mEosFP-Based Green-to-Red Photoconvertible Subcellular Probes for Plants
Bibliographic record
Abstract
Photoconvertible fluorescent proteins (FPs) are recent additions to the biologists' toolbox for understanding the living cell. Like green fluorescent protein (GFP), monomeric EosFP is bright green in color but is efficiently photoconverted into a red fluorescent form using a mild violet-blue excitation. Here, we report mEosFP-based probes that localize to the cytosol, plasma membrane invaginations, endosomes, prevacuolar vesicles, vacuoles, the endoplasmic reticulum, Golgi bodies, mitochondria, peroxisomes, and the two major cytoskeletal elements, filamentous actin and cortical microtubules. The mEosFP fusion proteins are smaller than GFP/red fluorescent protein-based probes and, as demonstrated here, provide several significant advantages for imaging of living plant cells. These include an ability to differentially color label a single cell or a group of cells in a developing organ, selectively highlight a region of a cell or a subpopulation of organelles and vesicles within a cell for tracking them, and understanding spatiotemporal aspects of interactions between similar as well as different organelles. In addition, mEosFP probes introduce a milder alternative to fluorescence recovery after photobleaching, whereby instead of photobleaching, photoconversion followed by recovery of green fluorescence can be used for estimating subcellular dynamics. Most importantly, the two fluorescent forms of mEosFP furnish bright internal controls during imaging experiments and are fully compatible with cyan fluorescent protein, GFP, yellow fluorescent protein, and red fluorescent protein fluorochromes for use in simultaneous, multicolor labeling schemes. Photoconvertible mEosFP-based subcellular probes promise to usher in a much higher degree of precision to live imaging of plant cells than has been possible so far using single-colored FPs.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".