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Cited by 172

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Imaging Mass Spectrometry: Principles and Potentials

Chaurand, Pierre, Schwartz, Sarah A., Reyzer, Michelle L., Caprioli, Richard M.·2005·Toxicologic Pathology

Abstract

Direct tissue profiling and imaging mass spectrometry (MS) allow for detailed mapping of the complex protein pattern across a tissue sample. Utilization of these tools provides spatial information across a tissue section for target protein expression and can be used to correlate changes in expression levels with specific disease states or drug response. Protein patterns can be directly correlated to known histological regions within the tissue, allowing for the direct monitoring of proteins specific for morphological regions within a tissue sample. Profiling and imaging MS have been used to characterize multiple tissues, including human gliomas and lung cancers, as well as tumor response to specific therapeutics, suggesting the use of proteomic information in assessing disease progression as well as predicting patient response to specific treatments. This article discusses both the technology and methods involved in analyzing proteins directly from tissue samples as well as several MS applications, including profiling human tumors, characterizing protein differences between tumor grades, and monitoring protein changes due to drug therapy.

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Crossref · 10.1080/01926230590881862

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Metadata

Authors
Chaurand, P., Schwartz, S. A., Reyzer, M. L., & Caprioli, R. M.
Venue
Toxicologic Pathology
Published
2005-01
Volume
33
Issue
1
Pages
92-101
ISSN
  • 0192-6233 (ISSN-L)
  • 1533-1601
OpenAlex
W2128748524
Cited by
172
References
4
FWCI
8.24
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Imaging Mass Spectrometry: Principles and Potentials. https://app.cochat.ai/papers/10.1080/01926230590881862
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Chaurand, P., Schwartz, S. A., Reyzer, M. L., & Caprioli, R. M. (2005). Imaging Mass Spectrometry: Principles and Potentials. Toxicologic Pathology. https://doi.org/10.1080/01926230590881862

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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.