Advances in LCMS Analysis of Lipids and Xenobiotics via Improvements in TrEnDi Derivatization
Bibliographic record
Abstract
Mass spectrometry (MS) has become a critical technology for the analysis of complex mixtures by scientists across many fields of research.Its ability to identify and quantify small amounts of biomolecules has allowed for a deeper understanding of biological systems previously thought possible.The interaction between proteins, lipids, metabolites and DNA, etc. heavily relies on the use of diagnostics tools like mass spectrometry to gain a superior understanding of how humans are affected by disease and environmental exposure to xenobiotics.This thesis is focused on chemical derivatization techniques to improve the MS analysis of lipids and the commonly used pesticide, glyphosate.Chemical derivatization, or alternatively the application of chemical reactions to biologically relevant molecules is used to amplify the precision and accuracy of MS analyses. The primary Chemical derivatization technique outlined here is TrimethylationEnhancement using Diazomethane (TrEnDi) whereby analytes undergo methylation to uniformly produce net positively charged species that contain amine functional groups.Glycerophospholipids, are a highly diverse class of biomolecules that can be readily methylated to net positively charged cations to greatly improve ionization efficiency prior to MS analysis.We successfully applied 13 C-TrEnDi with improved conditions to methylate plasmalogen lipid species with no acidic cleavage of vinyl ether moiety.The identification of twenty-nine 13 CH3 plasmalogen lipids were observed from a complex lipid extract and shows increased signal-to-noise ratios compared to their unmethylated counterparts.Furthermore, a novel apparatus for delivering a small amount of diazomethane (DZM) to lipid samples was developed to decrease the handling of the hazardous reagent.Micro-scale delivery of diazomethane (iTrEnDi) was determined to be less effective than in solution derivatization of lipid samples but had greatly reduced the safety concerns associated with large-scale DZM preparations.The most commonly used herbicide glyphosate and its major degradation product aminomethylphosphonic acid have been scrutinized for their long-term health effects and environmental impacts.Both compounds are challenging to analyze via classical LCMS methodologies due to low molecular weight, high polarity and anionic nature.Herein, iTrEnDi modification was used to produce permethylated GLY and AMPA with improved LC and ionization characteristics.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".