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.

  • Protocol
  • Published:

Cyborg organoids integrated with stretchable nanoelectronics can be functionally mapped during development

Abstract

Organoids are in vitro miniaturized cellular models of organs that offer opportunities for studying organ development, disease mechanisms and drug screening. Understanding the complex processes governing organoid development and function requires methods suitable for the continuous, long-term monitoring of cell activities (for example, electrophysiological and mechanical activity) at single-cell resolution throughout the entire three-dimensional (3D) structure. Cyborg organoid technology addresses this need by seamlessly integrating stretchable mesh nanoelectronics with tissue-like properties, such as tissue-level flexibility, subcellular feature size and mesh-like networks, into 3D organoids through a 2D-to-3D tissue reconfiguration process during organogenesis. This approach enables longitudinal, tissue-wide, single-cell functional mapping, thereby overcoming the limitations of existing techniques including recording duration, spatial coverage, and the ability to maintain stable contact with the tissue during organoid development. This protocol describes the fabrication and characterization of stretchable mesh nanoelectronics, their electrical performance, their integration with organoids and the acquisition of long-term functional organoid activity requiring multimodal data analysis techniques. Cyborg organoid technology represents a transformative tool for investigating organoid development and function, with potential for improving in vitro disease models, drug screening and personalized medicine. The procedure is suitable for users within a multidisciplinary team with expertise in stem cell biology, tissue engineering, nanoelectronics fabrication, electrophysiology and data science.

Key points

  • The procedure includes the fabrication of stretchable mesh nanoelectronics, the nanoelectronics’ integration within developing organoids and the long-term electrical measurements of organoid function.

  • Alternatives include imaging-based techniques, intracellular recordings and extracellular recordings using multielectrode arrays. Cyborg organoids provide stable 3D bioelectronics interfaces and enable the continuous monitoring of electrophysiological activity during development.

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

Access options

Buy this article

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

Fig. 1: Overview of the cyborg organoid protocol.
Fig. 2: Fabrication of tissue-level flexible and stretchable mesh nanoelectronics.
Fig. 3: Packaging and characterization of stretchable mesh nanoelectronics.
Fig. 4: Integration of hPSCs-derived progenitors with mesh nanoelectronics.
Fig. 5: Long-term electrophysiological recording of cyborg organoids.
Fig. 6: Integrative data analysis strategies of cyborg organoids.

Similar content being viewed by others

Data availability

The authors declare that the main data discussed in this protocol are available in the supporting primary research papers (https://doi.org/10.1021/acs.nanolett.9b02512, https://doi.org/10.1002/adma.202106829, https://doi.org/10.1126/sciadv.ade8513, and https://doi.org/10.1016/j.cell.2023.03.023, https://doi.org/10.1101/2024.03.18.585551).

References

  1. Kim, J., Koo, B. K. & Knoblich, J. A. Human organoids: model systems for human biology and medicine. Nat. Rev. Mol. Cell. Biol. 21, 571–584 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Zhao, Z. et al. Organoids. Nat. Rev. Methods Primers 2, 94 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Li, Q. et al. Cyborg organoids: implantation of nanoelectronics via organogenesis for tissue-wide electrophysiology. Nano Lett. 19, 5781–5789 (2019).

    Article  PubMed  CAS  Google Scholar 

  4. Le Floch, P. et al. Stretchable mesh nanoelectronics for 3D single-cell chronic electrophysiology from developing brain organoids. Adv. Mater. 34, e2106829 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Lin, Z. et al. Tissue-embedded stretchable nanoelectronics reveal endothelial cell-mediated electrical maturation of human 3D cardiac microtissues. Sci. Adv. 9, eade8513 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Li, Q. et al. Multimodal charting of molecular and functional cell states via in situ electro-sequencing. Cell 186, 2002–2017 e2021 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Wurst, W. & Bally-Cuif, L. Neural plate patterning: upstream and downstream of the isthmic organizer. Nat. Rev. Neurosci. 2, 99–108 (2001).

