Owing to the role of exosome as a cargo for intercellular communication, especially in cancer metastasis, the evidence has been consistently accumulated that exosomes can be used as a noninvasive indicator of cancer. Consequently, several studies applying exosome have been proposed for cancer diagnostic methods such as ELISA assay. However, it has been still challenging to get reliable results due to the requirement of a labeling process and high concentration of exosome. Here, we demonstrate a label-free and highly sensitive classification method of exosome by combining surface-enhanced Raman scattering (SERS) and statistical pattern analysis. Unlike the conventional method to read different peak positions and amplitudes of a spectrum, whole SERS spectra of exosomes were analyzed by principal component analysis (PCA). By employing this pattern analysis, lung cancer cell derived exosomes were clearly distinguished from normal cell derived exosomes by 95.3% sensitivity and 97.3% specificity. Moreover, by analyzing the PCA result, we could suggest that this difference was induced by 11 different points in SERS signals from lung cancer cell derived exosomes. This result paved the way for new real-time diagnosis and classification of lung cancer by using exosome as a cancer marker.

CML-derived exosomes could act as novel targets for the detection of the BCR-ABL transcript.

Isolation of pure extracellular vesicles (EVs), especially from blood, has been a major challenge in the field of EV research. The presence of lipoproteins and soluble proteins often hinders the isolation of high purity EVs upon utilization of conventional separation methods. To circumvent such problems, we designed a single-step dual size-exclusion chromatography (dSEC) column for effective isolation of highly pure EVs from bone marrow derived human plasma. With an aim to select appropriate column design parameters, we analyzed the physiochemical properties of the major substances in bone marrow derived plasma, which include EVs, lipoproteins, and soluble proteins. Based on these findings, we devised a novel dSEC column with two different types of porous beads sequentially stacked each other for efficient separation of EVs from other contaminants. The newly developed dSEC columns exhibited better performance in isolating highly pure EVs from AML plasma in comparison to conventional isolation methods.

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