GC–QTOFMS with a low-energy electron ionization source for advancing isotopologue analysis in 13C-based metabolic flux analysis

Mairinger, Teresa; Sanderson, J.

doi:10.1007/s00216-019-01590-y

Cited by 14 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Phaff 1956 (Komagataella phaffii Kurtzman) 27 Literature search confirmed our findings and additionally expanded the reported compounds [30][31][32][33][34][35][36][37][38][39] in our library which can be found in the Supplementary two participating laboratories or others [30][31][32][33][34][35][36][37][38][39] ). The remaining 25 metabolites were reported elsewhere [30][31][32][33]36,37 ( Supplementary Table S1, column, FinalMetaboliteList, "Additional literature hits"). The human metabolome database (HMDB) 43,44 was interrogated regarding overlaps with the yeast metabolome data base which is based on the Baker's/Brewer's yeast Saccharomyces cerevisiae 45 crosschecking our library for plausibility.…”

Section: Metabolites and Lipids Present In The Yeast Materialssupporting

confidence: 80%

“…Over the recent past, yeast-based standards have seen a slow but increasing acceptance in the metabolomics community. Several laboratories resorted to Pichia pastoris ethanolic yeast extracts [30][31][32][33][34][35][36][37][38][39] primarily for 13 C internal standardization. Only a few studies were related to instrument performance tests or method development in accordance to the here proposed application 33,[38][39][40] .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Benchmarking non-targeted metabolomics using yeast derived libraries

Rampler

Hermann

Grabmann³

et al. 2020

Preprint

View full text Add to dashboard Cite

Non-targeted analysis by high-resolution mass spectrometry (HRMS) is the essential discovery tool in metabolomics. Up to date, standardization and validation remain a challenge. Community wide accepted, cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts, derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of > 200 metabolites, reproducibly recovered in ethanolic extracts by orthogonal LCHRMS methods, different fermentations (over three years) and different laboratories. More specifically, compound identification was based on accurate mass, matching retention times, and MS/MS spectra as compared to authentic standards and internal databases. The library includes metabolites from the classes of 1) organic acids and derivatives (2) nucleosides, nucleotides and analogues, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds and (7) benzoids at expected concentrations ranges of sub-nM to µM. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the Human metabolome data base (HMDB). A large fraction of metabolites was found to be stable for several years when stored at −80°C. Thus, the yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments, but enabled in-house routines for instrumental performance tests. Finally, the benchmark material opens new avenues for batch to batch corrections in large scale non-targeted metabolomics studies.

show abstract

Section: Metabolites and Lipids Present In The Yeast Materialssupporting

confidence: 80%

Section: Resultsmentioning

confidence: 99%

Benchmarking non-targeted metabolomics using yeast derived libraries

Rampler

Hermann

Grabmann³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…To minimize fragmentation, different analytical strategies have been implemented, including chemical derivatization with N-methyl N-(tertbutylsilyl)trifluoroacetamide (MTBSTFA) 15 , EI ionization at 15 eV 31 or the use of chemical ionization 37 . The latter has been traditionally implemented using methane as reagent gas 31 , however, isobutane produces superior intact analyte signal abundance to methane 45 , resulting also in a predominant protonated adduct ion and less fragmentation.…”

Section: Discussionmentioning

confidence: 99%

“…Afterwards, isotopologue abundances are added by considering the number of labelled atoms to which each isotopologue corresponds (see Figure 3B for an example in the case of isotopologues M+0 to M+3 of lactate 2TMS). 31 or the use of chemical ionization 37 . The latter has been traditionally implemented using methane as reagent gas 31 , however, isobutane produces superior intact analyte signal abundance to methane 45 , resulting also in a predominant protonated adduct ion and less fragmentation.…”

Section: Isoscan: An R Package To Process Gc-ci-ms Data From Low-massmentioning

confidence: 99%

“…The coverage of GC-EI MS is comprehensive enough to cover most central carbon metabolism (i.e., glycolysis, TCA, amino acids, pentose phosphate pathway) thanks to the use of chemical derivatization. Several software tools for automated analysis of GC-EI MS data are available [21][22][23][24][25] , including tools for stable-isotope labelling data analysis [26][27][28][29][30][31][32] , which must address the inherent difficulty of deconvoluting the intensity of overlapping isotopologues from EI mass spectral fragmentation. Interestingly, the use of GC-CI MS for stable-isotope tracing is largely unexplored [33][34][35][36][37] .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring the Use of Gas Chromatography Coupled to Chemical Ionization Mass Spectrometry (GC-CI-MS) for Stable Isotope Labelling in Metabolomics

