Improving negative liquid chromatography/electrospray ionization mass spectrometry lipidomic analysis of human plasma using acetic acid as a mobile‐phase additive
Bibliographic record
Abstract
RATIONALE: Mobile-phase additives in liquid chromatography/mass spectrometry (LC/MS) are used to improve peak shape, analyte ionization efficiency and method coverage. Both basic and acidic mobile phases have been used successfully for negative electrospray ionization (ESI), but very few systematic investigations exist to date to justify the choice of mobile phase. Acetic acid was previously shown to improve ionization in untargeted metabolomics of urine, but has not been investigated in lipidomics. The goal of this study was to systematically compare the performance of acetic acid to that of other commonly employed additives in negative LC/ESI-MS lipidomics. METHODS: The performance of acetic acid was compared to that of commonly used mobile-phase additives in lipidomics, namely ammonium acetate, ammonium acetate with acetic acid and ammonium hydroxide, using lipid standard solutions containing representatives of major mammalian lipid subclasses and isopropanol-precipitated human plasma. This design allowed comparison of the influence of additive and additive concentration on lipid signal intensity, lipid peak shape and lipid coverage in both simple and complex biological matrices using both Orbitrap and quadrupole time-of-flight MS platforms with different ESI source designs. RESULTS: Ammonium hydroxide caused 2- to 1000-fold signal suppression of all lipid classes in comparison to acetic acid. In comparison to ammonium acetate, acetic acid increased lipid signal intensity from 2- to 19-fold for 11 lipid subclasses, and decreased ionization efficiency only for ceramide and phosphatidylcholine lipid classes which can be effectively ionized in positive ESI mode. The improved ionization efficiency using acetic acid also increased lipid coverage by 21-50% versus ammonium acetate additive. CONCLUSIONS: Acetic acid at a concentration of 0.02% (v/v) is the suggested choice as a mobile-phase additive for lipidomics and targeted lipid profiling with negative LC/ESI-MS based on signal enhancement and improved lipid coverage compared to ammonium acetate, ammonium acetate with acetic acid and ammonium hydroxide mobile phases.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".