Spatiotemporal photocatalytic proximity labeling proteomics reveals ligand-activated extracellular and intracellular EGFR neighborhoods
Bibliographic record
Abstract
Abstract Photo-proximity labeling proteomics (PLP) methods have recently shown that the cell surface receptors can dynamically form lateral interactome networks. Here, we present a paired set of PLP workflows that simultaneously track neighborhood changes for oncogenic epidermal growth factor receptor (EGFR) with temporal resolution, both outside and inside of cells. We achieved this by augmenting the multiscale PLP workflow we call MultiMap, where three photo-probes with different labeling ranges were photo-activated by one photocatalyst, Eosin Y. By anchoring Eosin Y extracellularly and intracellularly on EGFR, we captured hundreds of proteins on both sides of the cell membrane that change in proximity to EGFR upon EGF activation. Neighbors engaged with EGFR within minutes to over an hour, reflecting dynamic interactomes during early, mid- and late-signaling including phosphorylation, internalization, degradation and transcriptional regulation. This rapid “photographic” labeling approach provides snapshots of signaling neighborhoods, revealing their dynamic nature, and potential for drug targeting.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".