Investigation of space charge effects and ion trapping capacity on direct introduction ultra-high-resolution mass spectrometry workflows for metabolomics.

Ultra-high-resolution mass spectrometry, in the absence of chromatography, is finding its place for direct analyses of highly complex mixtures, such as those encountered during untargeted metabolomics screening. Advances, however, have been tempered by difficulties such as uneven signal suppression experienced during electrospray ionization. Moreover, ultra-high-resolution mass spectrometers that use Orbitrap and ICR analyzers both suffer from limited ion trapping capacities, owing principally to space-charge effects. This study has evaluated and contrasted the above two types of Fourier transform mass spectrometers for their abilities to detect and identify by accurate mass measurement, small molecule metabolites present in complex mixtures. For these direct introduction studies, the Orbitrap Fusion showed a major advantage in terms of speed of analysis, enabling detection of 218 of 440 molecules (<2 ppm error, 500 000 resolution at m/z 200) present in a complex mixture in 5 min. This approach is the most viable for high-throughput workflows, such as those used in investigations involving very large cohorts of metabolomics samples. From the same mixture, 183 unique molecules were observed by FT-ICR in the broadband mode, but this number was raised to 235 when "selected ion monitoring-stitching" (SIM-stitching) was employed (<0.1 ppm error, 7 T magnet with dynamic harmonization cell, 1.8 million resolution at m/z 200, both cases). SIM-stitching FT-ICR thus offered the most complete detection, which may be of paramount importance in situations where it is essential to obtain the most complete metabolic profile possible. This added completeness, however, came at the cost of a more lengthy analysis time (120 min including manual treatment). Compared to the data presented here, future automation of processing, plus the use of absorption mode detection, segmented ion detection (stepwise detection of smaller width m/z sections), and higher magnetic field strengths, can substantially reduce FT-ICR acquisition times.

Effectiveness of Interventions to Modulate the Rumen Microbiota Composition and Function in Pre-ruminant and Ruminant Lambs.

Modulating the assembly of the ruminal microbiota might have practical implications in production. We tested how an early-life dietary intervention in lambs influences the diversity and function of the ruminal microbiota during and after the intervention. Microbiota resilience during a repeated dietary intervention was also tested. The treatment, aiming to mitigate enteric methane emissions, combined garlic essential oil and linseed oil. Fifty-six lambs and their dams were allocated to two groups and treatment (T1) or placebo (C1) was drenched from birth until 10 weeks of life. Lambs were weaned at 8 weeks. From 16 to 20 weeks, lambs in each group were divided in two subgroups that received (T1-T2 and C1-T2) or not (T1-C2 and C1-C2) the same treatment. Measurements were done at 8, 14, and 20 weeks. Average daily gain was similar between groups. Methane production was reduced by treatment at 8 and 20 weeks but at 14 weeks it was similar between C1 and T1. Interestingly, early-life treated lambs displayed a numerical increase (P = 0.12) in methane emissions at 20 weeks compared with non-treated lambs. Concentration of VFA was not affected by the intervention at 8 or 14 weeks but a lower concentration was observed in T2 lambs compared with C2 at week 20. Metataxonomics (rRNA gene) revealed differences in archaeal communities between groups of lambs when treatment was applied (weeks 8 and 20); whereas, in accord with methane emissions, these differences disappeared when treatment was discontinued (week 14). Protozoal community structure was not affected by treatment. In contrast, bacterial community structure differed between treated and non-treated lambs during and after the intervention. Rumen and urine LC-MS and NMR metabolomics at week 20 separated C2 from T2 lambs and correlation analysis highlighted interactions between microbes and metabolites, notably that of methylated compounds and Methanomassiliicocceae methanogens. This study demonstrates that a long-term early-life intervention induced modifications in the composition of the rumen bacterial community that persisted after the intervention ceased with little or no effect on archaeal and protozoal communities. However, there was no persistency of the early-life intervention on methanogenesis indicating resilience for this function.

Evaluation of the High-Field Orbitrap Fusion for Compound Annotation in Metabolomics.

Annotation of signals of interest represents a key point in mass spectrometry-based metabolomics studies. The first level of investigation is the elemental composition, which can be deduced from accurately measured masses and isotope patterns. However, accuracy of these two parameters remains to be evaluated on last generation mass spectrometers to determine the level of confidence that can be used during the annotation process. In this context, we evaluated the performance of the Orbitrap Fusion mass spectrometer for the first time and demonstrated huge potential for metabolite annotation via elemental composition determination. This work was performed using a set of 50 standard compounds analyzed under LC/MS conditions in solvent and biological media. Accurate control of the number of trapped ions proved mandatory to avoid space charge effects, ensure sub-ppm mass accuracy (using external calibration), and reliable measurement of isotopic patterns at 500,000 resolution. On the basis of the results, we propose standard optimized experimental conditions for performing robust and accurate untargeted metabolomics on the Orbitrap Fusion at high mass measurement and mass spectral accuracy.

proFIA: a data preprocessing workflow for flow injection analysis coupled to high-resolution mass spectrometry.

MOTIVATION: Flow Injection Analysis coupled to High-Resolution Mass Spectrometry (FIA-HRMS) is a promising approach for high-throughput metabolomics. FIA-HRMS data, however, cannot be preprocessed with current software tools which rely on liquid chromatography separation, or handle low resolution data only. RESULTS: We thus developed the proFIA package, which implements a suite of innovative algorithms to preprocess FIA-HRMS raw files, and generates the table of peak intensities. The workflow consists of 3 steps: (i) noise estimation, peak detection and quantification, (ii) peak grouping across samples and (iii) missing value imputation. In addition, we have implemented a new indicator to quantify the potential alteration of the feature peak shape due to matrix effect. The preprocessing is fast (less than 15 s per file), and the value of the main parameters (ppm and dmz) can be easily inferred from the mass resolution of the instrument. Application to two metabolomics datasets (including spiked serum samples) showed high precision (96%) and recall (98%) compared with manual integration. These results demonstrate that proFIA achieves very efficient and robust detection and quantification of FIA-HRMS data, and opens new opportunities for high-throughput phenotyping. AVAILABILITY AND IMPLEMENTATION: The proFIA software (as well as the plasFIA dataset) is available as an R package on the Bioconductor repository (http://bioconductor.org/packages/proFIA), and as a Galaxy module on the Main Toolshed (https://toolshed.g2.bx.psu.edu), and on the Workflow4Metabolomics online infrastructure (http://workflow4metabolomics.org). CONTACT: alexis.delabriere@cea.fr or etienne.thevenot@cea.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.