Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Electro-metabolic coupling in multi-chambered vascularized human cardiac organoids

Nature Biomedical Engineering volume 7, pages 1493–1513 (2023)Cite this article

Subjects

Abstract

The study of cardiac physiology is hindered by physiological differences between humans and small-animal models. Here we report the generation of multi-chambered self-paced vascularized human cardiac organoids formed under anisotropic stress and their applicability to the study of cardiac arrhythmia. Sensors embedded in the cardiac organoids enabled the simultaneous measurement of oxygen uptake, extracellular field potentials and cardiac contraction at resolutions higher than 10 Hz. This microphysiological system revealed 1 Hz cardiac respiratory cycles that are coupled to the electrical rather than the mechanical activity of cardiomyocytes. This electro-mitochondrial coupling was driven by mitochondrial calcium oscillations driving respiration cycles. Pharmaceutical or genetic inhibition of this coupling results in arrhythmogenic behaviour. We show that the chemotherapeutic mitoxantrone induces arrhythmia through disruption of this pathway, a process that can be partially reversed by the co-administration of metformin. Our microphysiological cardiac systems may further facilitate the study of the mitochondrial dynamics of cardiac rhythms and advance our understanding of human cardiac physiology.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Generation of hiPSC-derived vascularized cardiac organoids.
Fig. 2: Generation of hiPSC-derived vascularized cardiac organoids.
Fig. 3: Functional characterization of human cardiac organoids.
Fig. 4: Integrated opto-electrical sensors permit real-time simultaneous measurement of cardiac metabolism, contraction and field potential.
Fig. 5: Disruption of electro-mitochondrial coupling induces arrhythmic behaviour.
Fig. 6: CRISPR/Cas9 knockout of MCU disrupts electro-mitochondrial coupling and induces arrhythmic behaviour.
Fig. 7: Mitoxantrone inhibition of electro-mitochondrial coupling and the resultant arrhythmia are partly reversed by metformin.
Fig. 8: Validation of electro-mitochondrial coupling in an ex vivo porcine model.

Similar content being viewed by others

Data availability

Sequencing data are available at NCBI GEO, under the accession number GSE234907. Data for the figures are provided with this paper. All data supporting the results of this study are available within the paper and its Supplementary Information. Source data are provided with this paper.

Code availability

The custom analysis software is available at https://github.com/mohammadghosheh95/Heart-on-a-Chip.

References

  1. Benjamin, E. J. et al. Heart disease and stroke statistics–2017 update: a report from the American Heart Association. Circulation 135, e146–e603 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Benjamin, E. J. et al. Heart disease and stroke statistics–2019 update: a report from the American Heart Association. Circulation 139, e56–e528 (2019).

    Article  PubMed  Google Scholar 

  3. Fryar, C. D., Chen, T. C. & Li, X. Prevalence of uncontrolled risk factors for cardiovascular disease: United States, 1999–2010. NCHS Data Brief 103, 1–8 (2012).

  4. Huch, M., Knoblich, J. A., Lutolf, M. P. & Martinez-Arias, A. The hope and the hype of organoid research. Development 144, 938–941 (2017).

    Article  CAS  PubMed  Google Scholar 

  5. Bertero, E. & Maack, C. Metabolic remodelling in heart failure. Nat. Rev. Cardiol. 15, 457–470 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Rosano, G. M. & Vitale, C. Metabolic modulation of cardiac metabolism in heart failure. Card. Fail. Rev. 4, 99–103 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Doehner, W., Frenneaux, M. & Anker, S. D. Metabolic impairment in heart failure: the myocardial and systemic perspective. J. Am. Coll. Cardiol. 64, 1388–1400 (2014).

    Article  PubMed  Google Scholar 

  8. von Haehling, S., Doehner, W. & Anker, S. D. Nutrition, metabolism, and the complex pathophysiology of cachexia in chronic heart failure. Cardiovasc. Res. 73, 298–309 (2007).

    Article  Google Scholar 

  9. Bonnet, D. et al. Arrhythmias and conduction defects as presenting symptoms of fatty acid oxidation disorders in children. Circulation 100, 2248–2253 (1999).

    Article  CAS  PubMed  Google Scholar 

  10. Gheorghiade, M., Colucci, W. S. & Swedberg, K. Beta-blockers in chronic heart failure. Circulation 107, 1570–1575 (2003).

    Article  PubMed  Google Scholar 

  11. Hamer, A. W., Arkles, L. B. & Johns, J. A. Beneficial effects of low dose amiodarone in patients with congestive cardiac failure: a placebo-controlled trial. J. Am. Coll. Cardiol. 14, 1768–1774 (1989).

    Article  CAS  PubMed  Google Scholar 

  12. Kloner, R. A., Shi, J. & Dai, W. New therapies for reducing post-myocardial left ventricular remodeling. Ann. Transl. Med. 3, 20 (2015).

    PubMed  PubMed Central  Google Scholar 

  13. Brown, D. A. et al. Mitochondrial function as a therapeutic target in heart failure. Nat. Rev. Cardiol. 14, 238–250 (2017).

    Article  CAS  PubMed  Google Scholar 

  14. Aon, M. A., Cortassa, S., Marbán, E. & O’Rourke, B. Synchronized whole cell oscillations in mitochondrial metabolism triggered by a local release of reactive oxygen species in cardiac myocytes. J. Biol. Chem. 278, 44735–44744 (2003).

