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Record W2009863844 · doi:10.1021/ac900166a

Differential <sup>12</sup>C-/<sup>13</sup>C-Isotope Dansylation Labeling and Fast Liquid Chromatography/Mass Spectrometry for Absolute and Relative Quantification of the Metabolome

2009· article· en· W2009863844 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnalytical Chemistry · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsUniversity of Alberta
FundersGenome AlbertaCanada Research ChairsGenome Canada
KeywordsChemistryChromatographyDerivatizationMetaboliteDansyl chlorideMass spectrometryMetabolomeReagentElectrospray ionizationMetabolomicsOrganic chemistry

Abstract

fetched live from OpenAlex

We report a new quantitative metabolome profiling technique based on differential (12)C-/(13)C-isotope dansylation labeling of metabolites, fast liquid chromatography (LC) separation and electrospray ionization Fourier-transform ion cyclotron resonance mass spectrometry (ESI-FTICR MS) detection. An isotope reagent, (13)C-dansyl chloride, can be readily synthesized. This reagent, along with (12)C-dansyl chloride, provides a simple and robust means of labeling metabolites containing primary amine, secondary amine, or phenolic hydroxyl group(s). It is shown that dansylation labeling offers 1-3 orders of magnitude ESI signal enhancement over the underivatized counterparts. Dansylation alters the chromatographic behaviors of polar and ionic metabolites normally not retainable on a reversed phase (RP) column to an extent that they can be retained and separated by RPLC with high efficiency. There is no isotopic effect on RPLC separation of the differential isotope labeled metabolites, and (12)C-/(13)C-labeled isoforms of metabolites are coeluted and detected by MS for precise and accurate quantification and confident metabolite identification. It is demonstrated that, in the analysis of 20 amino acids, a linear response of over 2 orders of magnitude is achieved for relative metabolite quantification with an average relative standard deviation (RSD) of about 5.3% from replicate experiments. A dansylation standard compound library consisting of 121 known amines and phenols has been constructed and is proven to be useful for absolute metabolite quantification and MS-based metabolite identification in biological samples. As an example, the absolute concentrations of 93 metabolites, ranging from 30 nM to 2510 microM, can be determined from a pooled sample of human urines collected in 5 consecutive days labeled with (12)C-dansylation and spiked with the 121 (13)C-dansylated standards. Relative concentration variations of these metabolites in individual urine samples can also be monitored by mixing the (13)C-dansylated pooled urine sample with the (12)C-dansylated individual sample. With a 12 min fast LC separation combined with FTICR MS, 672 metabolites were detected in a human urine sample with each metabolite peak having a signal-to-noise ratio of greater than 20; the identities of most of the metabolites remain to be determined. This work illustrates that dansylation labeling and fast LC/FTICR MS can be a powerful technique for quantitative profiling of at least 672 metabolites in urine samples in 12 min.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.245
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it