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Updated: Jan 10, 2026

Electroporation of Sliced Human Cortical Organoids for Studies of Gene Function
Published on: November 29, 2024
Elastic and viscoelastic properties of human cortical organoids
Yasaman Samei1, Ingrid Cheung2, Paraskevi Papavasileiou1
1Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL, USA.
Abstract:
Human cortical organoids are becoming increasingly popular as models of disease and development in the human brain and as tools for energy-efficient computing. However, their mechanical properties, which are critical to understanding how they self-organize and how they interact with synthetic devices, remain poorly understood. In this study, we used microindentation to quantify the size, elastic properties, and viscoelastic properties of cortical organoids derived from three human induced pluripotent stem cell (hiPSC) lines. The organoids exhibited low stiffness (mean effective Young's modulus: 285 ± 148 Pa) and were highly viscoelastic, with significant reductions in effective shear modulus between 0.5 and 180 s. Mechanical properties varied by cell line and batch, underscoring the need to consider biological variability in experimental and computational studies. Organoids were significantly more compliant than the synthetic materials commonly used in organoid-on-chip systems, resembling hydrogels like Matrigel in stiffness. These findings will increase the accuracy of computational models of cortical organoids and support the rational design of microphysiological systems, electrode interfaces, and injury models involving these tissues. STATEMENT OF SIGNIFICANCE: This study provides the first measurements of the viscoelastic properties of human cortical organoids. The findings reveal that organoids are highly compliant and time-dependent in their mechanical behavior, with variability across cell lines and batches within each cell line. These data are critical to understanding how organoids assume their form and how they interact with human-made devices in biomedical and energy-efficient computing applications.
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