    Article  CAS  PubMed  Google Scholar 

  15. Romashko, D. N., Marban, E. & O’Rourke, B. Subcellular metabolic transients and mitochondrial redox waves in heart cells. Proc. Natl Acad. Sci. USA 95, 1618–1623 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Bers, D. M. Cardiac excitation–contraction coupling. Nature 415, 198–205 (2002).

    Article  CAS  PubMed  Google Scholar 

  17. Gottlieb, R. A. & Bernstein, D. Mitochondria shape cardiac metabolism. Science 350, 1162–1163 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Wang, S. C. et al. Pathological basis of cardiac arrhythmias: vicious cycle of immune-metabolic dysregulation. Cardiovasc. Disord. Med. https://doi.org/10.15761/CDM.1000158 (2018).

  19. Edwards, A. G. & Louch, W. E. Species-dependent mechanisms of cardiac arrhythmia: a cellular focus. Clin. Med. Insights Cardiol. 11, 1179546816686061 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Milani-Nejad, N. & Janssen, P. M. L. Small and large animal models in cardiac contraction research: advantages and disadvantages. Pharmacol. Ther. 141, 235–249 (2014).

    Article  CAS  PubMed  Google Scholar 

  21. Ebert, A. et al. Proteasome-dependent regulation of distinct metabolic states during long-term culture of human iPSC-derived cardiomyocytes. Circ. Res. 125, 90–103 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Giacomelli, E. et al. Three-dimensional cardiac microtissues composed of cardiomyocytes and endothelial cells co-differentiated from human pluripotent stem cells. Development 144, 1008–1017 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Noor, N. et al. 3D printing of personalized thick and perfusable cardiac patches and hearts. Adv. Sci. 6, 1900344 (2019).

    Article  Google Scholar 

  25. Skylar-Scott, M. A. et al. Biomanufacturing of organ-specific tissues with high cellular density and embedded vascular channels. Sci. Adv. 5, eaaw2459 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Mills, R. J. et al. Functional screening in human cardiac organoids reveals a metabolic mechanism for cardiomyocyte cell cycle arrest. Proc. Natl Acad. Sci. USA 114, E8372–e8381 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Mills, R. J. et al. Drug screening in human PSC-cardiac organoids identifies pro-proliferative compounds acting via the mevalonate pathway. Cell Stem Cell 24, 895–907.e6 (2019).

    Article  CAS  PubMed  Google Scholar 

  28. Richards, D. J. et al. Human cardiac organoids for the modelling of myocardial infarction and drug cardiotoxicity. Nat. Biomed. Eng. 4, 446–462 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Li, R. A. et al. Bioengineering an electro-mechanically functional miniature ventricular heart chamber from human pluripotent stem cells. Biomaterials 163, 116–127 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Goldfracht, I. et al. Generating ring-shaped engineered heart tissues from ventricular and atrial human pluripotent stem cell-derived cardiomyocytes. Nat. Commun. 11, 75 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhao, Y. et al. A platform for generation of chamber-specific cardiac tissues and disease modeling. Cell 176, 913–927.e8 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lind, J. U. et al. Instrumented cardiac microphysiological devices via multimaterial three-dimensional printing. Nat. Mater. 16, 303–308 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Lind, J. U. et al. Cardiac microphysiological devices with flexible thin-film sensors for higher-throughput drug screening. Lab Chip 17, 3692–3703 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Oleaga, C. et al. Long-term electrical and mechanical function monitoring of a human-on-a-chip system. Adv. Funct. Mater. 29, 1805792 (2019).

    Article  PubMed  Google Scholar 

  35. 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).

    Article  CAS  PubMed  Google Scholar 

  36. Tian, B. et al. Macroporous nanowire nanoelectronic scaffolds for synthetic tissues. Nat. Mater. 11, 986–994 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Feiner, R. et al. Engineered hybrid cardiac patches with multifunctional electronics for online monitoring and regulation of tissue function. Nat. Mater. 15, 679–685 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Drakhlis, L. et al. Human heart-forming organoids recapitulate early heart and foregut development. Nat. Biotechnol. 39, 737–746 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Miquerol, L. & Kelly, R. G. Organogenesis of the vertebrate heart. Wiley Interdiscip. Rev. Dev. Biol. 2, 17–29 (2013).

    Article  CAS  PubMed  Google Scholar 

  40. Moorman, A., Webb, S., Brown, N. A., Lamers, W. & Anderson, R. H. Development of the heart: (1) formation of the cardiac chambers and arterial trunks. Heart 89, 806–814 (2003).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lin, C. J., Lin, C. Y., Chen, C. H., Zhou, B. & Chang, C. P. Partitioning the heart: mechanisms of cardiac septation and valve development. Development 139, 3277–3299 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Swift, J. et al. Nuclear lamin-A scales with tissue stiffness and enhances matrix-directed differentiation. Science 341, 1240104 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Dupont, S. et al. Role of YAP/TAZ in mechanotransduction. Nature 474, 179–183 (2011).

    Article  CAS  PubMed  Google Scholar 

  44. Gordan, R., Gwathmey, J. K. & Xie, L.-H. Autonomic and endocrine control of cardiovascular function. World J. Cardiol. 7, 204–214 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Mackin, C. et al. Intravenous amiodarone and sotalol impair contractility and cardiac output, but procainamide does not: a Langendorff study. J. Cardiovasc. Pharmacol. Ther. 24, 288–297 (2019).

