Validation of a fast and sensitive UPLC-MS/MS quantitative method for N-acyl taurine analysis in biological samples
Bibliographic record
Abstract
The recent discovery of N-acyl taurines (NATs) as a class of endogenous bioactive lipids and the perspective of their possible pharmacological applications stimulated the development of mass spectrometry-based methods for their quantitative measurements in biological tissues and fluids. We report here for the first time a procedure validated both in liver surrogate matrix and neat solvent (MeOH) based on UPLC-ESI-QqQ analysis for the identification and quantification of NATs in biological tissue extracts. The LC-MS method was based on five representative lipid analogues, including saturated, monounsaturated and polyunsaturated species, namely N-palmitoyl taurine (C16:0 NAT), N-oleoyl taurine (C18:1 NAT), N-arachidonoyl taurine (C20:4 NAT), N-docosanoyl taurine (C22:0 NAT) and N-nervonoyl taurine (C24:1 NAT), and evaluated for specificity, linearity, matrix effect, recovery, repeatability and intermediate precision and accuracy. The method validated in MeOH by internal standard approach (d4-C20:4 NAT) showed excellent linearity in the range 1–300 ng/ml with R always ≥ 0.9996 for all NATs; intra-day and inter-day precision and accuracy were always within the acceptable range. Specificity was assessed on NAT standards in MeOH, applying the confirmation ratio of two diagnostic MRM ion transitions for product ions at m/z 80 and m/z 107 to true samples in the adopted BEH C18 UPLC conditions. Limit of detection (LOD) and limit of quantification (LOQ) were 0.3–0.4 and 1 ng/ml, respectively, for all compounds. The method was successfully applied to assess the levels of NATs in the mouse liver and, for the first time, in varying sections of the intestine (duodenum, jejunum, ileum and colon). NAT levels increased from duodenum to colon, evidencing a remarkable prevalence in the large intestine of C22:0 NAT, typically occurring mainly in the central nervous system. These findings prompt further studies to disclose the biological function of the various members of this class in different peripheral tissues.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".