| Literature DB >> 27536877 |
Elliott D SoRelle1,2,3,4, Orly Liba1,2,4,5, Jos L Campbell1,6, Roopa Dalal7, Cristina L Zavaleta1,6, Adam de la Zerda1,2,3,4,5.
Abstract
Nanoparticles are used extensively as biomedical imaging probes and potential therapeutic agents. As new particles are developed and tested in vivo, it is critical to characterize their biodistribution profiles. We demonstrate a new method that uses adaptive algorithms for the analysis of hyperspectral dark-field images to study the interactions between tissues and administered nanoparticles. This non-destructive technique quantitatively identifies particles in ex vivo tissue sections and enables detailed observations of accumulation patterns arising from organ-specific clearance mechanisms, particle size, and the molecular specificity of nanoparticle surface coatings. Unlike nanoparticle uptake studies with electron microscopy, this method is tractable for imaging large fields of view. Adaptive hyperspectral image analysis achieves excellent detection sensitivity and specificity and is capable of identifying single nanoparticles. Using this method, we collected the first data on the sub-organ distribution of several types of gold nanoparticles in mice and observed localization patterns in tumors.Entities:
Keywords: biodistribution; biomedical Imaging; cancer biology; histology; human biology; hyperspectral Imaging; medicine; microscopy; mouse; nanoparticles
Year: 2016 PMID: 27536877 PMCID: PMC5042654 DOI: 10.7554/eLife.16352
Source DB: PubMed Journal: Elife ISSN: 2050-084X Impact factor: 8.140