Molecular profiling via analysis of multiple disease biomarkers, is a powerful tool for disease diagnosis and risk prediction. Due to simplicity and minimal instrumentation requirement, colloidal-based colorimetric DNA/RNA assays are attractive for driving molecular profiling towards widespread clinical usage. Still, the reliability and speed of current colorimetric assays need to be further improved upon for eventual clinical use. Herein, we propose a "mix-to-go" colloidal strategy which utilizes the electrostatic attraction between negatively charged target sequences and positively charged silver nanoparticles (AgNPs) to induce aggregation of AgNPs, to profile a panel of clinically-validated urinary prostate cancer (PCa) RNA biomarkers (TMPRSS2:ERG, T2:ERG; prostate cancer antigen 3, PCA3; and kallikrein-related peptidase 2, KLK2). Our strategy is unique in inducing a rapid (10 sec), unambiguous solution color change in the presence of target sequences, without the additional NP aggregation agents that are used in existing electrostatic-mediated aggregation assays. Our strategy is analytically specific and sensitive for the detection of 105 and 104 target copies by the naked eye and UV-vis spectrophotometry, respectively. Analytical accuracies of our strategy in detecting T2:ERG, PCA3, and KLK2 RNA biomarkers were 95.9%, 97.3%, and 100% respectively, as validated by quantitative reverse transcription-polymerase chain reaction. To further evaluate clinical molecular profiling performance beyond conventional proof-of-concept demonstration, we tested our colloidal strategy for non-invasive PCa risk prediction of 73 patients, using the urinary RNA biomarker panel comprising of T2:ERG, PCA3, and KLK2. We found that elevated T2:ERG and PCA3 levels were positively associated with high-risk PCa and obtained corresponding area-under-the-curve values of 0.790 and 0.833 for predicting PCa and high-risk PCa on biopsy, respectively. We believe our "mix-to-go" strategy may serve as a reliable and accessible Ag colloidal-based molecular profiling approach for clinical applications.
Analytical chemistry. 2018 Sep 27 [Epub ahead of print]
Jing Wang, Kevin Maisheng Koo, Yuling Wang, Matt Trau