    Article  PubMed  CAS  Google Scholar 

  8. Harvey, R. P. Patterning the vertebrate heart. Nat. Rev. Genet. 3, 544–556 (2002).

    Article  PubMed  CAS  Google Scholar 

  9. Li, Q. et al. Cyborg islets: implanted flexible electronics reveal principles of human islet electrical maturation. Preprint at bioRxiv 2024.2003.2018.585551 (2024).

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Liu, J. et al. Syringe-injectable electronics. Nat Nanotechnol. 10, 629–636 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Kim, D. H., Ghaffari, R., Lu, N. & Rogers, J. A. Flexible and stretchable electronics for biointegrated devices. Annu. Rev. Biomed. Eng. 14, 113–128 (2012).

    Article  PubMed  CAS  Google Scholar 

  13. Tang, X., Shen, H., Zhao, S., Li, N. & Liu, J. Flexible brain–computer interfaces. Nat. Electron. 6, 109–118 (2023).

    Article  Google Scholar 

  14. Fan, J. A. et al. Fractal design concepts for stretchable electronics. Nat. Commun. 5, 3266 (2014).

    Article  PubMed  Google Scholar 

  15. Xu, S. et al. Assembly of micro/nanomaterials into complex, three-dimensional architectures by compressive buckling. Science 347, 154–159 (2015).

    Article  PubMed  CAS  Google Scholar 

  16. 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  PubMed  PubMed Central  CAS  Google Scholar 

  17. Feiner, R. & Dvir, T. Tissue–electronics interfaces: from implantable devices to engineered tissues. Nat. Rev. Mater. 3, 17076 (2017).

    Article  Google Scholar 

  18. Fu, T. M., Hong, G., Viveros, R. D., Zhou, T. & Lieber, C. M. Highly scalable multichannel mesh electronics for stable chronic brain electrophysiology. Proc. Natl Acad. Sci. USA 114, E10046–E10055 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Gao, H. et al. Graphene-integrated mesh electronics with converged multifunctionality for tracking multimodal excitation-contraction dynamics in cardiac microtissues. Nat. Commun. 15, 2321 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Quadrato, G., Brown, J. & Arlotta, P. The promises and challenges of human brain organoids as models of neuropsychiatric disease. Nat. Med. 22, 1220–1228 (2016).

    Article  PubMed  CAS  Google Scholar 

  21. Smirnova, L. Biocomputing with organoid intelligence. Nat. Rev. Bioeng. 2, 633–634 (2024).

    Article  CAS  Google Scholar 

  22. Kagan, B. J. et al. In vitro neurons learn and exhibit sentience when embodied in a simulated game-world. Neuron 110, 3952–3969 e3958 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Cai, H. et al. Brain organoid reservoir computing for artificial intelligence. Nat. Electron. 6, 1032–1039 (2023).

    Article  Google Scholar 

  24. Chung, W. G. et al. Recent advances in electrophysiological recording platforms for brain and heart organoids. Adv. NanoBiomed Res. 2, 2200081 (2022).

    Article  CAS  Google Scholar 

  25. 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  PubMed  PubMed Central  CAS  Google Scholar 

  26. Sakaguchi, H. et al. Self-organized synchronous calcium transients in a cultured human neural network derived from cerebral organoids. Stem Cell Reports 13, 458–473 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Yamamoto, Y., Hirose, S., Wuriyanghai, Y., Yoshinaga, D. & Makiyama, T. Electrophysiological analysis of hiPSC-derived cardiomyocytes using a patch-clamp technique. Methods Mol. Biol. 2320, 121–133 (2021).

