Label-Free Classification of Bacterial Extracellular Vesicles by Combining Nanoplasmonic Sensors With Machine Learning

Extracellular Vesicles

Bacterial extracellular vesicles (EVs) are nano- scale 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.

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Cigarette smoke (CS) represents one of the most relevant environmental risk factors for several chronic pathologies. Tissue damage caused by CS exposure is mediated, at least in part, by oxidative stress induced by its toxic and pro-oxidant components. Evidence demonstrates that extracellular vesicles (EVs) released by various cell types exposed to CS extract (CSE) are characterized by altered biochemical cargo and gained pathological properties. In the present study, we evaluated the content of oxidized proteins and phospholipid fatty acid profiles of EVs released by human bronchial epithelial BEAS-2B cells treated with CSE. This specific molecular characterization has hitherto not been performed. After confirmation that CSE reduces viability of BEAS-2B cells and elevates intracellular ROS levels, in a dose-dependent manner, we demonstrated that 24 h exposure at 1% CSE, a concentration that only slight modifies cell viability but increases ROS levels, was able to increase carbonylated protein levels in cells and released EVs. The release of oxidatively modified proteins via EVs might represent a mechanism used by cells to remove toxic proteins in order to avoid their intracellular overloading. Moreover, 1% CSE induced only few changes in the fatty acid asset in BEAS-2B cell membrane phospholipids, whereas several rearrangements were observed in EVs released by CSE-treated cells. The impact of changes in acyl chain composition of CSE-EVs accounted for the increased saturation levels of phospholipids, a membrane parameter that might influence EV stability, uptake and, at least in part, EV-mediated biological effects. The present in vitro study adds new information concerning the biochemical composition of CSE-related EVs, useful to predict their biological effects on target cells. Furthermore, the information regarding the presence of oxidized proteins and the specific membrane features of CSE-related EVs can be useful to define the utilization of circulating EVs as marker for diagnosing of CS-induced lung damage and/or CS-related diseases.

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