Quantum Dot Encoding for In-Solution Single-Molecule Biomarker Counting in Metastatic Prostate Cancer

This is the authors' abstract. We don't add key points for this paper.

Digital assays are in wide development for biomarker quantification at the single-molecule level, but the common use of surface pull-down steps limits both analytical sensitivity and throughput. Here, we develop surface-free, wash-free, in-solution assays with an analytical sensitivity slope approaching unity for sequence-specific counting of microRNAs (miRs) relevant to metastatic castration-resistant prostate cancer (mCRPC). These assays are enabled by DNA nanoflowers (DNFs) densely encoded with ∼200 fluorescent quantum dots (QDs) that assemble in situ stoichiometrically to miRs. The QD-DNFs are detected as single events in solution by fluorescence microscopy or flow cytometry without washing away unbound labels. A ∼10 aM limit of detection and high agreement with absolute target count (intraclass correlation coefficient = 0.95) were achieved by machine learning-guided assay optimization, providing the potential for calibration-free measurements. Multiple miR sequences could be distinguished through ratiometric and colorimetric (5-color) QD signatures with a single excitation source for flexible detection in static solution or flow streams. The assays were applied to detect exosomal miRs from small-volume plasma from mCRPC patients and showed strong agreement with RT-qPCR, with more reliable detection of the trace prognostic biomarker miR-375. Consistent with our prior reports using large volume blood draws, higher plasma levels of miR-375 were associated with poor survival of patients with mCRPC. We anticipate that in-solution counting can increase the robustness of trace biomarker analysis needed for the advancement of cancer precision medicine.