Simultaneous Real-Time Imaging of Signal Oscillations Using Multiple Fluorescence-Based Reporters
Bibliographic record
Abstract
It is now well understood that G protein-coupled receptor (GPCR)-mediated cell signalling is subject to extensive spatial-temporal control, and that a meaningful understanding of this complexity requires techniques to study signalling at the molecular and sub-cellular level. This complexity in cell signal pattern begins with ligand binding to the receptor and its coupling to a variety of different effector systems. These signal transduction cascades within a cell involve a very complex series of molecular events requiring the generation of multiple second messenger responses and the activation a multiple effector proteins. In the present chapter, we will describe methodology for the simultaneous assessment of the spatial-temporal measurement of increases in intracellular Ca2+ concentrations and the activation of protein kinase C (PKC) in response to the agonist activation of a Gαq/11-coupled GPCR. Specifically, we will describe a confocal imaging approach to simultaneously measure oscillilations in intracellular Ca2+ levels and PKC translocation to the plasma membrane in response to mGluR1 stimulation in transiently transfected human embryonic kidney (HEK293) cells. The changes in intracellular Ca2+ were imaged using the fluorescent indicator Oregon Green 488 BAPTA and a recombinant PKCβII-DsRed fusion protein was used to image the sub-cellular distribution of the PKCβII isoform.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".