Label-Free Imaging of Nanoparticle Uptake Competition in Single Cells by Hyperspectral Stimulated Raman Scattering

Imaging and quantification of nanoparticles in single cells in their most natural condition are expected to facilitate the biotechnological applications of nanoparticles and allow for better assessment of their biosafety risks. However, current imaging modalities either require tedious sample preparation or only apply to nanoparticles with specific physicochemical characteristics. Here, the emerging hyperspectral stimulated Raman scattering (SRS) microscopy, as a label-free and nondestructive imaging method, is used for the first time to investigate the subcellular distribution of nanoparticles in the protozoan Tetrahymena thermophila. The two frequently studied nanoparticles, polyacrylate-coated α-Fe₂ O₃ and TiO₂ , are found to have different subcellular distribution pattern as a result of their dissimilar uptake routes. Significant uptake competition between these two types of nanoparticles is further discovered, which should be paid attention to in future bioapplications of nanoparticles. Overall, this study illustrates the great promise of hyperspectral SRS as an analytical imaging tool in nanobiotechnology and nanotoxicology.

A machine learning framework to analyze hyperspectral stimulated Raman scattering microscopy images of expressed human meibum

We develop and discuss a methodology for batch-level analysis of hyperspectral stimulated Raman scattering (hsSRS) data sets of human meibum in the CH-stretching vibrational range. The analysis consists of two steps. The first step uses a training set (n=19) to determine chemically meaningful reference spectra that jointly constitute a basis set for the sample. This procedure makes use of batch-level vertex component analysis (VCA), followed by unsupervised k-means clustering to express the data set in terms of spectra that represent lipid and protein mixtures in changing proportions. The second step uses a random forest classifier to rapidly classify hsSRS stacks in terms of the pre-determined basis set. The overall procedure allows a rapid quantitative analysis of large hsSRS data sets, enabling a direct comparison among samples using a single set of reference spectra. We apply this procedure to assess 50 specimens of expressed human meibum, rich in both protein and lipid, and show that the batch-level analysis reveals marked variation among samples that potentially correlate with meibum health quality.