A method for comprehensive analysis of urinary acylglycines by using ultra-performance liquid chromatography quadrupole linear ion trap mass spectrometry
Bibliographic record
Abstract
Acylglycines are an important class of metabolites that have been used in the diagnosis of several inborn errors of metabolism (IEM). However, current analytical methods detect only a few acylglycines. There is a need to profile these metabolites in a comprehensive manner for studying their functions and improving their diagnostic values for different IEM and potentially other diseases. We describe a sensitive method that combines the chromatographic resolving power of ultra-performance liquid chromatography (UPLC) to separate closely related metabolites including isomers with tandem mass spectrometry (MS/MS). Acylglycines were extracted from urine using an anion exchange solid-phase extraction (SPE) cartridge. After UPLC separation, the acylglycines were detected on a hybrid triple quadrupole linear ion trap mass spectrometer. A set of standards were used for the development of an optimal MS acquisition method. Several acquisition modes using information derived from collision-induced dissociation breakdown curves were used to detect acylglycines. Using this method, 18 acylglycines were detected in the urine of healthy individuals and confirmed using standards, while 47 additional acylglycines were detected and tentatively identified, based on their retention and fragmentation pattern. Among the 65 acylglycines detected, only 18 of them have been previously reported in biofluids of healthy individuals. These results will be deposited in a public human metabolome database. This example illustrates that by developing a method tailored to the analysis of a class of metabolites sharing similar structural moieties, we can potentially identify many more new metabolites, thereby expanding the overall metabolome coverage.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".