Development of Isotope Labeling Liquid Chromatography–Mass Spectrometry for Metabolic Profiling of Bacterial Cells and Its Application for Bacterial Differentiation
Bibliographic record
Abstract
Quantitative and comprehensive profiling of cellular metabolites is currently a challenging task in bacterial metabolomics. In this work, a simple and robust method for profiling the amine- and phenol-containing metabolome of bacterial cells is described. The overall workflow consists of methanol-based cell lysis and metabolite extraction with ultrasonication, differential isotope dansylation labeling of cellular metabolites, and analysis of the labeled metabolites by liquid chromatography-mass spectrometry (LC-MS). Over a thousand peak pairs or putative metabolites can be detected from bacterial cells in a 25 min LC-MS run and near 2500 putative metabolites can be found in one bacterium from combined results of multiple analyses. After careful examination and optimization of the sample preparation process, this method is shown to be effective for both Gram-positive and Gram-negative bacteria. An idea of applying LC-ultraviolet (UV) detection to quantify the total amount of labeled metabolites is shown to be effective for normalizing the amounts of metabolites present in different samples for metabolome comparison. The use of differential isotopic labeling allows relative quantification of each individual metabolite, which facilitates comparative metabolomics studies and the generation of a metabolic fingerprint of a bacterium. Finally, this method is demonstrated to be useful for the differentiation of three bacterial species in cultured media and spiked human urine 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.001 |
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".