Expansion microscopy

• Main Authors: Chen, Fei (Contributor), Tillberg, Paul W. (Contributor), Boyden, Edward (Author)
• Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Media Laboratory (Contributor), McGovern Institute for Brain Research at MIT (Contributor), Massachusetts Institute of Technology. Center for Neurobiological Engineering (Contributor), Boyden, Edward Stuart (Contributor)
• Format: Article
• Language: English
• Published: American Association for the Advancement of Science (AAAS), 2016-07-08T17:24:37Z.
• Subjects: Article
• Online Access: Get fulltext

Available in PMC 2015 July 30.
In optical microscopy, fine structural details are resolved by using refraction to magnify images of a specimen. We discovered that by synthesizing a swellable polymer network within a specimen, it can be physically expanded, resulting in physical magnification. By covalently anchoring specific labels located within the specimen directly to the polymer network, labels spaced closer than the optical diffraction limit can be isotropically separated and optically resolved, a process we call expansion microscopy (ExM). Thus, this process can be used to perform scalable superresolution microscopy with diffraction-limited microscopes. We demonstrate ExM with apparent ~70-nanometer lateral resolution in both cultured cells and brain tissue, performing three-color superresolution imaging of ~107 cubic micrometers of the mouse hippocampus with a conventional confocal microscope.
National Institutes of Health (U.S.) (NIH Director's Pioneer Award 1DP1NS087724)
National Institutes of Health (U.S.) (NIH Transformative Research Award 1R01MH103910-01)
New York Stem Cell Foundation (Robertson Investigator Award)
National Science Foundation (U.S.). Center for Brains, Minds and Machines (CBMM) (NSF CCF-1231216)
National Science Foundation (U.S.) (NSF CAREER Award CBET 1053233)