IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v1y2010i1d10.1038_ncomms1035.html
   My bibliography  Save this article

Motion-based DNA detection using catalytic nanomotors

Author

Listed:
  • Jie Wu

    (University of California San Diego)

  • Shankar Balasubramanian

    (University of California San Diego)

  • Daniel Kagan

    (University of California San Diego)

  • Kalayil Manian Manesh

    (University of California San Diego)

  • Susana Campuzano

    (University of California San Diego)

  • Joseph Wang

    (University of California San Diego)

Abstract

Synthetic nanomotors, which convert chemical energy into autonomous motion, hold considerable promise for diverse applications. In this paper, we show the use of synthetic nanomotors for detecting DNA and bacterial ribosomal RNA in a fast, simple and sensitive manner. The new motion-driven DNA-sensing concept relies on measuring changes in the speed of unmodified catalytic nanomotors induced by the dissolution of silver nanoparticle tags captured in a sandwich DNA hybridization assay. The concentration-dependent distance signals are visualized using optical microscopy, particularly through straight-line traces by magnetically aligned 'racing' nanomotors. This nanomotor biodetection strategy could be extended to monitor a wide range of biomolecular interactions using different motion transduction schemes, thus providing a versatile and powerful tool for detecting biological targets.

Suggested Citation

  • Jie Wu & Shankar Balasubramanian & Daniel Kagan & Kalayil Manian Manesh & Susana Campuzano & Joseph Wang, 2010. "Motion-based DNA detection using catalytic nanomotors," Nature Communications, Nature, vol. 1(1), pages 1-6, December.
    • Handle: RePEc:nat:natcom:v:1:y:2010:i:1:d:10.1038_ncomms1035
    DOI: 10.1038/ncomms1035
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/ncomms1035
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/ncomms1035?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chia Hsiang Chen & Vincent Gau & Donna D Zhang & Joseph C Liao & Fei-Yue Wang & Pak Kin Wong, 2010. "Statistical Metamodeling for Revealing Synergistic Antimicrobial Interactions," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-7, November.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:1:y:2010:i:1:d:10.1038_ncomms1035. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.