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Machine learning assisted interferometric structured illumination microscopy for dynamic biological imaging

Published version
Peer-reviewed

Type

Article

Change log

Authors

Hecker, Lisa 
Christensen, Charles N 
Lamb, Jacob R 
Lu, Meng 

Abstract

jats:titleAbstract</jats:title>jats:pStructured 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.</jats:p>

Description

Keywords

Journal Title

Nature Communications

Conference Name

Journal ISSN

2041-1723
2041-1723

Volume Title

Publisher

Springer Science and Business Media LLC
Sponsorship
Engineering and Physical Sciences Research Council (EP/L015889/1)
Engineering and Physical Sciences Research Council (EP/H018301/1)
Wellcome Trust (089703/Z/09/Z)
Medical Research Council (MR/K015850/1)
Medical Research Council (MR/K02292X/1)