Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies

Abstract

Extracellular vesicles (EVs) have been widely investigated as promising biomarkers for the liquid biopsy of diseases, owing to their countless roles in biological systems. Furthermore, with the notable progress of exosome research, the use of label-free surface-enhanced Raman spectroscopy (SERS) to identify and distinguish disease-related EVs has emerged. Even in the absence of specific markers for disease-related EVs, label-free SERS enables the identification of unique patterns of disease-related EVs through their molecular fingerprints. In this review, we describe label-free SERS approaches for disease-related EV pattern identification in terms of substrate design and signal analysis strategies. We first describe the general characteristics of EVs and their SERS signals. We then present recent works on applied plasmonic nanostructures to sensitively detect EVs and notable methods to interpret complex spectral data. This review also discusses current challenges and future prospects of label-free SERS-based disease-related EV pattern identification.

Deep Convolutional Neural Networks as a Unified Solution for Raman Spectroscopy-Based Classification in Biomedical Applications

“…Although commercial confocal Raman microscopes are much less sensitive to weak calibration and reproducibility issues related to spectral baselines compared to custom-built low resolution Raman spectrometers, there are still some major challenges regarding SERS spectra of EVs. Firstly, due to heterogeneous nature of EVs [48] and their lack of chromophore molecules [49], their SERS spectra are very weak and diverse and can be easily mixed with the short band Raman spectra of plasmonic nanoparticles in SERS. Another major challenge is the high variance of the EVs' SERS intensities across the sensor's surface, potentially caused by the wide EV size distribution (from roughly 30 to hundreds of nanometers depending on their subtype) and natural tendency to aggregate in patches.…”

Surface enhanced Raman scattering of extracellular vesicles for cancer diagnostics despite isolation dependent lipoprotein contamination

“… 33–39 The application of SERS to identify EVs in a label-free manner has been recently reviewed. 40 Besides diagnostic evaluation of EVs, spontaneous Raman spectroscopy has been reported to be able to assess purity, yet that study did not consider the effects of lipoprotein contamination, analyze combinations of methods, nor evaluate clinically significant samples acquired from patients. 41 …”

Surface-Enhanced Raman Scattering (SERS) Spectroscopy for Sensing and Characterization of Exosomes in Cancer Diagnosis

“…Nonetheless, the current literature survey on exosome SERS analysis shows a larger number of reports based on direct approaches [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ] than indirect ones [ 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 ]. This apparent anomaly may be explained by taking into considerations the intrinsic exosomal heterogeneity that currently burdens the identification of specific disease-related biomarkers for selective separation and labelling of clinically relevant exosome subpopulations [ 90 , 99 , 102 ]. Thus, the more holistic approach of acquiring the vibrational fingerprint of the whole ensemble of molecular constituents, including known and unknown biomarkers, remains a very valuable and effective sensing strategy as compared to indirect analytical methods that selectively inform about one or few structural features.…”