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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".