Imaging signal transduction during phagocytosis: phospholipids, surface charge, and electrostatic interactions
Bibliographic record
Abstract
Together with the development of genetically encoded fluorescent probes, digital imaging has provided great impetus to the study of cell signaling by providing enhanced sensitivity and much-improved spatial and temporal resolution. We have used phagocytosis as a paradigm of signal transduction, taking advantage of the generous size of phagosomes and of their comparatively leisurely rate of formation. Aided by the design of specific probes, we demonstrated a highly localized and elegantly choreographed sequence of changes in the level of several phosphoinositides and were able to also monitor the fate of phosphatidylserine. The net changes in the content of these anionic phospholipids are accompanied by marked alterations in the surface charge of the membrane of nascent phagosomes. These, in turn, cause the relocation of proteins that associate with the membrane by electrostatic interactions. Our studies suggest that anionic lipids control protein targeting not only through stereospecific recognition by specialized domains but also by electrostatic association mediated by polycationic motifs. The "electrostatic switch" can be turned on or off by altering the charge of the protein ligand (e.g., by phosphorylation) or, alternatively, by modifying the lipid composition of the target membrane.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".