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

Extracellular vesicles (EVs) secreted by cancer cellsprovide an important insight into cancer biology and could beleveraged to enhance diagnostics and disease monitoring. Thispaper details a high-throughput label-free extracellular vesicleanalysis approach to study fundamental EV biology, towarddiagnosis and monitoring of cancer in a minimally invasivemanner and with the elimination of interpreter bias. We presentthe next generation of our single particle automated Ramantrapping analysisSPARTAsystem through the development ofa dedicated standalone device optimized for single particleanalysis of EVs. Our visualization approach, dubbed dimensionalreduction analysis (DRA), presents a convenient and comprehensive method of comparing multiple EV spectra. Wedemonstrate that the dedicated SPARTA system can differentiate between cancer and noncancer EVs with a high degree ofsensitivity and specificity (>95% for both). We further show that the predictive ability of our approach is consistent acrossmultiple EV isolations from the same cell types. Detailed modeling reveals accurate classification between EVs derived fromvarious closely related breast cancer subtypes, further supporting the utility of our SPARTA-based approach for detailed EVprofiling