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The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.

Superresolution Imaging of Chemical Synapses in the Brain

Open access
Dani, Adish, Huang, Bo, Bergan, Joseph, Dulac, Catherine, Zhuang, Xiaowei·2010·Neuron

Abstract

Determination of the molecular architecture of synapses requires nanoscopic image resolution and specific molecular recognition, a task that has so far defied many conventional imaging approaches. Here we present a super-resolution fluorescence imaging method to visualize the molecular architecture of synapses in the brain. Using multicolor, three-dimensional stochastic optical reconstruction microscopy, the distributions of synaptic proteins can be measured with nanometer precision. Furthermore, the wide-field, volumetric imaging method enables high-throughput, quantitative analysis of a large number of synapses from different brain regions. To demonstrate the capabilities of this approach, we have determined the organization of ten protein components of the presynaptic active zone and the postsynaptic density. Variations in synapse morphology, neurotransmitter receptor composition, and receptor distribution were observed both among synapses and across different brain regions. Combination with optogenetics further allowed molecular events associated with synaptic plasticity to be resolved at the single-synapse level.

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Crossref · 10.1016/j.neuron.2010.11.021

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Metadata

Authors
Dani, A., Huang, B., Bergan, J., Dulac, C., & Zhuang, X.
Venue
Neuron
Publisher
Elsevier BV
Published
2010-12
Volume
68
Issue
5
Pages
843-856
ISSN
0896-6273 (ISSN-L)
OpenAlex
W2150192750
Cited by
725
References
11
FWCI
21.66
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Superresolution Imaging of Chemical Synapses in the Brain. https://app.cochat.ai/papers/10.1016/j.neuron.2010.11.021
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Dani, A., Huang, B., Bergan, J., Dulac, C., & Zhuang, X. (2010). Superresolution Imaging of Chemical Synapses in the Brain. Neuron. https://doi.org/10.1016/j.neuron.2010.11.021

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.