Abstract

Proteins work together in nanostructures in many physiological contexts and disease states. We recently developed expansion revealing (ExR), which expands proteins away from each other, in order to support better labeling with antibody tags and nanoscale imaging on conventional microscopes. Here, we report multiplexed expansion revealing (multiExR), which enables high-fidelity antibody visualization of >20 proteins in the same specimen, over serial rounds of staining and imaging. Across all datasets examined, multiExR exhibits a median round-to-round registration error of 39 nm, with a median registration error of 25 nm when the most stringent form of the protocol is used. We precisely map 23 proteins in the brain of 5xFAD Alzheimer’s model mice, and find reductions in synaptic protein cluster volume, and co-localization of specific AMPA receptor subunits with amyloid-beta nanoclusters. We visualize 20 synaptic proteins in specimens of mouse primary somatosensory cortex. multiExR may be of broad use in analyzing how different kinds of protein are organized amidst normal and pathological processes in biology.

Mapping the nature of multiprotein nanostructures in cellular contexts remains challenging. Here, Kang and Schroeder et al. report multiplexed expansion revealing, a technique which expands proteins away from each other, for nanoscale localisation and antibody visualisation of >20 proteins in the same specimen.

Details

Title
Multiplexed expansion revealing for imaging multiprotein nanostructures in healthy and diseased brain
Author
Kang, Jinyoung 1 ; Schroeder, Margaret E. 2   VIAFID ORCID Logo  ; Lee, Youngmi 3 ; Kapoor, Chaitanya 4   VIAFID ORCID Logo  ; Yu, Eunah 3 ; Tarr, Tyler B. 5 ; Titterton, Kat 3 ; Zeng, Menglong 3 ; Park, Demian 3 ; Niederst, Emily 6 ; Wei, Donglai 7 ; Feng, Guoping 8   VIAFID ORCID Logo  ; Boyden, Edward S. 9   VIAFID ORCID Logo 

 MIT, McGovern Institute for Brain Research, Cambridge, USA (GRID:grid.511294.a); MIT, Yang Tan Collective, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
 MIT, McGovern Institute for Brain Research, Cambridge, USA (GRID:grid.511294.a); MIT, Department of Brain and Cognitive Sciences, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
 MIT, McGovern Institute for Brain Research, Cambridge, USA (GRID:grid.511294.a) 
 Department of Electrical and Electronics Engineering, BITS Pilani, India (GRID:grid.418391.6) (ISNI:0000 0001 1015 3164) 
 University of Pittsburgh, Department of Neuroscience, Pittsburgh, USA (GRID:grid.21925.3d) (ISNI:0000 0004 1936 9000) 
 MIT, The Picower Institute for Learning and Memory, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
 Boston College, Department of Computer Science, Chestnut Hill, USA (GRID:grid.208226.c) (ISNI:0000 0004 0444 7053) 
 MIT, McGovern Institute for Brain Research, Cambridge, USA (GRID:grid.511294.a); MIT, Yang Tan Collective, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); MIT, Department of Brain and Cognitive Sciences, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Broad Institute of MIT and Harvard, Cambridge, USA (GRID:grid.66859.34) (ISNI:0000 0004 0546 1623) 
 MIT, McGovern Institute for Brain Research, Cambridge, USA (GRID:grid.511294.a); MIT, Yang Tan Collective, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); MIT, Department of Brain and Cognitive Sciences, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); MIT, Center for Neurobiological Engineering and K. Lisa Yang Center for Bionics, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); MIT, Department of Biological Engineering, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); MIT, Koch Institute, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786); Howard Hughes Medical Institute, Cambridge, USA (GRID:grid.413575.1) (ISNI:0000 0001 2167 1581); MIT, Media Arts and Sciences, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
Pages
9722
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3126443036
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.