Rapid and sensitive MRM‐based mass spectrometry approach for systematically exploring ganglioside‐protein interactions
Bibliographic record
Abstract
Gangliosides are ubiquitous components of cell membranes. Their interactions with bacterial toxins and membrane-associated proteins (e.g. receptor tyrosine kinases) have important roles in the regulation of multiple cellular functions. Currently, an effective approach for measuring ganglioside-protein interactions especially in a large-scale fashion is largely missing. To this end, we report a facile MS-based approach to explore gangliosides extracted from cells and measure their interactions with protein of interest globally. We optimized a two-step protocol for extracting total gangliosides from cells within 2 h. Easy-to-use magnetic beads conjugated with a protein of interest were used to capture interacting gangliosides. To measure ganglioside-protein interaction on a global scale, we applied a high-sensitive LC-MS system, containing hydrophilic interaction LC separation and multiple reaction monitoring-based MS for ganglioside detection. Sensitivity for ganglioside GM1 is below 100 pg, and the whole analysis can be done in 20 min with isocratic elution. To measure ganglioside interactions with soluble vascular endothelial growth factor receptor 1 (sFlt1), we extracted and readily detected 36 species of gangliosides from perivascular retinal pigment epithelium cells across eight different classes. Twenty-three ganglioside species have significant interactions with sFlt1 as compared with IgG control based on p value cutoff <0.05. These results show that the described method provides a rapid and high-sensitive approach for systematically measuring ganglioside-protein interactions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".