Evidence map›Paper›PMID 39939319›Full record

ArticleNature communications2025

Dense, continuous membrane labeling and expansion microscopy visualization of ultrastructure in tissues.

Tay Won Shin, Hao Wang, Chi Zhang, Bobae An, Yangning Lu, Elizabeth Zhang, Xiaotang Lu, Emmanouil D Karagiannis, Jeong Seuk Kang, Amauche Emenari and 9 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

  1. Article
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  10. Article
  11. Article
  12. Article
  13. Synthetic Lipid Biology.Chemical reviews · 2025
    Review
  14. Review
  15. Article
  16. Review
  17. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

19 authors.

Tay Won ShinMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Hao Wang *McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0003-1966-7940
Chi Zhang *McGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Bobae AnMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Yangning LuMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Elizabeth ZhangMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0009-0006-8032-9421
Xiaotang LuDepartment of Cellular and Molecular Biology, Harvard University, Cambridge, MA, 02138, USA.ORCID http://orcid.org/0000-0002-8575-5394
Emmanouil D KaragiannisMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Jeong Seuk KangMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Amauche EmenariMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Panagiotis SymvoulidisMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0002-1396-3426
Shoh AsanoMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Leanne LinDepartment of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0009-0000-5030-9434
Emma K CostaDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
IMAXT Grand Challenge Consortium
Adam H MarblestoneMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
Narayanan KasthuriCenter for Nanoscale Materials, Argonne National Laboratory, Lemont, IL, 60439, USA.
Li-Huei TsaiDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.ORCID http://orcid.org/0000-0003-1262-0592
Edward S BoydenMcGovern Institute, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. edboyden@mit.edu.ORCID http://orcid.org/0000-0002-0419-3351

Funding

Redesigning General Anesthesia (Admin Supp)R01GM104948 · NIGMS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BOYDEN, EDWARD S., BROWN, EMERY N · 2012 to 2016
$4.4M
High-throughput robotic analysis of integrated neuronal phenotypesR01EY023173 · NEI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BOYDEN, EDWARD S., FOREST, CRAIG · 2012 to 2016
$4.3M
Millisecond-Timescale Whole-Brain Neural Activity Mapping in Health and DiseaseDP1NS087724 · NINDS · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BOYDEN, EDWARD S. · 2013 to 2017
$3.9M
High-throughput approaches to local and long-range synaptic connectivityRF1MH123403 · NIMH · COLD SPRING HARBOR LABORATORY · PI BOYDEN, EDWARD S., ZADOR, ANTHONY M · 2020 to 2020
$3.3M
Mechanisms of pathology and neuronal hyperactivity in a memory circuit in Alzheimer's diseaseR01AG070831 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BOYDEN, EDWARD S., TSAI, LI-HUEI · 2021 to 2025
$3.2M
Multiplexed Nanoscale Protein Mapping Through Expansion Microscopy and Immuno-SABERRF1MH124606 · NIMH · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BOYDEN, EDWARD S., YIN, PENG · 2020 to 2020
$2.7M
Lipid imaging expansion microscopy to study Alzheimer's diseaseR01AG087374 · NIA · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Edward S. Boyden, Li-Huei Tsai · 2024 to 2026
$2.3M
Ultra-Multiplexed Nanoscale In Situ Proteomics for Understanding Synapse TypesU01MH106011 · NIMH · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI BATHE, MARK, BOYDEN, EDWARD S. · 2014 to 2016
$2.2M
New Methodologies for ConnectomicsK99MH128891 · NIMH · HARVARD UNIVERSITY · PI LU, XIAOTANG · 2022 to 2023
$266k
Howard Hughes Medical Institute (HHMI) InvestigatorNEI NIH HHS R01 EY023173NIA NIH HHS R01 AG070831NIA NIH HHS R01 AG087374NIGMS NIH HHS R01 GM104948NIMH NIH HHS K99 MH128891NIMH NIH HHS RF1 MH123403NIMH NIH HHS RF1 MH124606NIMH NIH HHS U01 MH106011NINDS NIH HHS DP1 NS087724U.S. Department of Health & Human Services | National Institutes of Health (NIH) 1R01GM104948
6 · The paper itself

Abstract

Lipid membranes are key to the nanoscale compartmentalization of biological systems, but fluorescent visualization of them in intact tissues, with nanoscale precision, is challenging to do with high labeling density. Here, we report ultrastructural membrane expansion microscopy (umExM), which combines an innovative membrane label and optimized expansion microscopy protocol, to support dense labeling of membranes in tissues for nanoscale visualization. We validate the high signal-to-background ratio, and uniformity and continuity, of umExM membrane labeling in brain slices, which supports the imaging of membranes and proteins at a resolution of ~60 nm on a confocal microscope. We demonstrate the utility of umExM for the segmentation and tracing of neuronal processes, such as axons, in mouse brain tissue. Combining umExM with optical fluctuation imaging, or iterating the expansion process, yields ~35 nm resolution imaging, pointing towards the potential for electron microscopy resolution visualization of brain membranes on ordinary light microscopes.

Indexed as

BrainCell MembraneAnimalsMiceMice, Inbred C57BLMicroscopy, ConfocalNeuronsStaining and Labeling

Identifiers

PMID39939319
PMCPMC11821914

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.