Beyond Photobleaching, Laser Illumination Unbinds Fluorescent Proteins
Bibliographic record
Abstract
Confocal and two-photon fluorescence microscopy techniques using genetically encoded fluorescent probes are widely used in cell biology. Beyond the common problems of photobleaching and phototoxicity, we present evidence that photounbinding also has the potential to compromise such methods, especially in quantitative studies. We show that laser intensities within excitation regimes typical for imaging approaches such as as fluorescence recovery after photobleaching (FRAP), photolysis, or fluorescence correlation spectroscopy (FCS) experiments can cause the dissociation of antibodies from their ligands. Indeed, both one- and two-photon excitation of a fluorescent anti-GFP antibody caused its dissociation from immobilized GFP in vitro. Importantly, with two-photon excitation, the laser intensity threshold for photobleaching was the same as for photounbinding. By contrast, with single-photon excitation, we found a range of laser intensities where photobleaching can be separated from photounbinding. This photounbinding effect was visualized and measured by rebinding a second fluorescent anti-GFP (Green Fluorescent Protein) antibody, indicating that the GFP remained functional for reassociation following the photoinduced dissociation. Finally, we show that this unbinding effect occurs only when at least one binding partner carries a fluorescent label. Our results show that this photounbinding effect can readily remain masked or be misinterpreted as photobleaching, which can compromise the quantitative interpretation of binding studies made using fluorescence microscopy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 0.002 |
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".