bioRxivBiophysics
Rapid Kinetic Fingerprinting of Single Nucleic Acid Molecules by a FRET-based Dynamic Nanosensor
K. Khanna, S. Mandal, A. T. Blanchard, M. Tewari, A. Johnson-Buck, N. G. Walter
Abstract
Biofluids contain cell-free nucleic acids such as microRNAs (miRNAs) and circulating tumor-derived DNAs (ctDNAs) that have emerged as promising disease biomarkers. Conventional detection of these biomarkers by digital PCR and next generation sequencing, although highly sensitive, requires time-consuming extraction and amplification steps that increase the risk of sample loss and cross-contamination, respectively. To achieve the direct, rapid detection of miRNAs and ctDNAs with near-perfect specificity and single-molecule level sensitivity, we herein describe an accelerated amplification-free single-molecule kinetic fingerprinting. This approach, termed intramolecular single-molecule recognition through equilibrium Poisson sampling (iSiMREPS), exploits a dynamic DNA nanosensor comprising a surface anchor and a pair of fluorescent detection probes: one probe captures individual target molecules onto the surface, while the other transiently interrogates them to generate kinetic fingerprints by intramolecular single-molecule Forster resonance energy transfer (smFRET). Formamide is used to further accelerate the kinetics of probe-target interactions and fingerprinting, while background signals are reduced by removing non-target-bound probes from the surface using toehold-mediated strand displacement. We show that iSiMREPS can detect in as little as 10 seconds two distinct, promising cancer biomarkers--miR-141 and a common EGFR exon 19 deletion--reaching a limit of detection (LOD) of ~3 fM and a mutant allele fraction among excess wild-type as low as 1 in 1 million, or 0.0001%. We anticipate that iSiMREPS will find utility in research and clinical diagnostics based on its features of rapid detection, high specificity, sensitivity, and generalizability.
- assay: DNA methylation
- assay: proteomics
- research focus: differential expression
- approach: deep learning
- approach: experimental assay
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