Capillary electrophoresis‐mass spectrometry using a flow‐through microvial interface for cationic metabolome analysis
Bibliographic record
Abstract
The application of CE-MS in the field of metabolomics is underrepresented, even though it is in principle highly suited for the analysis of small charged compounds, as many metabolites are. Moreover, a robust coupling, using the sheath liquid (SL)-assisted interface was already presented more than a decade ago. A lack of concentration sensitivity is often mentioned as a reason for the underrepresentation of CE-MS in metabolomics. This is caused by postcolumn dilution of the sample with SL, which is typically delivered at a flow rate of 1-10 μL/min. In this study, we investigated the performance of the flow-through microvial (MV) assisted CE-MS interface for cationic metabolomics. With this interface, only a little liquid is added postcolumn, that is, typically 100-500 nL/min. For the evaluation, we used a metabolite mix comprising 45 important cationic metabolites and compared the sensitivity and LOD of both devices. The performance of the CE-MS system was significantly improved by using the MV-assisted interface; the sensitivity was increased more than three times and the LOD decreased more than five times. Then, we analyzed single zebrafish embryos to demonstrate the method on a volume-limited biological sample. In comparison with SL-assisted CE-MS, twice as many molecular features were found, of which several could be identified. These results demonstrate the good potential of the MV interface for enhancing the coverage of the metabolome.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".