Back

High-speed AFM imaging of the Entropic Disassembly of DNA Origami for Single-Molecule Biosensing

Chau, C. C.; Gupta, V.; Heath, G. R.; Walti, C.; Actis, P.

2025-08-19 biophysics
10.1101/2025.08.15.670511 bioRxiv
Show abstract

Circulating microRNAs (miRNAs) are promising biomarkers for disease diagnosis, but their small size and instability hinder direct detection. The detection of small RNA, such as miRNA, using solid-state nanopores typically involves the binding of miRNA to a larger carrier molecule to generate detectable signals. However, this approach is prone to RNAse degradation in the environment leading to accidental digestion of miRNA prior to analysis. Here, we present an alternative approach based on DNA origami disassembly driven by toehold-mediated strand displacement (TMSD). Specifically, we designed a symmetric DNA origami dimer that undergoes TMSD-driven separation into monomers using miRNAs as invading strands. We visualized the real-time dynamics of dimer separation at high resolution using high-speed atomic force microscopy (HS-AFM), directly capturing nanoscale mechanical dynamics of the TMSD process that are inaccessible to ensemble or fluorescence-based measurements. Following the TMSD, single molecule nanopore sensing enables quantitative endpoint analysis of dimer separation by measuring the ratio of dimers to monomers. This direct read-out method enabled the multiplexed detection of miRNAs. Due to the near irreversible process of the TMSD, we performed the detection of miRNA in crude RNA tissue extracts under the presence of RNAse and showed that our approach allowed the robust detection of small RNA that is unaffected by the complex degrading environment.

Published in Angewandte Chemie International Edition (predicted rank #9) · training set

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.