Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging.

Journal: Nature communications
PMID: 36543776

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.

Authors

  • Edward N Ward
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
  • Lisa Hecker
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
  • Charles N Christensen
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
  • Jacob R Lamb
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
  • Meng Lu
    Department of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, United States; Department of Mechanical Engineering, Iowa State University, Ames, IA 500110, United States. Electronic address: menglu@iastate.edu.
  • Luca Mascheroni
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
  • Chyi Wei Chung
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
  • Anna Wang
    Department of Physics, Oxford University, Oxford, UK.
  • Christopher J Rowlands
    Department of Bioengineering, Imperial College London, London, UK.
  • Gabriele S Kaminski Schierle
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK.
  • Clemens F Kaminski
    Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, United Kingdom.

Keywords

External Resources

Popular Topics
Recent Journals