Influence of Mass Resolving Power in Orbital Ion-Trap Mass Spectrometry-Based Metabolomics

Abstract

Modern separation methods in conjunction with high-resolution accurate mass (HRAM) spectrometry can provide an enormous number of features characterized by exact mass and chromatographic behavior. Higher mass resolving power usually requires longer scanning times, and thus fewer data points are acquired across the target peak. This could cause difficulties for quantification, feature detection and deconvolution. The aim of this work was to describe the influence of mass spectrometry resolving power on profiling metabolomics experiments. From metabolic databases (HMDB, LipidMaps, KEGG), a list of compounds (41 474) was compiled and potential adducts and isotopes were calculated (622 110 features). The number of distinguishable masses was calculated for up to 3840k resolution. To evaluate these models, human plasma samples were analyzed by LC-HRMS on an Orbitrap Elite hybrid mass spectrometer (Thermo Fisher Scientific, CA, USA) at resolving power settings of 15k (7.8 Hz) up to a maximum of 480k (1.2 Hz). Software XCMS 1.44, MZmine 2.13.1, and Compound Discoverer 2.0.0.303 were used for evaluation. In plasma samples, the number of detected features increased sharply up to 60k in both positive and negative mode. However, beyond these values, it either flattened out or decreased owing to technical limitations. In conclusion, the most effective mass resolving powers for profiling analyses of metabolite rich biofluids on the Orbitrap Elite were around 60 000-120 000 fwhm to retrieve the highest amount of information. The region between 400-800 m/z was influenced the most by resolution.

Automated optimization of XCMS parameters for improved peak picking of liquid chromatography–mass spectrometry data using the coefficient of variation and parameter sweeping for untargeted metabolomics

“…Since it is usually not possible to obtain technical replicates from unknown analytes, this optimization could be conducted using pooled sample quality controls. All of the described results implicated that a method for untargeted metabolomics benefits from a high resolving power concerning the analysis but also indicate that this requires stricter setting for peak picking and are in accordance to the literature considered for the optimization of the mass spectrometer settings . It should also be noted that this approach was intended to be a dynamic optimization approach for individual experiments, which should be run in prior to every preprocessing using XCMS.…”

“…The relevance of high resolving power for metabolomic studies was investigated by Najdekr et al Although recommending a range of 60 000 to 120 000 fwhm in positive mode due to restrictions in the number of scans per peak and to limit the detection of “noise features,” they did not specify the data acquisition mode applied for their studies. Additionally, they did not adapt the applied parameter settings for peak picking using XCMS which might explain why they detected more noise features using a higher resolving power.…”

“…The HESI-II source conditions were as follows: ionization mode, positive; sheath gas, 53 AU; auxiliary gas, The optimization was conducted regarding previously published investigations for LC-HRMS/MS metabolomics in matters of scan mode, resolution of the mass spectrometer and cycle time. [24][25][26] The obtained results were evaluated regarding the recovery of the investigated compounds as well as peak shapes.…”

“…The results of the metabolomic-like evaluation are summarized in stricter setting for peak picking and are in accordance to the literature considered for the optimization of the mass spectrometer settings. [24][25][26] It should also be noted that this approach was intended to be a dynamic optimization approach for individual experiments, which should be run in prior to every preprocessing using XCMS. The presented approach does not provide a universal result for peak picking.…”

Endogenous and xenobiotic metabolic stability of primary human hepatocytes in long‐term 3D spheroid cultures revealed by a combination of targeted and untargeted metabolomics

“…We then assessed metabolomic signatures measured by a new untargeted UHPLC–ESI–HR-MS quantification method, which has been shown to provide high-quality quantification results (55–57). Metabolic patterns and their responses to nutritional, viral, or xenobiotic challenges have been studied in primary rodent hepatocytes and human hepatic cell lines, such as HepG2, Huh7.5, and HepaRG, and such approaches have been used for mechanistic investigations and to predict DILI (58–62).…”