Zhang Linghao, Yang Huixiao, Yan Yumin, Zhao Hongyang, Han Da, Su Xin
State Key Laboratory of Organic-Inorganic Composites, Beijing Key Laboratory of Bioprocess, Beijing Advanced Innovation Center for Soft Matter Science and Engineering, College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, 100029, China.
Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
Adv Mater. 2025 Mar;37(10):e2413198. doi: 10.1002/adma.202413198. Epub 2025 Jan 31.
DNA-based molecular computing systems for biomarkers have emerged as powerful tools for intelligent diagnostics. However, with the variety of feature biomarkers expanding, current molecular computing systems suffer from the use of a large number of oligonucleotides and limited encoding capability. Here, the study develops an alternative molecular computing approach termed Digital DNA Strand Displacement (DDSD) which recognizes targets and operates target valence through DNA polymerase-based extension and strand release. DDSD significantly reduced the number of used oligonucleotide species, provided robust molecular classifiers. In clinical blood samples, a 96% accuracy rate is achieved with a DDSD-based binary classifier for distinguishing bacterial and viral infections, a 100% accuracy rate is achieved with a multiclass classifier for identifying pathogen types, surpassing existing classifier systems. Moreover, DDSD can be readily expanded. Cascade DDSD is developed, enabling simultaneous computing of up to 14 valence states with a maximum valence of 25. Multiway junction DDSD is implemented to achieve high-valence computing by compact DNA nanostructures rather than split DNA computing units, reducing the potential leakage. The implementation of DDSD enhances the capability of valence-based intelligent molecular diagnostics and multiplexed biomarker detection.
用于生物标志物的基于DNA的分子计算系统已成为智能诊断的强大工具。然而，随着特征生物标志物种类的不断增加，当前的分子计算系统存在寡核苷酸使用量大和编码能力有限的问题。在此，该研究开发了一种名为数字DNA链置换（DDSD）的替代分子计算方法，该方法通过基于DNA聚合酶的延伸和链释放来识别靶标并操作靶标价态。DDSD显著减少了所用寡核苷酸种类的数量，提供了强大的分子分类器。在临床血液样本中，基于DDSD的二元分类器区分细菌和病毒感染的准确率达到96%，多类分类器识别病原体类型的准确率达到100%，超过了现有的分类器系统。此外，DDSD可以很容易地扩展。开发了级联DDSD，能够同时计算多达14个价态，最大价态为25。实现了多路连接DDSD，通过紧凑的DNA纳米结构而非分裂的DNA计算单元实现高价态计算，减少了潜在的泄漏。DDSD的实现增强了基于价态的智能分子诊断和多重生物标志物检测的能力。
