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Published July 7, 2009 | Published + Supplemental Material
Journal Article Open

Tunable interplay between epidermal growth factor and cell–cell contact governs the spatial dynamics of epithelial growth


Contact-inhibition of proliferation constrains epithelial tissue growth, and the loss of contact-inhibition is a hallmark of cancer cells. In most physiological scenarios, cell–cell contact inhibits proliferation in the presence of other growth-promoting cues, such as soluble growth factors (GFs). How cells quantitatively reconcile the opposing effects of cell–cell contact and GFs, such as epidermal growth factor (EGF), remains unclear. Here, using quantitative analysis of single cells within multicellular clusters, we show that contact is not a "master switch" that overrides EGF. Only when EGF recedes below a threshold level, contact inhibits proliferation, causing spatial patterns in cell cycle activity within epithelial cell clusters. Furthermore, we demonstrate that the onset of contact-inhibition and the timing of spatial patterns in proliferation may be reengineered. Using micropatterned surfaces to amplify cell–cell interactions, we induce contact-inhibition at a higher threshold level of EGF. Using a complementary molecular genetics approach to enhance cell–cell interactions by overexpressing E-cadherin also increases the threshold level of EGF at which contact-inhibition is triggered. These results lead us to propose a state diagram in which epithelial cells transition from a contact-uninhibited state to a contact-inhibited state at a critical threshold level of EGF, a property that may be tuned by modulating the extent of cell–cell contacts. This quantitative model of contact-inhibition has direct implications for how tissue size may be determined and deregulated during development and tumor formation, respectively, and provides design principles for engineering epithelial tissue growth in applications such as tissue engineering.

Additional Information

Copyright ©2009 by the National Academy of Sciences. Edited by Tony Hunter, The Salk Institute for Biological Studies, La Jolla, CA, and approved May 14, 2009 (received for review December 11, 2008). This article is a PNAS Direct Submission. We thank members of the Asthagiri group for helpful discussions, An-Tu Xie for his involvement in the early stages of image analysis, and Celeste Nelson and Casim Sarkar for comments on the manuscript. This work was supported by the Concern Foundation for Cancer Research and the Jacobs Institute for Molecular Engineering for Medicine. Author contributions: J.-H.K. and A.R.A. designed research; J.-H.K., K.K., and N.A.G. performed research; J.-H.K. and K.K. contributed new reagents/analytic tools; J.-H.K. and A.R.A. analyzed data; and J.-H.K. and A.R.A. wrote the paper. The authors declare no conflict of interest. This article contains supporting information online at www.pnas.org/cgi/content/full/0812651106/DCSupplemental.

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Published - Kim2009p5066P_Natl_Acad_Sci_Usa.pdf

Supplemental Material - Asthagiri0812651106SI.pdf


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