Trimethylation Enhancement Using <sup>13</sup>C-Diazomethane (<sup>13</sup>C-TrEnDi): Increased Sensitivity and Selectivity of Phosphatidylethanolamine, Phosphatidylcholine, and Phosphatidylserine Lipids Derived from Complex Biological Samples
Bibliographic record
Abstract
Significant sensitivity enhancements in the tandem mass spectrometry-based analysis of complex mixtures of several phospholipid classes has been achieved via (13)C-TrEnDi. (13)C-TrEnDi-modified phosphatidylethanolamine (PE), phosphatidylserine (PS), and phosphatidylcholine (PC) lipids extracted from HeLa cells demonstrated greater sensitivity via precursor ion scans (PISs) than their unmodified counterparts. Sphingomyelin (SM) species exhibited neither an increased nor decreased sensitivity following modification. The use of isotopically labeled diazomethane enabled the distinction of modified PE and modified PC species that would yield isobaric species with unlabeled diazomethane. (13)C-TrEnDi created a PE-exclusive PIS of m/z 202.1, two PS-exclusive PISs of m/z 148.1 and m/z 261.1, and a PIS of m/z 199.1 for PC species (observed at odd m/z values) and SM species (observed at even m/z values). The standardized average area increase after TrEnDi modification was 10.72-fold for PE species, 2.36-fold for PC, and 1.05-fold for SM species. The sensitivity increase of PS species was not quantifiable, as there were no unmodified PS species identified prior to derivatization. (13)C-TrEnDi allowed for the identification of 4 PE and 7 PS species as well as the identification and quantitation of an additional 4 PE and 4 PS species that were below the limit of detection (LoD) prior to modification. (13)C-TrEnDi also pushed 24 PE and 6 PC lipids over the limit of quantitation (LoQ) that prior to modification were above the LoD only.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".