Quantification of receptor tyrosine kinase transactivation through direct dimerization and surface density measurements in single cells
Bibliographic record
Abstract
Cell signaling involves dynamic changes in protein oligomerization leading to the formation of different signaling complexes and modulation of activity. Spatial intensity distribution analysis (SpIDA) is an image analysis method that can directly measure oligomerization and trafficking of endogenous proteins in single cells. Here, we show the use of SpIDA to quantify dimerization/activation and surface transport of receptor protein kinases--EGF receptor and TrkB--at early stages of their transactivation by several G protein-coupled receptors (GPCRs). Transactivation occurred on the same timescale and was directly limited by GPCR activation but independent of G-protein coupling types. Early receptor protein kinase transactivation and internalization were not interdependent for all receptor pairs tested, revealing heterogeneity between groups of GPCRs. SpIDA also detected transactivation of TrkB by dopamine receptors in intact neurons. By allowing for time and space resolved quantification of protein populations with heterogeneous oligomeric states, SpIDA provides a unique approach to undertake single cell multivariate quantification of signaling processes involving changes in protein interactions, trafficking, and activity.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".