32 (2023).

    Article  Google Scholar 

  15. Au, S. H. et al. Clusters of circulating tumor cells traverse capillary-sized vessels. Proc. Natl Acad. Sci. USA 113, 4947–4952 (2016).

    Article  Google Scholar 

  16. Peng, F. et al. Nanoparticles promote in vivo breast cancer cell intravasation and extravasation by inducing endothelial leakiness. Nat. Nanotechnol. 14, 279–286 (2019).

    Article  Google Scholar 

  17. Kim, Y. et al. Quantification of cancer cell extravasation in vivo. Nat. Protoc. 11, 937–948 (2016).

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. Aung, A., Kumar, V., Theprungsirikul, J., Davey, S. K. & Varghese, S. An engineered tumor-on-a-chip device with breast cancer–immune cell interactions for assessing T-cell recruitment. Cancer Res. 80, 263–275 (2020).

    Article  Google Scholar 

  20. Ayuso, J. M. et al. Microfluidic tumor-on-a-chip model to evaluate the role of tumor environmental stress on NK cell exhaustion. Sci. Adv. 7, eabc2331 (2021).

    Article  Google Scholar 

  21. Duval, K. et al. Modeling physiological events in 2D vs. 3D cell culture. Physiology 32, 266–277 (2017).

    Article  Google Scholar 

  22. Kim, S., Chung, M., Ahn, J., Lee, S. & Jeon, N. L. Interstitial flow regulates the angiogenic response and phenotype of endothelial cells in a 3D culture model. Lab Chip 16, 4189–4199 (2016).

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Clay, N. E. et al. Modulation of matrix softness and interstitial flow for 3D cell culture using a cell-microenvironment-on-a-chip system. ACS Biomater. Sci. Eng. 2, 1968–1975 (2016).

    Article  Google Scholar 

  25. Shin, J. et al. Monolithic digital patterning of polydimethylsiloxane with successive laser pyrolysis. Nat. Mater. 20, 100–107 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. 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).

    Article  Google Scholar 

  28. Novak, R. et al. Robotic fluidic coupling and interrogation of multiple vascularized organ chips. Nat. Biomed. Eng. 4, 407–420 (2020). This article introduces liquid-handling robotics to maintain multiorgan chips for 3 weeks and evaluate drug pharmacodynamics and pharmacokinetics.

    Article  Google Scholar 

  29. Lam, J. et al. A microphysiological system-based potency bioassay for the functional quality assessment of mesenchymal stromal cells targeting vasculogenesis. Biomaterials 290, 121826 (2022).

    Article  Google Scholar 

  30. Rajasekar, S. et al. IFlowPlate — a customized 384‐well plate for the culture of perfusable vascularized colon organoids. Adv. Mater. 32, 2002974 (2020). This articles offers an example of achieving both biological relevance and high throughput by using organoids and a standard well-plate format.

    Article  Google Scholar 

  31. Ao, Z. et al. Microfluidics guided by deep learning for cancer immunotherapy screening. Proc. Natl Acad. Sci. USA 119, e2214569119 (2022).

    Article  Google Scholar 

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

    Article  Google Scholar 

  33. Vunjak-Novakovic, G., Ronaldson-Bouchard, K. & Radisic, M. Organs-on-a-chip models for biological research. Cell 184, 4597–4611 (2021).

    Article  Google Scholar 

  34. Ingber, D. E. Human organs-on-chips for disease modelling, drug development and personalized medicine. Nat. Rev. Genet. 23, 467–491 (2022).

    Article  Google Scholar 

  35. 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).

    Article  Google Scholar 

  36. Wang, Y. & Jeon, H. 3D cell cultures toward quantitative high-throughput drug screening. Trends Pharmacol. Sci. 43, 569–581 (2022).

    Article  Google Scholar 

  37. Pan, C., Kumar, C., Bohl, S., Klingmueller, U. & Mann, M. Comparative proteomic phenotyping of cell lines and primary cells to assess preservation of cell type-specific functions. Mol. Cell. Proteom. 8, 443–450 (2009).

    Article  Google Scholar 

  38. Hashemzadeh, H. et al. A combined microfluidic deep learning approach for lung cancer cell high throughput screening toward automatic cancer screening applications. Sci. Rep. 11, 9804 (2021).

    Article  Google Scholar 

  39. 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).

    Article  Google Scholar 

  40. Huebsch, N. et al. Metabolically driven maturation of human-induced-pluripotent-stem-cell-derived cardiac microtissues on microfluidic chips. Nat. Biomed. Eng. 6, 372–388 (2022).

    Article  Google Scholar 

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

    Article  Google Scholar 

  42. Zheng, Y., Shao, Y. & Fu, J. A microfluidics-based stem cell model of early post-implantation human development. Nat. Protoc. 16, 309–326 (2021).

    Article  Google Scholar 

  43. Zheng, Y. et al. Controlled modelling of human epiblast and amnion development using stem cells. Nature 573, 421–425 (2019).

    Article  Google Scholar 

  44. Lei, Y. & Schaffer, D. V. A fully defined and scalable 3D culture system for human pluripotent stem cell expansion and differentiation. Proc. Natl Acad. Sci. USA 110, E5039–E5048 (2013).

