Advanced Materials · 2024 · 22 citations · 45 references
EngineeringBioengineeringBiomedical EngineeringBiosensorsElectrophysiologyMicroelectromechanical SystemsBiomedical InstrumentationImplantable Biomedical DevicesImplantable MicrodevicesMicroelectrode ArraysMicroscale BiosensorsOrganoid EngineeringCardiac OrganoidsElectrophysiological MeasurementElectrophysiological RecordingsMicroelectrode Array RecordingMultielectrode Array TechnologyOrganoidsStretchable MaterialsThree-dimensional Electrode Arrays
Organoids are 3D biological models that recapitulate the complex structures and functions of human organs. Despite the rapid growth in the generation of organoids, in vitro assay tools are still limited to 2D forms. Thus, a comprehensive and continuous functional evaluation of the electrogenic organoids remains a challenge. Here, a highly stretchable 3D multielectrode array (sMEA) with protruding microelectrodes is presented for functional evaluation of electrogenic organoids. The optimized serpentine structures with bridge structures cover the surface of the organoids conformally even in immersion. The protruding microelectrodes form a stable contact with the organoids and allow electrophysiological recordings with high signal-to-noise ratio (SNR). sMEAs are fabricated in wafer-scale for repeatable, scalable, and mass production and packed into an easy-to-use, user-friendly, and robust microwell for fast dissemination of technology. The versatility of sMEA is validated by measuring electrophysiological signals from cardiac spheroids and midbrain organoids with a wide range of sizes from 500 to 1500 µm. Also, electrophysiological signals recorded with high SNR enable functional evaluation of the effects of drugs. The proposed sMEA with high SNR and user-friendly interface could be the key player in high-throughput drug screening, 3D spatiotemporal mapping of electrogenic organoids, and standardization of protocols for quality assessment.
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Cerebral organoids model human brain development and microcephaly
Madeline A. Lancaster, Magdalena Renner, Carol-Anne Martin et al. · Nature · 2013 · 5.4K citations · Full text
Kip A. Ludwig, Jeffrey D. Uram, Junyan Yang et al. · Journal of Neural Engineering · 2006 · 636 citations · Full text