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Laser Ablation Electrospray Ionization for Atmospheric Pressure, in Vivo, and Imaging Mass Spectrometry

Nemes, Peter, Vertes, Akos·2007·Analytical Chemistry

Abstract

Mass spectrometric analysis of biomolecules under ambient conditions promises to enable the in vivo investigation of diverse biochemical changes in organisms with high specificity. Here we report on a novel combination of infrared laser ablation with electrospray ionization (LAESI) as an ambient ion source for mass spectrometry. As a result of the interactions between the ablation plume and the spray, LAESI accomplishes electrospray-like ionization. Without any sample preparation or pretreatment, this technique was capable of detecting a variety of molecular classes and size ranges (up to 66 kDa) with a detection limit of 8 and 25 fmol for verapamil and reserpine, respectively, and quantitation capabilities with a four-decade dynamic range. We demonstrated the utility of LAESI in a broad variety of applications ranging from plant biology to clinical analysis. Proteins, lipids, and metabolites were identified, and antihistamine excretion was followed via the direct analysis of bodily fluids (urine, blood, and serum). We also performed in vivo spatial profiling (on leaf, stem, and root) of metabolites in a French marigold (Tagetes patula) seedling.

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Metadata

Authors
Nemes, P., & Vertes, A.
Venue
Analytical Chemistry
Published
2007-09-27
Volume
79
Issue
21
Pages
8098-8106
ISSN
  • 0003-2700 (ISSN-L)
  • 1520-6882
OpenAlex
W4249005591
Cited by
817
References
0
FWCI
17.28
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Laser Ablation Electrospray Ionization for Atmospheric Pressure, in Vivo, and Imaging Mass Spectrometry. https://app.cochat.ai/papers/10.1021/ac071181r
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Nemes, P., & Vertes, A. (2007). Laser Ablation Electrospray Ionization for Atmospheric Pressure, in Vivo, and Imaging Mass Spectrometry. Analytical Chemistry. https://doi.org/10.1021/ac071181r

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