    Article  Google Scholar 

  45. Vatine, G. D. et al. Human iPSC-derived blood-brain barrier chips enable disease modeling and personalized medicine applications. Cell Stem Cell 24, 995–1005 (2019). This article describes a blood–brain barrier based on iPS-cell-derived endothelial cells, with enhanced barrier function induced by shear flow and coculture.

    Article  Google Scholar 

  46. Hu, Y. et al. Lung cancer organoids analyzed on microwell arrays predict drug responses of patients within a week. Nat. Commun. 12, 2581 (2021).

    Article  Google Scholar 

  47. Shirure, V. S. et al. Tumor-on-a-chip platform to investigate progression and drug sensitivity in cell lines and patient-derived organoids. Lab Chip 18, 3687–3702 (2018).

    Article  Google Scholar 

  48. Lai, B. F. L. et al. Recapitulating pancreatic tumor microenvironment through synergistic use of patient organoids and organ‐on‐a‐chip vasculature. Adv. Funct. Mater. 30, 2000545 (2020).

    Article  Google Scholar 

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

    Article  Google Scholar 

  50. Garreta, E. et al. Rethinking organoid technology through bioengineering. Nat. Mater. 20, 145–155 (2021).

    Article  Google Scholar 

  51. Kim, S.-J., Kim, E. M., Yamamoto, M., Park, H. & Shin, H. Engineering multi-cellular spheroids for tissue engineering and regenerative medicine. Adv. Healthc. Mater. 9, 2000608 (2020).

    Article  Google Scholar 

  52. Kang, S.-M., Kim, D., Lee, J.-H., Takayama, S. & Park, J. Y. Engineered microsystems for spheroid and organoid studies. Adv. Healthc. Mater. 10, 2001284 (2021).

    Article  Google Scholar 

  53. Hofer, M. & Lutolf, M. P. Engineering organoids. Nat. Rev. Mater. 6, 402–420 (2021).

    Article  Google Scholar 

  54. Lee, H. N. et al. Effect of biochemical and biomechanical factors on vascularization of kidney organoid-on-a-chip. Nano Converg. 8, 35 (2021).

    Article  Google Scholar 

  55. Bonanini, F. et al. In vitro grafting of hepatic spheroids and organoids on a microfluidic vascular bed. Angiogenesis 25, 455–470 (2022).

    Article  Google Scholar 

  56. Prince, E. et al. Microfluidic arrays of breast tumor spheroids for drug screening and personalized cancer therapies. Adv. Healthc. Mater. 11, 2101085 (2022).

    Article  Google Scholar 

  57. Nashimoto, Y. et al. Vascularized cancer on a chip: the effect of perfusion on growth and drug delivery of tumor spheroid. Biomaterials 229, 119547 (2020).

    Article  Google Scholar 

  58. Haase, K., Offeddu, G. S., Gillrie, M. R. & Kamm, R. D. Endothelial regulation of drug transport in a 3D vascularized tumor model. Adv. Funct. Mater. 30, 2002444 (2020).

    Article  Google Scholar 

  59. Seiler, S. T. et al. Modular automated microfluidic cell culture platform reduces glycolytic stress in cerebral cortex organoids. Sci. Rep. 12, 20173 (2022).

    Article  Google Scholar 

  60. Salmon, I. et al. Engineering neurovascular organoids with 3D printed microfluidic chips. Lab Chip 22, 1615–1629 (2022).

    Article  Google Scholar 

  61. Cho, A.-N. et al. Microfluidic device with brain extracellular matrix promotes structural and functional maturation of human brain organoids. Nat. Commun. 12, 4730 (2021).

    Article  Google Scholar 

  62. Byrne, A. T. et al. Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat. Rev. Cancer 17, 254–268 (2017).

    Article  Google Scholar 

  63. Dobrolecki, L. E. et al. Patient-derived xenograft (PDX) models in basic and translational breast cancer research. Cancer Metastasis Rev. 35, 547–573 (2016).

    Article  Google Scholar 

  64. Mathur, T., Tronolone, J. J. & Jain, A. Comparative analysis of blood‐derived endothelial cells for designing next‐generation personalized organ‐on‐chips. J. Am. Heart Assoc. 10, e022795 (2021).

    Article  Google Scholar 

  65. Pediaditakis, I. et al. Modeling alpha-synuclein pathology in a human brain-chip to assess blood–brain barrier disruption. Nat. Commun. 12, 5907 (2021).

    Article  Google Scholar 

  66. 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).

    Article  Google Scholar 

  67. Ibrahim, lI., Hajal, C., Offeddu, G. S., Gillrie, M. R. & Kamm, R. D. Omentum-on-a-chip: a multicellular, vascularized microfluidic model of the human peritoneum for the study of ovarian cancer metastases. Biomaterials 288, 121728 (2022).

    Article  Google Scholar 

  68. Zhao, J. et al. Separation and single-cell analysis for free gastric cancer cells in ascites and peritoneal lavages based on microfluidic chips. Ebiomedicine 90, 104522 (2023).

    Article  Google Scholar 

  69. Hyung, S. et al. Patient-derived exosomes facilitate therapeutic targeting of oncogenic MET in advanced gastric cancer. Sci. Adv. 9, eadk1098 (2023).

    Article  Google Scholar 

  70. Schwab, F. D. et al. MyCTC chip: microfluidic-based drug screen with patient-derived tumour cells from liquid biopsies. Microsyst. Nanoeng. 8, 130 (2022).

