Open AccessK-means clustering of coherent Raman spectra from extracellular vesicles visualized by label-free multiphoton imaging
Author(s) -
Yi Sun,
Ethan W. Chen,
Jalen Thomas,
Yuan Liu,
Haohua Tu,
Stephen A. Boppart
Publication year - 2020
Publication title -
optics letters/optics index
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.524
H-Index - 272
eISSN - 1071-2763
pISSN - 0146-9592
DOI - 10.1364/ol.395838
Subject(s) - hyperspectral imaging , autofluorescence , chemical imaging , optics , raman spectroscopy , raman scattering , molecular imaging , extracellular vesicles , medical imaging , materials science , nuclear magnetic resonance , physics , fluorescence , computer science , biology , artificial intelligence , in vivo , microbiology and biotechnology
Extracellular vesicles (EVs) have emerged as potential biomarkers in cancer research and for clinical diagnosis. Little is known, however, about their spatial distributions in tissue and the different subpopulations that may exist. Here we report the use of label-free nonlinear optical imaging techniques to provide spatially resolved chemical information of EVs within untreated tissues. A multimodal nonlinear optical imaging system incorporating multiphoton autofluorescence and hyperspectral coherent anti-Stokes Raman scattering (CARS) imaging was built to visualize the spatial tissue distribution and probe the spectra of EVs. K-means clustering is performed on the CARS spectra from EVs in rat mammary tissues and human breast tumor tissue to reveal both the spatial distribution of EV clusters and their different chemical signatures. Correlations are identified between EV clusters and metabolic information.