Label-Free Identification of Exosomes using Raman Spectroscopy and Machine Learning

Jul 1, 2013ยท
Ugur Parlatan
,
Mehmet Ozgun Ozen
,
Ibrahim Kecoglu
Batuhan Koyuncu
Batuhan Koyuncu
,
Hulya Torun
,
Davod Khalafkhany
,
Irem Loc
,
Mehmet Giray Ogut
,
Fatih Inci
,
Demir Akin
,
Ihsan Solaroglu
,
Nesrin Ozoren
,
Mehmet Burcin Unlu
,
Utkan Demirci
ยท 0 min read
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.
Type
Publication
In Small