An Improved UPLC-MS/MS Platform for Quantitative Analysis of Glycerophosphoinositol in Mammalian Cells
Bibliographic record
Abstract
The glycerophosphoinositols constitute a class of biologically active lipid-derived mediators whose intracellular levels are modulated during physiological and pathological cell processes. Comprehensive assessment of the role of these compounds expands beyond the cellular biology of lipids and includes rapid and unambiguous measurement in cells and tissues. Here we describe a sensitive and simple liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for quantitative analysis of the most abundant among these phosphoinositide derivatives in mammalian cells, the glycerophosphoinositol (GroPIns). The method has been developed in mouse Raw 264.7 macrophages with limits of quantitation at 3 ng/ml. Validation on the same cell line showed excellent response in terms of linear dynamic range (from 3 to 3,000 ng/ml), intra-day and inter-day precision (coefficient of variation ≤ 7.10%) and accuracy (between 98.1 and 109.0%) in the range 10-320 ng/ml. As proof of concept, a simplified analytical platform based on this method and external calibration was also tested on four stimulated and unstimulated cell lines, including Raw 264.7 macrophages, Jurkat T-cells, A375MM melanoma cells and rat basophilic leukemia RBL-2H3 cells. The results indicate a wide variation in GroPIns levels among different cell lines and stimulation conditions, although the measurements were always in line with the literature. No significant matrix effects were observed thus indicating that the here proposed method can be of general use for similar determinations in cells of different origin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".