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Cited by 308

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Advances in Imaging Secondary Ion Mass Spectrometry for Biological Samples

Open access
Boxer, Steven G., Kraft, Mary L., Weber, Peter K.·2009·Annual Review of Biophysics

Abstract

Imaging mass spectrometry combines the power of mass spectrometry to identify complex molecules based on mass with sample imaging. Recent advances in secondary ion mass spectrometry have improved sensitivity and spatial resolution, so that these methods have the potential to bridge between high-resolution structures obtained by X-ray crystallography and cyro-electron microscopy and ultrastructure visualized by conventional light microscopy. Following background information on the method and instrumentation, we address the key issue of sample preparation. Because mass spectrometry is performed in high vacuum, it is essential to preserve the lateral organization of the sample while removing bulk water, and this has been a major barrier for applications to biological systems. Recent applications of imaging mass spectrometry to cell biology, microbial communities, and biosynthetic pathways are summarized briefly, and studies of biological membrane organization are described in greater depth.

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Crossref · 10.1146/annurev.biophys.050708.133634

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Metadata

Authors
Boxer, S. G., Kraft, M. L., & Weber, P. K.
Venue
Annual Review of Biophysics
Published
2009-06-01
Volume
38
Issue
1
Pages
53-74
ISSN
  • 1936-122X (ISSN-L)
  • 1936-1238
OpenAlex
W2119080963
Cited by
308
References
1
FWCI
0.72
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Advances in Imaging Secondary Ion Mass Spectrometry for Biological Samples. https://app.cochat.ai/papers/10.1146/annurev.biophys.050708.133634
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Boxer, S. G., Kraft, M. L., & Weber, P. K. (2009). Advances in Imaging Secondary Ion Mass Spectrometry for Biological Samples. Annual Review of Biophysics. https://doi.org/10.1146/annurev.biophys.050708.133634

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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.