    Article  Google Scholar 

  71. Descamps, L. et al. MagPure chip: an immunomagnetic-based microfluidic device for high purification of circulating tumor cells from liquid biopsies. Lab Chip 22, 4151–4166 (2022).

    Article  Google Scholar 

  72. Meran, L., Tullie, L., Eaton, S., De Coppi, P. & Li, V. S. Bioengineering human intestinal mucosal grafts using patient-derived organoids, fibroblasts and scaffolds. Nat. Protoc. 18, 108–135 (2023).

    Article  Google Scholar 

  73. Phifer, C. J. et al. Obtaining patient-derived cancer organoid cultures via fine-needle aspiration. STAR. Protoc. 2, 100220 (2021).

    Article  Google Scholar 

  74. Huang, L. et al. Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell–and patient-derived tumor organoids. Nat. Med. 21, 1364–1371 (2015).

    Article  Google Scholar 

  75. Lou, J. & Mooney, D. J. Chemical strategies to engineer hydrogels for cell culture. Nat. Rev. Chem. 6, 726–744 (2022).

    Article  Google Scholar 

  76. Jiménez, G. et al. A soft 3D polyacrylate hydrogel recapitulates the cartilage niche and allows growth-factor free tissue engineering of human articular cartilage. Acta Biomater. 90, 146–156 (2019).

    Article  Google Scholar 

  77. Son, K. J., Gheibi, P., Stybayeva, G., Rahimian, A. & Revzin, A. Detecting cell-secreted growth factors in microfluidic devices using bead-based biosensors. Microsyst. Nanoeng. 3, 1–9 (2017).

    Article  Google Scholar 

  78. Clancy, A. et al. Hydrogel-based microfluidic device with multiplexed 3D in vitro cell culture. Sci. Rep. 12, 17781 (2022).

    Article  Google Scholar 

  79. Burdick, J. A. & Murphy, W. L. Moving from static to dynamic complexity in hydrogel design. Nat. Commun. 3, 1269 (2012).

    Article  Google Scholar 

  80. Akther, F., Little, P., Li, Z., Nguyen, N.-T. & Ta, H. T. Hydrogels as artificial matrices for cell seeding in microfluidic devices. RSC Adv. 10, 43682–43703 (2020).

    Article  Google Scholar 

  81. Han, S. et al. Hydrophobic patterning‐based 3D microfluidic cell culture assay. Adv. Healthc. Mater. 7, 1800122 (2018).

    Article  Google Scholar 

  82. Angelidakis, E. et al. Impact of fibrinogen, fibrin thrombi and thrombin on cancer cell extravasation using in vitro microvascular networks. Adv. Healthc. Mater. 12, 2202984 (2023).

    Article  Google Scholar 

  83. Bang, S., Na, S., Jang, J. M., Kim, J. & Jeon, N. L. Engineering‐aligned 3D neural circuit in microfluidic device. Adv. Healthc. Mater. 5, 159–166 (2016).

    Article  Google Scholar 

  84. Fridman, I. B. et al. High-throughput microfluidic 3D biomimetic model enabling quantitative description of the human breast tumor microenvironment. Acta Biomater. 132, 473–488 (2021).

    Article  Google Scholar 

  85. Kwak, T. J. & Lee, E. In vitro modeling of solid tumor interactions with perfused blood vessels. Sci. Rep. 10, 20142 (2020).

    Article  Google Scholar 

  86. Kim, S., Lee, H., Chung, M. & Jeon, N. L. Engineering of functional, perfusable 3D microvascular networks on a chip. Lab Chip 13, 1489–1500 (2013).

    Article  Google Scholar 

  87. Shin, Y. et al. Microfluidic assay for simultaneous culture of multiple cell types on surfaces or within hydrogels. Nat. Protoc. 7, 1247–1259 (2012).

    Article  Google Scholar 

  88. Berthier, E., Dostie, A. M., Lee, U. N., Berthier, J. & Theberge, A. B. Open microfluidic capillary systems. Anal. Chem. 91, 8739–8750 (2019). This article discusses open microfluidic platforms that can provide variable configurations of cell layout and high throughput.

    Article  Google Scholar 

  89. Berry, S. B. et al. Upgrading well plates using open microfluidic patterning. Lab Chip 17, 4253–4264 (2017).

    Article  Google Scholar 

  90. Huang, C. P. et al. Engineering microscale cellular niches for three-dimensional multicellular co-cultures. Lab Chip 9, 1740–1748 (2009).

    Article  Google Scholar 

  91. Lee, Y. et al. Microfluidics within a well: an injection-molded plastic array 3D culture platform. Lab Chip 18, 2433–2440 (2018). This article describes the methodology for designing injection-moulded microfluidic devices with open microfluidic analysis and 3D-printed prototyping.

    Article  Google Scholar 

  92. Chaudhuri, O. et al. Hydrogels with tunable stress relaxation regulate stem cell fate and activity. Nat. Mater. 15, 326–334 (2016).

    Article  Google Scholar 

  93. Rosales, A. M. & Anseth, K. S. The design of reversible hydrogels to capture extracellular matrix dynamics. Nat. Rev. Mater. 1, 15012 (2016).

    Article  Google Scholar 

  94. Hudalla, G. A. & Murphy, W. L. Biomaterials that regulate growth factor activity via bioinspired interactions. Adv. Funct. Mater. 21, 1754–1768 (2011).

