Proton Transfer Reactions for the Gas-Phase Separation, Concentration, and Identification of Cardiolipins
Bibliographic record
Abstract
Cardiolipin (CL) analysis demands high specificity, due to the extensive diversity of CL structures, and high sensitivity, due to their low relative abundance within the lipidome. While electrospray ionization mass spectrometry (ESI-MS) is the most widely used technology in lipidomics, the potential for multiple charging presents unique challenges for CL identification and quantification. Depending on the conditions, ESI-MS of lipid extracts in negative ion mode can give rise to cardiolipins ionized as both singly and doubly deprotonated anions. This signal degeneracy diminishes the signal-to-noise ratio, while in addition (for direct infusion), the dianion population falls within a m / z range already heavily congested with monoanions from more abundant glycerophospholipid subclasses. Herein, we describe a direct infusion strategy for CL profiling from total lipid extracts utilizing gas-phase proton-transfer ion/ion reactions. In this approach, lipid extracts are ionized by negative ion ESI generating both singly deprotonated phospholipids and doubly deprotonated CL anions. Charge reduction of the negative ion population by ion/ion reactions leads to an enhancement in singly deprotonated [CL – H] − species via proton transfer to the corresponding [CL – 2H] 2– ̅ dianions. To concentrate the [CL – H] − anion signal, multiple iterations of ion accumulation and proton-transfer ion/ion reaction can be performed prior to subsequent interrogation. Mass selection and collisional activation of the enriched population of [CL – H] − anions facilitates the assignment of individual fatty acyl substituents and phosphatidic acid moieties. Demonstrated advantages of this new approach derive from the improved performance in complex mixture analysis affording detailed characterization of low abundant CLs directly from a total biological extract.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".