Label-free Classification of Bacterial Extracellular Vesicles by
Combining Nanoplasmonic Sensors with Machine Learning
Abstract
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.