Topological Transformation‐Based Nanobarcoding for Detection and Enumeration of MicroRNAs and Single Nucleotide Polymorphism. Issue 7 (13th June 2019) - Yuk, Ji Soo; Shin, Seung Won; Chun, Sang Hun; Ku, Bo Mi; Choi, Yu Jeong; Lim, Yong Taik; Luo, Dan; Ahn, Myung‐Ju; Um, Soong Ho
Abstract: RNA biomarkers have been recently reported to be associated tightly with the diagnosis and prognosis of many diseases. Particularly, cancers considered to be a serious threat to primates are known to be vastly dominated by genetic networks where RNA plays a key role. RNAs are thus recognized as a major target group that can be used for numerous cancer treatments and it is still required to identify and enumerate them in an effective manner. Here, a new topological transformation‐based nanobarcoding technique (TNT) is first reported using fluorescence‐DNA barcodes engaged with graphene oxide (GO x ) for effectively discriminating short RNAs such as miRNAs and their single nucleotide polyporphisms in tissue and plasma. Through topological transformation into 3D DNA–RNA polygonal structures, various kinds of microRNAs have been read at the same time and analyzed quantitatively. Also, it positively discerned epidermal growth factor receptor (EGFR) mutations known as single base variations of typical lung cancer specific RNAs. A single variant of 0.785% in target EGFR mutations is explicitly detected. It is speculated that the TNT may be a versatile method for polymerase chain reaction (PCR)‐free practical diagnosis of several clinical genetic deviations such as significant biotic RNA and genic fragments and would be a promising alternative to conventional PCR terrains. Abstract : A topological transformation‐based nanobarcoding technique is systematically developed for discrimination of small non‐coding RNAs without the polymerase chain reaction. The side edge of the triangle prism nanobarcode combined with the target extends in a minute, instantaneously releasing fluorescence as the phase of the fluorescence moves away from the graphene. Yet this simple approach can distinguish single point mutations.