Optimized 13C-TrEnDi enhances the sensitivity of plasmenyl ether glycerophospholipids and demonstrates compatibility with other derivatization strategies
Bibliographic record
Abstract
The identification and quantitation of plasmalogen glycerophospholipids is challenging due to their isobaric overlap with plasmanyl ether-linked glycerophospholipids, susceptibility to acid degradation, and their typically low abundance in biological samples. Trimethylation enhancement using diazomethane (TrEnDi) can be used to significantly enhance the signal of glycerophospholipids through the creation of quaternary ammonium groups producing fixed positive charges using 13C-diazomethane in complex lipid extracts. Although TrEnDi requires a strong acid for complete methylation, we report an optimized protocol using 9.6 mM HBF4 with the subsequent addition of a buffer solution that prevents acidic hydrolysis of plasmalogen species and enables the benefits of TrEnDi to be realized for this class of lipids. These optimized conditions were applied to aliquots of bovine liver extract (BLE) to achieve permethylation of plasmalogen lipids within a complex mixture. Treating aliquots of unmodified and TrEnDi-derivatized BLE samples with 80% formic acid and comparing their liquid chromatography mass spectrometry (LCMS) results to analogous samples not treated with formic acid, enabled the identification of 29 plasmalogen species. On average, methylated plasmalogen species from BLE demonstrated 2.81-fold and 28.1-fold sensitivity gains over unmodified counterparts for phosphatidylcholine and phosphatidylethanolamine plasmalogen species, respectively. Furthermore, the compatibility of employing 13C-TrEnDi and a previously reported iodoacetalization strategy was demonstrated to effectively identify plasmenyl-ether lipids in complex biological extracts at greater levels of sensitivity. Overall, we detail an optimized 13C-TrEnDi derivatization strategy that enables the analysis of plasmalogen glycerophospholipids with no undesired cleavage of radyl groups, boosting their sensitivity in LCMS and LCMS/MS analyses.
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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 | 0.001 | 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".