Impedance Responses Reveal β2-Adrenergic Receptor Signaling Pluridimensionality and Allow Classification of Ligands with Distinct Signaling Profiles
Bibliographic record
Abstract
The discovery that drugs targeting a single G protein-coupled receptor (GPCR) can differentially modulate distinct subsets of the receptor signaling repertoire has created a challenge for drug discovery at these important therapeutic targets. Here, we demonstrate that a single label-free assay based on cellular impedance provides a real-time integration of multiple signaling events engaged upon GPCR activation. Stimulation of the β₂-adrenergic receptor (β₂AR) in living cells with the prototypical agonist isoproterenol generated a complex, multi-featured impedance response over time. Selective pharmacological inhibition of specific arms of the β₂AR signaling network revealed the differential contribution of G(s)-, G(i)- and Gβγ-dependent signaling events, including activation of the canonical cAMP and ERK1/2 pathways, to specific components of the impedance response. Further dissection revealed the essential role of intracellular Ca²⁺ in the impedance response and led to the discovery of a novel β₂AR-promoted Ca²⁺ mobilization event. Recognizing that impedance responses provide an integrative assessment of ligand activity, we screened a collection of β-adrenergic ligands to determine if differences in the signaling repertoire engaged by compounds would lead to distinct impedance signatures. An unsupervised clustering analysis of the impedance responses revealed the existence of 5 distinct compound classes, revealing a richer signaling texture than previously recognized for this receptor. Taken together, these data indicate that the pluridimensionality of GPCR signaling can be captured using integrative approaches to provide a comprehensive readout of drug 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".