Fluorescence complementation enables quantitative imaging of cell penetrating peptide-mediated protein delivery in plants including WUSCHEL transcription factor
Bibliographic record
Abstract
Abstract Protein delivery to plants offers many opportunities for plant bioengineering via gene editing and through direction of protein-protein interactions. However, the delivery and confirmation of successful protein delivery to plants presents both practical and analytical challenges. We present a GFP bimolecular fluorescence complementation-based tool, delivered complementation in planta (DCIP), which allows for unambiguous and quantitative measurement of protein delivery in leaves. Using DCIP, we demonstrate cell-penetrating peptide mediated cytosolic delivery of peptides and recombinant proteins in Nicotiana benthamiana . We show that DCIP enables measurement of delivery efficiency and enables functional screening of cell penetrating peptide efficacies for in-plant protein delivery. Finally, we demonstrate that DCIP detects cell penetrating peptide mediated delivery of recombinantly expressed proteins such as mCherry and Lifeact into intact leaves. Finally, we also demonstrate, for the first time, delivery of a recombinant plant transcription factor, WUSCHEL (AtWUS), in N. benthamiana . RT-qPCR analysis of AtWUS delivery in Arabidopsis seedlings also suggests delivered WUS can recapitulate AtWUS-overexpression transcriptional changes. All combined, DCIP offers a new and powerful tool for interrogating cytosolic delivery of proteins in plants and highlights future avenues for engineering plant physiology.
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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 | 0.000 |
| 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 | 0.001 |
| 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".