    Article  Google Scholar 

  95. Kumachev, A. et al. High-throughput generation of hydrogel microbeads with varying elasticity for cell encapsulation. Biomaterials 32, 1477–1483 (2011).

    Article  Google Scholar 

  96. Arık, Y. B. et al. Collagen I based enzymatically degradable membranes for organ-on-a-chip barrier models. ACS Biomater. Sci. Eng. 7, 2998–3005 (2021).

    Article  Google Scholar 

  97. Mondrinos, M. J., Yi, Y.-S., Wu, N.-K., Ding, X. & Huh, D. Native extracellular matrix-derived semipermeable, optically transparent, and inexpensive membrane inserts for microfluidic cell culture. Lab Chip 17, 3146–3158 (2017).

    Article  Google Scholar 

  98. 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).

    Article  Google Scholar 

  99. Park, J. Y. et al. A microphysiological model of human trophoblast invasion during implantation. Nat. Commun. 13, 1252 (2022).

    Article  Google Scholar 

  100. Park, S. E., Georgescu, A., Oh, J. M., Kwon, K. W. & Huh, D. Polydopamine-based interfacial engineering of extracellular matrix hydrogels for the construction and long-term maintenance of living three-dimensional tissues. ACS Appl. Mater. Interfaces 11, 23919–23925 (2019).

    Article  Google Scholar 

  101. Subedi, N. et al. An automated real-time microfluidic platform to probe single NK cell heterogeneity and cytotoxicity on-chip. Sci. Rep. 11, 17084 (2021).

    Article  Google Scholar 

  102. Kellogg, R. A., Gómez-Sjöberg, R., Leyrat, A. A. & Tay, S. High-throughput microfluidic single-cell analysis pipeline for studies of signaling dynamics. Nat. Protoc. 9, 1713–1726 (2014).

    Article  Google Scholar 

  103. Lecault, V. et al. High-throughput analysis of single hematopoietic stem cell proliferation in microfluidic cell culture arrays. Nat. Methods 8, 581–586 (2011).

    Article  Google Scholar 

  104. Kim, S., Chung, M. & Jeon, N. L. Three-dimensional biomimetic model to reconstitute sprouting lymphangiogenesis in vitro. Biomaterials 78, 115–128 (2016).

    Article  Google Scholar 

  105. Mousavi Shaegh, S. A. et al. A microfluidic optical platform for real-time monitoring of pH and oxygen in microfluidic bioreactors and organ-on-chip devices. Biomicrofluidics 10, 044111 (2016).

    Article  Google Scholar 

  106. Dornhof, J. et al. in 2021 21st International Conference on Solid-State Sensors, Actuators and Microsystems (Transducers), 703–706 (IEEE, 2021).

  107. Nashimoto, Y. et al. Electrochemical sensing of oxygen metabolism for a three-dimensional cultured model with biomimetic vascular flow. Biosens. Bioelectron. 219, 114808 (2023).

    Article  Google Scholar 

  108. Önen, S. et al. A pumpless monolayer microfluidic device based on mesenchymal stem cell-conditioned medium promotes neonatal mouse in vitro spermatogenesis. Stem Cell Res. Ther. 14, 127 (2023).

    Article  Google Scholar 

  109. Zhang, F., Lin, D. S., Rajasekar, S., Sotra, A. & Zhang, B. Pump‐less platform enables long‐term recirculating perfusion of 3D printed tubular tissues. Adv. Healthc. Mater. 12, 2300423 (2023).

    Article  Google Scholar 

  110. Lai, B. F. L. et al. A well plate–based multiplexed platform for incorporation of organoids into an organ-on-a-chip system with a perfusable vasculature. Nat. Protoc. 16, 2158–2189 (2021).

    Article  Google Scholar 

  111. Huh, D. et al. Reconstituting organ-level lung functions on a chip. Science 328, 1662–1668 (2010).

    Article  Google Scholar 

  112. 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).

    Article  Google Scholar 

  113. 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).

    Article  Google Scholar 

  114. Nikolaev, M. et al. Homeostatic mini-intestines through scaffold-guided organoid morphogenesis. Nature 585, 574–578 (2020).

    Article  Google Scholar 

  115. Chien, S. Mechanotransduction and endothelial cell homeostasis: the wisdom of the cell. Am. J. Physiol.Heart Circ. Physiol. 292, H1209–H1224 (2007).

    Article  Google Scholar 

  116. Song, J. W. & Munn, L. L. Fluid forces control endothelial sprouting. Proc. Natl Acad. Sci. USA 108, 15342–15347 (2011).

    Article  Google Scholar 

  117. Seo, J. et al. Multiscale reverse engineering of the human ocular surface. Nat. Med. 25, 1310–1318 (2019).

    Article  Google Scholar 

  118. Michas, C. et al. Engineering a living cardiac pump on a chip using high-precision fabrication. Sci. Adv. 8, eabm3791 (2022).

    Article  Google Scholar 

  119. Hajal, C., Ibrahim, L., Serrano, J. C., Offeddu, G. S. & Kamm, R. D. The effects of luminal and trans-endothelial fluid flows on the extravasation and tissue invasion of tumor cells in a 3D in vitro microvascular platform. Biomaterials 265, 120470 (2021).

    Article  Google Scholar 

  120. 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, 1–12 (2018).

