Machine learning-powered single-molecule cancer diagnosis using DNA origami tags

Single-molecule detection (SMD) holds considerable promise in biomedical research. Although atomic force microscopy (AFM) provides an important technique with nanoscale resolution for SMD, its broader application is limited by labeling challenges and slow data processing. Here, we present a machine learning (ML)-powered strategy combining AFM and DNA nanotags for SMD and cancer diagnosis. Nickases are applied to create specific single-strand breaks in target DNA, allowing insertion of exogenous DNA to attach shape-distinct nanotags for AFM imaging. A YOLOv5l algorithm is adopted to automatically recognize target objects in AFM images, which can classify 370 structures in 1.21 seconds with 98% accuracy. The proof of concept of this strategy is confirmed by identifying nickase-edited sites on both linear and circular DNA. Its practical applicability is demonstrated by detecting KRAS Gly12Arg (G12R) and p53 Arg175His (R175H) mutations in samples from patients with pancreatic and colorectal cancer, with accuracy rivaling Sanger sequencing and quantitative polymerase chain reaction, opening avenues for SMD.