    Article  CAS  PubMed  Google Scholar 

  46. Karbassi, E. et al. Cardiomyocyte maturation: advances in knowledge and implications for regenerative medicine. Nat. Rev. Cardiol. 17, 341–359 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Wegner, A., Meiser, J., Weindl, D. & Hiller, K. How metabolites modulate metabolic flux. Curr. Opin. Biotechnol. 34, 16–22 (2015).

    Article  CAS  PubMed  Google Scholar 

  48. Collins-Nakai, R. L., Noseworthy, D. & Lopaschuk, G. D. Epinephrine increases ATP production in hearts by preferentially increasing glucose metabolism. Am. J. Physiol. 267, H1862–H1871 (1994).

    CAS  PubMed  Google Scholar 

  49. Itzhaki, I. et al. Modeling of catecholaminergic polymorphic ventricular tachycardia with patient-specific human-induced pluripotent stem cells. J. Am. Coll. Cardiol. 60, 990–1000 (2012).

    Article  CAS  PubMed  Google Scholar 

  50. Lee, C. H. & Ruben, P. C. Interaction between voltage-gated sodium channels and the neurotoxin, tetrodotoxin. Channels 2, 407–412 (2008).

    Article  PubMed  Google Scholar 

  51. Farman, G. P. et al. Blebbistatin: use as inhibitor of muscle contraction. Pflugers Arch. 455, 995–1005 (2008).

    Article  CAS  PubMed  Google Scholar 

  52. Taxin, Z. H., Neymotin, S. A., Mohan, A., Lipton, P. & Lytton, W. W. Modeling molecular pathways of neuronal ischemia. Prog. Mol. Biol. Transl. Sci. 123, 249–275 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Fonteriz, R. I. et al. Monitoring mitochondrial [Ca(2+)] dynamics with rhod-2, ratiometric pericam and aequorin. Cell Calcium 48, 61–69 (2010).

    Article  CAS  PubMed  Google Scholar 

  54. Tsien, R. Y. A non-disruptive technique for loading calcium buffers and indicators into cells. Nature 290, 527–528 (1981).

    Article  CAS  PubMed  Google Scholar 

  55. Berry, W., Dakhil, S., Modiano, M., Gregurich, M. & Asmar, L. Phase III study of mitoxantrone plus low dose prednisone versus low dose prednisone alone in patients with asymptomatic hormone refractory prostate cancer. J. Urol. 168, 2439–2443 (2002).

    Article  CAS  PubMed  Google Scholar 

  56. Evison, B. J., Sleebs, B. E., Watson, K. G., Phillips, D. R. & Cutts, S. M. Mitoxantrone, more than just another topoisomerase II poison. Med. Res. Rev. 36, 248–299 (2016).

    Article  CAS  PubMed  Google Scholar 

  57. Scott, L. J. & Figgitt, D. P. Mitoxantrone: a review of its use in multiple sclerosis. CNS Drugs 18, 379–396 (2004).

    Article  CAS  PubMed  Google Scholar 

  58. Ewer, M. S. & Ewer, S. M. Cardiotoxicity of anticancer treatments. Nat. Rev. Cardiol. 12, 547–558 (2015).

    Article  CAS  PubMed  Google Scholar 

  59. Romitan, D. M. et al. Cardiomyopathies and arrhythmias induced by cancer therapies. Biomedicines https://doi.org/10.3390/biomedicines8110496 (2020).

  60. Guglin, M., Aljayeh, M., Saiyad, S., Ali, R. & Curtis, A. B. Introducing a new entity: chemotherapy-induced arrhythmia. Europace 11, 1579–1586 (2009).

    Article  PubMed  Google Scholar 

  61. Neri, B., Cini-Neri, G. & D’Alterio, M. Effect of anthracyclines and mitoxantrone on oxygen uptake and ATP intracellular concentration in rat heart slices. Biochem. Biophys. Res. Commun. 125, 954–960 (1984).

    Article  CAS  PubMed  Google Scholar 

  62. Arduino, D. M. et al. Systematic identification of MCU modulators by orthogonal interspecies chemical screening. Mol. Cell 67, 711–723.e7 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. De Mario, A. et al. Identification and functional validation of FDA-approved positive and negative modulators of the mitochondrial calcium uniporter. Cell Rep. 35, 109275 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Loubiere, C. et al. The energy disruptor metformin targets mitochondrial integrity via modification of calcium flux in cancer cells. Sci. Rep. 7, 5040 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Zhao, H. et al. AMPK-mediated activation of MCU stimulates mitochondrial Ca(2+) entry to promote mitotic progression. Nat. Cell Biol. 21, 476–486 (2019).

    Article  CAS  PubMed  Google Scholar 

  66. Moscovitz, H. L., Donoso, E., Gelb, I. J. & Welkowitz, W. Intracardiac phonocardiography: correlation of mechanical, acoustic and electric events of the cardiac cycle. Circulation 18, 983–988 (1958).

