A comprehensive review of in planta stable transformation strategies
Bibliographic record
Abstract
Plant transformation remains a major bottleneck to the improvement of plant science, both on fundamental and practical levels. The recalcitrant nature of most commercial and minor crops to genetic transformation slows scientific progress for a large range of crops that are essential for food security on a global scale. Over the years, novel stable transformation strategies loosely grouped under the term "in planta" have been proposed and validated in a large number of model (e.g. Arabidopsis and rice), major (e.g. wheat and soybean) and minor (e.g. chickpea and lablab bean) species. The in planta approach is revolutionary as it is considered genotype-independent, technically simple (i.e. devoid of or with minimal tissue culture steps), affordable, and easy to implement in a broad range of experimental settings. In this article, we reviewed and categorized over 300 research articles, patents, theses, and videos demonstrating the applicability of different in planta transformation strategies in 105 different genera across 139 plant species. To support this review process, we propose a classification system for the in planta techniques based on five categories and a new nomenclature for more than 30 different in planta techniques. In complement to this, we clarified some grey areas regarding the in planta conceptual framework and provided insights regarding the past, current, and future scientific impacts of these techniques. To support the diffusion of this concept across the community, this review article will serve as an introductory point for an online compendium about in planta transformation strategies that will be available to all scientists. By expanding our knowledge about in planta transformation, we can find innovative approaches to unlock the full potential of plants, support the growth of scientific knowledge, and stimulate an equitable development of plant research in all countries and institutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".