Mother transformer: A High-Throughput, Cost-Effective in Planta Hairy Root Transformation Method for Cannabis
Bibliographic record
Abstract
BACKGROUND: Hairy root (HR) transformation assays mediated by Agrobacterium rhizogenes, both in vitro and ex vitro, are essential tools in plant biotechnology and functional genomics. These assays can be significantly influenced by various factors, which ultimately can enhance the efficiency. In this study, we optimized a two-step ex vitro HR transformation method using the actual mother plant combined with the RUBY system and compared with existing methods. RESULTS: The two-step ex vitro method proved more efficient than both the one-step ex vitro and in vitro methods, with the highest transformation efficiency of 90% observed in the actual plant. This technique also demonstrated a faster and less complicated approach, reducing time to achieve massive transgenic HR formation by 9-29 days compared to other methods. CONCLUSIONS: A novel, quicker, less complicated, and more efficient two-step transformation method for cannabis has been established, presenting a significantly lower risk of contamination. This protocol is particularly interesting to produce secondary metabolites using the CRISPR/Cas system in cannabis. We anticipate that this method will facilitate substantial time savings by rapidly producing hundreds of transformed samples.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".