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Cited by 489

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Electron microscopy of whole cells in liquid with nanometer resolution

Open access
Jonge, N. de, Peckys, D. B., Kremers, G. J., Piston, D. W.·2009·Proceedings of the National Academy of Sciences

Abstract

Single gold-tagged epidermal growth factor (EGF) molecules bound to cellular EGF receptors of fixed fibroblast cells were imaged in liquid with a scanning transmission electron microscope (STEM). The cells were placed in buffer solution in a microfluidic device with electron transparent windows inside the vacuum of the electron microscope. A spatial resolution of 4 nm and a pixel dwell time of 20 μs were obtained. The liquid layer was sufficiently thick to contain the cells with a thickness of 7 ± 1 μm. The experimental findings are consistent with a theoretical calculation. Liquid STEM is a unique approach for imaging single molecules in whole cells with significantly improved resolution and imaging speed over existing methods.

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Crossref · 10.1073/pnas.0809567106

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Metadata

Authors
Jonge, N. D., Peckys, D. B., Kremers, G. J., & Piston, D. W.
Venue
Proceedings of the National Academy of Sciences
Published
2009-02-17
Volume
106
Issue
7
Pages
2159-2164
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2080853943
Cited by
489
References
1
FWCI
17.63
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Electron microscopy of whole cells in liquid with nanometer resolution. https://app.cochat.ai/papers/10.1073/pnas.0809567106
The paper
Jonge, N. D., Peckys, D. B., Kremers, G. J., & Piston, D. W. (2009). Electron microscopy of whole cells in liquid with nanometer resolution. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0809567106

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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.