Ultrasensitive miRNA Detection for Disease Diagnosis | AIChE

Ultrasensitive miRNA Detection for Disease Diagnosis

Authors 

Ozay, B. - Presenter, Montana State University
McCalla, S., Montana State University
Early detection of diseases has a huge impact on treatment success. Recent studies show that microRNA (miRNA) expression levels change in response to disease (such as cancer, heart attack, Alzheimer’s disease, malaria, and tuberculosis), often in the early stages, which makes them potential biomarkers for early diagnostics. Moreover, they are present and stable in many body matrices such as tears, saliva, sputum, breast milk, urine and seminal fluid, which makes them advantageous biomarkers that can be screened from multiple places. However, miRNAs have short sequences and are usually in low concentrations, such that cost-effective specific detection is a challenge. Currently, qPCR is considered the gold standard for miRNA detection. However, qPCR is a time-consuming, expensive method that is not suitable for limited resource settings. In this study, we developed and characterized a new tunable DNA amplification chemistry, UDAR (Ultrasensitive DNA Amplification Reaction)1, that mimics the switch-like characteristics of biological systems such as cell signaling and genetic regulation. UDAR is an isothermal reaction, which eliminates the need for a thermocycler. This high-yield switch creates a definitive change in fluorescence output and can be imaged using a cell phone. Currently, 3 different miRNAs were successfully detected with UDAR, with a limit of detection on the order of 10 fM. In the future, UDAR can be converted into a quantitative assay by counting single amplified miRNA molecules with the help of digital microfluidic tools. The aim of this study is to develop a novel miRNA detection method that is simple, inexpensive and rapid without compromising sensitivity and specificity. Our ultimate goal is to develop a miRNA assay that can be used in doctor’s office or in limited resource settings.

[1] B. Özay, C. Robertus, J. Negri, S. McCalla, Analyst 2018, 143.