Ovarian cancer has become one of the most common malignant tumors threatening female genital health. Recently, biomechanical properties of single cell have been reported as a potential index for early cancer detection. In this study, the viscoelastic properties of ovarian cancer cells were determined using stress-relaxation approach by atomic force microscopy (AFM). Individual force-time curves were recorded at maximum loads of 0.5, 1 and 2 nN, and the stressrelaxation time was 2 s for all the stress-relaxation measurements. A theoretical method of stress relaxation was proposed and the viscoelasticity of the cells was obtained according to a linear solid model. The results showed that the values of average viscosity of ovarian cancer cells were respectively 54.0±6.5 Pa-s, 100.5±13.2 Pa-s and 113.6±13.2 Pa-s using the three different loading forces from 0.5 nN to 2 nN. Furthermore, the values of average elasticity modulus were respectively 657.0±69.9 Pa, 730.9±67.0 Pa, 895.0±71.3 Pa. In conclusion, the viscoelasticity properties of the cells increased as the loading force increased from 0.5 nN to 2 nN. Our study indicates that the viscoelasticity of the ovarian cancer cells can be acquired by stress-relaxation approach and the loading force is an important factor that can affect the cellular viscoelasticity. It will shed new light on cancer early detection based on cellular viscoelasticity index at single cell level.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.