    Article  PubMed  CAS  Google Scholar 

  28. Xiang, Y. et al. Fusion of regionally specified hPSC-derived organoids models human brain development and interneuron migration. Cell Stem Cell 21, 383–398 e387 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Lewis-Israeli, Y. R. et al. Self-assembling human heart organoids for the modeling of cardiac development and congenital heart disease. Nat. Commun. 12, 5142 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Negraes, P. D. et al. Altered network and rescue of human neurons derived from individuals with early-onset genetic epilepsy. Mol. Psychiatry 26, 7047–7068 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Velasco, S. et al. Individual brain organoids reproducibly form cell diversity of the human cerebral cortex. Nature 570, 523–527 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Di Lullo, E. & Kriegstein, A. R. The use of brain organoids to investigate neural development and disease. Nat. Rev. Neurosci. 18, 573–584 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Chung, J. E. et al. A fully automated approach to spike sorting. Neuron 95, 1381–1394 e1386 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Pachitariu, M., Sridhar, S., Pennington, J. & Stringer, C. Spike sorting with Kilosort4. Nat Methods 21, 914–921 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).

    Article  PubMed  CAS  Google Scholar 

  36. Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 e3529 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Lian, X. et al. Directed cardiomyocyte differentiation from human pluripotent stem cells by modulating Wnt/beta-catenin signaling under fully defined conditions. Nat. Protoc. 8, 162–175 (2013).

    Article  PubMed  CAS  Google Scholar 

  38. Lian, X. et al. Robust cardiomyocyte differentiation from human pluripotent stem cells via temporal modulation of canonical Wnt signaling. Proc. Natl Acad. Sci. USA 109, E1848–E1857 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Yoon, S. J. et al. Reliability of human cortical organoid generation. Nat. Methods 16, 75–78 (2019).

    Article  PubMed  CAS  Google Scholar 

  40. Pollock, S. D., Galicia-Silva, I. M., Liu, M., Gruskin, Z. L. & Alvarez-Dominguez, J. R. Scalable generation of 3D pancreatic islet organoids from human pluripotent stem cells in suspension bioreactors. STAR Protoc. 4, 102715 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Alvarez-Dominguez, J. R. et al. Circadian entrainment triggers maturation of human in vitro islets. Cell Stem Cell 26, 108–122 e110 (2020).

    Article  PubMed  CAS  Google Scholar 

  42. Buccino, A. P. et al. SpikeInterface, a unified framework for spike sorting. Elife 9, e61834 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  44. Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Gala, R. et al. Consistent cross-modal identification of cortical neurons with coupled autoencoders. Nat. Comput. Sci. 1, 120–127 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Liu, R. et al. An AI-cyborg system for adaptive intelligent modulation of organoid maturation. Preprint at bioRxiv https://doi.org/10.1101/2024.12.07.627355 (2024).

Download references

Acknowledgements

We acknowledge the valuable discussions with A.ML., X.Z. and H.S. J.Liu acknowledges the support of NIH/NIMH 1RF1MH123948; NIH/NIDDK 1DP1DK130673; NSF/ECCS-2038603; NIH/NLM 5R01LM014465.

Author information

Authors and Affiliations

Authors

Contributions

Z.L., W.W., R.L., Q.L. and J.Liu. developed the protocol and drafted the manuscript with input from C.H. and J.Lee. All authors read, edited and approved the final manuscript.

Corresponding author

Correspondence to Jia Liu.

Ethics declarations

Competing interests

J. Liu is a co-founder of Axoft, Inc.

Peer review

Peer review information

Nature Protocols thanks Gi Doo Cha 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.

Key references

Li, Q. et al. Nano Lett. 19, 5781–5789 (2019): https://doi.org/10.1021/acs.nanolett.9b02512

Lin, Z. et al. Sci. Adv. 9, eade8513 (2023): https://doi.org/10.1126/sciadv.ade8513

Li, Q. et al. Cell 186, 2002–2017 e2021 (2023): https://doi.org/10.1016/j.cell.2023.03.023

Le Floch, P. et al. Adv. Mater. 34, e2106829 (2022): https://doi.org/10.1002/adma.202106829

Supplementary information

Supplementary Information

Supplementary Figures 1-2.

Reporting Summary

Supplementary Data 1

Photomask of mesh nanoelectronics.

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

Lin, Z., Wang, W., Liu, R. et al. Cyborg organoids integrated with stretchable nanoelectronics can be functionally mapped during development. Nat Protoc 20, 2528–2559 (2025). https://doi.org/10.1038/s41596-025-01147-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41596-025-01147-7

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing