Stable-Isotope Dimethylation Labeling Combined with LC−ESI MS for Quantification of Amine-Containing Metabolites in Biological Samples
Bibliographic record
Abstract
One of the challenges associated with metabolome profiling in complex biological samples is to generate quantitative information on the metabolites of interest. In this work, a targeted metabolome analysis strategy is presented for the quantification of amine-containing metabolites. A dimethylation reaction is used to introduce a stable isotopic tag onto amine-containing metabolites followed by LC-ESI MS analysis. This labeling reaction employs a common reagent, formaldehyde, to label globally the amine groups through reductive amination. The performance of this strategy was investigated in the analysis of 20 amino acids and 15 amines by LC-ESI MS. It is shown that the labeling chemistry is simple, fast (<10-min reaction time), specific, and provides high yields under mild reaction conditions. The issue of isotopic effects of the labeled amines on reversed-phase (RP) and hydrophilic interaction (HILIC) LC separations was examined. It was found that deuterium labeling causes an isotope effect on the elution of labeled amines on RPLC but has no effect on HILIC LC. However, 13C-dimethylation does not show any isotope effect on either RPLC or HILIC LC, indicating that 13C-labeling is a preferred approach for relative quantification of amine-containing metabolites in different samples. The isotopically labeled 35 amine-containing analogues were found to be stable and proved to be effective in overcoming matrix effects in both relative and absolute quantification of these analytes present in a complicated sample, human urine. Finally, the characteristic mass difference provides additional structural information that reveals the existence of primary or secondary amine functional groups in amine-containing metabolites. As an example, for a human urine sample, a total of 438 pairs of different amine-containing metabolites were detected, at signal-to-noise ratios of greater than 10, by using the labeling strategy in conjunction with RP LC-ESI Fourier-transform ion cyclotron resonance MS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".