High-Throughput and Automated Acoustic Trapping of Extracellular Vesicles to Identify microRNAs With Diagnostic Potential for Prostate Cancer.

Molecular profiling of extracellular vesicles (EVs) offers novel opportunities for diagnostic applications, but the current major obstacle for clinical translation is the lack of efficient, robust, and reproducible isolation methods. To bridge that gap, we developed a microfluidic, non-contact, and low-input volume compatible acoustic trapping technology for EV isolation that enabled downstream small RNA sequencing. In the current study, we have further automated the acoustic microfluidics-based EV enrichment technique that enables us to serially process 32 clinical samples per run. We utilized the system to enrich EVs from urine collected as the first morning void from 207 men referred to 10-core prostate biopsy performed the same day. Using automated acoustic trapping, we successfully enriched EVs from 199/207 samples (96%). After RNA extraction, size selection, and library preparation, a total of 173/199 samples (87%) provided sufficient materials for next-generation sequencing that generated an average of 2 × 10 reads per sample mapping to the human reference genome. The predominant RNA species identified were fragments of long RNAs such as protein coding and retained introns, whereas small RNAs such as microRNAs (miRNA) accounted for less than 1% of the reads suggesting that partially degraded long RNAs out-competed miRNAs during sequencing. We found that the expression of six miRNAs was significantly different (P < 0.05) in EVs isolated from patients found to have high grade prostate cancer [ISUP 2005 Grade Group (GG) 4 or higher] compared to those with GG3 or lower, including those with no evidence of prostate cancer at biopsy. These included miR-23b-3p, miR-27a-3p, and miR-27b-3p showing higher expression in patients with GG4 or high grade prostate cancer, whereas miR-1-3p, miR-10a-5p, and miR-423-3p had lower expression in the GG4 PCa cases. Cross referencing our differentially expressed miRNAs to two large prostate cancer datasets revealed that the putative tumor suppressors miR-1, miR-23b, and miR-27a are consistently deregulated in prostate cancer. Taken together, this is the first time that our automated microfluidic EV enrichment technique has been found to be capable of enriching EVs on a large scale from 900 μl of urine for small RNA sequencing in a robust and disease discriminatory manner.