Fourier Transform Infrared Spectroscopy as a useful tool for the automated classification of cancer cell-derived exosomes obtained under different culture conditions

Exosomes possess great potential as cancer biomarkers in personalized medicine due to their easy accessibility and capability of representing their parental cells. To boost the translational process of exosomes in diagnostics, the development of novel and effective strategies for their label-free and automated characterization is highly desirable. In this context, Fourier Transform Infrared Spectroscopy (FTIR) has great potential as it provides direct access to specific biomolecular bands that give compositional information on exosomes in terms of their protein, lipid and genetic content. Here, we used FTIR spectroscopy in the mid-Infrared (mid-IR) range to study exosomes released from human colorectal adenocarcinoma HT-29 cancer cells cultured in different media. To this purpose, cells were studied in well-fed condition of growth, with 10% of exosome-depleted FBS (EVd-FBS), and under serum starvation with 0.5% EVd-FBS. Our data show the presence of statistically significant differences in the shape of the Amide I and II bands in the two conditions. Based on these differences, we showed the possibility to automatically classify cancer cell-derived exosomes using Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA); we tested the effectiveness of the classifier with a cross-validation approach, obtaining very high accuracy, precision, and recall. Aside from classification purposes, our FTIR data provide hints on the underlying cellular mechanisms responsible for the compositional differences in exosomes, suggesting a possible role of starvation-induced autophagy. [Display omitted] •Exosomes possess great potential as cancer biomarkers for liquid biopsy applications in personalized medicine.•mid-IR spectroscopy allows the label-free characterization of exosomes, detecting clinically relevant modifications.•Combining the mid-IR spectroscopy and statistical learning allows for the automated classification of exosomes.