High‐throughput Targeted Lipidomics Analysis of Dihydroceramide Desaturase‐1 (DES1) Knockout Mice
Bibliographic record
Abstract
Retention time scheduling for MRM transitions during targeted assays enables more compounds to be quantified with higher quality results. In this assay, MRMs to quantitate over 1150 lipid molecular species were combined into a single assay. The selected amide column chemistry provides reproducible isomer separation based on lipid class. This Liquid Chromatography separation strategy, is then coupled with the time scheduled targeted assay. To validate this method, we selected DES1 knockout mice to measure lipid changes in liver and adipose tissues. The MRM™ Pro Builder tool (SCIEX) was then used to optimize window width and dwell weight to enhance sensitivity and coverage of method. This method provides extensive, reproducible lipid coverage in complex biological samples like tissue, cells, and plasma. Amide column chemistry was chosen for lipid class separation and minimize isomeric interference. The target list of lipids is comprehensive, covering most major lipid classes and categories, and MRMs were selected to cover lipids containing fatty acids with 14 to 22 carbons and 0 to 6 double bonds. The method is customizable, so new lipid categories, classes and molecular species can be added to the MRMs list. This method provides quantitative measurement of over 1150 different lipid molecular species in a rapid, highly reproducible manner. The sMRM Pro Builder template which was developed to streamline the method optimization process, enables assigning the retention time, optimize dwell weight and set window size per MRM to enhanced coverage and sensitivity of the method. This optimization improved results quality especially on low abundant lipids. Liver and eWAT tissues were harvested from dihydroceramide desaturase‐1 (DES1) knockout mice. DES1 is the enzyme responsible for inserting the 4,5‐trans‐double bond into the sphingolipid backbone causing the dihydroceramide conversion to ceramide. While both of these classes of lipids are lower in abundance in the chosen tissues, this method shows significant changes in these lipid classes in a quantitative manner. However, lipids from another 17 classes were not changing. Lipid standards from 19 different classes, which are either heavy isotopic labeled lipids or odd chain lipids, served as internal standard. This method provided extensive lipid class coverages including, CE, CER, DCER, HCER, LCER, TAG, DAG, MAG, LPC, PC, LPE, PE, LPG, PG, LPI, PI, LPS and PS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".