Imaging plant cell death: GFP-Nit1 aggregation marks an early step of wound and herbicide induced cell death
Bibliographic record
Abstract
BACKGROUND: A great deal is known about the morphological endpoints of plant cell death, but relatively little is known about its sequence of events and/or its execution at the biochemical level. Live cell imaging using GFP-tagged markers is a powerful way to provide dynamic portraits of a cellular process that can in turn provide a descriptive foundation valuable for future biochemical and genetic investigations. RESULTS: While characterizing a collection of random GFP-protein fusion markers we discovered that mechanical wounding induces rapid aggregation of a GFP-Nitrilase 1 fusion protein in Arabidopsis cells directly abutting wound sites. Time-lapse imaging of this response shows that the aggregation occurs in cells that subsequently die 30-60 minutes post-wounding, indicating that GFP-Nit1 aggregation is an early marker of cell death at wound sites. Time-lapse confocal imaging was used to characterize wound-induced cell death using GFP-Nit1 and markers of the nucleus and endoplasmic reticulum. These analyses provide dynamic portraits of well-known death-associated responses such as nuclear contraction and cellular collapse and reveal novel features such as nuclear envelope separation, ER vesiculation and loss of nuclear-lumen contents. As a parallel system for imaging cell death, we developed a chemical method for rapidly triggering cell death using the herbicides bromoxynil or chloroxynil which cause rapid GFP-Nit1 aggregation, loss of nuclear contents and cellular collapse, but not nuclear contraction, separating this response from others during plant cell death. CONCLUSION: Our observations place aggregation of Nitrilase 1 as one of the earliest events associated with wound and herbicide-induced cell death and highlight several novel cellular events that occur as plant cells die. Our data create a detailed descriptive framework for future investigations of plant cell death and provide new tools for both its cellular and biochemical analysis.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".