    Article  CAS  PubMed  Google Scholar 

  67. Morad, M. & Orkand, R. K. Excitation-concentration coupling in frog ventricle: evidence from voltage clamp studies. J. Physiol. 219, 167–189 (1971).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Wendt-Gallitelli, M. F. & Isenberg, G. Total and free myoplasmic calcium during a contraction cycle: x-ray microanalysis in guinea-pig ventricular myocytes. J. Physiol. 435, 349–372 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Lee, K. S. A new technique for the simultaneous recording of oxygen consumption and contraction of muscle: the effect of ouabain on cat papillary muscle. J. Pharmacol. Exp. Ther. 109, 304–312 (1953).

    CAS  PubMed  Google Scholar 

  70. Lentini, E. A. Myocardial oxygen consumption as influenced by isotonic contractions. Proc. Soc. Exp. Biol. Med. 109, 869–872 (1962).

    Article  CAS  PubMed  Google Scholar 

  71. Isenberg, G., Han, S., Schiefer, A. & Wendt-Gallitelli, M. F. Changes in mitochondrial calcium concentration during the cardiac contraction cycle. Cardiovasc. Res. 27, 1800–1809 (1993).

    Article  CAS  PubMed  Google Scholar 

  72. Reiermann, H. J., Herzig, J. W. & Rüegg, J. C. Ca++ activation of ATPase activity, ATP-Pi exchange, and tension in briefly glycerinated heart muscle. Basic Res. Cardiol. 72, 133–139 (1977).

    Article  CAS  PubMed  Google Scholar 

  73. O’Rourke, B., Ramza, B. M. & Marban, E. Oscillations of membrane current and excitability driven by metabolic oscillations in heart cells. Science 265, 962–966 (1994).

    Article  PubMed  Google Scholar 

  74. Robert, V. et al. Beat-to-beat oscillations of mitochondrial [Ca2+] in cardiac cells. EMBO J. 20, 4998–5007 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Wescott, A. P., Kao, J. P. Y., Lederer, W. J. & Boyman, L. Voltage-energized calcium-sensitive ATP production by mitochondria. Nat. Metab. 1, 975–984 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Patterson, A. J. & Zhang, L. Hypoxia and fetal heart development. Curr. Mol. Med. 10, 653–666 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Bakkehaug, J. P. et al. Myosin activator omecamtiv mecarbil increases myocardial oxygen consumption and impairs cardiac efficiency mediated by resting myosin ATPase activity. Circ. Heart Fail. 8, 766–775 (2015).

    Article  CAS  PubMed  Google Scholar 

  78. Lewis, G. D. et al. Effect of omecamtiv mecarbil on exercise capacity in chronic heart failure with reduced ejection fraction: the METEORIC-HF randomized clinical trial. JAMA 328, 259–269 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Teerlink, J. R. et al. Chronic Oral Study of Myosin Activation to Increase Contractility in Heart Failure (COSMIC-HF): a phase 2, pharmacokinetic, randomised, placebo-controlled trial. Lancet 388, 2895–2903 (2016).

    Article  CAS  PubMed  Google Scholar 

  80. Bick, A. G. et al. Cardiovascular homeostasis dependence on MICU2, a regulatory subunit of the mitochondrial calcium uniporter. Proc. Natl Acad. Sci. USA 114, E9096–e9104 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Pan, X. et al. The physiological role of mitochondrial calcium revealed by mice lacking the mitochondrial calcium uniporter. Nat. Cell Biol. 15, 1464–1472 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Lambert, J. P. et al. MCUB regulates the molecular composition of the mitochondrial calcium uniporter channel to limit mitochondrial calcium overload during stress. Circulation 140, 1720–1733 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Wu, Y. et al. The mitochondrial uniporter controls fight or flight heart rate increases. Nat. Commun. 6, 6081 (2015).

    Article  CAS  PubMed  Google Scholar 

  84. Yellon, D. M. & Hausenloy, D. J. Myocardial reperfusion injury. N. Engl. J. Med. 357, 1121–1135 (2007).

    Article  CAS  PubMed  Google Scholar 

  85. Kwong, J. C. et al. Acute myocardial infarction after laboratory-confirmed influenza infection. N. Engl. J. Med. 378, 345–353 (2018).

    Article  PubMed  Google Scholar 

  86. Tiburcy, M. et al. Defined engineered human myocardium with advanced maturation for applications in heart failure modeling and repair. Circulation 135, 1832–1847 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Fleischer, V. et al. Cardiotoxicity of mitoxantrone treatment in a German cohort of 639 multiple sclerosis patients. J. Clin. Neurol. 10, 289–295 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Xie, A. et al. Mitochondrial Ca(2+) influx contributes to arrhythmic risk in nonischemic cardiomyopathy. J. Am. Heart Assoc. https://doi.org/10.1161/jaha.117.007805 (2018).

  89. Schweitzer, M. K. et al. Suppression of arrhythmia by enhancing mitochondrial Ca(2+) uptake in catecholaminergic ventricular tachycardia models. JACC Basic Transl. Sci. 2, 737–747 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Burridge, P. W. et al. Chemically defined generation of human cardiomyocytes. Nat. Methods 11, 855–860 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Pires, R. H., Shree, N., Manu, E., Guzniczak, E. & Otto, O. Cardiomyocyte mechanodynamics under conditions of actin remodelling. Phil. Trans. R. Soc. Lond. B 374, 20190081 (2019).

