Development of a Liquid Chromatography–High Resolution Mass Spectrometry Metabolomics Method with High Specificity for Metabolite Identification Using All Ion Fragmentation Acquisition
Bibliographic record
Abstract
High-resolution mass spectrometry (HRMS)-based metabolomics approaches have made significant advances. However, metabolite identification is still a major challenge with significant bottleneck in translating metabolomics data into biological context. In the current study, a liquid chromatography (LC)-HRMS metabolomics method was developed using an all ion fragmentation (AIF) acquisition approach. To increase the specificity in metabolite annotation, four criteria were considered: (i) accurate mass (AM), (ii) retention time (RT), (iii) MS/MS spectrum, and (iv) product/precursor ion intensity ratios. We constructed an in-house mass spectral library of 408 metabolites containing AMRT and MS/MS spectra information at four collision energies. The percent relative standard deviations between ion ratios of a metabolite in an analytical standard vs sample matrix were used as an additional metric for establishing metabolite identity. A data processing method for targeted metabolite screening was then created, merging m/z, RT, MS/MS, and ion ratio information for each of the 413 metabolites. In the data processing method, the precursor ion and product ion were considered as the quantifier and qualifier ion, respectively. We also included a scheme to distinguish coeluting isobaric compounds by selecting a specific product ion as the quantifier ion instead of the precursor ion. An advantage of the current AIF approach is the concurrent collection of full scan data, enabling identification of metabolites not included in the database. Our data acquisition strategy enables a simultaneous mixture of database-dependent targeted and nontargeted metabolomics in combination with improved accuracy in metabolite identification, increasing the quality of the biological information acquired in a metabolomics experiment.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.002 |
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".