A rapid lipid-based approach for normalization of quantum dot-detected biomarker expression on extracellular vesicles in complex biological samples

Extracellular Vesicles (EVs), particularly exosomes, are of considerable interest as tumor biomarkers since tumor-derived EVs contain a broad array of information about tumor pathophysiology including its metabolic and metastatic status. However, current EV based assays cannot distinguish between EV biomarker changes by altered secretion of EVs during diseased conditions like cancer, inflammation, etc. that express a constant level of a given biomarker, stable secretion of EVs with altered biomarker expression, or a combination of these two factors. This issue was addressed by developing a nanoparticle and dye-based fluorescent immunoassay that can distinguish among these possibilities by normalizing EV biomarker level(s) to EV abundance, revealing average expression levels of EV biomarker under observation. In this approach, EVs are captured from complex samples (e.g. serum), stained with a lipophilic dye and hybridized with antibody-conjugated quantum dot probes for specific EV surface biomarkers. EV dye signal is used to quantify EV abundance and normalize EV surface biomarker expression levels. EVs from malignant (PANC-1) and nonmalignant pancreatic cell lines (HPNE) exhibited similar staining, and probe-to-dye ratios did not change with EV abundance, allowing direct analysis of normalized EV biomarker expression without a separate EV quantification step. This EV biomarker normalization approach markedly improved the ability of serum levels of two pancreatic cancer biomarkers, EV EpCAM, and EV EphA2, to discriminate pancreatic cancer patients from nonmalignant control subjects. The streamlined workflow and robust results of this assay are suitable for rapid translation to clinical applications and its flexible design permits it to be rapidly adapted to quantitate other EV biomarkers by the simple swapping of the antibody-conjugated quantum dot probes for those that recognize a different disease-specific EV biomarker utilizing a workflow that is suitable for rapid clinical translation.