FRET and colocalization analyzer—A method to validate measurements of sensitized emission FRET acquired by confocal microscopy and available as an ImageJ Plug‐in
Bibliographic record
Abstract
Fluorescence resonance energy transfer (FRET) between an adequate pair of fluorophores is an indication of closer proximity than colocalization and is used by biologists to study fluorescently modified protein interactions inside cells. We present a method for visualization of FRET images acquired by confocal sensitized emission, involving excitation of the donor fluorophore and detection of the energy transfer as an emission from the acceptor fluorophore into the FRET channel. Authentic FRET signal measurements require the correction from the FRET channel of the undesired bleed-through signals (BT) resulting from both the leak-through of the donor emission and the direct acceptor emission. Our method reduces the interference of the user to a minimum by analyzing the entire image, pixel by pixel. It proposes imaging treatments and the display of control images to validate the BT calculation and the image corrections. It displays FRET images as a function of the colocalization of the two fluorescent partners. Finally, it proposes an alternative to normalization of the FRET intensities to compare FRET signal variations between samples. This method called "FRET and Colocalization Analyzer" has been implemented in a Plug-in of the freely available ImageJ software. It is particularly adapted when transient expression of the fluorescent proteins is used thereby giving very variable expression levels or when the colocalization of the two partners is varying in proportion, in amount, and in size, as a function of time. The method and program are validated using the analysis of the spatio-temporal interactions between a G-protein coupled receptor, the tachykinin NK2 receptor, and the beta-arrestin 2 as an example.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".