Automated Nanoplasmonic Analysis of Spherical Nucleic Acids Clusters in Single Cells.

Abstract

Spherical nucleic acids (SNAs) have been extensively used in the field of biosensing, drug delivery, and theranostics. Precise engineering of SNAs and their clinical application require better understanding of their cellular internalization process. We demonstrate a colorimetry-based algorithm that can analyze the aggregation states of SNAs clusters on the basis of the changes of plasmonic colors of SNAs. The dark-field microscopy (DFM) images of cytoplasmic region of single cells are imported as raw data. All the image spots are analyzed in the interference reduction process, and the clustering states of target image spots are assigned on the basis of the distribution of coordinates of all the pixels in the CIE map. This method provides faster analysis on clustering states of extracellular and intracellular SNAs with good accuracy. Moreover, the clustering states of SNAs in 20 single cells (generally >1000) can be efficiently distinguished within 200 s. Therefore, our method provides an automatic, quantitative, objective, and repeatable way to analyze SNAs aggregations, and shows good application potential in robust and quantitative nanoplasmonic analysis in single cells.