Label‐Free Identification of Exosomes using Raman Spectroscopy and Machine Learning. Issue 9 (15th January 2023) - Parlatan, Ugur; Ozen, Mehmet Ozgun; Kecoglu, Ibrahim; Koyuncu, Batuhan; Torun, Hulya; Khalafkhany, Davod; Loc, Irem; Ogut, Mehmet Giray; Inci, Fatih; Akin, Demir; Solaroglu, Ihsan; Ozoren, Nesrin; et al.
Abstract: Exosomes, nano‐sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface‐enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label‐free Raman spectroscopy method's prediction ratio correlates with the ratio of HT‐1080 exosomes in the mixture. This machine learning‐assisted SERS method enables a new direction through label‐free investigation of EV preparations by differentiating cancer cell‐derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer. Abstract : It is shown that the EVs isolated with the exoTIC technology have different molecular signatures measured by surface‐enhanced Raman spectroscopy. The cellular origins of the EVs with the help of a neural network algorithm are predicted. Besides, using spectral analysis, it is shown that the chemical signatures from nucleic acids differentiate for different exosome types.