Systematic Evaluation of Solid-Phase Microextraction Coatings for Untargeted Metabolomic Profiling of Biological Fluids by Liquid Chromatography−Mass Spectrometry
Bibliographic record
Abstract
In this study, we propose for the first time the use of solid-phase microextraction (SPME) in combination with liquid chromatography-mass spectrometry for untargeted metabolomic profiling of biological fluids. To achieve this goal, we first systematically evaluated 42 different SPME coatings for the extraction of 36 metabolites from different chemical classes and of widely varying polarities (log P range of -7.9 to 7.4) in order to identify SPME coatings which are the most suitable for metabolomic studies and to improve the extraction of polar metabolites over the existing commercial SPME devices. Three types of SPME coatings (mixed-mode coatings, polar-enhanced polystyrene-divinylbenzene, and phenylboronic acid) performed the best for simultaneous extraction of both hydrophilic and hydrophobic metabolites at physiological conditions, thus making them suitable for untargeted metabolomic profiling applications. A rapid and simple SPME method was then developed with single-use biocompatible mixed-mode coating for the metabolomic profiling of human plasma in combination with liquid chromatography-high-resolution mass spectrometry on a benchtop Orbitrap system. This optimized SPME method was evaluated versus ultrafiltration and solvent precipitation in terms of metabolite coverage and method precision. SPME detected 1592-3320 features versus 2082-3245 features detected by solvent precipitation methods and 2093-2686 detected for ultrafiltration using the same pooled human plasma sample. Method precision of SPME ranged between 11% and 18% (expressed as median relative standard deviation (RSD) of n = 7 replicates) versus 8-19% for solvent precipitation and 20-22% for ultrafiltration. The results demonstrate that the proposed SPME methodology reduces ionization suppression, provides free concentration information for hydrophobic analytes which are not detected by ultrafiltration methods, and can improve metabolite coverage over existing methodologies.
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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) | 0.001 | 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) | 0.000 | 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".