Seed‐expressed fluorescent proteins as versatile tools for easy (co)transformation and high‐throughput functional genomics in <i>Arabidopsis</i>
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it