Amplified EQCM-D detection of extracellular vesicles using 2D gold nanostructure arrays fabricated by block copolymer self-assembly

Extracellular Vesicles
/References

Extracellular vesicles (EVs) are routinely released from nearly all cell types as transport vehicles and for cell communication. Crucially, they contain biomolecular content for the identification of health and disease states that can be detected from readily accessible physiological fluids, including urine, plasma, or saliva. Despite their clinical utility within noninvasive diagnostic platforms such as liquid biopsies, the currently available portfolio of analytical approaches are challenged by EV heterogeneity in size and composition, as well as the complexity of native biofluids. Quartz crystal microbalance with dissipation monitoring (QCM-D) has recently emerged as a powerful alternative for the phenotypic detection of EVs, offering multiple modes of analyte discrimination by frequency and dissipation. While providing rich data for sensor development, further progress is required to reduce detection limits and fully exploit the technique’s potential within biosensing. Herein, we investigate the impact of nanostructuring the sensor electrode surface for enhancing its detection capabilities. We employ self-assembly of the block copolymer polystyrene-block-poly(4-vinylpyridine) to create well defined 2D gold islands via selective impregnation of the pyridine domain with gold precursors and subsequent removal of the template. When matched to the EV length scale, we find a 4-fold improvement in sensitivity despite a 4-fold reduction in area for analyte and ligand anchoring in comparison to a flat sensor surface. Creation of tailored and confined sensing regions interspersed by non-binding silica provides optimal spatial orientation for EV capture with reduced steric effects and negative cooperativity of grafted antibodies, offering a promising route for enhanced binding efficiency and performance of sensor platforms.

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Recent Publications

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.

2023
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