Evidence map›Paper›PMID 25372979›Full record

ArticleAnalytical chemistry2014

Untargeted profiling of tracer-derived metabolites using stable isotopic labeling and fast polarity-switching LC-ESI-HRMS.

Bernhard Kluger, Christoph Bueschl, Nora Neumann, Romana Stückler, Maria Doppler, Alexander W Chassy, Andrew L Waterhouse, Justyna Rechthaler, Niklas Kampleitner, Gerhard G Thallinger and 3 more

Open access · hybridAbstract read
In one paragraph

Article in Analytical chemistry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
3.1field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 45 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. An optimization method for untargeted MS-based isotopic tracing investigations of metabolism.Metabolomics : Official journal of the Metabolomic Society · 2022
    Article
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  7. Article
  8. Review
  9. Article
  10. Article
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  12. Article
  13. Article
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  15. Article
  16. Time is ripe: maturation of metabolomics in chronobiology.Current opinion in biotechnology · 2017
    Review
  17. Article
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 2 countries.

Bernhard KlugerCenter for Analytical Chemistry, Department for Agrobiotechnology (IFA-Tulln), University of Natural Resources and Life Sciences Vienna (BOKU) , Konrad-Lorenz-Strasse 20, 3430 Tulln, Austria.
Christoph Bueschl
Nora Neumann
Romana Stückler
Maria Doppler
Alexander W Chassy
Andrew L Waterhouse
Justyna Rechthaler
Niklas Kampleitner
Gerhard G Thallinger
Gerhard Adam
Rudolf Krska
Rainer Schuhmacher
BOKU University · ATUniversitätsklinikum Tulln · ATUniversity of California, Davis · USBioTechMed-Graz · AT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

An untargeted metabolomics workflow for the detection of metabolites derived from endogenous or exogenous tracer substances is presented. To this end, a recently developed stable isotope-assisted LC-HRMS-based metabolomics workflow for the global annotation of biological samples has been further developed and extended. For untargeted detection of metabolites arising from labeled tracer substances, isotope pattern recognition has been adjusted to account for nonlabeled moieties conjugated to the native and labeled tracer molecules. Furthermore, the workflow has been extended by (i) an optional ion intensity ratio check, (ii) the automated combination of positive and negative ionization mode mass spectra derived from fast polarity switching, and (iii) metabolic feature annotation. These extensions enable the automated, unbiased, and global detection of tracer-derived metabolites in complex biological samples. The workflow is demonstrated with the metabolism of (13)C9-phenylalanine in wheat cell suspension cultures in the presence of the mycotoxin deoxynivalenol (DON). In total, 341 metabolic features (150 in positive and 191 in negative ionization mode) corresponding to 139 metabolites were detected. The benefit of fast polarity switching was evident, with 32 and 58 of these metabolites having exclusively been detected in the positive and negative modes, respectively. Moreover, for 19 of the remaining 49 phenylalanine-derived metabolites, the assignment of ion species and, thus, molecular weight was possible only by the use of complementary features of the two ion polarity modes. Statistical evaluation showed that treatment with DON increased or decreased the abundances of many detected metabolites.

Indexed as

Isotope LabelingCarbon IsotopesChromatography, High Pressure LiquidMolecular StructurePhenylalanineSpectrometry, Mass, Electrospray IonizationTriticumCarbon IsotopesPhenylalanine

Identifiers

PMID25372979
PMCPMC4255957
OpenAlexW2023249945

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.