Identification and Quantitation of Changes in the Platelet Activating Factor Family of Glycerophospholipids over the Course of Neuronal Differentiation by High-Performance Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry
Bibliographic record
Abstract
Glycerophospholipids are important structural lipids in membranes with changes associated with progressive neurodegenerative disorders such as Alzheimer disease. Synthesis of the platelet activating factor (PAF) glycerophospholipid subclass is implicated in the control of neuronal differentiation and death. In this article, we combine nanoflow HPLC and mass spectrometry to screen, identify, and quantitate changes in glycerophospholipid subspecies, specifically PAF family members, over the course of neuronal differentiation. Furthermore, precursor ion scans for fragments characteristic of PAF phosphocholine family members and the standard additions of PAF subspecies were combined to perform absolute quantitation of PAF lipids in undifferentiated and differentiated PC12 cells. Surprisingly, a marked asymmetry was detected in the two predominant PAF species (C16:0, C18:0) over the course of differentiation. These results describe a new technique for the sensitive analysis of lipids combining nanoflow HPLC, ESI-MS, and precursor ion scan. Limits of detection of as little as 2 pg of PAF and LPC were obtained, and analysis of the lipidome of as little as 70,000 cells was performed on this system. Furthermore, application to the PC12 model identified a quantifiable difference between PAF molecular species produced over the course of neuronal differentiation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".