Multiplexed and scalable super-resolution imaging of three-dimensional protein localization in size-adjustable tissues
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Author(s)
Ku, Taeyun
Swaney, Justin Mark
Park, Jeong-Yoon
Albanese, Alexandre
Murray, Evan
Cho, Jae Hun
Park, Young-Gyun
Mangena, Vamsi
Chen, Jiapei
Chung, Kwanghun
Date Issued
July 2016
Journal
Nature Biotechnology
Publisher
Nature Publishing Group
Citation
Ku, Taeyun; Swaney, Justin; Park, Jeong-Yoon; Albanese, Alexandre; Murray, Evan; Cho, Jae Hun; Park, Young-Gyun; Mangena, Vamsi; Chen, Jiapei and Chung, Kwanghun “Multiplexed and Scalable Super-Resolution Imaging of Three-Dimensional Protein Localization in Size-Adjustable Tissues.” Nature Biotechnology 34, no. 9 (July 2016): 973–981. © 2016 Macmillan Publishers Limited, part of Springer Nature
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Author's final manuscript
Abstract
The biology of multicellular organisms is coordinated across multiple size scales, from the subnanoscale of molecules to the macroscale, tissue-wide interconnectivity of cell populations. Here we introduce a method for super-resolution imaging of the multiscale organization of intact tissues. The method, called magnified analysis of the proteome (MAP), linearly expands entire organs fourfold while preserving their overall architecture and three-dimensional proteome organization. MAP is based on the observation that preventing crosslinking within and between endogenous proteins during hydrogel-tissue hybridization allows for natural expansion upon protein denaturation and dissociation. The expanded tissue preserves its protein content, its fine subcellular details, and its organ-scale intercellular connectivity. We use off-the-shelf antibodies for multiple rounds of immunolabeling and imaging of a tissue's magnified proteome, and our experiments demonstrate a success rate of 82% (100/122 antibodies tested). We show that specimen size can be reversibly modulated to image both inter-regional connections and fine synaptic architectures in the mouse brain.
MIT Department
Institute for Medical Engineering and Science
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Picower Institute for Learning and Memory
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DOI of Published Version
https://doi.org/10.1038/nbt.3641
