Photo‐convertible fluorescent proteins as tools for fresh insights on subcellular interactions in plants
Bibliographic record
Abstract
Optical highlighters comprise photo-activatable, photo-switchable and photo-convertible fluorescent proteins and are relatively recent additions to the toolbox utilized for live cell imaging research. Here, we provide an overview of four photo-convertible fluorescent proteins (pcFP) that are being used in plant cell research: Eos, Kaede, Maple and Dendra2. Each of these proteins has a significant advantage over other optical highlighters since their green fluorescent nonconverted forms and red fluorescent converted forms are generally clearly visible at expression levels that do not appear to interfere with subcellular dynamics and plant development. These proteins have become increasingly useful for understanding the role of transient and sustained interactions between similar organelles. Tracking of single organelles after green-to-red conversion has provided novel insights on plastids and their stroma-filled extensions and on the formation of mega-mitochondria. Similarly colour recovery after photo-conversion has permitted the estimation of nuclear endo-reduplication events and is being developed further to image protein trafficking within the lumen of the endoplasmic reticulum. We have also applied photo-convertible proteins to create colour-differentiation between similar cell types to follow their development. Both the green and red fluorescent forms of these proteins are compatible with other commonly used single coloured FPs. This has allowed us to develop simultaneous visualization schemes for up to five types of organelles and investigate organelle interactivity. The advantages and caveats associated with the use of photo-convertible fluorescent proteins are discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".