Virtual Quantification of Metabolites by Capillary Electrophoresis-Electrospray Ionization-Mass Spectrometry: Predicting Ionization Efficiency Without Chemical Standards
Bibliographic record
Abstract
A major obstacle in metabolomics remains the identification and quantification of a large fraction of unknown metabolites in complex biological samples when purified standards are unavailable. Herein we introduce a multivariate strategy for de novo quantification of cationic/zwitterionic metabolites using capillary electrophoresis-electrospray ionization-mass spectrometry (CE-ESI-MS) based on fundamental molecular, thermodynamic, and electrokinetic properties of an ion. Multivariate calibration was used to derive a quantitative relationship between the measured relative response factor (RRF) of polar metabolites with respect to four physicochemical properties associated with ion evaporation in ESI-MS, namely, molecular volume (MV), octanol-water distribution coefficient (log D), absolute mobility (mu(o)), and effective charge (z(eff)). Our studies revealed that a limited set of intrinsic solute properties can be used to predict the RRF of various classes of metabolites (e.g., amino acids, amines, peptides, acylcarnitines, nucleosides, etc.) with reasonable accuracy and robustness provided that an appropriate training set is validated and ion responses are normalized to an internal standard(s). The applicability of the multivariate model to quantify micromolar levels of metabolites spiked in red blood cell (RBC) lysates was also examined by CE-ESI-MS without significant matrix effects caused by involatile salts and/or major co-ion interferences. This work demonstrates the feasibility for virtual quantification of low-abundance metabolites and their isomers in real-world samples using physicochemical properties estimated by computer modeling, while providing deeper insight into the wide disparity of solute responses in ESI-MS. New strategies for predicting ionization efficiency in silico allow for rapid and semiquantitative analysis of newly discovered biomarkers and/or drug metabolites in metabolomics research when chemical standards do not exist.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| 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".