    Article  CAS  Google Scholar 

  92. Li, Z., Guo, X., Palmer, A. F., Das, H. & Guan, J. High-efficiency matrix modulus-induced cardiac differentiation of human mesenchymal stem cells inside a thermosensitive hydrogel. Acta Biomater. 8, 3586–3595 (2012).

    Article  CAS  PubMed  Google Scholar 

  93. Liu, M., Sun, J., Sun, Y., Bock, C. & Chen, Q. Thickness-dependent mechanical properties of polydimethylsiloxane membranes. J. Micromech. Microeng. 19, 035028 (2009).

    Article  Google Scholar 

  94. Soofi, S. S., Last, J. A., Liliensiek, S. J., Nealey, P. F. & Murphy, C. J. The elastic modulus of Matrigel as determined by atomic force microscopy. J. Struct. Biol. 167, 216–219 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Ebrahimi, A. P. Mechanical properties of normal and diseased cerebrovascular system. J. Vasc. Interv. Neurol. 2, 155–162 (2009).

    PubMed  PubMed Central  Google Scholar 

  96. Dutta, D. et al. Non-invasive assessment of elastic modulus of arterial constructs during cell culture using ultrasound elasticity imaging. Ultrasound Med. Biol. 39, 2103–2115 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  97. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Huang, D. W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).

    Article  CAS  PubMed  Google Scholar 

  101. 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 (2018).

    Article  PubMed Central  Google Scholar 

  102. Xia, J., Gill, E. E. & Hancock, R. E. W. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat. Protoc. 10, 823–844 (2015).

    Article  CAS  PubMed  Google Scholar 

  103. Ehrlich, A. et al. Microphysiological flux balance platform unravels the dynamics of drug induced steatosis. Lab Chip 18, 2510–2522 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Joung, J. et al. Genome-scale CRISPR-Cas9 knockout and transcriptional activation screening. Nat. Protoc. 12, 828–863 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Ou, Q. et al. Physiological biomimetic culture system for pig and human heart slices. Circ. Res. 125, 628–642 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Ludikhuize, M. C., Meerlo, M., Burgering, B. M. T. & Rodríguez Colman, M. J. Protocol to profile the bioenergetics of organoids using Seahorse. STAR Protoc. 2, 100386 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Sigg, C. & Buhmann, J. Expectation-maximization for sparse and non-negative PCA. In Proc. 25th International Conference on Machine Learning 960–967 (Association for Computing Machinery, 2008).

  108. Wickham, H. & Sievert, C. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).

Download references

Acknowledgements

Funding was provided by the European Research Council Consolidator Grant OCLD (project no. 681870) and generous gifts from the Nikoh Foundation and the Sam and Rina Frankel Foundation. M.G. was supported by a Neubauer Foundation Graduate Fellowship. We thank O. Leitersdorf, Y. Kroiz, J. Gotlib, D. Viner, I. Shweky, H. Naimi, B. A. Berke, M. Ehrlich and S. Regenbaum. Figure 8a was generated using BioRender.com.

Author information

Authors and Affiliations

  1. Alexander Grass Center for Bioengineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    Mohammad Ghosheh, Avner Ehrlich, Konstantinos Ioannidis, Muneef Ayyash, Merav Cohen & Yaakov Nahmias

  2. The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    Mohammad Ghosheh, Avner Ehrlich, Konstantinos Ioannidis, Muneef Ayyash, Merav Cohen & Yaakov Nahmias

  3. Tissue Dynamics, LTD, Jerusalem, Israel

    Avner Ehrlich, Konstantinos Ioannidis & Yaakov Nahmias

  4. Sohnis Research Laboratory for Cardiac Electrophysiology and Regenerative Medicine, the Rappaport Faculty of Medicine and Research Institute, Technion- Israel Institute of Technology, Haifa, Israel

    Idit Goldfracht & Lior Gepstein

  5. Department of Cell and Developmental Biology, The Hebrew University of Jerusalem, Jerusalem, Israel

    Merav Cohen & Yaakov Nahmias

  6. Department of Biological Chemistry, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem, Israel

    Amit Fischer

  7. Department of General Surgery, Hadassah Hebrew University Medical Center, Jerusalem, Israel

    Yoav Mintz

  8. Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel

    Yoav Mintz

  9. Cardiology Department, Rambam Health Care Campus, Haifa, Israel

    Lior Gepstein

Authors
  1. Mohammad Ghosheh
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Avner Ehrlich
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Konstantinos Ioannidis
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Muneef Ayyash
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Idit Goldfracht
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Merav Cohen
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Amit Fischer
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Yoav Mintz
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Lior Gepstein
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Yaakov Nahmias
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

M.G., A.E. and Y.N. conceived the hypothesis. M.G., A.E., M.A., L.G. and Y.N. designed the experiment. M.G., A.E., M.A., K.I., M.C., A.F. and Y.N. performed the experiments. Y.M. provided the porcine heart and prepared the ex-vivo heart tissue. M.G. and A.E. analysed the results. M.G., A.E. and Y.M. wrote the manuscript. M.G., A.E. and I.G. built the system. M.C., L.G. and Y.N supervised the project. All authors read the manuscript and agree with its contents.

Corresponding author

Correspondence to Yaakov Nahmias.

