Towards analytically useful two-dimensional Fourier transform ion cyclotron resonance mass spectrometry View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-01

AUTHORS

Maria A. van Agthoven, Marc-André Delsuc, Geoffrey Bodenhausen, Christian Rolando

ABSTRACT

Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS) achieves high resolution and mass accuracy, allowing the identification of the raw chemical formulae of ions in complex samples. Using ion isolation and fragmentation (MS/MS), we can obtain more structural information, but MS/MS is time- and sample-consuming because each ion must be isolated before fragmentation. In 1987, Pfändler et al. proposed an experiment for 2D FT-ICR MS in order to fragment ions without isolating them and to visualize the fragmentations of complex samples in a single 2D mass spectrum, like 2D NMR spectroscopy. Because of limitations of electronics and computers, few studies have been conducted with this technique. The improvement of modern computers and the use of digital electronics for FT-ICR hardware now make it possible to acquire 2D mass spectra over a broad mass range. The original experiments used in-cell collision-induced dissociation, which caused a loss of resolution. Gas-free fragmentation modes such as infrared multiphoton dissociation and electron capture dissociation allow one to measure high-resolution 2D mass spectra. Consequently, there is renewed interest to develop 2D FT-ICR MS into an efficient analytical method. Improvements introduced in 2D NMR spectroscopy can also be transposed to 2D FT-ICR MS. We describe the history of 2D FT-ICR MS, introduce recent improvements, and present analytical applications to map the fragmentation of peptides. Finally, we provide a glossary which defines a few keywords for the 2D FT-ICR MS field. More... »

PAGES

51-61

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00216-012-6422-8

DOI

http://dx.doi.org/10.1007/s00216-012-6422-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1020750020

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23076397


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