Retention time shift analysis and correction in chemical isotope labeling liquid chromatography/mass spectrometry for metabolome analysis
Bibliographic record
Abstract
RATIONALE: In chemical isotope labeling (CIL) liquid chromatography/mass spectrometry (LC/MS) metabolome analysis, the peak pairs of the same metabolite detected from different samples are aligned according to their mass and retention time (RT). Any RT shift of a peak pair in one of the sample files that falls outside the tolerance window will result in misalignment of the pair as a different metabolite. Thus, determination and correction of any significant RT shift are important to ensure the generation of high-quality metabolome results. METHODS: In CIL LC/MS, the heavy-isotope-labeled pooled sample is spiked into all light-isotope-labeled individual samples. As a result, in the analysis of labeled samples of the same type, many common metabolites are detectable with high intensity in all LC/MS runs. We have developed a method to select a few of these metabolites as internal RT reference markers to check the occurrence of any RT shift in an LC/MS run. If a significant shift is found, an expanded list of these markers with their RT values covering the entire LC RT window is selected to serve as internal RT calibrants to recalibrate the chromatogram to correct any RT shift. RESULTS: We developed a software program in R to perform RT check (RTC) and recalibration (RT-calib). This program can quickly determine the occurrence of any RT shift falling outside a user-defined threshold in an LC/MS run, thereby triggering a timely intervention to correct the problem (e.g., fixing a small leak or changing a column). In the analysis of 278 dansylation LC/MS runs of human urine samples, we show that the RT values can be corrected to be within a 30-second window. CONCLUSIONS: An RT-check method and program tailored to CIL LC/MS metabolome analysis have been developed for quick detection and correction of RT shifts during the course of running many metabolome samples.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".