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

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.

Automated summary

The study focuses on extracellular vesicles (EVs) and introduces a high-throughput label-free EV analysis method for cancer diagnosis and monitoring, eliminating interpreter bias. By developing a dedicated device and using dimensional reduction analysis (DRA), a convenient and comprehensive approach for comparing multiple EV spectra is demonstrated. The study shows the high sensitivity and specificity of the dedicated SPARTA system in differentiating cancer and noncancer EVs, and its consistent predictive ability across multiple EV isolations.