Finite element modeling to analyze TEER values across silicon nanomembranes View Full Text


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Article Info

DATE

2018-03

AUTHORS

Tejas S. Khire, Barrett J. Nehilla, Jirachai Getpreecharsawas, Maria E. Gracheva, Richard E. Waugh, James L. McGrath

ABSTRACT

Silicon nanomembranes are ultrathin, highly permeable, optically transparent and biocompatible substrates for the construction of barrier tissue models. Trans-epithelial/endothelial electrical resistance (TEER) is often used as a non-invasive, sensitive and quantitative technique to assess barrier function. The current study characterizes the electrical behavior of devices featuring silicon nanomembranes to facilitate their application in TEER studies. In conventional practice with commercial systems, raw resistance values are multiplied by the area of the membrane supporting cell growth to normalize TEER measurements. We demonstrate that under most circumstances, this multiplication does not 'normalize' TEER values as is assumed, and that the assumption is worse if applied to nanomembrane chips with a limited active area. To compare the TEER values from nanomembrane devices to those obtained from conventional polymer track-etched (TE) membranes, we develop finite element models (FEM) of the electrical behavior of the two membrane systems. Using FEM and parallel cell-culture experiments on both types of membranes, we successfully model the evolution of resistance values during the growth of endothelial monolayers. Further, by exploring the relationship between the models we develop a 'correction' function, which when applied to nanomembrane TEER, maps to experiments on conventional TE membranes. In summary, our work advances the the utility of silicon nanomembranes as substrates for barrier tissue models by developing an interpretation of TEER values compatible with conventional systems. More... »

PAGES

11

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10544-017-0251-7

DOI

http://dx.doi.org/10.1007/s10544-017-0251-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100196451

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29305767


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