Ethics declarations

Competing interests

Y.N. and A.E. are employees of Tissue Dynamics. M.G., A.E. and Y.N. filed a patent application through Hebrew University (US202163242091P; 2019, Israel). The other authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Alessandro Prigione and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 Vascular network development during cardiac organoid formation.

a, Representative time-lapse sequence confocal images depicting the formation of vascular networks in cardiac organoids formed from UN-1, ACS-1021, and ACS-1028 hiPSC-derived cardiomyocytes. Confocal microscopy shows the distribution of GFP-expressing rat microvascular cardiac endothelial cells (CECs) in the organoid. Vascular networks are apparent by day 10, stabilizing into a capillary-like network, distributed within the organoid at 25 d. UN-1 cardiac organoid images were taken from Fig.1b. Scale bar, 100 μm. b, Relative gene expression of the rat microvascular cardiac endothelial cells (CECs) and isolated rat primary endothelial cells. The CECs are showing expression signatures comparable with those of isolated rat primary endothelial cells across multiple endothelial markers. Anti-phospho-Nuclear Receptor (NR4A1), Cadherin 5 (CDH5), Von Willebrand factor (VWF), and tyrosine kinase with immunoglobulin-like and EGF-like domains 1 (TIE1) are similarly expressed among the CECs used and isolated rat primary endothelial cells.

Source data

Extended Data Fig. 2 Functional comparison of hiPSC-CMs and human cardiac organoids.

a, Principal component analysis (PCA) of 513 genes differentially expressed between hiPS-derived cardiomyocytes and vascularized cardiac organoids (methods). Cardiac organoids cluster with adult, but not fetal cardiomyocytes. b, Principal component analysis (PCA) of 513 genes differentially expressed between hiPS-derived cardiomyocytes and vascularized cardiac organoids (methods) separated into PC components. PC1 gene set is enriched for angiogenesis and cell adhesion, clustering the non-beating AC16 cell line, the negative control, with the mature heart tissue. This clearly suggests that PC1 is less relevant for comparative analysis. c, Visual contraction analysis of UN-1 vascularized cardiac organoids treated with DMSO (Control), 10 µM amiodarone, or 100 µM epinephrine (Supplementary. Video 3), normalized to the highest and lowest signal recorded through the entire measurement duration (30 seconds; methods). Analysis shows that untreated organoids acquire a homogenous synchronized spontaneous beating of 66 ± 5 beats per minute. Stimulation with 100 µM epinephrine increases the contraction rate to 88 ± 7 bpm and relative contraction by 18% (n = 5, p < 0.001), while stimulation with 10 µM amiodarone decreased the rate to 52 ± 4 bpm and contraction by 28% (n = 5, p < 0.001), resulting in a physiological-like response to the drugs. Mean of 5 biological replicates; error bars, s.e.m. Significance was determined using a one-way ANOVA with Dunnett correction. Graphs were taken from Fig. 3d. d, Visual contraction analysis of UN-1 hiPSC-derived cardiomyocytes (hiPSC-CMs) treated with DMSO (Control), 10 µM amiodarone, or 100 µM epinephrine. Analysis shows that untreated hiPSC-CMs acquire a homogenous unsynchronized beating of 97 ± 4 beats per minute. Stimulation with 100 µM epinephrine increases the contraction rate to 106 ± 4 bpm and relative contraction by 18% (n = 3, p < 0.01), while stimulation with 10 µM amiodarone decreased the rate to 92 ± 3 bpm and contraction by 28% (n = 3, p < 0.01). Mean of 3 biological replicates; error bars, s.e.m. Significance was determined using a one-way ANOVA with Dunnett correction. e, Seahorse MitoStress assay nested analysis of UN-1 cardiac organoids, UN-1 hiPSC-derived cardiomyocytes (hiPSC-CMs), and cardiac endothelial cells (CECs). CECs show basal respiration equal to 4.5% of the basal respiration of the cardiac organoids and less than 2% of maximal respiration, indicating that changes in respiration are attributed to changes in the cardiomyocytes (n = 9, p < 0.001). Significance was determined using one-way ANOVA and Dunnett multiple comparison correction. Lines represent independent experiments, Error bars mark standard error of mean among n = 3 biological repeats.

Source data

Extended Data Fig. 3 Establishment of an Integrated 2-PMT Heart-on-a-chip platform.

a, Scheme and typical measurements depicting the advantages of using a 2-PMT system over a single PMT system. The addition of the second detector (cPMT), which measures the excitation signal, reduces the noise and enables accurate measurements at sub-second resolution. The second PMT also allows an emission-independent measurement of tissue contraction (methods). b, Representative calibration measurements of the reflected signal measured by the second PMT in different displacements. Curve fitting displays a sigmoidal relationship between the emission intensity, measured by peak-to-peak voltage (VP-P), and the sensor displacement. Cardiac displacement was measured by the embedded oxygen beads inside the cardiac organoids during a contraction when the beads move at different distances from the focal point. The Sigmoidal fit shows a correlation of R-square: 0.9835 and RMSE below 4. c, Representative continuous interstitial oxygen measurements in cardiac organoids measured continuously over 10 hours. Measurements show steady recordings that are unaffected by photobleaching or system sensitivity loss at prolonged measurements. Oxygen content and oscillation were unchanged even after 10 hours of measurement.

