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Nanoparticles have been explored extensively as potential biomedical imaging and therapeutic agents. One critical aspect of in vivo nanoparticle use is the characterization of biodistribution profiles. Such studies improve our understanding of particle uptake, specificity, and clearance mechanisms. Currently, the most prevalent nanoparticle biodistribution methods provide either aspatial quantification of whole-organ particle accumulation or nanometerresolution images of uptake in single cells. Few existing techniques are well-suited to study particle uptake on the micron to millimeter scales relevant to sub-tissue physiology. Here we demonstrate a new method called Hyperspectral Microscopy with Adaptive Detection (HSM-AD) that uses machine learning classification of hyperspectral dark-field images to study interactions between tissues and administered nanoparticles. This label-free, non-destructive method enables quantitative particle identification in histological sections and detailed observations of sub-organ accumulation patterns consistent with organ-specific clearance mechanisms, particle size, and the molecular specificity of the nanoparticle surface. Unlike studies with electron microscopy, HSM-AD is readily applied for large fields of view. HSM-AD achieves excellent detection sensitivity (99.4%) and specificity (99.7%) and can identify single nanoparticles. To demonstrate HSM-AD’s potential for novel nanoparticle uptake studies, we collected the first data on the sub-organ localization of large gold nanorods (LGNRs) in mice. We also observed differences in particle accumulation and localization patterns in tumors as a function of conjugated molecular targeting moieties. Thus, HSM-AD affords new degrees of detail for the study of nanoparticle uptake at physiological scales. HSM-AD may offer an auxiliary or alternative approach to study the biodistribution profiles of existing and novel nanoparticles.
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