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Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging

Author

Listed:
  • Edward N. Ward

    (University of Cambridge)

  • Lisa Hecker

    (University of Cambridge)

  • Charles N. Christensen

    (University of Cambridge)

  • Jacob R. Lamb

    (University of Cambridge)

  • Meng Lu

    (University of Cambridge)

  • Luca Mascheroni

    (University of Cambridge)

  • Chyi Wei Chung

    (University of Cambridge)

  • Anna Wang

    (Oxford University)

  • Christopher J. Rowlands

    (Imperial College London)

  • Gabriele S. Kaminski Schierle

    (University of Cambridge)

  • Clemens F. Kaminski

    (University of Cambridge)

Abstract

Structured Illumination Microscopy, SIM, is one of the most powerful optical imaging methods available to visualize biological environments at subcellular resolution. Its limitations stem from a difficulty of imaging in multiple color channels at once, which reduces imaging speed. Furthermore, there is substantial experimental complexity in setting up SIM systems, preventing a widespread adoption. Here, we present Machine-learning Assisted, Interferometric Structured Illumination Microscopy, MAI-SIM, as an easy-to-implement method for live cell super-resolution imaging at high speed and in multiple colors. The instrument is based on an interferometer design in which illumination patterns are generated, rotated, and stepped in phase through movement of a single galvanometric mirror element. The design is robust, flexible, and works for all wavelengths. We complement the unique properties of the microscope with an open source machine-learning toolbox that permits real-time reconstructions to be performed, providing instant visualization of super-resolved images from live biological samples.

Suggested Citation

  • Edward N. Ward & Lisa Hecker & Charles N. Christensen & Jacob R. Lamb & Meng Lu & Luca Mascheroni & Chyi Wei Chung & Anna Wang & Christopher J. Rowlands & Gabriele S. Kaminski Schierle & Clemens F. Ka, 2022. "Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35307-0
    DOI: 10.1038/s41467-022-35307-0
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    References listed on IDEAS

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    1. Marcel Müller & Viola Mönkemöller & Simon Hennig & Wolfgang Hübner & Thomas Huser, 2016. "Open-source image reconstruction of super-resolution structured illumination microscopy data in ImageJ," Nature Communications, Nature, vol. 7(1), pages 1-6, April.
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