Engineered tissues can be used to model human pathophysiology and test the efficacy and safety of drugs. Yet, to model whole-body physiology and systemic diseases, engineered tissues with preserved phenotypes need to physiologically communicate. Here we report the development and applicability of a tissue-chip system in which matured human heart, liver, bone and skin tissue niches are linked by recirculating vascular flow to allow for the recapitulation of interdependent organ functions. Each tissue is cultured in its own optimized environment and is separated from the common vascular flow by a selectively permeable endothelial barrier. The interlinked tissues maintained their molecular, structural and functional phenotypes over 4 weeks of culture, recapitulated the pharmacokinetic and pharmacodynamic profiles of doxorubicin in humans, allowed for the identification of early miRNA biomarkers of cardiotoxicity, and increased the predictive values of clinically observed miRNA responses relative to tissues cultured in isolation and to fluidically interlinked tissues in the absence of endothelial barriers. Vascularly linked and phenotypically stable matured human tissues may facilitate the clinical applicability of tissue chips.
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Source data for some graphs in the main figures and in the Extended Data figures are available as Supplementary Information. All raw and analysed datasets generated during the study are available from the corresponding author on request. Source data are provided with this paper.
All software used to analyse the data is commercial or open-source. The custom code for the analyses of pixel movement can be obtained at https://www.nature.com/articles/s41596-019-0189-8.
Di, L. et al. A perspective on the prediction of drug pharmacokinetics and disposition in drug research and development. Drug Metab. Dispos. 41, 1975–1993 (2013).
Mak, I. W., Evaniew, N. & Ghert, M. Lost in translation: animal models and clinical trials in cancer treatment. Am. J. Transl. Res. 6, 114–118 (2014).
Tuntland, T. et al. Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research. Front. Pharmacol. 5, 174 (2014).
Pound, P. & Ritskes-Hoitinga, M. Is it possible to overcome issues of external validity in preclinical animal research? Why most animal models are bound to fail. J. Transl. Med. 16, 304 (2018).
Huh, D. et al. Reconstituting organ-level lung functions on a chip. Science 328, 1662–1668 (2010).
Wikswo, J. P. The relevance and potential roles of microphysiological systems in biology and medicine. Exp. Biol. Med. 239, 1061–1072 (2014).
Polacheck, W. J. et al. A non-canonical Notch complex regulates adherens junctions and vascular barrier function. Nature 552, 258–262 (2017).
Shinha, K., Nihei, W., Ono, T., Nakazato, R. & Kimura, H. A pharmacokinetic-pharmacodynamic model based on multi-organ-on-a-chip for drug-drug interaction studies. Biomicrofluidics 14, 044108 (2020).
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).
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).
Ronaldson-Bouchard, K. et al. Advanced maturation of human cardiac tissue grown from pluripotent stem cells. Nature 556, 239–243 (2018).
Novak, R. et al. Robotic fluidic coupling and interrogation of multiple vascularized organ chips. Nat. Biomed. Eng. 4, 407–420 (2020).
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).
Leger, K. J. et al. Circulating microRNAs: potential markers of cardiotoxicity in children and young adults treated with anthracycline chemotherapy. J. Am. Heart Assoc. 6, e004653 (2017).
McAleer, C. W. et al. Multi-organ system for the evaluation of efficacy and off-target toxicity of anticancer therapeutics. Sci. Transl. Med. 11, eaav1386 (2019).
Oatmen, K. E. et al. Identification of a novel microRNA profile in pediatric patients with cancer treated with anthracycline chemotherapy. Am. J. Physiol. Heart Circ. Physiol. 315, H1443–H1452 (2018).
Tacar, O., Sriamornsak, P. & Dass, C. R. Doxorubicin: an update on anticancer molecular action, toxicity and novel drug delivery systems. J. Pharm. Pharmacol. 65, 157–170 (2013).
Thorn, C. F. et al. Doxorubicin pathways: pharmacodynamics and adverse effects. Pharmacogenet. Genomics 21, 440–446 (2011).
Yadi, W. et al. Bioinformatic analysis of peripheral blood miRNA of breast cancer patients in relation with anthracycline cardiotoxicity. BMC Cardiovasc. Disord. 20, 43 (2020).
Schepers, A., Li, C., Chhabra, A., Seney, B. T. & Bhatia, S. Engineering a perfusable 3D human liver platform from iPS cells. Lab Chip 16, 2644–2653 (2016).
