A Quantitative Assessment of The Yeast Lipidome using Electrospray Ionization Mass Spectrometry
Bibliographic record
Abstract
Lipids are one of the major classes of biomolecules and play important roles membrane dynamics, energy storage, and signalling(1-4). The budding yeast Saccharomyces cerevisiae, a genetically and biochemically manipulable unicellular eukaryote with annotated genome and very simple lipidome, is a valuable model for studying biological functions of various lipid species in multicellular eukaryotes(2,3,5). S. cerevisiae has 10 major classes of lipids with chain lengths mainly of 16 or 18 carbon atoms and either zero or one degree of unsaturation(6,7). Existing methods for lipid identification and quantification - such as high performance liquid chromatography, thin-layer chromatography, fluorescence microscopy, and gas chromatography followed by MS - are well established but have low sensitivity, insufficiently separate various molecular forms of lipids, require lipid derivitization prior to analysis, or can be quite time consuming. Here we present a detailed description of our experimental approach to solve these inherent limitations by using survey-scan ESI/MS for the identification and quantification of the entire complement of lipids in yeast cells. The described method does not require chromatographic separation of complex lipid mixtures recovered from yeast cells, thereby greatly accelerating the process of data acquisition. This method enables lipid identification and quantification at the concentrations as low as g/ml and has been successfully applied to assessing lipidomes of whole yeast cells and their purified organelles. Lipids extraction from whole yeast cells for using this method of lipid analysis takes two to three hours. It takes only five to ten minutes to run each sample of extracted and dried lipids on a Q-TOF mass spectrometer equipped with a nano-electrospray source.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".