A Highly Sensitive Quantitative Cytosensor Technique for the Identification of Receptor Ligands in Tissue Extracts
Bibliographic record
Abstract
Because G-protein-coupled receptors (GPCRs) constitute excellent putative therapeutic targets, functional characterization of orphan GPCRs through identification of their endogenous ligands has great potential for drug discovery. We propose here a novel single cell-based assay for identification of these ligands. This assay involves (a) fluorescent tagging of the GPCR, (b) expression of the tagged receptor in a heterologous expression system, (c) incubation of the transfected cells with fractions purified from tissue extracts, and (d) imaging of ligand-induced receptor internalization by confocal microscopy coupled to digital image quantification. We tested this approach in CHO cells stably expressing the NT1 neurotensin receptor fused to EGFP (enhanced green fluorescent protein), in which neurotensin promoted internalization of the NT1-EGFP receptor in a dose-dependent fashion (EC(50) = 0.98 nM). Similarly, four of 120 consecutive reversed-phase HPLC fractions of frog brain extracts promoted internalization of the NT1-EGFP receptor. The same four fractions selectively contained neurotensin, an endogenous ligand of the NT1 receptor, as detected by radioimmunoassay and inositol phosphate production. The present internalization assay provides a highly specific quantitative cytosensor technique with sensitivity in the nanomolar range that should prove useful for the identification of putative natural and synthetic ligands for GPCRs.
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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) | 0.002 | 0.001 |
| 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) | 0.002 | 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".