Single cell analysis of population-wide nuclear and cytosolic drug responses using high-content FRET imaging: measuring protein kinase activation in rat primary striatal neurons
Bibliographic record
Abstract
Abstract Genetically-encoded biosensors are used to track biochemical activities in living cells by measuring changes in fluorescence emitted by one or more fluorescent proteins. In the present article, we describe the application of genetically-encoded FRET biosensors with high content microscopy to image the signaling responses of thousands of neurons in response to drug treatments. We applied this approach to reveal intercellular variation in signaling responses among cultured striatal neurons stimulated with multiple drugs. The striatum is largely composed of medium-spiny GABAergic neurons which are divided into two broad sub-types based in part on their expression of dopamine D1 vs. D2 receptors. Using high content FRET imaging and immunofluorescence, we identified neuronal sub-populations with unique responses to pharmacological manipulation. Focusing on dopamine- and glutamate-regulated PKA and ERK1/2 signaling in both the cytoplasm and nucleus, we identified pronounced intercellular differences, in both the magnitude and kinetics of signaling responses to drug application. Importantly, we found that a conventional “bulk” analysis that included all cells in culture yielded a different rank order of drug potency than that revealed by our single-cell analysis. The high degree of heterogeneity that we observed at the single cell level would not have been detectable using common population-level analyses, derived for example from western blotting or plate reader-based measurements. In conclusion, our single-cell analytical approach highlights the limitations of population-level analyses, and provides a novel way to study signaling biology.
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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) | 0.001 | 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) | 0.000 | 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".