A1 Refereed original research article in a scientific journal
High-sensitivity, protein-independent detection of dsDNA sequences
Authors: Yan, Jiaqi; Bhadane, Rajendra; Xu, Wentao; Ran, Meixin; Ma, Xiaochao; Li, Yuanqiang; Jahnke, Kevin; Ma, Xiaodong; Salo-Ahen, Outi M. H.; Kostiainen, Mauri A.; Weitz, David A.; Zhang, Hongbo
Publisher: Proceedings of the National Academy of Sciences
Publication year: 2026
Journal: Proceedings of the National Academy of Sciences of the United States of America
Article number: e2515765123
Volume: 123
Issue: 6
ISSN: 0027-8424
eISSN: 1091-6490
DOI: https://doi.org/10.1073/pnas.2515765123
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1073/pnas.2515765123
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/515634781
Self-archived copy's licence: CC BY NC ND
Self-archived copy's version: Publisher`s PDF
Current methodologies for detecting the sequence of double-stranded DNA (dsDNA) require amplifying and denaturing the target into single-stranded DNA (ssDNA) to enable sequence detection through Watson-Crick base pairing. However, these approaches are limited by the risks of nonspecific amplification, reliance on complex, temperature-sensitive protein enzymes, and harsh reaction conditions, such as in strong base or acidic environments. Here, we introduce a dsDNA detection platform that integrates a peptide nucleic acid (PNA) as the dsDNA denaturation agent, with multicomponent deoxyribozyme as the ssDNA detection tool, in a droplet-based system. This protein- and amplification-free method offers single-nucleotide resolution, detects down to a single dsDNA molecule, and delivers results within 1 h at room temperature. This work introduces a conceptually unique approach, that may be useful for both diagnostics and therapeutics.
Downloadable publication This is an electronic reprint of the original article. |  |
Funding information in the publication:
We thank the Zhejiang Provincial Natural Science Foundation of China [BD24H180001 (H.Z.)], the Research Project [Grant No. 347897 (H.Z.)], Solution for Health Profile [Grant No. 336355 (H.Z.)], InFLAMES Flagship [Grant No. 337531 (H.Z.)], and “Printed Intelligence Infrastructure” (PII-FIRI) (H.Z.)” from Research Council of Finland; NIH/University of Pittsburgh [Grant No. R01AI153156 (D.A.W)]; and the Tor, Joe, and Pentti Borg Memorial Fund (O.S). J.Y. thanks the Finnish Culture Foundation Post Doc Pool grant. K.J. thanks the Alexander von Humboldt Foundation for financial support. We acknowledge the Academy of Finland Centers of Excellence Program (2022–2029) in Life-Inspired Hybrid Materials (LIBER) 346110 (M.A.K). We thank the Center for Nanoscale Systems (CNS) in Harvard University. We thank Electron Microscopy Laboratory, Institute of Biomedicine, University of Turku, and Biocenter Finland. Also, the Biocenter Finland Bioinformatics, CSC IT Center for Science, and Prof. Mark Johnson and Dr. Jukka Lehtonen are gratefully acknowledged for the excellent computational infrastructure at the Åbo Akademi University.
