On the basis of the characteristics of EVs in the THG-contrast images, an automated segmentation algorithm (Fig. 3A) was developed to extract the EV signal from the background and subsequently quantify EV density. The principles of this segmentation algorithm relied on the spatial and nonlinear optical properties of the EVs: that they are small (40 to 2000 nm) (6), point-like, and generate exceptionally strong THG signal due to the good phase-matching condition provided by their large surface-to-volume interface ratio (28). Therefore, an intensity threshold was used to segment the EVs from the background (Fig. 3A). This threshold was automatically set to be the pixel intensity value at a fixed percentage out of the intensity histogram generated from each THG image, and this specific percentage for all images was deliberately determined to leave only the in-focus EVs while suppressing the background noise. By applying this algorithm to THG-contrast images of the tumor microenvironment (Fig. 2C), binary images (Fig. 2D) were generated to reveal the spatial distribution and density of the EVs. These black points in the binary image were subsequently quantified to represent the density of EVs in each FOV.

To validate this imaging and segmentation method for EV detection and quantification, we acquired THG-contrast images of EVs purified from human cancer cell lines with a known density of 3 × 1010 ml−1, measured by a standardized technique (40) with a commercial instrument (NS3000, NanoSight Ltd.). A representative example of a THG-contrast image of purified EVs was processed to highlight the EVs (Fig. 3B) using this segmentation algorithm. Considering the axial resolution and imaging FOV, the three-dimensional imaging volume of each image of purified EVs was approximately 100 μm by 100 μm by 1 μm = 10−8 ml. The density of EVs was then calculated to be 1.5 × 1010 ml−1 using the EV counts (152 ± 10), quantified from five THG-contrast images of purified EVs. To explain the density discrepancy, it is likely that some EVs underwent refractive index matching due to the diffusion and permeation of glycerol through the EV membrane and, thus, ceased to provide the phase-matching condition necessary for THG signal generation. Nevertheless, the EV density quantified from the THG images using the segmentation algorithm was of the same magnitude as the known density of EVs measured by the commercial instrument. Furthermore, the isolated EVs were mixed with human cells in culture. The drastic increase of EV density after mixing was identified by the quantification algorithm based on the THG images (fig. S2) and validated by the NanoSight measurement. Therefore, the EV densities quantified from the intraoperative THG-contrast images faithfully represented the distribution and density of EVs in the tumor microenvironment.

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