Capellades

Junza²,

Samino³

et al. 2020

Preprint

View full text Add to dashboard Cite

<p>Isotopic labelling experiments have been utterly valuable to monitor the flux of metabolic reactions in biological systems, which is crucial to understand homeostatic alterations with disease. Experimental determination of metabolic fluxes can be inferred from a characteristic rearrangement of stable isotope tracers (e.g., 13C or 15N) that can be detected by mass spectrometry (MS). Metabolites measured are generally members of well-known metabolic pathways, and most of them can be detected using both gas chromatography (GC)-MS and liquid chromatography (LC)-MS. In here, we show that GC methods coupled to chemical ionization (CI) MS have a clear advantage over alternative methodologies due to GC’s superior chromatography separation efficiency and the fact that CI is a soft ionization technique that yields identifiable protonated molecular ion peaks. We tested diverse GC-CI-MS setups, including methane and isobutane reagent gases, triple quadrupole (QqQ) MS in SIM mode or selected ion clusters using optimized narrow-windows (~10 Da) in scan mode, and standard full scan methods using high resolution GC-(q)TOF and GC-Orbitrap systems. Isobutane as reagent gas in combination with both low-resolution (LR) and high-resolution (HR) MS showed the best performance, enabling precise detection of isotopologues in most metabolic intermediates of central carbon metabolism. Finally, with the aim of overcoming manual operations, we developed an R-based tool called isoSCAN that automatically quantifies all isotopologues of intermediate metabolites of glycolysis, TCA cycle, amino acids, pentose phosphate pathway and urea cycle from LRMS and HRMS data.</p>

show abstract

Low‐energy electron ionization optimization for steroidomics analysis using high‐resolution mass spectrometry

Leogrande

Jardines

Brito

et al. 2021

Rapid Comm Mass Spectrometry

View full text Add to dashboard Cite

RationaleSystematic electron ionization fragmentation studies of steroids have been performed to elucidate and trace their characteristic fragmentation patterns. However, the electron ionization source setting at 70 eV electron energy is much higher than the ionization potential (7–15 eV) of most organic compounds, leading to extensive fragmentation. We present a multifactorial study on optimizing a low‐energy electron ionization source to maximize molecular ion formation while minimizing the extent of fragmentation to improve the analytical sensitivity of steroids, especially the more thermolabile ones, while preserving the information that can be extracted from the data.MethodsTwenty‐seven steroid reference materials, chosen to cover four main classes of urinary steroids, were considered; gas chromatography/quadrupole time‐of‐flight (GC/qTOF) analyses were carried out using an Agilent Technologies model 8890 gas chromatograph coupled to an Agilent Technologies model 7250 accurate‐mass quadrupole time‐of‐flight (GC/qTOF) instrument. The effects of electron energy, emission current, and source temperature, as well as their potential interactions on steroid fragmentation pathways, have been assessed in full factorial experimental designs.ResultsThree parameters were specifically evaluated to improve the chromatographic/spectrometric response of the selected steroids: (i) degree of fragmentation; (ii) relative abundance of the molecular ion; and (iii) peak width. The first two were evaluated by screening designs that highlighted collision energy and source temperature as the most influential factors on the analytical responses of the considered steroids, while emission current always showed a non‐significant influence. Then, an optimization design was performed to select the final source setting by searching for the combination of factors that minimize peak tailing.ConclusionsThe proposed analytical approach permits a faster selection of optimal experimental conditions for steroidomics analysis using low‐energy electron ionization and high‐resolution mass spectrometry. The development of these designs of experiments (DoE) in full factorial design (FFD) allowed multiple inputs to be monitored at the same time, highlighting the possible interactions and estimating the effects of a factor in the different levels of the other factors considered.

show abstract

GC–QTOFMS with a low-energy electron ionization source for advancing isotopologue analysis in 13C-based metabolic flux analysis

Cited by 14 publications

References 27 publications

Benchmarking non-targeted metabolomics using yeast derived libraries

Benchmarking non-targeted metabolomics using yeast derived libraries

Exploring the Use of Gas Chromatography Coupled to Chemical Ionization Mass Spectrometry (GC-CI-MS) for Stable Isotope Labelling in Metabolomics

Low‐energy electron ionization optimization for steroidomics analysis using high‐resolution mass spectrometry

Contact Info

Product

Resources

About