Automatic detection of nanoparticles using hyperspectral microscopy
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 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.
Image shows, from left to right: bright field microscopy image of stained section, dark-field microscopy, hyperspectral microscopy, detection of the nanoparticles, shown in orange.
This work was published in eLife in Aug 2016: https://elifesciences.org/content/5/e16352.
Please cite our paper if you use this code.
"A hyperspectral method to assay the microphysiological fates of nanomaterials in histological samples".
ED SoRelle, O Liba, JL Campbell, R Dalal, CL Zavaleta, A Zerda, eLife, 2016
Note: the code is based on images acquired on a [Cytoviva](http://www.cytoviva.com/) microscope that uses Envi for hyperspectral imaging.
This project includes Envi reading code from Matlab File Exchange:
https://www.mathworks.com/matlabcentral/fileexchange/27172-envi-file-reader-writer