Label-free Classification of Bacterial Extracellular Vesicles by Combining Nanoplasmonic Sensors with Machine Learning
preprintposted on 26.08.2021, 22:03 by Mohammadrahim KazemzadehMohammadrahim Kazemzadeh, Colin HiseyColin Hisey, Priscila Dauros SingorenkoPriscila Dauros Singorenko, Simon SwiftSimon Swift, Kamran ZargarKamran Zargar, Peter XuPeter Xu, Neil BroderickNeil Broderick
Bacterial extracellular vesicles (EVs) are nanoscale lipid-enclosed packages that are released by bacteria cells and shuttle various biomolecules between bacteria or host cells. They are implicated in playing several important roles, from infectious disease progression to maintaining proper gut health, however the tools available to characterise and classify them are limited and impractical for many applications. Surface-enhanced Raman Spectroscopy (SERS) provides a promising means of rapidly fingerprinting bacterial EVs in a label-free manner by taking advantage of plasmonic resonances that occur on nanopatterned surfaces, effectively amplifying the inelastic scattering of incident light. In this study, we demonstrate that by applying machine learning algorithms to bacterial EV SERS spectra, EVs from cultures of the same bacterial species Escherichia coli can be classified by strain, culture conditions, and purification method. While these EVs are highly purified and homogeneous compared to complex samples, the ability to classify them from a single species demonstrates the incredible power of SERS when combined with machine learning, and the importance of considering these parameters in future applications. We anticipate that these findings will play a crucial role in developing the laboratory and clinical utility of bacterial EVs, such as the label-free, noninvasive, and rapid diagnosis of infections without the need to culture samples from blood, urine, or other fluids.
Email Address of Submitting Authorcolin.firstname.lastname@example.org
Submitting Author's InstitutionUniversity of Auckland
Submitting Author's CountryNew Zealand
Read the peer-reviewed publication
in IEEE Sensors Journal