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Record W2767211704 · doi:10.1002/rcm.8024

Improving negative liquid chromatography/electrospray ionization mass spectrometry lipidomic analysis of human plasma using acetic acid as a mobile‐phase additive

2017· article· en· W2767211704 on OpenAlexafffund
Cian Monnin, Parsram Ramrup, Carolann Daigle‐Young, Dajana Vuckovic

Bibliographic record

VenueRapid Communications in Mass Spectrometry · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsConcordia University
FundersPfizer CanadaMerck CanadaFondation Brain CanadaOntario Brain InstitutePfizer
KeywordsChemistryChromatographyAcetic acidElectrospray ionizationAmmonium acetateLipidomicsMass spectrometryExtractive electrospray ionizationAmmonium hydroxideLiquid chromatography–mass spectrometrySelected reaction monitoringTriple quadrupole mass spectrometerTandem mass spectrometryHigh-performance liquid chromatographySample preparation in mass spectrometryBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.318
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2017
Admission routes2
Has abstractyes

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