Robust and High-Throughput Method for Anionic Metabolite Profiling: Preventing Polyimide Aminolysis and Capillary Breakages under Alkaline Conditions in Capillary Electrophoresis-Mass Spectrometry
Bibliographic record
Abstract
Capillary electrophoresis-mass spectrometry (CE-MS) represents a high efficiency microscale separation platform for untargeted profiling of polar/ionic metabolites that is ideal for volume-restricted biological specimens with minimal sample workup. Despite these advantages, the long-term stability of CE-MS remains a major obstacle hampering its widespread application in metabolomics notably for routine analysis of anionic metabolites under negative ion mode conditions. Herein, we report for the first time that commonly used ammonia containing buffers compatible with electrospray ionization (ESI)-MS can compromise the integrity of fused-silica capillaries via aminolysis of their outer polyimide coating. Unlike organic solvent swelling effects, this chemical process occurs under aqueous conditions that is dependent on ammonia concentration, buffer pH, and exposure time resulting in a higher incidence of capillary fractures and current errors during extended operation. Prevention of polyimide aminolysis is achieved by using weakly alkaline ammonia containing buffers (pH < 9) in order to preserve the tensile strength of the polyimide coated fused-silica capillary. Alternatively, less nucleophilic primary/secondary amines can be used as electrolytes without polyimide degradation, whereas chemically resistant polytetrafluoroethylene coating materials offer higher pH tolerance in ammonia. In this work, multisegment injection (MSI)-CE-MS was used as multiplexed separation platform for high throughput profiling of anionic metabolites when using optimized buffer conditions to prevent polyimide degradation. A diverse range of acidic metabolites in human urine were reliably measured by MSI-CE-MS via serial injection of seven urine samples within a single run, including organic acids, food-specific markers, microbial-derived compounds and over-the-counter drugs as their sulfate and glucuronide conjugates. This approach offers excellent throughput (<5 min/sample) and acceptable intermediate precision (average CV ≈ 16%) with high separation efficiency as reflected analysis of 30 anionic metabolites following 238 repeated sample injections of human urine over 3 days while using a single nonisotope internal standard for data normalization. Careful optimization and rigorous validation of CE-MS protocols are crucial for developing a rapid, low cost, and robust screening platform for metabolomics that is amenable to large-scale clinical and epidemiological studies.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".