Nanoflow LC–MS for High-Performance Chemical Isotope Labeling Quantitative Metabolomics
Bibliographic record
Abstract
Nanoflow liquid chromatography mass spectrometry (nLC-MS) is frequently used in the proteomics field to analyze a small amount of protein and peptide samples. However, this technique is currently not widespread in the metabolomics field. We report a detailed investigation on the development of an nLC-MS system equipped with a trap column for high-performance chemical isotope labeling (CIL) metabolomic profiling with deep coverage and high sensitivity. Experimental conditions were optimized for profiling the amine/phenol submetabolome with (13)C-/(12)C-dansylation labeling. Comparison of analytical results from nLC-MS and microbore LC-MS (mLC-MS) was made in the analysis of metabolite standards and labeled human urine and sweat samples. It is shown that, with a 5-μL loop injection, 7 labeled amino acid standards could be detected with S/N ranging from 7 to 150 by nLC-MS with an injection of 5 nM solution containing a total of 25 fmol labeled analyte. For urine metabolome profiling where the sample amount was not limited, nLC-MS detected 13% more metabolites than mLC-MS under optimal conditions (i.e., 4524 ± 37 peak pairs from 26 nmol injection in triplicate vs 4019 ± 40 peak pairs from 52 nmol injection). This gain was attributed to the increased dynamic range of peak detection in nLC-MS. In the analysis of human sweat where the sample amount could be limited, nLC-MS offered the advantage of providing much higher coverage than mLC-MS. Injecting 5 nmol of dansylated sweat, 3908 ± 62 peak pairs or metabolites were detected by nLC-MS, while only 1064 ± 6 peak pairs were detected by mLC-MS. Because dansyl labeled metabolites can be captured on a reversed phase (RP) trap column for large volume injection and are well separated by RPLC, the CIL platform can be readily implemented in existing nLC-MS instruments such as those widely used in shotgun proteomics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".