Separation and Simultaneous Trapping of Multiply Charged and Singly Charged Ions for Mass Spectrometry: Application to Lipid Mixtures
Bibliographic record
Abstract
Conventional electrospray ionization (ESI) of mixtures can give rise to singly and multiply charged analyte species that overlap in mass-to-charge ( m / z ) ratios, which can complicate the analysis of individual components. The overlap in m / z for ions of different mass and charge is particularly problematic when ions of low relative abundance are of interest. For example, cardiolipins (CLs) are structurally complex phospholipids present in low relative abundance in the lipidome but play crucial roles in mitochondrial metabolism and various regulatory processes. ESI of CLs in negative ion mode shows abundant doubly deprotonated ions and minor singly deprotonated ions. In the ESI of lipid extracts, highly abundant singly charged phospholipids extensively overlap in m / z space with CL dianions of much lesser abundance, thereby complicating the study of the CLs. To address this challenge, we employed a gas-phase approach to separate singly charged ions from a population of ions of mixed charge states while allowing for the storage of one or both of the separated ion populations. Herein, we describe the considerations for applying enhanced singly charged (ESC) and enhanced multiply charged (EMC) scans to perform a gas-phase separation of singly charged lipids from doubly charged lipids in an Escherichia coli extract. This method allows for improved signal-to-noise (S/N) ratio of low abundance ions with minimal overall signal loss, removal of “chemical noise” arising from singly charged ions, and allows for retention of spatially separated ions within a mass spectrometer.
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 | 0.001 | 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".