Trimethylation Enhancement Using Diazomethane (TrEnDi): A Novel Technique to Enhance the Sensitivity of MS-Based Analyses of Biological Molecules
Bibliographic record
Abstract
Defining cellular processes relies heavily on elucidating the temporal dynamics of both lipids and proteins. Different mass spectrometry (MS)-based quantitative strategies have emerged to map protein and lipid dynamics over the course of stimuli. We report the development of a novel MS-based quantitative proteomics and lipidomics strategy with unique analytical characteristics. By reacting with diazomethane, analytes are modified to contain fixed, permanent positive charges resulting in improved ionization characteristics and predictable dissociation pathways. Optimization and determination of reactive functional groups enabled a priori prediction of MS2 fragmentation patterns for both modified peptides and lipids. The strategy was tested on digested BSA and successfully quantified a peptide not observable prior to modification. Our chemistry eliminates the need for protonation during ionization, reduces ion suppression, and permits predictable MRM-based or precursor ion-based quantitation with improved sensitivity.
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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 | 0.000 |
| 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 | 0.001 | 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".