Imperial College London
Alternate

Professor Molly Stevens

Faculty of Engineering, Department of Materials

Professor of Biomedical Materials and Regenerative Medicine
 
 
 
//

Contact

 

+44 (0)20 7594 6804m.stevens

 
 
//

Location

 

208Royal School of MinesSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Penders:2021:10.1021/acsnano.1c07075,
author = {Penders, J and Nagelkerke, A and Cunnane, EM and Pedersen, S and Pence, I and Coombes, RC and Stevens, M},
doi = {10.1021/acsnano.1c07075},
journal = {ACS Nano},
pages = {18192--18205},
title = {Single particle automated Raman trapping analysis of breast cancer cell-derived extracellular vesicles as cancer biomarkers},
url = {http://dx.doi.org/10.1021/acsnano.1c07075},
volume = {15},
year = {2021}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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 analysisSPARTAsystem 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.
AU  - Penders,J
AU  - Nagelkerke,A
AU  - Cunnane,EM
AU  - Pedersen,S
AU  - Pence,I
AU  - Coombes,RC
AU  - Stevens,M
DO  - 10.1021/acsnano.1c07075
EP  - 18205
PY  - 2021///
SN  - 1936-0851
SP  - 18192
TI  - Single particle automated Raman trapping analysis of breast cancer cell-derived extracellular vesicles as cancer biomarkers
T2  - ACS Nano
UR  - http://dx.doi.org/10.1021/acsnano.1c07075
UR  - https://pubs.acs.org/doi/10.1021/acsnano.1c07075
UR  - http://hdl.handle.net/10044/1/92758
VL  - 15
ER  -
Download