Evaluation of dispersive solid-phase microextraction using hydrogel microparticles for global metabolomics by liquid chromatography – mass spectrometry
Bibliographic record
Abstract
An ideal sample-preparation method for LC-MS metabolomic analysis should be as non-selective as possible for metabolites but still capable to remove the interferences such as salts and proteins. Microextraction methods have not been widely used in this application despite their potential to reduce ionization suppression and/or increase metabolite coverage. The main goal of this M.Sc. project was to develop a new dispersive solid phase microextraction (D-SPME) sample preparation method and investigate whether this approach can improve the coverage of the metabolome from human plasma. Different types of poly-N-isopropylacrylamide hydrogel extraction phases functionalized with vinyl acetate (VAC), acrylic acid (AAC) or N-3-aminopropyl methacrylamide hydrochloride (APMAH) were tested. Sample analysis was performed using three complementary liquid chromatography–high resolution mass spectrometry methods for high, intermediate and low polarity sub-metabolomes respectively. The extraction conditions were optimized in terms of desorption solvent, influence of pH, centrifugation time, extraction time, sorbent to sample ratio, increasing portion of functional monomer and evaluation of reproducibility. Finally, the performance of the optimized D-SPME method was compared against protein precipitation using methanol, which is currently the gold standard method for global metabolomics of human plasma, and commercial core-shell nanoparticles (CERES Nanotrap) functionalized with acrylic acid or Cibacron blue cores. The main criteria used for the comparison were ionization suppression, metabolite coverage and precision. Hydrogel microparticles performed as well as nanotraps in terms of extraction and performed better than nanotraps and methanol precipitation in terms of ion suppression. Hydrogel D-SPME had lower total coverage than methanol precipitation, as expected for a microextraction method, but successfully revealed more than 568 low abundance metabolites in positive ESI mode and 48 metabolites in negative mode that could not be observed using the methanol method. Therefore, hydrogel D-SPME appears to be a promising new method for global metabolomics.
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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) | 0.000 | 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 | 0.001 | 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 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".