Using GCaMP3 to Study Ca2+ Signaling in Nicotiana Species
Bibliographic record
Abstract
Ca2+ signaling is a central component of plant biology; however, direct analysis of in vivo Ca2+ levels is experimentally challenging. In recent years, the use of genetically encoded Ca2+ indicators has revolutionized the study of plant Ca2+ signaling, although such studies have been largely restricted to the model plant Arabidopsis. We have developed stable transgenic Nicotiana benthamiana and Nicotiana tabacum lines expressing the single-wavelength fluorescent Ca2+ indicator, GCaMP3. Ca2+ levels in these plants can be imaged in situ using fluorescence microscopy, and these plants can be used qualitatively and semi-quantitatively to evaluate Ca2+ signals in response to a broad array of abiotic or biotic stimuli, such as cold shock or pathogen-associated molecular patterns (PAMPs). Furthermore, these tools can be used in conjunction with well-established N. benthamiana techniques such as virus-induced gene silencing (VIGS) or transient heterologous expression to assay the effects of loss or gain of function on Ca2+ signaling, an approach which we validated via silencing or transient expression of the PAMP receptors FLS2 (Flagellin Sensing 2) or EFR (EF-Tu receptor), respectively. Using these techniques, along with chemical inhibitor treatments, we demonstrate how these plants can be used to elucidate the molecular components governing Ca2+ signaling in response to specific stimuli.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".