Transcriptional Profiling of Arabidopsis Tissues Reveals the Unique Characteristics of the Pollen Transcriptome
Bibliographic record
Abstract
Pollen tubes are a good model for the study of cell growth and morphogenesis because of their extreme elongation without cell division. Yet, knowledge about the genetic basis of pollen germination and tube growth is still lagging behind advances in pollen physiology and biochemistry. In an effort to reduce this gap, we have developed a new method to obtain highly purified, hydrated pollen grains of Arabidopsis through flowcytometric sorting, and we used GeneChips (Affymetrix, Santa Clara, CA; representing approximately 8,200 genes) to compare the transcriptional profile of sorted pollen with those of four vegetative tissues (seedlings, leaves, roots, and siliques). We present a new graphical tool allowing genomic scale visualization of the unique transcriptional profile of pollen. The 1,584 genes expressed in pollen showed a 90% overlap with genes expressed in these vegetative tissues, whereas one-third of the genes constitutively expressed in the vegetative tissues were not expressed in pollen. Among the 469 genes enriched in pollen, 162 were selectively expressed, and most of these had not been associated previously with pollen. Their functional classification reveals several new candidate genes, mainly in the categories of signal transduction and cell wall biosynthesis and regulation. Thus, the results presented improve our knowledge of the molecular mechanisms underlying pollen germination and tube growth and provide new directions for deciphering their genetic basis. Because pollen expresses about one-third of the number of genes expressed on average in other organs, it may constitute an ideal system to study fundamental mechanisms of cell biology and, by omission, of cell division.
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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".