Villasante, A. et al. Tissue-engineered model of human osteolytic bone tumor. Tissue Eng. C Methods 23, 98–107 (2017).
Itoh, M. et al. Generation of 3D skin equivalents fully reconstituted from human induced pluripotent stem cells (iPSCs). PLoS ONE 8, e77673 (2013).
Neil, J. E., Brown, M. B. & Williams, A. C. Human skin explant model for the investigation of topical therapeutics. Sci. Rep. 10, 21192 (2020).
Davies, P. F. Flow-mediated endothelial mechanotransduction. Physiol. Rev. 75, 519–560 (1995).
Hirschi, K. K. & D’Amore, P. A. Pericytes in the microvasculature. Cardiovasc. Res. 32, 687–698 (1996).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Zeng, H., Wang, J., Clouse, H., Lagrutta, A. & Sannajust, F. Resolving the reversed rate effect of calcium channel blockers on human-induced pluripotent stem cell-derived cardiomyocytes and the impact on in vitro cardiac safety evaluation. Toxicol. Sci. 167, 573–580 (2019).
Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
Prantil-Baun, R. et al. Physiologically based pharmacokinetic and pharmacodynamic analysis enabled by microfluidically linked organs-on-chips. Annu. Rev. Pharmacol. Toxicol. 58, 37–64 (2018).
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).
Przekwas, A. & Somayaji, M. R. in Organ-on-a-Chip (eds Hoeng, J. et al.) 311–361 (Academic Press, 2020). https://doi.org/10.1016/B978-0-12-817202-5.00011-5
Somayaji, M. R., Das, D. & Przekwas, A. Computational approaches for modeling and analysis of human-on-chip systems for drug testing and characterization. Drug Discov. Today 21, 1859–1862 (2016).
Chramiec, A. et al. Integrated human organ-on-a-chip model for predictive studies of anti-tumor drug efficacy and cardiac safety. Lab Chip 20, 4357–4372 (2020).
Wang, Y. et al. Taurine zinc solid dispersions attenuate doxorubicin-induced hepatotoxicity and cardiotoxicity in rats. Toxicol. Appl. Pharmacol. 289, 1–11 (2015).
Fan, C. et al. Combination breast cancer chemotherapy with doxorubicin and cyclophosphamide damages bone and bone marrow in a female rat model. Breast Cancer Res. Treat. 165, 41–51 (2017).
Rigaud, V. O.-C. et al. Circulating miR-1 as a potential biomarker of doxorubicin-induced cardiotoxicity in breast cancer patients. Oncotarget 8, 6994–7002 (2017).
Chen, Y. & Wang, X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. 48, D127–D131 (2020).
Ronaldson-Bouchard, K. et al. Engineering of human cardiac muscle electromechanically matured to an adult-like phenotype. Nat. Protoc. 14, 2781–2817 (2019).
Bhumiratana, S. et al. Tissue-engineered autologous grafts for facial bone reconstruction. Sci. Transl. Med. 8, 343ra83 (2016).
Marcos-Campos, I. et al. Bone scaffold architecture modulates the development of mineralized bone matrix by human embryonic stem cells. Biomaterials 33, 8329–8342 (2012).
Thomas, A. et al. Characterization of vascular permeability using a biomimetic microfluidic blood vessel model. Biomicrofluidics 11, 024102 (2017).
Mootha, V. K. et al. PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).
Navarrete-Perea, J., Yu, Q., Gygi, S. P. & Paulo, J. A. Streamlined Tandem Mass Tag (SL-TMT) protocol: an efficient strategy for quantitative (phospho)proteome profiling using tandem mass tag-synchronous precursor selection-MS3. J. Proteome Res. 17, 2226–2236 (2018).
Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).
Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).
Ge, S. X., Jung, D. & Yao, R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 36, 2628–2629 (2020).
Mi, H., Muruganujan, A., Ebert, D., Huang, X. & Thomas, P. D. PANTHER version 14: more genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res. 47, D419–D426 (2019).
Ge, S. X., Son, E. W. & Yao, R. iDEP: an integrated web application for differential expression and pathway analysis of RNA-seq data. BMC Bioinformatics 19, 534 (2018).
Ru, Y. et al. The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations. Nucleic Acids Res. 42, e133 (2014).
Zhang, J. & Storey, K. B. RBiomirGS: an all-in-one miRNA gene set analysis solution featuring target mRNA mapping and expression profile integration. PeerJ 6, e4262 (2018).
