Multisegment Injection-Capillary Electrophoresis-Mass Spectrometry: A High-Throughput Platform for Metabolomics with High Data Fidelity
Bibliographic record
Abstract
A major constraint in large-scale mass spectrometry (MS)-based metabolomic initiatives is the low sample throughput associated with chromatographic or electrophoretic separations. Herein, we introduce multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS) as a multiplexed separation platform for metabolomics that increases sample throughput up to one order of magnitude while improving overall data fidelity. We demonstrate that serial injection of seven or more discrete sample segments can be performed within a single capillary while maintaining isomeric resolution without ion suppression when using a high mass resolution time-of-flight-MS. Customized injection sequences can be devised to encode information temporally within a separation based on signal pattern recognition, which enables unambiguous identification and accurate quantification (mean bias <10%) of polar metabolites in human plasma with good reproducibility (CV ≈ 10%, n = 70). False discoveries are avoided when using a rigorous dilution trend filter to reject spurious signals and background peaks that comprise the majority (≈65%) of total detectable features. MSI-CE-MS offers an unprecedented approach to enhance sample throughput analogous to direct infusion-MS (≈3 min/sample) while delivering far greater selectivity, quantitative performance, and data quality since the same ion from different samples migrates into the ion source within a short time interval (≈2-6 min). These distinct analytical and bioinformatic merits are achieved without column switching, isotopic labeling, hardware modifications, or costly infrastructure investments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".