Seed‐expressed fluorescent proteins as versatile tools for easy (co)transformation and high‐throughput functional genomics in <i>Arabidopsis</i>
Bibliographic record
Abstract
We demonstrate that fluorescent proteins can be used as visual selection markers for the transformation of Arabidopsis thaliana by the floral dip method. Seed-specific expression of green fluorescent protein (GFP) variants, as well as DsRed, permits the identification of mature transformed seeds in a large background of untransformed seeds by fluorescence microscopy. In planta visualization of transformed seeds in siliques shows that susceptibility to floral dip transformation is limited to a small, defined window in flower development. In the competent stage, the random transformation of up to 25% of the seeds within a single silique may occur. The use of fluorescent proteins with different spectral characteristics allows a rapid identification and genetic analysis of seeds that have received multiple genes-of-interest in co-transformation experiments. The data reveal that co-transformation does not occur at random, since the co-transformed genes are integrated at a single genetic locus in approximately 70% of the cases. This genetic linkage of the co-transformed genes greatly simplifies metabolic pathway engineering by reverse genetics in Arabidopsis. Additional advantages of using visual selection instead of antibiotic resistance include a rapid identification of the effect of the T-DNA insertion or the transgene on seed development and/or germination. This technology, of tagging and identifying transformed seeds by fluorescence provides a novel high-throughput screening system with many potential applications in plant biotechnology.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".