Investigation of the Effects of Labware Contamination on Mass Spectrometry-Based Human Serum Lipidome Analysis
Bibliographic record
Abstract
Polypropylene microcentrifuge tubes (MCTs) are increasingly used in lipidome sample preparation. In the absence of a comprehensive study evaluating ramifications of plasticware utilization in mass spectrometry-based lipidomic analyses, we conducted a systematic analysis to elucidate potential negative effects ascribable to labware contamination in serum lipidomics. During serum lipid extractions, tested glassware introduced 24 labware contaminants. In contrast, Eppendorf polypropylene MCTs contributed 485 contaminant features, many of which could be erroneously putatively identified as lipids via their m / z values. Eppendorf MCTs contamination engendered severe ion-suppression of 40 low abundance serum lipids, while generating mild to modest lipid ion-suppression across a multitude of higher abundance coeluting lipids. Less compatible polypropylene MCTs from an alternative manufacturer introduced a staggering 2,949 contaminant m / z values, severely affecting 75 coeluting serum lipids and causing more frequent and pronounced ion-suppression instances. Furthermore, by performing serum extractions with varied initial volumes, it was ascertained that labware-induced lipid ion-suppression is a dynamic phenomenon, contingent on both lipid and labware contaminant concentrations where low-abundance lipids are disproportionately impacted by coelutes of suppressive contaminants. In addition to lipid ion-suppression, the identification and quantification of 7 fatty acid endogenous serum lipids were compromised by the leaching of structurally identical surfactants from MCTs. MCTs artificially introduced 10 additional primary amides extraneous to serum samples. Utmost caution is imperative in interpreting data concerning primary amides and fatty acids when employing plastic labware. Through this investigation, we aspire to elevate awareness regarding the pernicious impact of labware contamination on lipidome analysis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".