MicroRNAs (miRNAs) are a class of regulatory small RNA molecules that play critical roles in a wide variety of biological processes. Abnormally expressed miRNAs have been increasingly utilized as biomarkers for cancer diagnosis. Generally, a specific cancer is associated with expression alterations of several species of miRNAs and different types of cancers are related to different miRNA species. Therefore, a universal method for multiplexed detection of miRNA targets of interest is now desirable for cancer diagnosis. In this paper, by adding an enzymatic digestion step to reduce the nonspecific adaptor dimers, we firstly improved the method to construct cDNA library of all miRNAs, which greatly increased the c... More
MicroRNAs (miRNAs) are a class of regulatory small RNA molecules that play critical roles in a wide variety of biological processes. Abnormally expressed miRNAs have been increasingly utilized as biomarkers for cancer diagnosis. Generally, a specific cancer is associated with expression alterations of several species of miRNAs and different types of cancers are related to different miRNA species. Therefore, a universal method for multiplexed detection of miRNA targets of interest is now desirable for cancer diagnosis. In this paper, by adding an enzymatic digestion step to reduce the nonspecific adaptor dimers, we firstly improved the method to construct cDNA library of all miRNAs, which greatly increased the cDNA yield. By specifically designing DNA probes to hybridize with the cDNAs at key positions and doubly encoding DNA probes with different lengths and different fluorophores during single-base extension, each miRNA could produce a unique product, which could be separated and detected by capillary electrophoresis. Thus, miRNA targets of interest could be simultaneously detected with great specificity at single-base resolution. By using seventeen randomly selected miRNAs as the model, as low as 1.0 fM of each miRNA target could be simultaneously determined. Furthermore, we had achieved accurate analysis of multiple miRNAs in real biological RNA samples and found that several miRNAs expressed differently between cancer cells and normal cells, indicating that the proposed method had the ability to pick out aberrant expression miRNAs in real biological samples. Compared with high-throughput sequencing methods, the proposed method is simpler and specific, and very suitable for the detection of specific miRNAs associated with a disease, which shows great potential for cancer diagnosis.