Source data

Extended Data Fig. 4 Quantitative and dynamic analyses of contraction, field potential, and oxygen content in vascularized cardiac organoids.

a, Representative simultaneous measurements and (b) Fast Fourier Transformation (FFT) analysis of contraction, field potential, and interstitial oxygen during spontaneous beating of cardiac organoids formed from UN-1, ACS-1021, and ACS-1028 hiPSC-derived cardiomyocytes. Interstitial oxygen concentration shows oscillatory behaviour during the cardiac cycle, yielding distinct single-frequency peaks in FFT analysis correlated to the mechanical and electrical behaviour of the cardiac tissue. UN-1 cardiac organoid graphs were taken from Fig. 4f–h. Nested analysis of the organoid’s contraction and oxygen oscillation (c) frequency and (d) amplitude in UN-1, ACS-1021, and ACS-1028 cardiac organoids. Treatment with 10 μM of myosin II inhibitor Blebbistatin. Blebbistatin completely inhibits the contraction of vascularized cardiac organoids (n = 3, p < 0.001) but does not affect the field potential or oxygen oscillation (n = 3, p > 0.05). Treatment with 25 μM of Nav channel inhibitor Tetrodotoxin (TTX) resulted in a complete loss of field potential generation, the coupled mechanical contraction (n = 3, p < 0.001), and a concurrent loss of oxygen oscillations (n = 3, p < 0.001). Middle represents mean of 3 biological repeats for each line; error bars,s.e.m. Significance was determined using a two-tailed nested t-test. e, Representative graph of the interstitial oxygen behaviours in ACS-1021 cardiac organoids following treatment with 25 μM of Nav channel inhibitor Tetrodotoxin (TTX). After 7 minutes of exposure to TTX, a complete loss of field potential generation occurs, coupled with decay in oxygen oscillations (n = 3). Graph was taken from Fig. 4l. Middle represents mean of 3 biological repeats for each line; error bars,s.e.m. Significance was determined using a two-tailed nested t-test.

Source data

Extended Data Fig. 5 Real-time metabolic measurement of vascularized cardiac organoids under epinephrine stimulation.

Nested analysis of the organoid’s contraction and oxygen oscillation (a) frequency and (b) amplitude during prolonged stimulation by 100 µM epinephrine in UN-1, ACS-1021, and ACS-1028 cardiac organoids. Analysis shows that epinephrine exposure significantly increases the frequency of cardiac contraction and oxygen oscillations (n = 3, p < 0.001), and the amplitude of cardiac contraction and oxygen oscillation amplitudes (n = 3, p < 0.001). c, Analysis of the kinetic behaviour of the organoid’s contraction rate during prolonged stimulation by 100 µM epinephrine. Kinetic analysis suggests that epinephrine stimulation results in a sigmoidal-like change in the organoid contraction rate. Representative relation graphs between (d) contraction amplitude (contractility) to contraction rate, and (e) interstitial oxygen content to contraction rate during prolonged epinephrine stimulation. Analysis suggests a correlation between an increase in cardiac organoid contractility and oxygen consumption. f, Representative frequency histograms of interstitial oxygen measurements at 0, 15, and 90 minutes after stimulation with 100 µM epinephrine. Analysis shows that an increase in oxygen consumption correlates to an increase in interstitial oxygen content variability correlative to the increased oxygen amplitudes measured. Representative correlation analysis between (g) oxygen oscillation frequency to contraction frequency and (h) oxygen oscillation amplitude to contractility reveals a direct linear correlation between the oscillatory behaviour of the interstitial oxygen and organoid contraction. i, Representative graphs of the cardiac organoid’s contraction and interstitial oxygen content 60 mins prior to epinephrine stimulation and 5 hours post-stimulation with 100 µM epinephrine. Oxygen returns to the baseline value after 300 minutes, indicating hypoxia, stimulation-induced or ischemia-like injuries did not occur. Middle represents mean of 3 biological repeats for each line; error bars,s.e.m. Significance was determined using a two-tailed nested t-test.

Source data

Extended Data Fig. 6 Live mitochondrial imaging reveals oscillations in mitochondrial membrane potential.

a, Immunofluorescent micrograph of mitochondrial membrane potential measured using live imaging of TMRE dye (Supplementary. Video 2; methods). b, Kinetic analysis of mitochondrial membrane potential using TMRE. The mitochondrial membrane potential of hiPSC-derived cardiomyocytes oscillates in the frequency of contraction. The mitochondrial membrane potential of non-beating cells did not oscillate and was lower overall. Rainbow heatmaps of (c) mean mitochondrial membrane potential and (d) major oscillating frequency of (MMP) measured using TMRE (methods). The heatmaps suggest a correlation between areas with high mean mitochondrial membrane potential and oscillation frequency. e, Immunofluorescent micrograph of mitochondrial membrane potential measured using live imaging of JC-1 dye (Supplementary. Video 3; methods). f, Kinetic analysis of mitochondrial membrane potential following aggregation of JC-1 dye. Mitochondrial membrane potential showed distinct polarization peaks in contracting cells, while non-beating cells did not oscillate and show lower mitochondrial membrane potential overall. Rainbow heatmaps of (g) mean mitochondrial membrane potential and (h) major oscillating frequency of (MMP) measured using JC-1 (methods). Similar to the behaviour measured by TMRE, JC-1 heatmaps suggest a correlation between areas with high mean mitochondrial membrane potential and oscillation frequency. Scale bar, 25 μm.