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).
We gratefully acknowledge funding of this research by the NIH (UG3 EB025765, P41 EB027062 and R01 CA249799 to G.V.-N.; R35 CA197745, S10 OD012351 and S10 OD021764 to An.C.; UL1 TR001873 to the Irving Institute for Clinical and Translational Research; P30 CA013696 to the Confocal and Specialized Microscopy Shared Resource), NSF (Engineering Reseach Center EEC-1647837 to C.S.C. and G.V.-N., Graduate Research Fellowship DGE1644869 to D.N.T.) and F.C.T. (PD/BD/105819/2014 to D.T.). We thank P. L. Graney, E. Öztürk, M. C. Samaritano, N. Valerio Dorrello and B. Fine for helpful discussions; R. Nandakumar and C. Qiao (Biomarkers Core Laboratory at the Irving Institute for Clinical Translational Research), M. Kissner (Columbia Stem Cell Initiative Flow Cytometry Core), C. Damoci (Oncology Precision Therapeutics and Imaging Core Shared Resource), the Molecular Pathology, Confocal and Specialized Microscopy, and the Oncology Precision Therapeutics and Imaging Core Shared Resources at the Columbia University Herbert Irving Comprehensive Cancer Center for technical support.
G.V.-N. is a co-founder and board director of epiBone, Tara Biosystems, Xylyx Bio and Immplacate and holds equity in all four companies. K.R-B. and Y.Z. are co-founders of TARA Biosystems and hold equity in the company. S.P.H. holds equity in Xylyx Bio. An.C. is founder, equity holder and consultant of DarwinHealth, Inc., a company that has licensed some of the algorithms used in this manuscript from Columbia University. Columbia University is also an equity holder in DarwinHealth, Inc. The other authors declare no competing interests.
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Extended Data Fig. 1 Tissue chip configurability and modularity.
a, Photograph of the assembled chip. b, Top view of the tissue chip including each compartment chamber and reservoir for circulating media. c, Bottom view of the chip. d, Configurability of the tissue chip can be established through connecting reactors in series for scaling of engineered organs. e, Alternative chip design for single-organ culture with perfusion and vascular barrier. f, Alternative chip design for dual-organ culture with perfusion and vascular barrier. g, Tissue chip with tubing attached to a peristaltic pump during integrated culture. h, Computational fluidic model of shear stress during perfusion with the vascular barrier, at a flow rate of 1.3 mL min−1 to yield a shear stress of 1.88 dyn cm−2 at the vascular barrier interface.
Extended Data Fig. 2 Tissue chip design details for integration of engineered tissues.
a, Images and dimensions of the engineered transwell insert and its location within the tissue chip. b, Top view of the reservoir to detail routing of fluid from the channel into the reservoir and subsequent pump driven routing into the tubing via the elbow connector. c, Schematic side view of the tissue chip with measurements. d, Schematic images detailing fluidic routing of vascular media via the designed channel entry and exit ports. e, Tissue chip details.
Extended Data Fig. 3 Characterization of tissue specific maturation.
a-c, Electromechanically matured cardiac tissues show aligned actinin alpha expression (green, scale bar, 10 µm) (a), functional improvements in maximum capture rate and excitation threshold (b) (n = 8-9 biological replicates), and increased force responses when exposed to increasing calcium concentrations (c) (n = 9 biological replicates). d-e, Liver tissues are matured via co-culture of cells in 3D aggregated as detailed by immunohistochemical staining (d) and increased albumin secretion (e) (n = 6 biological replicates). f, Immunohistochemical staining of engineered bone slices stain positive for osteocalcin, TRAP activity, and bone sialoprotein. g, Micro-computed tomography imaging of bone scaffolds over time details bone remodeling during the osteoblastic and osteolytic phases of induced bone maturation (n = 13 biological replicates). h, Immunohistochemical staining of engineered skin slices detail the formation of the epidermis and dermis over 4 weeks of maturation. i, TEER values detail the barrier function of the engineered skin, with reported values detailed by the red shaded region23 (n = 15 biological replicates). Data is shown as mean ± SD and statistics determined by unpaired t-test (b, c, e), or one-way ANOVA (g, i).
