MS/MS <sup>ALL</sup> with SelexION®: A High‐throughput Lipidomic Solution for Untargeted Profiling
Bibliographic record
Abstract
Global lipid profiling is a growing area of interest and is a primary means to measure the changes in lipid molecular species as a function of human disease or drug treatment. One of the most challenging aspects of lipid analysis by mass spectrometry is isobaric overlap, or molecules having the same or nearly the same mass. With direct infusion, or ‘Shotgun’ approaches, the user is able to rapidly profile biological samples. On the Sciex 6600+ TripleTOF®, the MSMS ALL method on this system generates data by collecting product ions of all precursor masses. Using this approach, thousands of lipids can be represented in a profile snapshot and allows for retrospective analysis of acquired data. However the issue of isobaric overlap is not addressed. Isobaric overlap is a complex problem in lipidomic workflows and can make positive identification or quantification of a lipid species very difficult. The issue of isobaric overlap is often approached with liquid chromatography separation. However, lengthy sample preparation, method development, and lengthy chromatographic run times for adequate separation can narrow the scope of the lipidome and diminish throughput. Differential ion mobility separation (DMS) is a relatively new ion mobility technology that uses a molecule’s dipole moment to isolate it rather than shape and size. The SelexION® device is installed at atmosphere, so the addition of chemical modifiers can be applied to the system to assist in additional separation. In this study, 1‐propanol is used as the chemical modifier to separate the six phospholipid subclasses while cardiolipin’s ‐2H charge state is used to separate this class without the assistance of chemical modifier. Unique compensation voltages are then found with standards and assigned to each lipid class to ensure lipid class isolation. In negative ion mode, all seven classes can be acquired in just under twelve minutes of total run time. This allows for untargeted, high‐throughput acquisition of the lipidome while simultaneously acquiring MS/MS spectra for lipid species identification. LipidView software is used to analyze the data collected from the SelexION® runs in a high throughput fashion. All samples from each class can be loaded and a lipid class can be assigned along with its corresponding COV. Since much of the isobaric overlap has been removed from the sample a positive identification and quantification can be made.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.021 |
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".