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

Area of Science:

• Biochemistry
• Molecular Biology
• Nanotechnology

Background:

• Current digital assays for single-molecule biomarker quantification often rely on surface-based methods, 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 a surface-free, wash-free, in-solution assay for highly sensitive and high-throughput sequence-specific counting of miRs.
• To enable calibration-free measurements and reliable detection of trace biomarkers for precision medicine.

Main Methods:

• Utilized DNA nanoflowers (DNFs) densely functionalized with quantum dots (QDs) for stoichiometric assembly with target miRs in solution.
• Employed fluorescence microscopy and flow cytometry for detecting QD-DNFs as single events without washing.
• Applied machine learning for assay optimization to achieve high sensitivity and accuracy.

Main Results:

• Achieved a limit of detection of approximately 50 aM and high agreement (0.95) with absolute target counts.
• Demonstrated multiplexed detection of multiple miR sequences using ratiometric and 5-color QD signatures.
• Successfully detected exosomal miRs from small plasma volumes in mCRPC patients, showing strong agreement with RT-qPCR and reliable detection of miR-375.

Conclusions:

• The developed in-solution assay offers enhanced analytical sensitivity and throughput compared to surface-based methods.
• This technology enables robust absolute counting of clinical biomarkers in plasma, facilitating the translation of cancer precision medicine.
• The assay's ability to detect trace biomarkers like miR-375 holds significant prognostic value for mCRPC patients.