Integrative Metabolomics for Characterizing Unknown Low-Abundance Metabolites by Capillary Electrophoresis-Mass Spectrometry with Computer Simulations
Bibliographic record
Abstract
Characterization of unknown low-abundance metabolites in biological samples is one the most significant challenges in metabolomic research. In this report, an integrative strategy based on capillary electrophoresis-electrospray ionization-ion trap mass spectrometry (CE-ESI-ITMS) with computer simulations is examined as a multiplexed approach for studying the selective nutrient uptake behavior of E. coli within a complex broth medium. On-line sample preconcentration with desalting by CE-ESI-ITMS was performed directly without off-line sample pretreatment in order to improve detector sensitivity over 50-fold for cationic metabolites with nanomolar detection limits. The migration behavior of charged metabolites were also modeled in CE as a qualitative tool to support MS characterization based on two fundamental analyte physicochemical properties, namely, absolute mobility (muo) and acid dissociation constant (pKa). Computer simulations using Simul 5.0 were used to better understand the dynamics of analyte electromigration, as well as aiding de novo identification of unknown nutrients. There was excellent agreement between computer-simulated and experimental electropherograms for several classes of cationic metabolites as reflected by their relative migration times with an average error of <2.0%. Our studies revealed differential uptake of specific amino acids and nucleoside nutrients associated with distinct stages of bacterial growth. Herein, we demonstrate that CE can serve as an effective preconcentrator, desalter, and separator prior to ESI-MS, while providing additional qualitative information for unambiguous identification among isobaric and isomeric metabolites. The proposed strategy is particularly relevant for characterizing unknown yet biologically relevant metabolites that are not readily synthesized or commercially available.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".