IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v10y2019i1d10.1038_s41467-019-10036-z.html
   My bibliography  Save this article

Information-rich localization microscopy through machine learning

Author

Listed:
  • Taehwan Kim

    (University of California)

  • Seonah Moon

    (University of California)

  • Ke Xu

    (University of California
    Chan Zuckerberg Biohub)

Abstract

Recent years have witnessed the development of single-molecule localization microscopy as a generic tool for sampling diverse biologically relevant information at the super-resolution level. While current approaches often rely on the target-specific alteration of the point spread function to encode the multidimensional contents of single fluorophores, the details of the point spread function in an unmodified microscope already contain rich information. Here we introduce a data-driven approach in which artificial neural networks are trained to make a direct link between an experimental point spread function image and its underlying, multidimensional parameters, and compare results with alternative approaches based on maximum likelihood estimation. To demonstrate this concept in real systems, we decipher in fixed cells both the colors and the axial positions of single molecules in regular localization microscopy data.

Suggested Citation

  • Taehwan Kim & Seonah Moon & Ke Xu, 2019. "Information-rich localization microscopy through machine learning," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10036-z
    DOI: 10.1038/s41467-019-10036-z
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-019-10036-z
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-019-10036-z?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
    ---><---

    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:10:y:2019:i:1:d:10.1038_s41467-019-10036-z. 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.