TrEnDi Enhances the LC-MS Detection of γ-Aminobutyric Acid
Bibliographic record
Abstract
Neurotransmitters are critical for the proper function, signal transmission, and physiological balance of the brain, with γ-aminobutyric acid (GABA) being the main inhibitory neurotransmitter in the central nervous system. GABA is present at relatively low concentrations compared to other neurotransmitters therefore requiring sensitive analytical methods for accurate identification and quantitation. Described herein is a rapid and facile liquid chromatography mass spectrometry (LC-MS)-based chemical derivatization method to enhance the detection of GABA, demonstrated in both saline culture media and Carassius auratus (goldfish) retina samples. We have expanded the use of trimethylation enhancement using diazomethane (TrEnDi) to permethylate GABA ([GABATr]+) at 98–100% yields across all matrix types. Quantitative methylation of the carboxylic acid and amino moieties nullifies any zwitterionic character and fixes a permanent positive charge on [GABATr]+ leading to MS sensitivity enhancement. In biological triplicates of goldfish retina samples, [GABATr]+ boasted 6.3–27.9-fold increases in MS sensitivity compared to its unmodified counterpart enabling quantitation with concentrations ranging between 78.6 to 806.5 nM. Calibration curve linearity for [GABATr]+ and unmodified GABA were R2 = 0.9996 and R2 = 0.9923, respectively. Limits of detection and quantitation (LOD/LOQ) for [GABATr]+ were 0.053 nM (1.1 fmol)/0.18 nM (3.6 fmol), compared to 2.5 nM (50 fmol)/8.3 nM (167 fmol) for unmodified GABA. This work demonstrates that TrEnDi has the ability to rapidly enhance LC-MS detection of GABA in a relatively facile manner, reducing the probability of reporting false negatives in the analysis of complex biological samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".