Characterization of Isotopic Abundance Measurements in High Resolution FT-ICR and Orbitrap Mass Spectra for Improved Confidence of Metabolite Identification

• Published in: Analytical chemistry (Washington) 2011-05, Vol.83 (10), p.3737-3743
• Main Authors: Weber, Ralf J. M, Southam, Andrew D, Sommer, Ulf, Viant, Mark R
• Format: Article
• Language: English
• Subjects: Accuracy; Analytical chemistry; Carbon Isotopes - chemistry; Chemistry; Exact sciences and technology; Fourier Analysis; Isotopes; Mass spectrometry; Mass Spectrometry - instrumentation; Mass Spectrometry - methods; Metabolism; Metabolome; Signal to noise ratio; Spectrometric and optical methods
• ISSN: 0003-2700; 1520-6882
• DOI: 10.1021/ac2001803

Currently there is limited information available on the accuracy and precision of relative isotopic abundance (RIA) measurements using high-resolution direct-infusion mass spectrometry (HR DIMS), and it is unclear if this information can benefit automated peak annotation in metabolomics. Here we characterize the accuracy of RIA measurements on the Thermo LTQ FT Ultra (resolution of 100 000–750 000) and LTQ Orbitrap (R = 100 000) mass spectrometers. This first involved reoptimizing the SIM-stitching method ( Southam A. D. Anal. Chem. 2007, 79, 4595−4602 ) for the LTQ FT Ultra, which achieved a ca. 3-fold sensitivity increase compared to the original method while maintaining a root-mean-squared mass error of 0.16 ppm. Using this method, we show the quality of RIA measurements is highly dependent on signal-to-noise ratio (SNR), with RIA accuracy increasing with higher SNR. Furthermore, a negative offset between the theoretical and empirically calculated numbers of carbon atoms was observed for both mass spectrometers. Increasing the resolution of the LTQ FT Ultra lowered both the sensitivity and the quality of RIA measurements. Overall, although the errors in the empirically calculated number of carbons can be large (e.g., 10 carbons), we demonstrate that RIA measurements do improve automated peak annotation, increasing the number of single empirical formula assignments by >3-fold compared to using accurate mass alone.