Proximity‐dependent sensors for signaling
Bibliographic record
Abstract
Understanding dynamic subcellular organization in living cells is key to unravelling mechanisms of intracellular signaling that goes awry in disease. Using proximity‐dependent biotinylation (BioID), we first established a reference map for the human cell at steady state (Go et al., Nature, 2021; humancellmap.org) that serves to identify protein baits that can report on the recruitment of proteins to selected organelles or subcellular structures. These BioID “sensors” can then be applied to look at dynamic changes in signaling. For example, exploiting late endosome/lysosome BioID sensors (VAMP7 and VAMP8), we recently revealed new intricacies in the regulation of amino acid sensing pathways (Hesketh et al., Science, 2020). We also coupled BioID sensor profiling to CRISPR‐mediated ablation of pathway components to investigate the consequences on the environment detected by the sensor. Using these approaches, we recently used fast‐acting biotinylation enzymes, including miniTurbo, to provide a space and time‐resolved analysis of EGFR pathway activation, revealing previously uncharacterized associations. This presentation will revisit key principles of dynamic organelle mapping in living cells, and the utilization of coincidence detection for dynamic proximal interactomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".