Simultaneous Recognition of Over- and Under-Expressed MicroRNAs Using Nanopore Decoding.

Abstract

This paper describes a strategy for simultaneous recognition of over- and under-expressed microRNAs (miRNAs) using the method of signal classification-based nanopore decoding. MiRNA has attracted attention as a promising biomarker for cancer diagnosis owing to its cancer-type-specific expression patterns. While nanopore technology has emerged as a simple and label-free method to detect miRNAs and their expression patterns, recognizing patterns involving simultaneous over/under-expression is still challenging due to the inherent working principles. Here, inspired by the sequence design for DNA computation with nanopore decoding, we designed diagnostic DNA probes targeting two individual over/under-expressed miRNAs in the serum of oral squamous cell carcinoma. Through nanopore measurements, our designed probes exhibited characteristic current signals depending on the hybridized miRNA species, which were plotted on the scatter plot of duration versus current blocking ratio. The classified signals reflected the relative abundance of target miRNAs, thereby enabling successful pattern recognition of over/under-expressed miRNAs, even when using clinical samples. We believe that our method paves the way for miRNA-targeting simple diagnosis as a liquid biopsy.