1. Rapid miRNA detection enhanced by exponential hybridization chain reaction in graphene field-effect transistors.
- Author
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Huang T, Li J, Chen H, Sun H, Jang DW, Alam MM, Yeung KK, Zhang Q, Xia H, Duan L, Mao C, and Gao Z
- Subjects
- Humans, Equipment Design, MicroRNAs blood, MicroRNAs analysis, Graphite chemistry, Biosensing Techniques instrumentation, Biosensing Techniques methods, Transistors, Electronic, Nucleic Acid Hybridization, Limit of Detection
- Abstract
Scalable electronic devices that can detect target biomarkers from clinical samples hold great promise for point-of-care nucleic acid testing, but still cannot achieve the detection of target molecules at an attomolar range within a short timeframe (<1 h). To tackle this daunting challenge, we integrate graphene field-effect transistors (GFETs) with exponential target recycling and hybridization chain reaction (TRHCR) to detect oligonucleotides (using miRNA as a model disease biomarker), achieving a detection limit of 100 aM and reducing the sensing time by 30-fold, from 15 h to 30 min. In contrast to traditional linear TRHCR, our exponential TRHCR enables the target miRNA to initiate an autocatalytic system with exponential kinetics, significantly accelerating the reaction speed. The resulting reaction products, long-necked double-stranded polymers with a negative charge, are effectively detected by the GFET through chemical gating, leading to a shift in the Dirac voltage. Therefore, by monitoring the magnitude of this voltage shift, the target miRNA is quantified with high sensitivity. Consequently, our approach successfully detects 22-mer miRNA at concentrations as low as 100 aM in human serum samples, achieving the desired short timeframe of 30 min, which is congruent with point-of-care testing, and demonstrates superior specificity against single-base mismatched interfering oligonucleotides., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)
- Published
- 2024
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