Open AccessSingle Particle Automated Raman Trapping Analysis of Breast Cancer Cell-Derived Extracellular Vesicles as Cancer Biomarkers
Author(s) -
Jelle Penders,
Anika Nagelkerke,
Eoghan M. Cunnane,
Simon Vilms Pedersen,
Isaac J. Pence,
R. Charles Coombes,
Molly M. Stevens
Publication year - 2021
Publication title -
acs nano
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.554
H-Index - 382
eISSN - 1936-086X
pISSN - 1936-0851
DOI - 10.1021/acsnano.1c07075
Subject(s) - extracellular vesicles , breast cancer , cancer , nanotechnology , extracellular vesicle , computational biology , raman spectroscopy , cancer cell , computer science , microvesicles , biology , cancer research , biological system , microbiology and biotechnology , materials science , physics , biochemistry , genetics , microrna , gene , optics
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.