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Updated: May 26, 2026

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

Chia-Wei Kuo1,2, Siva Nalla1,2, Suresh Sarkar1,3

  • 1Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.

ACS Nano
|May 25, 2026
PubMed
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This summary is machine-generated.

We developed novel surface-free assays for precise microRNA (miR) quantification in solution. These assays improve sensitivity and throughput for detecting cancer biomarkers, aiding precision medicine advancements.

Area of Science:

  • Biomarker quantification
  • Molecular diagnostics
  • Cancer research

Background:

  • Current digital assays for single-molecule biomarker quantification often rely on surface pull-down steps, limiting sensitivity and throughput.
  • Accurate quantification of microRNAs (miRs) is crucial for diagnosing and monitoring diseases like metastatic castration-resistant prostate cancer (mCRPC).

Purpose of the Study:

  • To develop novel surface-free, wash-free, in-solution assays for sequence-specific quantification of microRNAs (miRs).
  • To achieve high analytical sensitivity and throughput for biomarker detection, particularly for mCRPC-relevant miRs.
  • To enable reliable detection of trace biomarkers for cancer precision medicine.

Main Methods:

  • Development of DNA nanoflowers (DNFs) densely encoded with quantum dots (QDs) for in situ stoichiometric assembly with target miRs.
Keywords:
ampliconbarcodediagnosismultiplexnanocrystaloncologyself-assembly

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  • Detection of QD-DNFs as single events in solution using fluorescence microscopy or flow cytometry without washing steps.
  • Machine learning-guided assay optimization to achieve high analytical sensitivity and agreement with absolute target counts.
  • Utilizing ratiometric and multi-color (5-color) QD signatures for distinguishing multiple miR sequences.

    • Main Results:

    • Achieved a limit of detection of approximately 10 aM with high agreement to absolute target counts (intraclass correlation coefficient = 0.95).
    • Demonstrated the ability to distinguish multiple miR sequences using QD signatures with a single excitation source.
    • Successfully applied the assays to detect exosomal miRs from small plasma volumes in mCRPC patients, showing strong agreement with RT-qPCR.
    • Observed more reliable detection of the prognostic biomarker miR-375 compared to RT-qPCR, correlating higher levels with poor patient survival.

    Conclusions:

    • The developed in-solution, surface-free assays offer enhanced analytical sensitivity and throughput for miR quantification.
    • These assays have the potential for calibration-free measurements and reliable detection of trace biomarkers.
    • The technology shows promise for advancing cancer precision medicine through improved biomarker analysis in patient samples.