Spectral imaging of FRET‐based sensors reveals sustained cAMP gradients in three spatial dimensions
Bibliographic record
Abstract
Cyclic AMP is a ubiquitous second messenger that orchestrates a variety of cellular functions over different timescales. The mechanisms underlying specificity within this signaling pathway are still not well understood. Several lines of evidence suggest the existence of spatial cAMP gradients within cells, and that compartmentalization underlies specificity within the cAMP signaling pathway. However, to date, no studies have visualized cAMP gradients in three spatial dimensions (3D: x, y, z).This is in part due to the limitations of FRET-based cAMP sensors, specifically the low signal-to-noise ratio intrinsic to all intracellular FRET probes. Here, we overcome this limitation, at least in part, by implementing spectral imaging approaches to estimate FRET efficiency when multiple fluorescent labels are used and when signals are measured from weakly expressed fluorescent proteins in the presence of background autofluorescence and stray light. Analysis of spectral image stacks in two spatial dimensions (2D) from single confocal slices indicates little or no cAMP gradients formed within pulmonary microvascular endothelial cells (PMVECs) under baseline conditions or following 10 min treatment with the adenylyl cyclase activator forskolin. However, analysis of spectral image stacks in 3D demonstrates marked cAMP gradients from the apical to basolateral face of PMVECs. Results demonstrate that spectral imaging approaches can be used to assess cAMP gradients-and in general gradients in fluorescence and FRET-within intact cells. Results also demonstrate that 2D imaging studies of localized fluorescence signals and, in particular, cAMP signals, whether using epifluorescence or confocal microscopy, may lead to erroneous conclusions about the existence and/or magnitude of gradients in either FRET or the underlying cAMP signals. Thus, with the exception of cellular structures that can be considered in one spatial dimension, such as neuronal processes, 3D measurements are required to assess mechanisms underlying compartmentalization and specificity within intracellular signaling pathways.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".