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Electrical impedance tomography for real-time and label-free cellular viability assays of 3D tumour spheroids

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Abstract

There is currently a need to culture cells in 3D to better mimic the behaviour of cells growing in the natural environment. In parallel, this calls for novel technologies to assess cell growth in 3D cell culture. In this study, we demonstrated both in silico and in vitro that cell viability inside large cell spheroids could be monitored in real time and label-free with electrical impedance tomography (EIT). Simulations using a single shell model and the effective media approximation (EMA) method were performed to prove the performance of EIT on spheroid imaging and viability monitoring. Then in vitro experiments were conducted to measure in real time a loss of cell viability in MCF-7 breast cancer spheroids when exposed to Triton X-100 and validate with conventional biochemical assays. It is shown that EIT has a spatial resolution of 1.14% and it could monitor the cell mortality over 20% of a spheroid under laboratory noise level. The reconstructed conductivity images for cell mortality induced by the chemical are clear and match the result in the cellular metabolic viability assay. Furthermore, the image reconstruction speed in the experiment was less than 0.3 seconds. Taken together, the results show the potential of EIT for non-destructive real-time and label-free cellular assays in the miniature sensor, providing physiological information in the applications of 3D drug screening and tissue engineering.

Graphical abstract: Electrical impedance tomography for real-time and label-free cellular viability assays of 3D tumour spheroids

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Publication details

The article was received on 19 Apr 2018, accepted on 21 Jul 2018 and first published on 24 Jul 2018


Article type: Paper
DOI: 10.1039/C8AN00729B
Citation: Analyst, 2018, Advance Article
  • Open access: Creative Commons BY-NC license
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    Electrical impedance tomography for real-time and label-free cellular viability assays of 3D tumour spheroids

    H. Wu, Y. Yang, P. O. Bagnaninchi and J. Jia, Analyst, 2018, Advance Article , DOI: 10.1039/C8AN00729B

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