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A soft and ultrasensitive force sensing diaphragm for probing cardiac organoids instantaneously and wirelessly

Time-lapse mechanical properties of stem cell derived cardiac organoids are important biological cues for understanding contraction dynamics of human heart tissues, cardiovascular functions and diseases. However, it remains difficult to directly, instantaneously and accurately characterize such mech...

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Detalles Bibliográficos
Autores principales: Lyu, Quanxia, Gong, Shu, Lees, Jarmon G., Yin, Jialiang, Yap, Lim Wei, Kong, Anne M., Shi, Qianqian, Fu, Runfang, Zhu, Qiang, Dyer, Ash, Dyson, Jennifer M., Lim, Shiang Y., Cheng, Wenlong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9700778/
https://www.ncbi.nlm.nih.gov/pubmed/36433978
http://dx.doi.org/10.1038/s41467-022-34860-y
Descripción
Sumario:Time-lapse mechanical properties of stem cell derived cardiac organoids are important biological cues for understanding contraction dynamics of human heart tissues, cardiovascular functions and diseases. However, it remains difficult to directly, instantaneously and accurately characterize such mechanical properties in real-time and in situ because cardiac organoids are topologically complex, three-dimensional soft tissues suspended in biological media, which creates a mismatch in mechanics and topology with state-of-the-art force sensors that are typically rigid, planar and bulky. Here, we present a soft resistive force-sensing diaphragm based on ultrasensitive resistive nanocracked platinum film, which can be integrated into an all-soft culture well via an oxygen plasma-enabled bonding process. We show that a reliable organoid-diaphragm contact can be established by an ‘Atomic Force Microscope-like’ engaging process. This allows for instantaneous detection of the organoids’ minute contractile forces and beating patterns during electrical stimulation, resuscitation, drug dosing, tissue culture, and disease modelling.