Extended Data Fig. 4 Establishment of mature, selectively permeable vascular barrier.
a-b, Immunofluorescence imaging of vascular barriers (a) and FITC-Dextran transport through vascular barriers cultured on various transwell pore sizes (b) (n = 7-10 biological replicates). Scale bar, 50 µm. c-d, Immunofluorescence imaging of vascular barriers (c) and FITC-Dextran transport through vascular barriers cultured under different shear stress conditions (d) (n = 7-8 biological replicates). Scale bar, 50 µm. e, Barrier function demonstrated by tracking tagged dextran molecules of different size (n = 3 biological replicates). Data is shown as mean ± SD and statistics determined by two-way or mixed ANOVA.
Extended Data Fig. 5 Immune cell isolation, maturation, and characterization.
a, Initial cell population was made up of >98% CD14+ and ITGAM+ monocytes. b, Brightfield image detailing monocyte adherence to barrier surface within tissue chip. Scale bar, 50 µm. c, Flow cytometry characterization of monocyte viability after 3 days of culture (n = 3-4 biological replicates). d, Monocyte specification and differentiation over two weeks of culture (n = 3 biological replicates). Data is shown as mean ± SD and statistics determined by two-way ANOVA.
Extended Data Fig. 6 Immune function over four-week culture.
a-b, Heatmap of all measured cytokines (a) and individual cytokine expression for selected cytokines (b) over 28 days of culture in the different tissue chips configurations (Multi-organ, n = 5 biological replicates; Mixed and Isolated, n = 3 biological replicates). Data is shown as mean ± SD and statistics determined by one-way ANOVA.
Extended Data Fig. 7 Proteomic breakdown of bone, liver, and skin tissues studied over four weeks in Multi-organ multi-tissue chip, in the Mixed media approach, and in Isolation.
a-c, Proteomic breakdown of engineered bone (more than 6,000 unique proteins; a), liver (more than 2,000 proteins; b), and skin (more than 2,000 proteins; c) studied over 28 days in the integrated Multi-organ multi-tissue chip, chip with mixed media, and tissues cultured in isolation. Comparison of integrated versus mixed conditions via differential protein abundances is represented by Volcano plots. d-f, PGSEA plots of the top 30 GO Biological Process pathways, with red indicating activated pathways and blue indicating suppressed pathways for bone (d), liver (e), and skin (f). g, Immunohistochemical staining for picrosirius red details collagen within liver, skin, and bone tissues after 28 days. Scale bar for liver and skin stains, 50 µm. Scale bar for bone stains, 500 µm.
Extended Data Fig. 8 Proteomic breakdown of engineered cardiac tissues studied over four weeks in Multi-organ multi-tissue chip, in the Mixed media approach, and in Isolation.
a-b, Excitation threshold (a) and maximum capture rate (b) of cardiac tissues for each experimental condition (Data is shown as mean ± SD and statistics determined by Ordinary one-way ANOVA. Multi-organ and Mixed, n = 6 biological replicates; Isolated, n = 3 biological replicates). c, PCA clustering of each experimental condition. d, Comparison of integrated versus mixed conditions via differential protein abundances. e, Proteins important to cardiac tissue function, structure, energetics, and calcium handling. f, PGSEA pathway analysis showing the top 30 GO Biological Process pathways related to disease and function in integrated vs. mixed conditions, with red indicating activated and blue indicating suppressed pathways.
Extended Data Fig. 9 Development of a multi-compartment computational model of the multi-tissue chip.
a, Schematic of the entire mechanistic multi-compartment model. All tissue tanks have a similar configuration that is composed of a cylindrical tank divided in 3 sub-compartments (TT, MT, and BT), an endothelial membrane with 3 layers (TM, MM, and BM), a fluidic inflow segment (IFC), and a fluidic perfusion segment (FC). All the individual compartments were represented by species mass balance equations, calculated using drug flux (J) and volumetric medium flow (Qf). b, Schematic of the liver tissue chamber. c, Schematic of the heart tissue chamber. d, Schematic of the bone tissue chamber. e, Schematic of the skin tissue chamber. f, Schematic of the reservoir and tubing.
Extended Data Fig. 10 Comparison of the Multi-organ and Mixed computational PK models.
a-b, Doxorubicin (a) and doxorubicinol (b) levels over time within all tissue chambers and in the reservoir predicted by the computational PK model for the Multi-organ (blue line) and the Mixed platform (red bar).
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Ronaldson-Bouchard, K., Teles, D., Yeager, K. et al. A multi-organ chip with matured tissue niches linked by vascular flow. Nat. Biomed. Eng 6, 351–371 (2022). https://doi.org/10.1038/s41551-022-00882-6
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