Abstract
Nanoparticles (NPs) have been used in drug delivery therapies, medical diagnostic strategies, and as current Covid-19 vaccine carriers. Many microscope-based imaging systems have been introduced to facilitate detection and visualization of NPs. Unfortunately, none can differentiate the core and the shell of NPs. Spectral imaging has been used to distinguish a drug molecule and its metabolite. We have recently integrated this technology to a resolution of 9 nm by using artificial intelligence-driven analyses. Such a resolution allowed us to collect many robust datapoints for each pixel of an image. Our analyses could recognize 45 spectral points within a pixel to detect unlabeled Ag-NPs and Au-NPs in single live cells and tissues (liver, heart, spleen and kidneys). The improved resolution and software provided a more specific fingerprinting for each single molecule, allowing simultaneous analyses of 990 complex interactions from the 45 points for each molecule within a pixel of an image. This in turn allowed us to detect surface-functionalization of Ag-NPs to distinguish the core from the shell of Ag-NPs for the first time. Our studies were validated using various laborious and time-consuming conventional techniques. We propose that spectral imaging has tremendous potential to study NP localization and identification in biological samples at a high temporal and spatial resolution, based primarily on spectral identity information.