SAT-034 Photometry Based Measurement of G Protein-Coupled Receptor-Mediated Signalling
Bibliographic record
Abstract
Many hormones and neurotransmitters exert their effects by binding to G protein-coupled receptors (GPCRs). These membrane-spanning receptors couple primarily to heterotrimeric G proteins which can act on effector proteins to modulate cellular functions such as membrane excitability, second messenger production or gene expression. GPCRs represent the most druggable targets in the mammalian genome, however individual receptors can often activate multiple signaling pathways, only some of which may mediate therapeutic effects. Much of what is known about the signaling pathways linked to activation of specific GPCRs comes from studies using heterologous cell models, but evidence now suggests that the specific complement of effectors engaged by a given GPCR is dependent on the cellular and tissue context, such that the same receptor may induce different signaling patterns in different cell types. This phenomenon is particularly evident in the central nervous system which contains hundreds of specialized cell populations whose function are regulated by the combined action of hormones, neurotransmitters and neuromodulators, and disruption of select signaling pathways may contribute to disease. To study GPCR mediated signaling in cells of interest in their native context, we have developed a biosensor-based approach that would allow in vivo expression and recording of fluorescent reporters in behaving animals. Our approach uses adeno-associated viruses (AAV) to express genetically encoded sensors in animals using cell-type selective promoters and Cre-recombinase dependent expression. Biosensor responses are recorded in real time from live animals using a fiber-photometry-based FRET recording platform. Our approach allows simultaneous measurement of cell-specific signaling with behavioral or physiological measurements made from live animal subjects. This approach has wide applicability to the study of neuroendocrine cell populations, hormone action in the brain, and to evaluate drug action on target cells.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".