Comparison of High-Resolution Fourier Transform Mass Spectrometry Platforms for Putative Metabolite Annotation

Characterization of halogenated organic compounds by the Fourier transform ion cyclotron resonance mass spectrometry: A critical review

Halogenated organic compounds (HOCs), widely present in various environments, are generally formed by natural processes (e.g., photochemical halogenation) and anthropogenic activities (e.g., water disinfection and anthropogenic discharge of HOCs), posing health and environmental risks. Therefore, in-depth knowledge of the molecular composition, transformation, and fate of HOCs is crucial to regulate and reduce their formation. Because of the extremely complex nature of HOCs and their precursors, the molecular composition of HOCs remains largely unknown. The Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) offers the most powerful resolution and mass accuracy for the simultaneous molecular-level characterization of HOCs and their precursors. However, there is still a paucity of reviews regarding the comprehensive characterization of HOCs by FT-ICR MS. Based on the FT-ICR MS, the formation mechanism, sample pretreatment, and analysis methods were summarized for two typical HOCs classes, namely halogenated disinfection byproducts and per- and polyfluoroalkyl substances in this review. Moreover, we have highlighted data analysis methods and some typical applications of HOCs using FT-ICR MS and proposed suggestions for current issues. This review will deepen our understanding of the chemical characterization of HOCs and their formation mechanisms and transformation at the molecular level in aquatic systems, facilitating the application of the state-of-the-art FT-ICR MS in environmental and geochemical research.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

Hyphenation of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) with separation methods: The art of compromises and the possible - A review

This review provides an overview of the online hyphenation of Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS) with separation methods to date. The online coupling between separation techniques (gas and liquid chromatography, capillary electrophoresis) and FT-ICR MS essentially raises questions of compromise and is not look as straightforward as hyphenation with other analyzers (QTOF-MS for instance). FT-ICR MS requires time to reach its highest resolving power and accuracy in mass measurement capabilities whereas chromatographic and electrophoretic peaks are transient. In many applications, the strengths and the weaknesses of each technique are balanced by their hyphenation. Untargeted "Omics" (e.g. proteomics, metabolomics, petroleomics, …) is one of the main areas of application for FT-ICR MS hyphenated to online separation techniques because of the complexity of the sample. FT-ICR MS achieves the required high mass measurement accuracy to determine accurate molecular formulae and resolution for isobar distinction. Meanwhile separation techniques highlight isomers and reduce the ion suppression effects extending the dynamic range. Even if the implementation of FT-ICR MS hyphenated with online separation methods is a little trickier (the art of compromise), this review shows that it provides unparalleled results to the scientific community (the art of the possible), along with raising the issue of its future in the field with the relentless technological progress.

A Strategy for Uncovering the Serum Metabolome by Direct-Infusion High-Resolution Mass Spectrometry

Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.

The effect of root hairs on exudate composition: a comparative non-targeted metabolomics approach

Root exudation is a major pathway of organic carbon input into soils. It affects soil physical properties, element solubility as well as speciation, and impacts the microbial community in the rhizosphere. Root exudates contain a large number of primary and secondary plant metabolites, and the amount and composition are highly variable depending on plant species and developmental stage. Detailed information about exudate composition will allow for a better understanding of exudate-driven rhizosphere processes and their feedback loops. Although non-targeted metabolomics by high-resolution mass spectrometry is an established tool to characterize root exudate composition, the extent and depth of the information obtained depends strongly on the analytical approach applied. Here, two genotypes of Zea mays L., differing in root hair development, were used to compare six mass spectrometric approaches for the analysis of root exudates. Reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography combined with time-of-flight mass spectrometry (LC-TOF-MS), as well as direct infusion Fourier-transform ion cyclotron resonance mass spectrometry (DI-FT-ICR-MS), were applied with positive and negative ionization mode. By using the same statistical workflow, the six approaches resulted in different numbers of detected molecular features, ranging from 176 to 889, with a fraction of 48 to 69% of significant features (fold change between the two genotypes of > 2 and p-value < 0.05). All approaches revealed the same trend between genotypes, namely up-regulation of most metabolites in the root hair defective mutant (rth3). These results were in agreement with the higher total carbon and nitrogen exudation rate of the rth3-mutant as compared to the corresponding wild-type maize (WT). However, only a small fraction of features were commonly found across the different analytical approaches (20-79 features, 13-31% of the rth3-mutant up-regulated molecular formulas), highlighting the need for different mass spectrometric approaches to obtain a more comprehensive view into the composition of root exudates. In summary, 111 rth3-mutant up-regulated compounds (92 different molecular formulas) were detected with at least two different analytical approaches, while no WT up-regulated compound was found by both, LC-TOF-MS and DI-FT-ICR-MS. Zea mays L. exudate features obtained with multiple analytical approaches in our study were matched against the metabolome database of Zea mays L. (KEGG) and revealed 49 putative metabolites based on their molecular formula.

PFAS Analysis with Ultrahigh Resolution 21T FT-ICR MS: Suspect and Nontargeted Screening with Unrivaled Mass Resolving Power and Accuracy

Per- and polyfluoroalkyl substances (PFASs) are a large family of thousands of chemicals, many of which have been identified using nontargeted time-of-flight and Orbitrap mass spectrometry methods. Comprehensive characterization of complex PFAS mixtures is critical to assess their environmental transport, transformation, exposure, and uptake. Because 21 tesla (T) Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) offers the highest available mass resolving power and sub-ppm mass errors across a wide molecular weight range, we developed a nontargeted 21 T FT-ICR MS method to screen for PFASs in an aqueous film-forming foam (AFFF) using suspect screening, a targeted formula database (C, H, Cl, F, N, O, P, S; ≤865 Da), isotopologues, and Kendrick-analogous mass difference networks (KAMDNs). False-positive PFAS identifications in a natural organic matter (NOM) sample, which served as the negative control, suggested that a minimum length of 3 should be imposed when annotating CF₂-homologous series with positive mass defects. We putatively identified 163 known PFASs during suspect screening, as well as 134 novel PFASs during nontargeted screening, including a suspected polyethoxylated perfluoroalkane sulfonamide series. This study shows that 21 T FT-ICR MS analysis can provide unique insights into complex PFAS composition and expand our understanding of PFAS chemistries in impacted matrices.