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. 2016 Aug 18;5:e16352. doi: 10.7554/eLife.16352

Figure 1. Overview of nanoparticle biodistribution analysis with HSM-AD.

(a) Large gold nanorods (LGNRs, ~100 × 30 nm) exhibiting near infrared plasmon resonance were synthesized, functionalized, and intravenously injected into live nude mice. (b,c) 24 hr post-injection, the animals were euthanized and tissues were resected and prepared as normal histological sections for characterization with bright-field (b) and dark-field microscopy (c) neither of which was able to visualize the distribution of the LGNRs. (d) The same section was then imaged with hyperspectral microscopy, which showed clear signs of LGNRs accumulation (denoted by red hues) in various areas of the tissue and exhibited spectral peaks matching the LGNR plasmon resonance. (e) We then trained an adaptive clustering algorithm for spectral identification of LGNRs with hyperspectral images from injected mice. The algorithm identified several characteristic spectra representing the tissue and the H&E staining, as well as one unique spectrum representing the LGNRs (depicted in orange), altogether representing a library of 5 spectra. Once a spectral cluster library is produced from the training dataset, images of unknown tissue samples can by analyzed for the presence of LGNRs via automated classification. (f) The resulting HSM-AD images depict the location of all points within the sample that exhibit the LGNR spectrum (orange for LGNRs, grayscale for tissue).

DOI: http://dx.doi.org/10.7554/eLife.16352.003

Figure 1.

Figure 1—figure supplement 1. Diagram of the CytoViva microscope used for dark-field and hyperspectral image acquisition.

Figure 1—figure supplement 1.

DOI: http://dx.doi.org/10.7554/eLife.16352.004
Figure 1—figure supplement 2. Image segmentation, including method for dynamic threshold determination.

Figure 1—figure supplement 2.

(a) A histogram of the peak intensities of each pixel in an image can be roughly divided into background (noise), tissue scattering, and LGNR and bright tissue scattering. (b) Detection of minHist and peakHist, as described in Methods. (c,d) A characteristic hyperspectral image (c) and its corresponding segmentation map (d) showing background (blue), tissue (cyan), and potential LGNRs and bright tissue (yellow).
DOI: http://dx.doi.org/10.7554/eLife.16352.005
Figure 1—figure supplement 3. Detailed flowchart of steps used in HSM-AD algorithm.

Figure 1—figure supplement 3.

DOI: http://dx.doi.org/10.7554/eLife.16352.006
Figure 1—figure supplement 4. Typical cluster results for pixel classification in an image of tissues with injected LGNRs.

Figure 1—figure supplement 4.

For a given image (>250,000 pixels), each pixel is binned into one of the five spectral clusters. This plot depicts the means (solid lines) and standard deviations (shaded areas) of all classified pixel spectra. Although the adaptive clustering algorithm is agnostic with respect to defining the spectral clusters (with the exception of chromatic aberration, which is user-defined), the learned clusters can be readily correlated to the major scattering components present in each sample, i.e., hematoxylin-stained nuclei (green), eosin-stained cytoplasm (blue), and LGNRs (red).
DOI: http://dx.doi.org/10.7554/eLife.16352.007