A Proximity Ligation Assay Using Transiently Transfected, Epitope-Tagged Proteins: Application for in Situ Detection of Dimerized Receptor Tyrosine Kinases
Bibliographic record
Abstract
The development of small molecule and antibody inhibitors targeting the interaction of receptor tyrosine kinases (RTKs), such as epidermal growth factor receptor (EGFR), is of high pharmacological and biological interest. Unfortunately, conventional biochemical techniques using cell or tissue lysates and co-immunoprecipitation experiments to investigate EGFR dimerization are not always conclusive. Here we describe a series of technical and biological validation experiments demonstrating the utility of a proximity ligation assay (PLA)-based methodology for in situ visualization and quantification of ligand-dependent EGFR receptor dimerization in intact cells. Using the PLA approach combined with a universally applicable epitope tagging strategy, we detected EGFR dimers in cells transiently co-expressing FLAG-tagged and MYC-tagged human EGFRs. Our data strongly suggest that PLA can be used to detect ligand-dependent EGFR dimerization and this signal is generated in a protein interaction-based manner, rather than solely due to proximity of target proteins. This application represents a generalized RTK expression strategy for protein-interaction analysis in a transient expression system where antibody epitopes are not known or not unique enough to discriminate between interaction partners. This assay also holds promise as a general RTK dimerization screening tool in tissue specimens to identify potential dimerization inhibitors with clinical relevance.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".