    Article  Google Scholar 

  121. Ko, J., Lee, Y., Lee, S., Lee, S. R. & Jeon, N. L. Human ocular angiogenesis‐inspired vascular models on an injection‐molded microfluidic chip. Adv. Healthc. Mater. 8, 1900328 (2019).

    Article  Google Scholar 

  122. Lee, S.-R. et al. U-IMPACT: a universal 3D microfluidic cell culture platform. Microsyst. Nanoeng. 8, 126 (2022).

    Article  Google Scholar 

  123. Stoppel, W. L., Kaplan, D. L. & Black, L. D. Electrical and mechanical stimulation of cardiac cells and tissue constructs. Adv. Drug. Delivery Rev. 96, 135–155 (2016).

    Article  Google Scholar 

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

    Article  Google Scholar 

  125. Nunes, S. S. et al. Biowire: a platform for maturation of human pluripotent stem cell–derived cardiomyocytes. Nat. Methods 10, 781–787 (2013).

    Article  Google Scholar 

  126. 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).

    Article  Google Scholar 

  127. Habibey, R., Golabchi, A., Latifi, S., Difato, F. & Blau, A. A microchannel device tailored to laser axotomy and long-term microelectrode array electrophysiology of functional regeneration. Lab Chip 15, 4578–4590 (2015).

    Article  Google Scholar 

  128. 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).

    Article  Google Scholar 

  129. 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).

    Article  Google Scholar 

  130. Taylor, A. M. et al. A microfluidic culture platform for CNS axonal injury, regeneration and transport. Nat. Methods 2, 599–605 (2005).

    Article  Google Scholar 

  131. Vulto, P. et al. Phaseguides: a paradigm shift in microfluidic priming and emptying. Lab Chip 11, 1596–1602 (2011).

    Article  Google Scholar 

  132. Cho, H., Kim, H.-Y., Kang, J. Y. & Kim, T. S. How the capillary burst microvalve works. J. Colloid Interface Sci. 306, 379–385 (2007).

    Article  Google Scholar 

  133. Berthier, J., Brakke, K. A. & Berthier, E. Open Microfluidics (Wiley, 2016).

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

    Article  Google Scholar 

  135. Kim, S., Park, J., Kim, J. & Jeon, J. S. Microfluidic tumor vasculature model to recapitulate an endothelial immune barrier expressing FasL. ACS Biomater. Sci. Eng. 7, 1230–1241 (2021).

    Article  Google Scholar 

  136. Xiao, Y. et al. Ex vivo dynamics of human glioblastoma cells in a microvasculature-on-a-chip system correlates with tumor heterogeneity and subtypes. Adv. Sci. 6, 1801531 (2019).

    Article  Google Scholar 

  137. Yu, J. et al. Perfusable micro-vascularized 3D tissue array for high-throughput vascular phenotypic screening. Nano Converg. 9, 16 (2022).

    Article  Google Scholar 

  138. Ko, J. et al. Tumor spheroid-on-a-chip: a standardized microfluidic culture platform for investigating tumor angiogenesis. Lab Chip 19, 2822–2833 (2019). This article describes a microfluidic design that can be used for spheroid culture in a high-throughput manner and co-culture with endothelial cells.

    Article  Google Scholar 

  139. 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).

    Article  Google Scholar 

  140. Ronaldson-Bouchard, K. et al. A multi-organ chip with matured tissue niches linked by vascular flow. Nat. Biomed. Eng. 6, 351–371 (2022). This article reports a multiorgan chip comprising four tissues interconnected by vascular flow.

    Article  Google Scholar 

  141. Regehr, K. J. et al. Biological implications of polydimethylsiloxane-based microfluidic cell culture. Lab Chip 9, 2132–2139 (2009).

    Article  Google Scholar 

  142. Leung, C. M. et al. A guide to the organ-on-a-chip. Nat. Rev. Methods Primers 2, 33 (2022). This article discusses general considerations for engineering organ-on-a-chip devices.

    Article  Google Scholar 

  143. Ho, C. M. B., Ng, S. H., Li, K. H. H. & Yoon, Y.-J. 3D printed microfluidics for biological applications. Lab Chip 15, 3627–3637 (2015).

    Article  Google Scholar 

  144. Razavi Bazaz, S. et al. Rapid softlithography using 3D‐printed molds. Adv. Mater. Technol. 4, 1900425 (2019).

    Article  Google Scholar 

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

    Article  Google Scholar 

  146. O’Grady, B. J. et al. Rapid prototyping of cell culture microdevices using parylene-coated 3D prints. Lab Chip 21, 4814–4822 (2021).

    Article  Google Scholar 

  147. Park, D. et al. Aspiration-mediated hydrogel micropatterning using rail-based open microfluidic devices for high-throughput 3D cell culture. Sci. Rep. 11, 19986 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  150. Lee, U. N. et al. Fundamentals of rapid injection molding for microfluidic cell-based assays. Lab Chip 18, 496–504 (2018).

    Article  Google Scholar 

  151. Lerman, M. J., Lembong, J., Muramoto, S., Gillen, G. & Fisher, J. P. The evolution of polystyrene as a cell culture material. Tissue Eng. B 24, 359–372 (2018).

    Article  Google Scholar 

  152. Berthier, E., Young, E. W. & Beebe, D. Engineers are from PDMS-land, biologists are from Polystyrenia. Lab Chip 12, 1224–1237 (2012).

