Vendor-dependent mobile phase contaminants affect neutral lipid analysis in lipidomics protocols
Bibliographic record
Abstract
Lipidomics is a well-established field, enabled by modern liquid chromatography mass spectrometry (LCMS) technology, rapidly generating large amounts of data. Lipid extracts derived from biological samples are complex and most spectral features in LCMS lipidomics datasets remain unidentified, colloquially termed lipidomics “dark matter”. In-depth analyses of triacylglycerol, diacylglycerol, and cholesterol ester species revealed the expected ammoniated and sodiated ions as well as 5 additional higher mass dark matter peaks. These additional peaks were of relatively high intensity and resulted from analyte adduction with alkylated amine contaminants from LCMS-grade methanol and isopropanol. Tandem MS (MS/MS) of adduct peaks yielded no lipid structural information, producing only an intense ion of the adducted contaminant. Analysis of bovine liver extract identified 33 neutral lipids with an additional 73 alkyl amine adducts. Removing alcohols in place for acetonitrile and methyl tert-butyl ether in the mobile phase resulted in a 60% decrease in neutral lipid annotations, but eliminated the formation of alkyl amine adducts. Analysis of LCMS-grade methanol and isopropanol from different vendors revealed alkyl amine adduct formation in one out of three different brands that were tested. Substituting solvents increased lipid annotations by 36.5% or 27.4%, depending on the vendor and resulted in >2.5-fold increases in peak area for neutral lipid species, dramatically affecting their quantification and detection. Using principal component analysis, the same bovine liver sample separated into vendor-based clusters. These findings demonstrate the importance of solvent selection and disclosure during lipidomics protocols and highlight the challenges when comparing data between experiments.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.010 |
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".