Single Particle Automated Raman Trapping Analysis of Breast Cancer Cell-Derived Extracellular Vesicles as Cancer Biomarkers

Abstract

Extracellular vesicles (EVs) secreted by cancer cells provide an important insight into cancer biology and could be leveraged to enhance diagnostics and disease monitoring. This paper details a high-throughput label-free extracellular vesicle analysis approach to study fundamental EV biology, toward diagnosis and monitoring of cancer in a minimally invasive manner and with the elimination of interpreter bias. We present the next generation of our single particle automated Raman trapping analysisSPARTAsystem through the development of a dedicated standalone device optimized for single particle analysis of EVs. Our visualization approach, dubbed dimensional reduction analysis (DRA), presents a convenient and comprehensive method of comparing multiple EV spectra. We demonstrate that the dedicated SPARTA system can differentiate between cancer and noncancer EVs with a high degree of sensitivity and specificity (>95% for both). We further show that the predictive ability of our approach is consistent across multiple EV isolations from the same cell types. Detailed modeling reveals accurate classification between EVs derived from various closely related breast cancer subtypes, further supporting the utility of our SPARTA-based approach for detailed EV profiling.

Artificial Intelligent Label-Free SERS Profiling of Serum Exosomes for Breast Cancer Diagnosis and Postoperative Assessment

“…Thus, the SERS detection of exosomes could be clinically suitable for assessment of therapeutic responses and cancer prognosis. Recent studies have exemplified the utility of label-free or label-based SERS for exosome-based cancer diagnosis. − However, these studies mainly target a single exosomal biomarker or a panel of exosomal biomarkers, consequently leading to the partial missing of molecular information of paramount importance. Additionally, subtle variations of SERS signatures from different cancer subtypes impose a major challenge in exosome-based diagnosis in a label-free manner .…”

The Role of Extracellular Vesicles in Osteoporosis: A Scoping Review

“…According to Daniel Liu et al, plasma EVs contain unannotated small RNA clusters, so they are suitable to be biomarkers for the detection of early hepatocellular carcinoma. Likewise, the abnormal content of EVs may could be used as the markers of age-related osteoporosis [ 22 , 29 , 30 , 31 ] ( Table 1 ).…”

Recent advances in optical label-free characterization of extracellular vesicles

“…Raman spectroscopy has been employed to characterize EVs in bulk [ 158 – 160 ] as well as single EVs [ 155 – 166 ]. Bulk characterization of EVs via Raman spectroscopy showed differences in tissue specific biochemical composition of EVs and the ability to distinguish EVs based on their Raman spectra with high accuracy (>90%) [ 161 , 159 ].…”

“…Such single-particle analysis techniques are essential in the field of biomarkers discovery due to their ability to provide information about the protein, lipid, and nucleic acid content of a single EV [ 161 , 163 , 165 ]. Moreover, the combination of single EV Raman spectroscopy with machine learning algorithms has great potential to be used as a rapid and sensitive diagnostic tool [ 155 , 161 , 164 , 166 ].…”