    Article  Google Scholar 

  153. Agha, A. et al. A review of cyclic olefin copolymer applications in microfluidics and microdevices. Macromol. Mater. Eng. 307, 2200053 (2022).

    Article  Google Scholar 

  154. Baker, M. Academic screening goes high-throughput. Nat. Methods 7, 787–792 (2010).

    Article  Google Scholar 

  155. Tan, K. et al. A high-throughput microfluidic microphysiological system (PREDICT-96) to recapitulate hepatocyte function in dynamic, re-circulating flow conditions. Lab Chip 19, 1556–1566 (2019).

    Article  Google Scholar 

  156. Gijzen, L. et al. Culture and analysis of kidney tubuloids and perfused tubuloid cells-on-a-chip. Nat. Protoc. 16, 2023–2050 (2021).

    Article  Google Scholar 

  157. Baran, S. W. et al. Perspectives on the evaluation and adoption of complex in vitro models in drug development: workshop with the FDA and the pharmaceutical industry (IQ MPS Affiliate). ALTEX 39, 297–314 (2022). This article reports case studies of microphysiological systems used by pharmaceutical companies.

    Google Scholar 

  158. Nosrati, R. et al. Microfluidics for sperm analysis and selection. Nat. Rev. Urol. 14, 707–730 (2017).

    Article  Google Scholar 

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

    Article  Google Scholar 

  160. Yue, T. et al. A modular microfluidic system based on a multilayered configuration to generate large-scale perfusable microvascular networks. Microsyst. Nanoeng. 7, 4 (2021).

    Article  Google Scholar 

  161. Lam, S. F., Shirure, V. S., Chu, Y. E., Soetikno, A. G. & George, S. C. Microfluidic device to attain high spatial and temporal control of oxygen. PLoS ONE 13, e0209574 (2018).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  164. Shi, Q. et al. Co‐culture of human primary hepatocytes and nonparenchymal liver cells in the emulate® liver‐chip for the study of drug‐induced liver injury. Curr. Protoc. 2, e478 (2022).

    Article  Google Scholar 

  165. Shin, H. et al. 3D high-density microelectrode array with optical stimulation and drug delivery for investigating neural circuit dynamics. Nat. Commun. 12, 492 (2021).

    Article  Google Scholar 

  166. Liu, H. et al. Heart-on-a-chip model with integrated extra-and intracellular bioelectronics for monitoring cardiac electrophysiology under acute hypoxia. Nano Lett. 20, 2585–2593 (2020).

    Article  Google Scholar 

  167. Abulaiti, M. et al. Establishment of a heart-on-a-chip microdevice based on human iPS cells for the evaluation of human heart tissue function. Sci. Rep. 10, 19201 (2020).

    Article  Google Scholar 

  168. Liu, L., He, F., Yu, Y. & Wang, Y. Application of FRET biosensors in mechanobiology and mechanopharmacological screening. Front. Bioeng. Biotechnol. 8, 595497 (2020).

    Article  Google Scholar 

  169. Kogler, S. et al. Organoids, organ-on-a-chip, separation science and mass spectrometry: an update. TrAC Trends Anal. Chem. 161, 116996 (2023).

    Article  Google Scholar 

  170. Brandenberg, N. et al. High-throughput automated organoid culture via stem-cell aggregation in microcavity arrays. Nat. Biomed. Eng. 4, 863–874 (2020).

    Article  Google Scholar 

  171. Eduati, F. et al. A microfluidics platform for combinatorial drug screening on cancer biopsies. Nat. Commun. 9, 2434 (2018).

    Article  Google Scholar 

  172. Picard, M., Scott-Boyer, M.-P., Bodein, A., Périn, O. & Droit, A. Integration strategies of multi-omics data for machine learning analysis. Comput. Struct. Biotechnol. J. 19, 3735–3746 (2021).

    Article  Google Scholar 

  173. Zitnik, M. et al. Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. Inf. Fusion. 50, 71–91 (2019).

    Article  Google Scholar 

  174. Pham, T.-H., Qiu, Y., Zeng, J., Xie, L. & Zhang, P. A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing. Nat. Mach. Intell. 3, 247–257 (2021).

    Article  Google Scholar 

  175. Paek, K. et al. A high-throughput biomimetic bone-on-a-chip platform with artificial intelligence-assisted image analysis for osteoporosis drug testing. Bioeng. Transl. Med. 8, e10313 (2023). This article demonstrates artificial intelligence-based image analysis for high-throughput data analysis.

    Article  Google Scholar 

  176. Bian, X. et al. A deep learning model for detection and tracking in high-throughput images of organoid. Comput. Biol. Med. 134, 104490 (2021).

    Article  Google Scholar 

  177. Fan, K. et al. A machine learning assisted, label-free, non-invasive approach for somatic reprogramming in induced pluripotent stem cell colony formation detection and prediction. Sci. Rep. 7, 13496 (2017).

    Article  Google Scholar 

  178. Tetteh, G. et al. DeepVesselNet: vessel segmentation, centerline prediction, and bifurcation detection in 3-D angiographic volumes. Front. Neurosci. 14, 592352 (2020).

    Article  Google Scholar 

  179. Gegundez-Arias, M. E., Marin-Santos, D., Perez-Borrero, I. & Vasallo-Vazquez, M. J. A new deep learning method for blood vessel segmentation in retinal images based on convolutional kernels and modified U-Net model. Comput. Methods Prog. Biomed. 205, 106081 (2021).

