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Utilizing artificial neural networks in MATLAB to achieve parts-per-billion mass measurement accuracy with a fourier transform ion cyclotron resonance mass spectrometer.

ABSTRACT: Fourier transform ion cyclotron resonance mass spectrometry has the ability to realize exceptional mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ICR cell for a particular measurement, as well as relative ion abundance of a given species. Artificial neural network calibration in conjunction with automatic gain control (AGC) is utilized in these experiments to formally account for the differences in total ion population in the ICR cell between the external calibration spectra and experimental spectra. In addition, artificial neural network calibration is used to account for both differences in total ion population in the ICR cell as well as relative ion abundance of a given species, which also affords mean MMA values at the parts-per-billion level.

SUBMITTER: Williams DK

PROVIDER: S-EPMC2752871 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Utilizing artificial neural networks in MATLAB to achieve parts-per-billion mass measurement accuracy with a fourier transform ion cyclotron resonance mass spectrometer.

Williams D Keith DK Kovach Alexander L AL Muddiman David C DC Hanck Kenneth W KW

Journal of the American Society for Mass Spectrometry 20090311 7

Fourier transform ion cyclotron resonance mass spectrometry has the ability to realize exceptional mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ICR cell for a particular measurement, as well as relative ion abundance of a given species. Artificial neural net ...[more]

PMID: 19362012

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Project description:The reactivity of 10 charged phenyl radicals toward several amino acids was examined in the gas phase in a dual-cell Fourier transform ion cyclotron resonance mass spectrometer. All radicals abstract a hydrogen atom from the amino acids, as expected. The most electrophilic radicals (with the greatest calculated vertical electron affinities (EA) at the radical site) also react with these amino acids via NH(2) abstraction (a nonradical nucleophilic addition-elimination reaction). Both the radical (hydrogen atom abstraction) and nonradical (NH(2) abstraction) reaction efficiencies were found to increase with the electrophilicity (EA) of the radical. However, NH(2) abstraction is more strongly influenced by EA. In contrast to an earlier report, the ionization energies of the amino acids do not appear to play a general reactivity-controlling role. Studies using several partially deuterium-labeled amino acids revealed that abstraction of a hydrogen atom from the α-carbon is only preferred for glycine; for the other amino acids, a hydrogen atom is preferentially abstracted from the side chain. The electrophilicity of the radicals does not appear to have a major influence on the site from which the hydrogen atom is abstracted. Hence, the regioselectivity of hydrogen atom abstraction appears to be independent of the structure of the radical but dependent on the structure of the amino acid. Surprisingly, abstraction of two hydrogen atoms was observed for the N-(3-nitro-5-dehydrophenyl)pyridinium radical, indicating that substituents on the radical not only influence the EA of the radical but also can be involved in the reaction. In disagreement with an earlier report, proline was found to display several unprecedented reaction pathways that likely do not proceed via a radical mechanism but rather by a nucleophilic addition-elimination mechanism. Both NH(2) and (15)NH(2) groups were abstracted from lysine labeled with (15)N on the side chain, indicating that NH(2) abstraction occurs both from the amino terminus and from the side chain. Quantum chemical calculations were employed to obtain insights into some of the reaction mechanisms.

Project description:Metabolomics is an interdisciplinary field that aims to study all metabolites < 1500 Da that are ubiquitously found within all organisms. Metabolomics is experiencing exponential growth and commonly relies on high-resolution mass spectrometry (HRMS). Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a form of HRMS that is particularly well suited for metabolomics research due to its exceptionally high resolution (105-106) and sensitivity with a mass accuracy in parts per billion (ppb). In this regard, FT-ICR-MS can provide valuable insights into the metabolomics analysis of complex biological systems due to unique capabilities such as the easy separation of isobaric and isomeric species, isotopic fine structure analysis, spatial resolution of metabolites in cells and tissues, and a high confidence (<1 ppm mass error) in metabolite identification. Alternatively, the large and complex data sets, long acquisition times, high cost, and limited access mainly through national mass spectrometry facilities may impede the routine adoption of FT-ICR-MS by metabolomics researchers. This review examines recent applications of FT-ICR-MS metabolomics in the search for clinical and non-human biomarkers; for the analysis of food, beverage, and environmental samples; and for the high-resolution imaging of tissues and other biological samples. We provide recent examples of metabolomics studies that highlight the advantages of FT-ICR-MS for the detailed and reliable characterization of the metabolome. Additionally, we offer some practical considerations for implementing FT-ICR-MS into a research program by providing a list of FT-ICR-MS facilities and by identifying different high-throughput interfaces, varieties of sample types, analysis methods (e.g., van Krevelen diagrams, Kendrick mass defect plot, etc.), and sample preparation and handling protocols used in FT-ICR-MS experiments. Overall, FT-ICR-MS holds great promise as a vital research tool for advancing metabolomics investigations.

Dataset Information

Utilizing artificial neural networks in MATLAB to achieve parts-per-billion mass measurement accuracy with a fourier transform ion cyclotron resonance mass spectrometer.

Publications

Utilizing artificial neural networks in MATLAB to achieve parts-per-billion mass measurement accuracy with a fourier transform ion cyclotron resonance mass spectrometer.

Similar Datasets

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