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Rapid Lipid-Based Approach for Normalization of Quantum-Dot-Detected Biomarker Expression on Extracellular Vesicles in Complex Biological Samples.

ABSTRACT: Extracellular vesicles (EVs) are of considerable interest as tumor biomarkers because tumor-derived EVs contain a broad array of information about tumor pathophysiology. However, current EV assays cannot distinguish between EV biomarker differences resulting from altered abundance of a target EV population with stable biomarker expression, altered biomarker expression in a stable target EV population, or effects arising from changes in both parameters. We now describe a rapid nanoparticle- and dye-based fluorescent immunoassay that can distinguish among these possibilities by normalizing EV biomarker levels to EV abundance. 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 and nonmalignant pancreatic cell lines 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 modular design permits it to be rapidly adapted to quantitate other EV biomarkers by the simple expedient of swapping the antibody-conjugated quantum dot probes for those that recognize a different disease-specific EV biomarker.

SUBMITTER: Rodrigues M

PROVIDER: S-EPMC8162763 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Antibody-based proteomics is an enabling technology that has significant implications for cancer biomarker discovery, diagnostic screening, prognostic and pharmacodynamic evaluation of disease state, and targeted therapeutics. Quantum dot based fluoro-immunoconjugates possess promising features toward realization of this goal such as high photostability, brightness, and multispectral tunability. However, current strategies to generate such conjugates are riddled with complications such as improper orientation of antigen binding sites of the antibody, aggregation, and stability issues. We report a facile yet effective strategy to conjugate anti-epidermal growth factor receptor (EGFR) antibody to quantum dots using copper-free click reaction, and compared them to similar constructs prepared using traditional strategies such as succinimidyl-4-(N-maleimidomethyl) cyclohexane-1-carboxylate (SMCC) and biotin-streptavidin schemes. The Fc and Fab regions of the conjugates retain their binding potential, compared to those generated through the traditional schemes. We further applied the conjugates in testing a novel microsphere array device designed to carry out sensitive detection of cancer biomarkers through fluoroimmunoassays. Using purified EGFR, we determined the limit of detection of the microscopy centric system to be 12.5 ng/mL. The biological assay, in silico, was successfully tested and validated by using tumor cell lysates, as well as human serum from breast cancer patients, and the results were compared to normal serum. A pattern consistent with established clinical data was observed, which further validates the effectiveness of the developed conjugates and its successful implementation both in vitro as well as in silico fluoroimmunoassays. The results suggest the potential development of a high throughput in silico paradigm for predicting the class of patient cancer based on EGFR expression levels relative to normal reference levels in blood.

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Rapid Lipid-Based Approach for Normalization of Quantum-Dot-Detected Biomarker Expression on Extracellular Vesicles in Complex Biological Samples.

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