    Article  Google Scholar 

  180. Rivenson, Y. et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat. Biomed. Eng. 3, 466–477 (2019).

    Article  Google Scholar 

  181. Kim, S. et al. Angio-Net: deep learning-based label-free detection and morphometric analysis of in vitro angiogenesis. Lab Chip https://doi.org/10.1039/d3lc00935a (2024).

  182. Nehme, E., Weiss, L. E., Michaeli, T. & Shechtman, Y. Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica 5, 458–464 (2018).

    Article  Google Scholar 

  183. Chen, J. et al. Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes. Nat. Methods 18, 678–687 (2021).

    Article  Google Scholar 

  184. Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinform. 18, 529 (2017).

    Article  Google Scholar 

  185. Ouyang, W. et al. Bioimage Model Zoo: a community-driven resource for accessible deep learning in bioimage analysis. Preprint at bioRxiv https://doi.org/10.1101/2022.06.07.495102 (2022).

  186. Wang, A. et al. A novel deep learning-based 3D cell segmentation framework for future image-based disease detection. Sci. Rep. 12, 342 (2022).

    Article  Google Scholar 

  187. Greenwald, N. F. et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning. Nat. Biotechnol. 40, 555–565 (2022).

    Article  Google Scholar 

  188. Schmauch, B. et al. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat. Commun. 11, 3877 (2020).

    Article  Google Scholar 

  189. Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).

    Article  Google Scholar 

  190. Palasantzas, V. E. J. M. et al. iPSC-derived organ-on-a-chip models for personalized human genetics and pharmacogenomics studies. Trends Genet. 39, 268–284 (2023).

    Article  Google Scholar 

  191. Park, S. E., Georgescu, A. & Huh, D. Organoids-on-a-chip. Science 364, 960–965 (2019).

    Article  Google Scholar 

  192. Macdonald, N. P. et al. Comparing microfluidic performance of three-dimensional (3D) printing platforms. Anal. Chem. 89, 3858–3866 (2017).

    Article  Google Scholar 

  193. Bhattacharjee, N., Urrios, A., Kang, S. & Folch, A. The upcoming 3D-printing revolution in microfluidics. Lab Chip 16, 1720–1742 (2016).

    Article  Google Scholar 

  194. Suthiwanich, K. & Hagiwara, M. Localization of multiple hydrogels with MultiCUBE platform spatially guides 3D tissue morphogenesis in vitro. Adv. Mater. Technol. 8, 2201660 (2023).

    Article  Google Scholar 

  195. Carvalho, M. R. et al. Colorectal tumor-on-a-chip system: a 3D tool for precision onco-nanomedicine. Sci. Adv. 5, eaaw1317 (2019).

    Article  Google Scholar 

  196. Lee, S. et al. Angiogenesis-on-a-chip coupled with single-cell RNA sequencing reveals spatially differential activations of autophagy along angiogenic sprouts. Nat. Commun. 15, 230 (2024).

    Article  Google Scholar 

  197. Gebreyesus, S. T. et al. Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry. Nat. Commun. 13, 37 (2022).

    Article  Google Scholar 

  198. Bi, Y. et al. Tumor-on-a-chip platform to interrogate the role of macrophages in tumor progression. Integr. Biol. 12, 221–232 (2020).

    Article  Google Scholar 

  199. Shin, W. & Kim, H. J. 3D in vitro morphogenesis of human intestinal epithelium in a gut-on-a-chip or a hybrid chip with a cell culture insert. Nat. Protoc. 17, 910–939 (2022).

    Article  Google Scholar 

  200. Fridman, I. B., Ugolini, G. S., VanDelinder, V., Cohen, S. & Konry, T. High throughput microfluidic system with multiple oxygen levels for the study of hypoxia in tumor spheroids. Biofabrication 13, 035037 (2021).

    Article  Google Scholar 

  201. Phan, D. T. et al. A vascularized and perfused organ-on-a-chip platform for large-scale drug screening applications. Lab Chip 17, 511–520 (2017).

    Article  Google Scholar 

  202. Yu, J. et al. Reconfigurable open microfluidics for studying the spatiotemporal dynamics of paracrine signalling. Nat. Biomed. Eng. 3, 830–841 (2019).

    Article  Google Scholar 

  203. Peel, S. et al. Introducing an automated high content confocal imaging approach for organs-on-chips. Lab Chip 19, 410–421 (2019).

    Article  MathSciNet  Google Scholar 

  204. 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 

  205. Ehlers, H. et al. Vascular inflammation on a chip: a scalable platform for trans-endothelial electrical resistance and immune cell migration. Front. Immunol. 14, 207 (2023).

    Article  Google Scholar 

  206. Oliver, C. R. et al. A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche. Lab Chip 19, 1162–1173 (2019).

    Article  Google Scholar 

  207. Kim, D., Min, Y., Oh, J. M. & Cho, Y.-K. AI-powered transmitted light microscopy for functional analysis of live cells. Sci. Rep. 9, 18428 (2019).

    Article  Google Scholar 

  208. Song, J. W. et al. Computer-controlled microcirculatory support system for endothelial cell culture and shearing. Anal. Chem. 77, 3993–3999 (2005).

    Article  Google Scholar 

  209. Sonmez, U. M., Cheng, Y.-W., Watkins, S. C., Roman, B. L. & Davidson, L. A. Endothelial cell polarization and orientation to flow in a novel microfluidic multimodal shear stress generator. Lab Chip 20, 4373–4390 (2020).

