Standardized measure for phosphatidylinositol, 4,5-bisphosphate by mass spectrometry
Bibliographic record
Abstract
Phosphoinositides (PIs) play a fundamental role in many physiological processes such as cell surface signal transduction, membrane trafficking, cytoskeleton regulation, nuclear events and permeability and transport functions of membranes. Levels of PIs vary under different physiological conditions thus PI profiling may be an important step in elucidation of importance in the progression towards many diseases. Previous methods for PI analysis have several disadvantages in time-constraints, use of radioactive samples and inconsistent results due to lack of sensitivity. Mass spectrometry has previously been utilized to quantify low abundance peptides, metabolites and lipids. Here we propose a novel approach for PI quantitation based on inositol head cleavage coupled to high-performance liquid-mass spectrometry (HPLC-MS) to overcome these issues. First, we highlight the extraction and deacylation of PIs from S. Ceravisiae, followed by purification via reverse phase chromatography with a Sep-Pak. Next, using commercially purchased PI (4,5) P2 standards, we created a tune file which provide the correct conditions sensitive enough to identify prolific ion peaks to be utilized in characterization of biological samples. Using the standard tune file, we successfully identified and quantitated the PI (4,5) P2 in cells lacking INP51 which have 2-4 fold increase in PI (4,5)P(subscript 2) and comparing them to the wild type cells. The methods described may form a basis for further optimization of mass spectral based quantitation of PIs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".