DNA-Gold Nanoparticle Reversible Networks Grown on Cell Surface Marker Sites: Application in Diagnostics

Kyuwan Lee, Purdue University
Vladimir P. Drachev, Birck Nanotechnology Center, Purdue University
Joseph Irudayaraj, Birck Nanotechnology Center, Purdue University

Abstract

Effective identification of breast cancer stem cells (CSC) benefits from a multiplexed approach to detect cell surface markers that can distinguish this subpopulation, which can invade and proliferate at sites of metastasis. We present a new approach for dual-mode sensing based on targeting using pointer and signal enhancement using enhancer particle networks for detection by surface plasmon resonance (SPR) and surface-enhanced Raman scattering (SERS). We demonstrate our concept to detect cell surface markers, CD44 and CD24, in three breast cancer cell lines to identify a CD44(+)/CD24(-) subpopulation of CSCs. The designed network structure can be well-controlled and has improved sensitivity compared to conventional approaches with ability to detect a single target on the membrane of a living cell. We have also developed a fractal approach to model the dimension of the network structure and developed an empirical relationship to estimate the number of particles in the network and its size. The empirical equation was validated with experiments and finite-difference time-domain simulations, and the cell phenotyping results were found to be in good agreement with published data from conventional sorting by flow cytometry.

Discipline(s)

Nanoscience and Nanotechnology