## 2 The Label-Free Approach
The time-dependent measurements revealed intense SERS signals from cancer-derived exosomes to that of the SERS signals of exosomes from the normal cell line. The effective discrimination between exosomes derived from healthy and colorectal cancer cell lines was achieved by utilizing super-hydrophobic silicon micropillars coated with silver nanograins, as shown in Figure 2[97]. The exosomes were concentrated in the SERS active area due to the reduced friction coefficient, which results in the greatest signal enhancement of Raman exosome signatures  
Figure 1: Surface-enhanced Raman spectroscopy (SERS) substrates fabrication: (**a**) A nanobowl-like substrate; Reproduced with permission from [95], The Royal Society of Chemistry, 2015; (**b**) a bimetallic substrate designed as an AuNR array surface with self-assembled AgNC. Reproduced with permission from [96], Elsevier, 2017.  
compared to other areas on the SERS substrate. The spectral analysis exhibited differences in the biochemical composition of healthy and tumor exosomes. It was found that the exosomes derived from the CCD841-CoN cell line showed higher SERS intensity peaks related to vibrations of lipids, whereas the peaks assigned to proteins and ribonucle acid bases were dominated in the SERS spectra of exosomes purified from the HCT116 cell line. To distinguish non-small cell lung cancer (NSCLC) and healthy cell lines, [PERSON] et al. combined a label-free SERS approach and statistical pattern analysis, conducting the whole SERS spectra analysis by PCA instead of taking into account only the positions and intensities of peaks. For SERS signals they used spherical gold nanoparticles. Extracting meaningful patterns from obtained Raman spectra and analyzing first and second principal components (PCA1 and PCA respectively) the authors demonstrated a good distinction between exosomes from lung cancer cells (H522 and H1299) and normal alveolar cells, as shown in Figure 3. However, when applied to blood samples of real patients, this approach did not show a clear separation of the two diseased and two healthy patients. This finding suggests that a real blood sample contains lots of exosomes of different origins and only few exosomes are from cancer cells, so the classification is not clear, but the tendency can be seen, thus it is a subject for future studies [98]. Figure 3: Exosome classification by principal component analysis (PCA): (**a**) Principal component scatter plot; (**b**) highlighting points related to different exosome types. Reproduced with permission from [98], American Chemical Society, 2017. Figure 2: A super-hydrophobic substrate: (**a**) A scanning electron microscope (SEM) picture of the substrate made as a periodic hexagonal pattern of cylindrical micropillars; (**b**) a SEM picture of the single silicon pillar with randomly distributed silver nanograins; (**c**) a drop put on the implemented super-hydrophobic surface. Reproduced with permission from [97], Elsevier, 2012. A similar approach based on PCA was used to investigate the origin of the peaks of NSCLC-derived exosomes and several exosomal surface protein markers using gold aggregated clusters on a glass substrate [99]. To reveal differences between normal and cancerous exosomes (derived from PC9 and H1299 cells), the 26 peaks were evaluated based on the first principal component loading data. The obtained peaks were selected as unique Raman bands. Also, ratiometric analysis revealed that 13 out of 20 Raman bands are well correlated with cancerous exosomes ration, therefore they were assigned as unique Raman bands of NSCLC exosomes. To investigate a correlation between the Raman bands of CD9, CD81, EGFR, and EpCAM protein markers and unique Raman bands of NSCLC exosomes, an antibody-coated SERS substrate was used before and after the conjugation of antigens. Proteins CD9 and CD81 are common on the surfaces of all exosomes, while EGFR and EpCAM are diagnostic and prognostic markers for NSCLC exosomes.