Source data

Extended Data Fig. 7 CRISPR/Cas9 knockout of MCU disrupts electro-mitochondrial coupling and induces arrhythmic behaviour.

a, Kinetic measurements of mitochondrial calcium in beating 2D-cultured hiPSC-derived cardiomyocytes. Non-targeting sgRNA had no effect on [Ca2+]m showing a dominant frequency of 0.8 Hz, while MCU knockout (MCUKO) showed a 35–50% decrease in [Ca2+]m and oscillation magnitude while increasing oscillation rate to 1.3–1.4 Hz. b, Immunofluorescent confocal microscopy demonstrated a marked reduction in MCU expression in the MCU knockout (MCUKO) compared to the non-targeting control. Scale bar, 100 µm. c, Relative gene expression of the MCU gene was markedly reduced by 46% (n = 3, p < 0.001) and 27% (n = 3, p < 0.1) in the MCU knockout MCUKO cardiac organoids compared to the non-targeting cardiac organoids in ACS-1021 and UN-1 cell lines respectively. Mean of 3 biological replicates; error bars, s.e.m. d, Interstitial oxygen showed a 78% (n = 3, p < 0.001) and 126% (n = 4, p < 0.001) increase in the MCUKO cardiac organoids compared to the non-targeting organoids in ACS-1021 and UN-1 cell lines, respectively. Mean of 3 biological replicates; error bars,s.e.m. Significance was determined using a one-way ANOVA with Dunnett correction. UN-1 cardiac organoid graphs were taken from Fig. 6a.

Source data

Extended Data Fig. 8 Reduced oxygen consumption coordinated with electro-mitochondrial decoupling.

a, Representative kinetic measurements of interstitial oxygen content and mean oxygen content analysis in non-homogenous MCUKO or Non-targeting sgRNA cardiac organoids. Analysis shows a decrease in oscillation amplitude and an increase in oscillation frequency in the MCUKO organoids, coupled with an increase in mean oxygen content (n = 3, p < 0.001). Mean of 3 biological replicates; error bars,s.e.m. Significance was determined using two tailed t-test. Cardiac organoid graphs were taken from Fig. 6c. b, Representative kinetic measurements of interstitial oxygen content and mean oxygen content analysis in cardiac organoids treated with DMSO (Control), 10 μM mitoxantrone (Mitoxantrone) or 10 μM mitoxantrone, and 100 μM AMP-activated protein kinase (AMPK) activator metformin (Mitoxantrone + Metformin). Analysis shows a decrease in oscillation amplitude and an increase in oscillation frequency in the mitoxantrone-treated organoids, coupled with an increase in mean oxygen content (n = 3, p < 0.001). Metformin reverts these changes, restoring mean oxygen content to levels not significantly different than the control (n = 3, p > 0.05). Cardiac organoid graphs were taken from Fig. 6e. Mean of 3 biological replicates; error bars,s.e.m. Significance was determined using two tailed t-test. c, Representative kinetic measurements of interstitial oxygen content and mean oxygen content analysis in porcine cardiac tissue exposed to DMSO (control), 10 μM of blebbistatin, 10 µM mitoxantrone, or 10 µM mitoxantrone, and 100 μM metformin (mitoxantrone + metformin). Analysis shows that blebbistatin does not change oscillation amplitude and oscillation frequency or mean oxygen content (n = 3, p > 0.05). Mitoxantrone-treated organoids show a decrease in oscillation amplitude and an increase in oscillation frequency, coupled with an increase in mean oxygen content (n = 3, p < 0.001). Metformin partly reverts these changes, increasing mean oxygen content by 29% (n = 3, p < 0.001). Porcine cardiac tissue graphs were taken from Fig. 8d. Mean of 3 biological replicates; error bars,s.e.m. Significance was determined using one-way ANOVA with Dunnett multiple comparison correction.

Source data

Supplementary information

Supplementary Information

Supplementary figures.

Reporting Summary

Supplementary Video 1

Representative time-lapse sequence of brightfield images showing vascularized cardiac organoids during organoid formation.

Supplementary Video 2

Synchronized beating cardiac organoid embedded with oxygen sensors.

Supplementary Video 3

The effect of pharmaceutical stimulation of epinephrine and amiodarone on the contraction rate and contractility of a cardiac organoid.

Supplementary Video 4

Immunofluorescent live imaging of mitochondrial membrane-potential oscillations by TMRE stain in homogeneous beating hiPSC-derived cardiomyocytes.

Supplementary Video 5

Beating cardiac organoids under epinephrine stimulation at different timepoints.

Supplementary Video 6

MATLAB projection of the desynchronization of cardiac organoids due to MCU inhibition by KB-R7943.

Supplementary Video 7

Beating cardiac organoids embedded with oxygen sensors on an MEA chip.

Supplementary Video 8

Non-targeting CRISPR knockout cardiomyocyte wells display cardiac contractions, whereas none of the MCU CRISPR KO wells display cardiac contraction.

Supplementary Video 9

Time-lapse sequence of brightfield and fluorescent images of cardiac organoids loaded with the cell-impermeable Dextran-Texas Red.

Source data

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ghosheh, M., Ehrlich, A., Ioannidis, K. et al. Electro-metabolic coupling in multi-chambered vascularized human cardiac organoids. Nat. Biomed. Eng 7, 1493–1513 (2023). https://doi.org/10.1038/s41551-023-01071-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-023-01071-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research