Isotope‐labeled differential profiling of metabolites using <i>N</i> ‐benzoyloxysuccinimide derivatization coupled to liquid chromatography/high‐resolution tandem mass spectrometry
Bibliographic record
Abstract
RATIONALE: An isotopic labeling strategy based on derivatizing amine-containing metabolites has been developed using light ((12) C6 ) and heavy ((13) C6 ) N-benzoyloxysuccinimide reagents for semi-targeted metabolomic applications. METHODS: Differentially labeled samples were combined and analyzed simultaneously by liquid chromatography/high-resolution tandem mass spectrometry (LC/HR-MS/MS) to compare relative amounts of amine-containing metabolites. The selectivity of the reaction was determined with model metabolites and was shown to also be applicable to thiol and phenol moieties. The potential for relative quantitation was evaluated in cell extracts and the method was then applied to quantify metabolic perturbations occurring in human cultured cells under normal vs. oxidative stress conditions. RESULTS: A total of 279 derivatized features were detected in HL60 cell extracts, 77 of which yielded significant concentration changes upon oxidative stress treatment. Based on accurate mass measurements and MS/MS spectral matching with reference standard solutions, 10 metabolites were clearly identified. Derivatized compounds were found to have diagnostic fragment ions from the reagent itself, as well as structurally informative ions useful for metabolite identification. CONCLUSIONS: This simple derivatization reaction can be applied to the relative quantitation of amine-, thiol- and phenol-containing compounds, with improved sensitivity and chromatographic peak shapes due to the increased hydrophobicity of polar metabolites not readily amenable to reversed-phase LC/MS analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".