Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging

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 and Clemens F. Kaminski ()
Additional contact information
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

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: 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.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-022-35307-0 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35307-0

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-022-35307-0

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().