    Article  Google Scholar 

  210. Suntharalingam, G. et al. Cytokine storm in a phase 1 trial of the anti-CD28 monoclonal antibody TGN1412. N. Engl. J. Med. 355, 1018–1028 (2006).

    Article  Google Scholar 

  211. Blumenrath, S. H., Lee, B. Y., Low, L., Prithviraj, R. & Tagle, D. Tackling rare diseases: clinical trials on chips. Exp. Biol. Med. 245, 1155–1162 (2020).

    Article  Google Scholar 

  212. Shik Mun, K. et al. Patient-derived pancreas-on-a-chip to model cystic fibrosis-related disorders. Nat. Commun. 10, 3124 (2019).

    Article  Google Scholar 

  213. FDA. Rare Diseases: Considerations for the Development of Drugs and Biological Products. fda.gov www.fda.gov/regulatory-information/search-fda-guidance-documents/rare-diseases-considerations-development-drugs-and-biological-products (2023).

  214. Center for Drug Evaluation and Research/Center for Biologics Evaluation and Research. Rare Diseases: Common Issues in Drug Development. Guidance for Industry. fda.gov www.fda.gov/regulatory-information/search-fda-guidance-documents/rare-diseases-considerations-development-drugs-and-biological-products (2019).

  215. Center for Drug Evaluation and Research/Center for Biologics Evaluation and Research. Human Gene Therapy for Rare Disease. Guidance for Industry. fda.gov www.fda.gov/regulatory-information/search-fda-guidance-documents/human-gene-therapy-rare-diseases (2020).

  216. Junaid, A. et al. Ebola hemorrhagic shock syndrome-on-a-chip. iScience 23, 100765 (2020).

    Article  Google Scholar 

  217. Ribas, J. et al. Biomechanical strain exacerbates inflammation on a progeria-on-a-chip model. Small 13, 1603737 (2017).

    Article  Google Scholar 

  218. Chou, D. B. et al. On-chip recapitulation of clinical bone marrow toxicities and patient-specific pathophysiology. Nat. Biomed. Eng. 4, 394–406 (2020). This article reports a vascularized human bone marrow-on-a-chip that supports the differentiation and maturation of blood cells.

    Article  Google Scholar 

  219. Orlova, V. V. et al. Vascular defects associated with hereditary hemorrhagic telangiectasia revealed in patient-derived isogenic iPSCs in 3D vessels on chip. Stem Cell Rep. 17, 1536–1545 (2022).

    Article  Google Scholar 

  220. Virlogeux, A. et al. Reconstituting corticostriatal network on-a-chip reveals the contribution of the presynaptic compartment to Huntington’s disease. Cell Rep. 22, 110–122 (2018).

    Article  Google Scholar 

  221. Food and Drug Administration. Context of Use Transcript. FDA https://www.fda.gov/drugs/biomarker-qualification-program/context-use-transcript (2017).

  222. NIH. Validation, Qualification, and Regulatory Acceptance of New Approach Methodologies. National Toxicology Program ntp.niehs.nih.gov (2023).

  223. Food and Drug Administration. Innovative Science and Technology Approaches for New Drugs (ISTAND) Pilot Program. FDA www.fda.gov/drugs/drug-development-tool-ddt-qualification-programs/innovative-science-and-technology-approaches-new-drugs-istand-pilot-program (2023).

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Acknowledgements

This work was supported by National Research Foundation of Korea grants funded by the Korean government (MSIT) (no. 2021R1A3B1077481 to N.L.J.; RS-2023-00253722 to J.K.; RS-202300222838 to J.L.).

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  1. These authors contributed equally: Jihoon Ko, Dohyun Park.

Authors and Affiliations

  1. Department of BioNano Technology, Gachon University, Seongnam-si, Republic of Korea

    Jihoon Ko

  2. Mechanical Engineering, Seoul National University, Seoul, Republic of Korea

    Dohyun Park, Jungseub Lee, Sangmin Jung & Noo Li Jeon

  3. Qureator, Inc., San Diego, CA, USA

    Kyusuk Baek & Noo Li Jeon

  4. US Food and Drug Administration, Center for Biologics Evaluation and Research, Office of Cellular Therapy and Human Tissue, Cellular and Tissue Therapy Branch, Silver Spring, MD, USA

    Kyung E. Sung

  5. Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University, Samsung Medical Center, Seoul, Republic of Korea

    Jeeyun Lee

  6. Institute of Advanced Machines and Design, Seoul National University, Seoul, Republic of Korea

    Noo Li Jeon

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N.L.J. conceived, wrote and edited the manuscript. All authors contributed to the writing of the manuscript. K.S. provided insights from a regulatory perspective. K.B. offered an industry viewpoint. J.L. shared clinical insights.

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K.B. discloses a relationship with Qureator, Inc., which encompasses participation on the board, employment, and ownership of equity or stocks. N.L.J. discloses an affiliation with Qureator, Inc., involving board membership and ownership of equity or stocks. The other authors declare no competing interests. The opinions expressed in this article are those of the authors and may not necessarily reflect those of the FDA.

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Ko, J., Park, D., Lee, J. et al. Microfluidic high-throughput 3D cell culture. Nat Rev Bioeng (2024). https://doi.org/10.1038/s44222-024-00163-8

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