Cell type-specific dynamics underlie cellular growth variability in plants
Bibliographic record
Abstract
Coordination of growth, patterning and differentiation is required for shaping organs in multicellular organisms. In plants, cell growth is controlled by positional information, yet the behavior of individual cells is often highly heterogeneous. The origin of this variability is still unclear. Using time-lapse imaging, we determined the source and relevance of cellular growth variability in developing organs of Arabidopsis thaliana. We show that growth is more heterogeneous in the leaf blade than in the midrib and petiole, correlating with higher local differences in growth rates between neighboring cells in the blade. This local growth variability coincides with developing stomata. Stomatal lineages follow a specific, time-dependent growth program that is different from that of their surroundings. Quantification of cellular dynamics in the leaves of a mutant lacking stomata, as well as analysis of floral organs, supports the idea that growth variability is mainly driven by stomata differentiation. Thus, the cell-autonomous behavior of specialized cells is the main source of local growth variability in otherwise homogeneously growing tissue. Those growth differences are buffered by the immediate neighbors of stomata and trichomes to achieve robust organ shapes.
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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 | 0.000 |
| 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 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".