Trimethylation Enhancement Using <sup>13</sup>C-Diazomethane: Gas-Phase Charge Inversion of Modified Phospholipid Cations for Enhanced Structural Characterization
Bibliographic record
Abstract
Methylation of phospholipids (PL) leads to increased uniformity in positive electrospray ionization (ESI) efficiencies across the various PL subclasses. This effect is realized in the approach referred to as “trimethylation enhancement using 13 C-diazomethane” ( 13 C-TrEnDi), which results in the methyl esterification of all acidic sites and the conversion of amines to quaternary ammonium sites. Collision-induced dissociation (CID) of these cationic modified lipids enables class identification by forming distinctive headgroup fragments based on the number of 13 C atoms incorporated during derivatization. However, there are no distinctive fragment ions in positive mode that provide fatty acyl information for any of the modified lipids. Gas-phase ion/ion reactions of 13 C-TrEnDi-modified phosphatidylethanolamine (PE), phosphatidylserine (PS), phosphatidylcholine (PC), and sphingomyelin (SM) cations with dicarboxylate anions are shown to charge-invert the positively charged phospholipids to the negative mode. An electrostatically bound complex anion is shown to fragment predominantly via a novel headgroup dication transfer to the reagent anion. Fragmentation of the resulting anionic product yields fatty acyl information, in the case of the glycerophospholipids (PE, PS, and PC), via ester bond cleavage. Analogous information is obtained from modified SM lipid anions via amide bond cleavage. Fragmentation of the anions generated from charge inversion of the 13 C-TrEnDi-modified phospholipids was also found to yield lipid class information without having to perform CID in positive mode. The combination of 13 C-TrEnDi modification of lipid mixtures with charge inversion to the negative-ion mode retains the advantages of uniform ionization efficiency in the positive-ion mode with the additional structural information available in the negative-ion mode without requiring the lipids to be ionized directly in both ionization modes.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".