Generation of guard cell RNA-seq transcriptomes during progressive drought and recovery using an adapted INTACT protocol for <i>Arabidopsis thaliana</i> shoot tissue
Bibliographic record
Abstract
Abstract Drought is an important environmental stress that limits crop production. Guard cells (GC) act to control the rate of water loss. To better understand how GCs change their gene expression during a progressive drought we generated guard cell-specific RNA-seq transcriptomes during mild, moderate, and severe drought stress. We additionally sampled re-watered plants that had experienced severe drought stress. These transcriptomes were generated using the INTACT system to capture the RNA from GC nuclei. We optimized the INTACT protocol for Arabidopsis thaliana leaf tissue, incorporating fixation to preserve RNA during nuclear isolation. To be able to identify gene expression changes unique to GCs, we additionally generated transcriptomes from all cell types, using a 35S viral promoter to capture the nuclei of all cell types in leaves. These data sets highlight shared and unique gene expression changes between GCs and the bulk leaf tissue. The timing of gene expression changes is different between GCs and other cell types: we found that only GCs had detectable gene expression changes at the earliest drought time point. The drought responsive GC and leaf RNA-seq transcriptomes are available in the Arabidopsis ePlant at the Bio-Analytic Resource for Plant Biology website.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".