Metabolic Profiling of Bile Acids in Human and Mouse Blood by LC–MS/MS in Combination with Phospholipid-Depletion Solid-Phase Extraction
Bibliographic record
Abstract
To obtain a more comprehensive profile of bile acids (BAs) in blood, we developed an ultrahigh performance liquid chromatography/multiple-reaction monitoring-mass spectrometry (UPLC-MRM-MS) method for the separation and detection of 50 known BAs. This method utilizes phospholipid-depletion solid-phase extraction as a new high-efficiency sample preparation procedure for BA assay. UPLC/scheduled MRM-MS with negative ion electrospray ionization enabled targeted quantitation of 43 and 44 BAs, respectively, in serum samples from seven individuals with and without fasting, as well as in plasma samples from six cholestatic gene knockout mice and six age- and gender-matched wild-type (FVB/NJ) animals. Many minor BAs were identified and quantitated in the blood for the first time. Method validation indicated good quantitation precision with intraday and interday relative standard deviations of ≤9.3% and ≤10.8%, respectively. Using a pooled human serum sample and a pooled mouse plasma sample as the two representative test samples, the quantitation accuracy was measured to be 80% to 120% for most of the BAs, using two standard-substance spiking approaches. To profile other potential BAs not included in the 50 known targets from the knockout versus wild-type mouse plasma, class-specific precursor/fragment ion transitions were used to perform UPLC-MRM-MS for untargeted detection of the structural isomers of glycine- and taurine-conjugated BAs and unconjugated tetra-hydroxy BAs. As a result, as many as 36 such compounds were detected. In summary, this UPLC-MRM-MS method has enabled the quantitation of the largest number of BAs in the blood thus far, and the results presented have revealed an unexpectedly complex BA profile in mouse plasma.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".